Journal of Zhengzhou University (Engineering Science)

User Login

　Author Register

　Author Login

　Reviewer Login

　Editor Login

Article Search

Previous Issue Next Issue Periodical Index Advanced Search Most Downloaded Most Read

News

MORE+

Indexing Services

MORE+

Link

MORE+

Statistics

Today's visit:67427

The line number:2399

Traffic statistics:343448432

Recommend

MORE+

2025 volumne 46 Issue 03

Cover - Table of Contents - Back Cover[0]

Abstract:

225| 150

Effects of Foot-ground Contact on the Dynamic Gait Behavior of Passive Walking Robot[1-10]

GAO Jianshe¹, ZHENG Ziyi¹, WAN Lei², LI Kui², WU Guangliang²

Doi:10.13705/j.issn.1671-6833.2025.03.011

Abstract: To address the issue of analyzing the complex dynamic behaviors of gait, such as slip and bounce, in passive walking robots, an arc-based bipedal passive walking robot model considering foot-ground contact was developed, to analyze various walking modes exhibited with complex walking conditions with changes in ground surface materials. It could reveal the gait instability mechanisms with multi-parameter co-evolution. Firstly, the Hertz contact model and Coulomb friction model was introduced to describe the normal contact force and tangential friction force between the foot and the ground. The second kind of Lagrangian equation was used for dynamic modeling. Secondly, by changing contact parameters to simulate different walking surfaces, numerical simulations were employed to study phenomena such as slipping, bouncing, and falling during walking, so as to obtaining foot-ground contact conditions that optimize the robot′s walking performance. Finally, tools such as bifurcation diagrams and time response plots were used to analyze the walking gait of the robot during the co-evolution of various structural parameters. The research results indicated that reducing the friction coefficient and damping coefficient could cause slipping and bouncing in the gait, respectively, and continued reduction could cause the robot to fall. Among the structural parameters, reducing the hip joint mass and moment of inertia, and increasing the foot radius were more conducive to the robot adapting to a wide range of walking slope angles. Seemed that regardless of how the structural parameters evolved, the robot would experience gait instability due to slipping as the slope angle increased.

268| 200

Trajectory Tracking Control of a Four-wheel Mobile Robot Based on Adaptive Sliding Mode Control[11-18]

GUO Lei¹, ZHANG Yuqing², SONG Yuan¹

Doi:10.13705/j.issn.1671-6833.2025.03.015

Abstract: This study aimed to address the estimation and compensation of unmatched uncertainties with unknown bounds in sliding mode control. A non-singular terminal sliding mode controller was designed for a four-wheel mobile robot. To ensure the existence of the sliding surface, Gaussian process regression (GPR) was employed for online estimation of the unmatched uncertainties. GPR not only estimated the bound of the uncertainties but also provided the mean and variance, which allows for a more robust estimation. On the one hand, the use of GPR for uncertainty estimation could help avoid the use of high-gain control, thereby reducing control chattering. On the other hand, the uncertainty compensation based on the estimates from GPR could enhance the adaptability of the modelbased sliding mode control algorithm. Additionally, an adaptive terminal sliding mode controller was designed based on the proximal policy optimization (PPO) algorithm. A reward function was constructed with the objective of improving control accuracy and minimizing control input chattering, which could enable the adaptive adjustment of the sliding mode controller’s parameters. The stability of the non-singular terminal sliding mode controller was proven through Lyapunov stability analysis. The effectiveness of the proposed control algorithm is validated through numerical simulations. The results demonstrated that the adaptive terminal sliding mode controller based on GPR significantly reduced by 90% while achieving high control accuracy, outperforming traditional control methods.

209| 154

Protective Equipment Wearing Detection Algorithm in Construction Scenarios Based on YOLOv8n[19-25]

LI Jun, ZHOU Keyu, ZOU Jun, ZENG Wenbing

Doi:10.13705/j.issn.1671-6833.2025.03.002

Abstract: In view of the problems of protective equipment detection, such as information interference, uneven illumination and occlusion in the construction scene, a lightweight algorithm with improved YOLOv8n was proposed, which was called YOLO-LA . Firstly, the weighted bidirectional feature pyramid network BiFPN was introduced into the neck, and the underlying details and high-level semantic information were improved through multi-path interactive fusion, the multi-scale feature fusion performance was enhanced, and the detection accuracy of the model for small targets in complex scenes was improved. Secondly, the C2f-ContextGuided module was used to transform the backbone network in the baseline model, and the global context information was used to calculate the weight vector, to refine the joint features of the local features and the surrounding context features, so as to improve the feature extraction ability of the model and reduce the complexity of the model. Then, a new LSCD lightweight detection head was proposed, which used shared convolution to reduce the number of parameters and computations of the model. Finally, EIoU was used to replace the original CIoU, and the border regression was optimized, and the convergence speed and regression accuracy of the algorithm were improved. Compared with the baseline model YOLOv8n, the number of parameters, the amount of computation, and the size of the model were reduced by 61.5%, 43.2% and 58.7%, respectively, and the mAP@0.5 was increased by 1.4 percentage points, and the FPS was 253 frames/s, which could meet the requirements of real-time, accuracy and lightweight of protective equipment wearing detection.

149| 117

AMRs Autonomous Collaboration Task Assignment Method Based on Multi-agent Reinforcement Learning[26-33]

ZHANG Fuqiang^1,2, ZHANG Yanrui^1,2, DING Kai ^1,2, CHANG Fengtian^1,2

Doi:10.13705/j.issn.1671-6833.2025.03.001

Abstract: In order to solve the task autonomy assignment problem of AMR in flexible production, a multi-agent deep deterministic policy gradient (MADDPG) algorithm based on improved multi-agent reinforcement learning algorithm was adopted. The attention mechanism was introduced to improve the algorithm. Firstly, the framework of centralized training decentralized execution was adopted, and then the action and state of AMR were set. Secondly, according to the size of the reward value, the coverage degree of the task node and the completion effect of the task were determined. The simulation results showed that the average reward value of MADDPG algorithm increase 3 than other algorithms, and the training times were reduced by 300 times. It could have faster learning speed and more stable convergence process while ensuring the completion of task allocation.

149| 113

Adaptive Control Method of Lower Limb Exoskeleton Based on Myoelectric Prediction Model[34-41]

SUN Guoan^1,2, ZHAO Ming¹, ZHANG Tingfeng², ZHANG Bi¹

Doi:10.13705/j.issn.1671-6833.2025.03.012

Abstract: In order to improve the adaptability of lower limb rehabilitation robot, such as its difficulty to respond to the patient′s intention in real time, and to adapt to individual motion needs, in this study, an adaptive control method of lower limb exoskeleton was proposed based on myoelectric prediction model. By collecting the surface EMG signals of biceps femoris, rectus femoris and lateral femoris muscles, the EMG prediction model was constructed to predict the expected motion trajectory of patients. Aiming at the uncertainties and model errors of the system, an adaptive sliding mode controller was designed to dynamically adjust sliding mode parameters according to muscle activation, so as to improve the tracking accuracy and compliance of the robot. The myoelectric model and sliding mode controller were tested in an experiment with 5 healthy subjects. The results showed that the RMSE of the model was 7.94 for the hip joint and 9.31 for the knee joint, which could meet the need of trajectory generation. Compared with the traditional PID control, the tracking accuracy of the adaptive sliding mode controller was improved by 28%, which proved the effectiveness of the method.

197| 151

Frequency Control Strategy of Photovoltaic Participation in Power System Based on TD3 Algorithm[42-49]

ZHANG Jianhua, TAO Ying, ZHAO Si

Doi:10.13705/j.issn.1671-6833.2024.06.023

Abstract: To address the challenges posed by the intermittency and randomness of photovoltaic (PV) power output to maintaining stable power system frequency, a rapid frequency regulation method based on the twin delayed deep deterministic policy gradient (TD3) algorithm was proposed. No need to rely on specific mechanistic models, this method could tackle the strong uncertainties associated with PV power generation. Firstly, a simplified model of the PV power generation system was constructed. Secondly, a novel frequency controller was designed leveraging the TD3 algorithm. Lastly, the proposed control strategy was compared with traditional droop control, sliding mode control, and a control strategy based on the deep deterministic policy gradient (DDPG) algorithm. The results demonstrated that, in two scenarios, single-step and continuous-step load disturbances respectively, the frequency deviations based on the proposed control strategy were significantly lower than those of the other three control algorithms. Specifically, the integral of time-weighted absolute error (ITAE) criterion showed a reduction of 41.7% and 31.8% compared to the worst-performing droop control, thoroughly validating the superiority of the proposed control strategy in terms of both dynamic and steady-state performance during frequency regulation.

191| 806

Wind Power Interval Prediction Based on Combined Deep Learning[50-58]

JIANG Jiandong¹, ZHAO Yunfei¹, HAN Wenxuan¹, YAN Yuehao², BAO Wei², LIU Xiaohui²

Doi:10.13705/j.issn.1671-6833.2025.03.020

Abstract: To improve the accuracy of wind power interval prediction, in this study, a combined deep learningbased wind power interval prediction model was proposed. Firstly, to address the imbalance between global optimization ability and local exploration in the traditional dung beetle optimization (DBO) algorithm, an improved version POTDBO was introduced. This algorithm enhanced the global search capability and improved the local search strategy. By optimizing the decomposition number K and penalty factor β in the variational mode decomposition (VMD), thus it improved the decomposition performance of VMD. Secondly, based on the optimized VMD decomposition results, a combined deep learning model, POTDBO-VMD-CNN-BiLSTM, was established. In this model, convolutional neural networks (CNN) were used to extract the spatial features of wind power, and a bidirectional long short-term memory (BiLSTM) network was applied to capture both historical and future signal features in the data. The individual components were predicted and then combined to reconstruct the wind power prediction accurately. To perform interval prediction for wind power, in this study the non-parametric kernel density estimation (KDE) method was introduced to fit the prediction errors of the combined model, then to obtain wind power interval prediction results at different confidence levels. Finally, the proposed model was validated using actual operation data from a wind farm in Xinjiang. Simulation results showed that, at a 95% confidence level, compared to the Gaussian and T-distribution models, the proposed method reduced the prediction interval coverage width (CWC) by 0.103 6 and 0.171 4, respectively, while improving the interval prediction accuracy.

148| 153

VMD-LSTM Short-term Wind Speed Prediction Model Based on BWO and WOA[59-66]

JIA Shihui^1,2 , LIU Lifu¹, CHI Xiaoni^2,3, LI Gaoxi⁴

Doi:10.13705/j.issn.1671-6833.2024.06.014

Abstract: In view of the power fluctuation and randomness existing in the operation of wind turbine networks, to improve the accuracy of wind speed prediction and the stability of wind turbine operation, in this study a VMDLSTM short-term wind speed prediction model was proposed based on the beluga whale optimization and the whale optimization algorithm. Firstly, the Beluga optimization algorithm was used to optimize the number of modes and penalty factors in VMD to obtain the reorganized subsequence. For parameters such as the number of hidden layer nodes, the maximum number of training generations, and the initial learning rate in LSTM, the whale optimization algorithm was used to determine these parameters. Finally, the monomer transplantation ability of LSTM was utilized to predict the data. The results indicated that the VMD-LSTM prediction model based on BWO and WOA proposed in this study achieved RMSE, MAE, and MAPE values of 0.223 4, 0.172 7, and 0.083 7, respectively, on the test set, all of which were lower than those of other comparative models. This validated the effectiveness of the proposed model in short-term wind speed prediction.

129| 167

Analysis of Influencing Factors of Noise Reduction Effectiveness of Reflective Sound Barrier in Metro Access Line[67-74]

XU Ping¹, DU Xuanqi¹, HE Kuang², YANG Yanfeng³

Doi:10.13705/j.issn.1671-6833.2025.03.003

Abstract: In order to improve resident living environment along the metro access line, one section of Zhengzhou metro access line was taken as the example, the fast multi-pole boundary element method was considered, the sound barrier model was constructed with Virtual Lab acoustic simulation software, the wheel rail noise of low-speed subway train was simplified as a two-point sound source, the insertion loss of sound pressure level was adopted to characterize the noise reduction effectiveness of the sound barrier, and the influence of some factors on noise reduction effectiveness were analyzed such as the sound source sound pressure level, sound barrier height, distance between sound barriers and sound sources, the top structure of the sound barrier and the location in the sound shadow area. According to the simulation results, the vertical Y-shaped reflective sound barrier with 4 m height and 3 m distance from the center of dual sound sources was set up on the metro access line, the noise signals were in-situ measured, the noise characteristic parameters were obtained, and the simulation results had good agreement with the test results, the simulation calculation was verified to be reasonable. The closest distance between buildings along the metro access line and the center of the track is 30 meters, the noise of residential buildings with sound barrier along the line meet with relevant regulatory requirements, Y-shaped sound barrier had better noise reduction performance, and the relevant achievements could provide parameters for the noise reduction design and performance evaluation of metro access line.

152| 172

A Method for Optimizing Mountainous Highway Routes Based on Data Optimization Multi-attribute Decision Model[75-81]

GE Wei¹, PENG Zhaohui², XU Bo², LIU Mu², WANG Yawei², ZHANG Yadong¹, WANG Siwei¹

Doi:10.13705/j.issn.1671-6833.2024.06.020

Abstract: There are problems in the optimization of mountainous highway routes, including complex evaluation indicators, difficulty in quantifying qualitative indicators, and the weight of subjectively set indicators do not match the actual situation. In this study, based on the principles of technology, economy, and safety, the Hall three-dimensional structure was to analyzed to explore the influencing factors of mountainous highway routes, and to construct an evaluation index system. cloud model theory was introduced to quantify qualitative evaluation indicators, and the variable weight theory was used to modify the constant weight of evaluation indicators considering the impact of the actual state of evaluation indicators on the evaluation results. Finally, a method for optimizing mountainous highway routes based on TOPSIS was proposed. This method was applied to the optimization of routes for the fourth risk point of the Alcia Highway Project in Bolivia. The results indicated that cloud models could effectively solve the problem of uncertainty in qualitative indicators, which made them difficult to quantify. The closeness degree of the three route schemes was 0.833, 0.606, and 0.684, respectively. Compared with traditional constant weight, the variable weight theory highlighted the influence of extreme indicators on the evaluation results in the evaluation process, and the results were more realistic.

136| 145

An Emergency Strategy for Subway Disruption Based on Resilience Theory[82-89]

CHEN Xiyang¹, LIU Chenhui^1,2,3,4, LIU Ling⁵, PENG Haibo^2,6, ZHANG Wang^2,6

Doi:10.13705/j.issn.1671-6833.2024.06.010

Abstract: To tackle frequent subway delays and interruptions, to address the shortcomings of common emergency strategies, in this study, an emergency strategy for localized subway line interruptions was proposed based on resilience theory. Firstly, the evolution process of subway line performance during emergencies were thoretically analyzed. Subsequently, a resilience evaluation model for subway lines was constructed using indicators such as line connectivity, delay resistance index, and passenger retention rate. Finally, aiming to enhance subway line resilience, a multi-section single-track bidirectional operation emergency strategy was proposed for common incidents of partial interruption on one side of subway tracks. This strategy divided the operable track in the opposite direction of the accident site into several sections, with a train operating in a single-track bidirectional mode within each section. To assess the effectiveness of the proposed strategy, a case study was conducted on the Line 4 of the Changsha subway during weekday morning peak hours. The results indicated that during emergencies, the majority of passengers experienced wait times concentrated within 5-7 minutes, with passenger waiting rates dropping below 50% within 10 minutes. Compared to traditional shuttle operation and single-track bidirectional operation schemes, the multi-section single-track bidirectional operation scheme improved passenger retention rates by 42.4% and 12.7%, respectively, with a corresponding increase in line resilience of 145% and 50%.

160| 164

Ecological Slope Protection Material with Self-compacting Concrete Incorporated with Silt[90-96]

ZHANG Xinyu¹, FENG Wei², SUN Xiao¹, ZHAO Yunpeng¹, LU Hao³

Doi:10.13705/j.issn.1671-6833.2024.06.018

Abstract: Aiming at the problems of large labor demand and high transport loss in manual assembly of prefabricated ecological brick slope protection site, and considering the resource utilization of silt waste, a method of using silt as a cementitious material to prepare self-compacting concrete (SCC) for non-structural ecological slope protection was proposed. This approach replaced prefabricated ecological bricks and could reduce costs while increasing efficiency. In this study, the physicochemical properties of silt were analyzed and the effects of silt admixture on working properties, mechanical properties, and pore structure was investigated. The results indicated that the working performance of SCC declined with the addition of silt. To meet SCC specifications and construction requirements, the amount of water reducing agent increased correspondingly with the increase in silt mixing. The mechanical properties of the material decreased as the amount of silt increases. Specifically, the 28-day compressive strength, split tensile strength, and modulus of elasticity were all reduced. This was due to the effect of silt on the pore structure of SCC, which led to an increase in cumulative pore volume and porosity.The results of this study showed that SCC with 10% silt incorporated could reach the strength of C30 at a slump extension of 700 mm, which verified its feasibility as an ecological slope protection material.

160| 133

Analyzing the Influence of the Number and Layout of Sensors on the Calibration of Urban Drainage Models[97-104]

HUANG Yuan¹, XU Xin’ai¹, ZHAO Min¹, LI Mingyu¹, ZHENG Feifei²

Doi:10.13705/j.issn.1671-6833.2024.06.012

Abstract: Urban drainage models (UDMs) often involved a large number of complex parameters, leading to significant challenges for model calibration. Existing studies focused on calibration methods but neglected the impact of monitoring point placement on the calibration effect. It led to the issues of poor generalization ability and low reliability of the model. In this study, the real-world UDM in the town of Bellinge, Denmark was taken as a case study and optimization methods were utilied to determine the optimal layouts of sensors under different numbers, therefore the impact of the number and layout of sensors on the UDM calibration were analyzed. Results showed that increasing the number of sensors and optimizing the sensor layout could significantly improve the accuracy and robustness of the model calibration. For instance, the average prediction accuracy of the calibration model at flooding nodes with the optimized layout scheme with five water level sensors improved by about 53% compared with the spatially uniform layout scheme. The study also revealed that the UDM calibration problem faced the challenge of "parameter equifinality", which hindered the accurate deduction of true parameter values. However, a calibration model that met practical requirements could still be obtained when there was sufficient observation information. For instance, increasing the number of sensors from one to more than five enhanced the overall prediction performance of the calibration model by over 38%.

139| 165

Research on Vehicle Yaw Stability Analysis Method Based on State Estimation[105-110]

KOU Farong, CHANG Hangtao, WANG Qianlei, FANG Bo

Doi:10.13705/j.issn.1671-6833.2024.06.015

Abstract: Aimed at the problem that the state parameters were difficult to obtain and the analysis results were single in the process of vehicle yaw stability analysis, a two-degree-of-freedom vehicle model was established as a reference model for yaw stability analysis and state estimation. The phase plane was constructed by using the sideslip angle and its angular velocity to analyze the yaw stability of the vehicle, and the adaptive phase plane stability domain based on multi-layer perceptron (MLP) was designed. According to the real-time state of the vehicle and the phase plane stability region, the yaw stability evaluation index was constructed. A vehicle state estimation algorithm based on extended Kalman filter (EKF) was designed, and a vehicle yaw stability analysis method based on state estimation was proposed. In order to verify the effectiveness and practicability of the proposed yaw stability analysis method, simulation tests at 100 km/h and real vehicle tests at 30 km/h were conducted in double lane change conditions. The simulation and real vehicle test results showed that the average error of sideslip angle estimation based on state estimation was less than 0.1°, and the average error of longitudinal velocity estimation was less than 0.03 m/s. This method could quantify the yaw stability from 0 to 1 based on the estimated vehicle state parameter input, reflecting the dynamic changes in vehicle yaw stability.

117| 177

A Dynamic Impedance Matching Method and Frequency Tracking of Piezoelectric Transducers[111-117]

JI Yuebo¹, YANG Yuheng¹, MENG Chenchen¹, PENG Yunfeng²

Doi:10.13705/j.issn.1671-6833.2024.06.013

Abstract: Dynamic impedance matching technology can improve the output power and energy conversion efficiency of piezoelectric transducers. Most of the existing dynamic impedance matching method was intelligent numerical optimization algorithm, but the intelligent algorithm had some problems, such as complex modeling, long iteration time and large amount of calculation. In order to solve these problems, a dynamic impedance matching method based on data fitting was proposed, and the corresponding T-shaped impedance matching network was designed. The proposed method obtained the observed values of the resistance and reactance components of the transducer at the corresponding frequency by fine-tuning the operating frequency, and obtained a set of equivalent circuit parameters of the transducer with the highest degree of fitting based on the minimum residual sum of squares. Combined with the relevant formulas, the matching network element parameters and the series resonant frequency of the transducer were calculated. The frequency tracking function was further implemented. The dynamic impedance matching method was simulated in Python, and the simulation circuit was built in MATLAB/Simulink to simulate the effect of impedance matching and frequency tracking. The results showed that the proposed method could accurately obtain the equivalent circuit parameters of the transducer, the voltage and current signals at both ends of the matching Ttype impedance matching network were basically in phase, the active power was significantly improved, the matching effect was good, and the matching speed was also significantly improved compared with the genetic algorithm.

176| 179

Influence Law of Parallelism Error in Linear Guide Rail on Motion Error of Worktable[118-127]

LI Haitao, GENG Ruilin, WANG Yawen, CHEN Jinglin

Doi:10.13705/j.issn.1671-6833.2025.03.022

Abstract: Most of the existing studies took the entire linear worktable as the research target, and merely obtained the local influence laws of different errors on the one-dimensional linear worktable and failed to fully disclosed the coupling mechanisms among different types of errors as well as the influence laws on the overall motion error of the worktable. In response to this deficiency, in this study, the linear worktable with double guide rails and four sliders was taken as the research target. By means of the equivalent stiffness of the dynamic interface, the constraint stiffness matrix of the sliders was solved. Then, combined with the position and posture state of the worktable after the coordinated deformation of the sliders with the double constraints of the guide rails and the worktable, the error coupling model of the one-dimensional linear worktable was established. Based on the error coupling model, multiple parallelism errors were mapped onto the initial errors of the sliders through homogeneous transformation, and ultimately, the mapping model from the parallelism error of guide rail installation to the motion error of the worktable was established. Based on the mapping model, the influence laws of parallelism errors on the motion error of the worktable were simulated and analyzed, and the consistency of the results was verified through finite element simulation and experiments. The maximum error between the parallelism error mapping model and the finite element model did not exceed 9.8%.This study indicated that different types of parallelism errors had significantly different degrees of influence on the linear worktable. By reducing the parallelism errors of yaw and pitch, the overall error of the worktable could be greatly reduced. The proposed method provided a powerful theoretical support for the error compensation and precision design of linear worktables.

155| 3004

Dynamic Contrastive Representation Enhancement Approach for Keyphrase Prediction[128-135]

GENG Xuelian^1,2, SONG Mingyang^1,2, FENG Yi^1,2, JING Liping^1,2, YU Jian^1,2

Doi:10.13705/j.issn.1671-6833.2025.03.004

Abstract: Keyphrase prediction often fail to fully utilize the complex hierarchies and semantic information within text structures. To address this issue, a novel keyphrase prediction method that enhanced semantic representation, called adaptive contrastive learning for keyphrase prediction(ACL-KP) was proposed. This method introduced an adaptive weighting mechanism to dynamically adjust sample weights, to solve the problem of distinguishing true samples from noise samples during contrastive learning, thereby reducing the impact of misidentifying noise samples and optimizing spatial representation. Additionally, to increase the diversity of training data, Gaussian white noise was incorporated to automatically generate some challenging virtual samples, thus enhancing the semantic representation of documents and keywords. Experimental results on multiple public datasets in the keyphrase prediction field showed that the model improved performance by 2% to 17% in F1@5 and F1@M metrics compared to current state-of-the-art models. Compared to sequence-to-sequence models and unified models, the proposed model demonstrated a more significant performance advantage.

121| 177

Unbalanced Node Classification Based on Minority Class Weighted and Anomalous Connectivity[136-142]

WANG Junfeng, YANG Jiayue, LI Dun

Doi:10.13705/j.issn.1671-6833.2024.06.019

Abstract: The robot detection methods based on GNN ignored the importance of minority class nodes when dealing with class imbalance problems, and did not consider the unique connectivity problem of graph structures, resulting in unsatisfactory node classification performance. Therefore, in response to the shortcomings of existing solutions, in this study, a class imbalanced node classification algorithm was proposed based on minority class weighted and abnormal connectivity margin loss, which extended the traditional imbalanced classification idea in the field of machine learning to graph structured data. Based on GraphSMOTE, minority class weighted aggregation was performed to enhance the feature aggregation of minority nodes. In the oversampling stage, the SMOTE algorithm was used to process imbalanced data, which considering node representation and topology structure. Simultaneously training an edge generator to model relational information and introducing anomalous connectivity margin loss to improve GNN′s perception of connectivity anomalies and enhance the model′s learning of connectivity information.Finally, experiments were conducted on publicly available Weibo, Twitter fake accounts, and BlogCatalog datasets. The comparison results with the five baselines of SMOTE, Re-weight, GraphSMOTE, DR-GCN, and mGNN showed that the average ACC of the algorithm proposed in this study reached 84.3%, with an accuracy improvement of 1.3% compared to the mGNN model on the Kaggle dataset.

113| 141

3D Model-based Visual Recognition Method of Aviation Electrical Connector′s Contacts[143-152]

WANG Yuntao¹, ZHANG Shang’an², XU Yingpeng³, GENG Junhao¹

Doi:10.13705/j.issn.1671-6833.2025.03.005

Abstract: The intelligent insertion assistance technology of aviation electrical connectors based on augmented reality or robotic arms relied on precise prior information such as precise contact positions and sorting information. However, the current acquisition of prior information relied entirely on manual collection, with low accuracy and completeness. To address this issue, in this study, a 3D model-based visual recognition method of aviation electrical connector′s contacts was proposed. This method coupled deep learning and image processing methods. The precise detection and positioning information acquisition of the 3D model contact for aviation electrical connectors was achieved through a two-step contact precise positioning method based on deep learning. Then, based on the circular layering idea, the positioned contacts were completed and sorted. Finally, fully automated, intelligent, and precise visual recognition of complex aviation electrical connector model contacts was achieved, and accurate contact positions and sorting information were obtained. The experiment showed that the contact recognition method proposed in this study was superior to the single deep learning method in both recognition rate and positioning accuracy. Among them, the fusion of YOLOv7 had the best effect, with an average recognition rate of 97.85%, an average positioning error of 0.025 mm, an average positioning time of 69 ms, a missing contact completion rate of 100%, and a sorting accuracy rate of 100%. It could provide accurate and effective prior information for intelligent insertion assistance of aviation electrical connectors based on augmented reality or robotic arms.

140| 170

Pre-publication

Task Offloading Strategy of UAV Edge Computing Based on Deep Reinforcement Learning[10]

WANG Feng, MA Xingyu, MENG Pengshuai, ZHAO Wei, ZHAI Weiguang

Doi:10.13705/j.issn.1671-6833.2025.01.018

Abstract: Aiming at the problems such as lack of infrastructure, high task delay and high bandwidth demand in complex geographical conditions, this paper proposes a multi-stage mobile edge computing system model which combines computing offload and power distribution. In this model, a server equipped with MEC is deployed near the UAV to provide computing services, and the problems such as task offloading, power consumption and computing resource allocation of the UAV are comprehensively analyzed and the measurement methods are given. At the same time, the types of tasks that the UAV can perform and the requirements of the CPU and GPU on the UAV are considered. The problem is expressed as a mixed integer nonlinear problem. Secondly, a task computing offloading algorithm based on deep reinforcement learning is proposed to solve this problem. Based on the improved double deep Q learning algorithm, the algorithm uses deep neural network to find the mapping between drones in deep reinforcement learning, find potential patterns from the state space and estimate the optimal action, and uses model-free DRL method. Enable each drone to make quick unloading decisions based on local observations. In order to verify the effectiveness of the proposed scheme, detailed simulation is carried out in this paper. Simulation results show that the proposed algorithm reduces the average unloading cost by 42.8% compared with LCGP algorithm. Compared with DDPG algorithm, the energy consumption is reduced by 16%. Compared with DDQN algorithm, the task execution delay is reduced by 12.9%

619| 631

Automated penetration testing method based on deep reinforcement learning Noisy Net-A3C algorithm[2]

DONG Weiyu1, LIU Pengkun2, LIU Chunling1, TANG Yonghe1 , MA Yupu 2

Doi:10.13705/j.issn.1671-6833.2024.02.011

Abstract: In the field of automated penetration testing, most existing attack path decision algorithms are based on partially observable Markov decision processes (POMDP), which have problems such as high algorithm complexity, slow convergence speed, and susceptibility to getting stuck in local optima. This article proposes a reinforcement learning algorithm NoisyNet-A3C based on Markov Decision Process (MDP) and applies it to the field of automated penetration testing. This algorithm trains Actor Critic through multiple threads, and the operation results of each thread are fed back to the main neural network. At the same time, the latest parameter updates are obtained from the main neural network, fully utilizing computer performance, reducing data correlation, and improving training efficiency. In addition, adding noise parameters and weight network training update parameters to the training network increases the randomness of the behavior strategy, facilitates faster exploration of effective paths, reduces the impact of data disturbances, and enhances the robustness of the algorithm. The experimental results show that compared with A3C, Q-learning, DQN, and NDSPI-DQN algorithms, the NoisyNet-A3C algorithm converges more than 30% faster, verifying that the algorithm proposed in this paper converges faster

765| 846

Mechanism Analysis of Soil Compaction Grouting Based on Modified Cam-clay Model[2]

XING Haipeng1,2, WU Guanghua1,2, WANG Ge1, CHEN Kunyang1, LI Xiaolong1, ZHANG Bei1

Doi:10.13705/j.issn.1671-6833.2025.01.004

Abstract: The existing compaction grouting simulation method can only analyze the stress distribution after grouting, and cannot obtain the parameter information reflecting the compaction effect of grouting, such as the void ratio and density of soil after compaction. Therefore, the MCC is introduced to describe the mechanical property of soil, and based on the elastic-plastic finite element theory, a simulation method is established to simulate the compaction grouting process of constant density slurry in soil. A more comprehensive and intuitive description of the formation compaction effect is achieved. The compaction grouting simulation analysis was carried out on clay, silty clay and other low permeability soil. Compared with the analytical and experimental results, t he overall average relative errors of the simulated values and analytical solutions of radial stress and void ratio under different grouting pressures are 4.04% and 0.29% respectively , and the average relative errors between the calculated elastic modulus and void ratio and the field test results are 2.85% and 5.69% respectively, which proves the applicability of this method. On this basis, the distribution characteristics of soil density, void ratio and elastic modulus around the grouting column after grouting reinforcement are analyzed. The results show that when the grouting pressure increases from 0.4 MPa to 1.0 MPa at the grouting depth of 1.5 m, the soil density, elastic modulus and void ratio at the grouting hole center of 0.05 m approximate linear changes, and the average change rates are 0.148 g/cm3/MPa, 0.808 and -0.126 MPa-1, respectively. When the grouting pressure is 0.4 MPa, the increase rates of soil density and elastic modulus and the decrease rate of void ratio at 0.05 m from the center of the grouting hole gradually decrease with the increase of grouting depth. Overall, the density and elastic modulus of the soil around the grouting column are greatly increased after grouting reinforcement, and the void ratio is significantly reduced. The soil parameters change less with the distance from the grouting hole, and gradually return to the initial state. Under the same grouting pressure condition, with the increase of grouting depth, the compaction effect gradually weakens

652| 1346

Short Term Wind Power Forecasting Based on Improved Dung Beetle Optimization Algorithm[2]

JIANG Jiandong1, ZHANG Haifenf1,2GUO jiaqi2

Doi:10.13705/j.issn.1671-6833.2025.01.015

Abstract: A short-term wind power prediction model based on POTDBO-VMD-CNN- BiLSTM is proposed in the thesis to improve the accuracy of short-term wind power prediction. Firstly, three strategies are adopted to improve the dung beetle optimization algorithm, including integrating Piecewise chaotic mapping, integrating Osprey optimization algorithm, and integrating adaptive T-distribution perturbation, in order to balance the global exploration and local development capabilities of the dung beetle optimization algorithm and accelerate its convergence speed . Secondly, the improved Dung Beetle Optimization algorithm ( POTDBO) is used to optimize the decomposition number and penalty factor of Variational Mode Decomposition (VMD) to improve the decomposition effect of VMD. Then, the POTDBO-VMD model is used to decompose the wind power . Finally, the decomposed frequency components and residual components are input into the CNN-BiLSTM hybrid model for prediction, and the prediction results of each frequency component and residual component are sequentially reconstructed to obtain the wind power prediction results. The proposed model is experimentally tested using actual data from wind farm s in Xinjiang and Jilin . Compared with the CNN-BiLSTM model , the results show that the model in this thesis increases by 4.21% and 7.14% on R 2 respectively, demonstrating better prediction accuracy demonstrates better prediction accuracy

651| 1274

Steel Surface Defect Detection Based on Improved YOLOv5 Algorithm[2]

YAN Yu，JING Yuchao，SHI Mengxiang，YANG Duo

Doi:10.13705/j.issn.1671-6833.2025.01.007

Abstract:

In order to solve the problem of low efficiency of steel defect detection and economic loss caused by false detection, an improved YOLOv5 algorithm was proposed for steel defect detection.Under the condition of keeping the original YOLOv5 detection layer unchanged, the improved algorithm added three auxiliary branches with adaptive weights to extract the shallow information of the YOLOv5 network, and the auxiliary branches could also enhance the gradient flow of the whole network, which made the training effect better. The EMA attention mechanism was added to the main part of the network, and the weighted feature information of the EMA module could help the model better focus on and understand the important target features. SIoU was used instead of the CIoU loss function, and the angle loss and shape loss introduced by SIoU could make the anchor frame more fast and accurate in the regression process to improve the stability and robustness of the detection. Through experiments on the NEUDET dataset, the proposed algorithm had an accuracy improvement of 3. 7 percentage points compared with the original YOLOv5s, and had better detection performance than other mainstream algorithms.

715| 876

Modulation Recognition Algorithm Based on Multi-Criteria Fusion and Intelligent Decision[2]

XIA Zhaoyu, LIN Yujie, HU Chunyuan, WU Zihao

Doi:10.13705/j.issn.1671-6833.2024.04.015

Abstract: Aiming at the requirement of high modulation order identification and the difficulty of modulation identification in low signal-to-noise ratio environment in 6G communication, a modulation recognition algorithm based on multi-criteria fusion and intelligent decision was proposed by combining artificial intelligence technology and modern signal processing technology. The algorithm was divided into two parts: multi-criteria fusion network and intelligent decision network. The multi-criteria fusion network calculated the higher-order cumulative extensions of the standard modulation signals, traversed all the potential thresholds by using local optimal solutions, and determined the judgment thresholds by Gini coefficient and the entropy of certainty gain. The intelligent decision network adopted a CART architecture to recognize the modulation format of unknown signals using the determined judgment thresholds, and the model was iterative optimized using a pruning algorithm to obtain the finally optimal decision tree, forming a modulation recognition algorithm based on multi-criteria fusion and intelligent decision making. Experimental results showed that the algorithm could accurately recognize 16QAM, 64QAM, 128QAM, 1024QAM, 2PSK, 4PSK, 8PSK, 2FSK, 4FSK at 0dB SNR, and the comprehensive recognition accuracy reached 99.4%. Compared with other methods, the modulation recognition accuracy and the types of recognizable modulation have been improved

518| 648

COP-based Feature Extraction and Analysis of Plantar Pressure in Tai Chi Stake[2]

WAN Hong1,2, GU Zhi yuan1,2, LI Mengmeng1,2

Doi:10.13705/j.issn.1671-6833.2025.01.016

Abstract: To explore the inherent characteristics of Tai Chi Stake training and digitally interpret its movement essentials, effective plantar pressure detection equipment was used to collect plantar pressure signals from participants, and the relative position, time-domain, frequency-domain, and regularity indicators of the center of pressure (COP) movement between the expert group and the trainee group were compared and analyzed. The results show that the relative position of COP of the expert group is closer to 50% compared to the trainee group. In terms of time-domain indicators, the root mean square of COP movement of the expert group is significantly larger than that of the trainee group in both the left -right and front-back directions, while the frequency-domain peak frequency of the movement is significantly lower than that of the trainee group in both directions. For the sample entropy analysis of COP that measures the regularity, the expert group showed significantly lower values in both the left-right and front-back directions compared to the trainee group. These results indicate that the Tai Chi Stake COP of the expert group is more concentrated in the central position, reflecting the technical essential of " stand straight and be centered". The regular low-frequency adjustment reflects the characteristic of " motion in quiescence" in Tai Chi Stake

735| 938

HE L. Key Target Person Detection and Tracking Based on Fragmented Video Information[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2023[2]

MA Liuyang1, HU Zhengzheng1, LI Wuhua1

Doi:10.13705/j.issn.1671-6833.2025.01.017

Abstract: To address the problem of target identity (identification, ID) fluctuation during target tracking, affecting the time-sensitive target recognition, this study proposed an "detection-decision" time-sensitive target recognition method (AR-SSVEP-YOLOV3) that integrates augmented reality (AR) technology, steady-state visual evoked potentials (SSVEP), and YOLOv3. The target perception module obtains the front-end scene video and presents it in real-time through an AR headset. The YOLOv3 algorithm completes the detection of sensitive targets in the scene video, and the AR-SSVEP EEG processing module decodes the EEG data of the subject during ID changes to identify time-sensitive targets. The correct recognition rate of time-sensitive targets is compared and analyzed, and the average improvement in the recognition rate of AR-SSVEP-YOLOV3 time-sensitive target recognition method compared to the YOLOv3 algorithm is about 40%, and the average improvement compared to the YOLOv3-Sort algorithm is about 15%. The experimental results show that the AR-SSVEP-YOLOV3 time-sensitive target recognition method can reduce the influence of target ID fluctuation on time-sensitive target recognition and improve the human-computer interaction ability and the correct recognition rate of time-sensitive targets.

681| 706

A Path Planning Method for Parallel Parking in Constrained Parking Channels[2]

QIN Dongchen, ZHANG Wencan, WANG Tingting, CHEN Jiangyi

Doi:10.13705/j.issn.1671-6833.2024.05.006

Abstract: Aiming at the problem of long time and low success rate of automatic parking planning in restricted parking channels, an improved hybrid A * algorithm for path planning is proposed. Firstly, the parking path is divided into two parts: the forward pose adjustment section and the backward reverse parking section. Secondly, the collision risk cost is introduced into the hybrid A * algorithm, the node expansion method is improved, and the collision detection is carried out by judging whether the vehicle contour line intersects with the obstacle line, so as to improve the real-time and safety of the parking segment planning. Finally, the objective function is designed with the path length, smoothness and deviation as indexes, and the initial path is smoothed by quadratic programming to get the final path. The improved algorithm and the original algorithm are simulated by MATLAB. The results show that the improved algorithm can obtain a smooth and collision-free parking path under the constrained parking channel, and the search time is reduced by 23.8% compared with the hybrid A * algorithm, and the obtained path is safer and easier to track.

518| 642

Dynamic Fusion of Attention Mechanism Imputation Model forMissing Data of Power Load[1-8]

ZHAO Dong, LI Yarui, WANG Wenxiang, SONG Wei

Doi:10.13705/j.issn.1671-6833.2024.05.004

Abstract: In order to improve the accuracy of missing value filling of power load data and ensure the efficient follow-up data analysis and application, a filling model based on dynamic fusion attention mechanism is proposed. The model consists of an attention mechanism module and a dynamic weighted fusion module, and the deep association between features and timestamps is mined through two different attention mechanisms of the attention mechanism module. The learnable weights are assigned to the two outputs of the attention mechanism module by the dynamic weighted fusion module to get the feature representation. Finally, the feature representation is used to replace the values at the missing positions to obtain accurate filling results. The proposed model is validated using the meteorological and load dataset of a certain area of New York City and the UCI power load dataset, and the experimental results show that DFAIM has certain advantages over statistical, machine learning, and deep learning filling models in MAE, RMSE, and MRE.

530| 679

2025

01 02 03

2024

01 02 03 04 05 06

2023

01 02 03 04 05 06

2022

01 02 03 04 05 06

2021

01 02 03 04 05 06

2020

01 02 03 04 05 06

2019

01 02 03 04 05 06

2018

01 02 03 04 05 06

2017

01 02 03 04 05 06

2016

01 02 03 04 05 06

2015

01 02 03 04 05 06

2014

01 02 03 04 05 06

2013

01 02 03 04 05 06

2012

01 02 03 04 05 06

2011

01 02 03 04 05 06

2010

01 02 03 04 05 06

2009

01 02 03 04

2008

01 02 03 04

2007

01 02 03 04

2006

01 02 03 04

2005

01 02 03 04

2004

01 02 03 04

2003

01 02 03 04

2002

01 02 03 04

2001

01 02 03 04

2000

01 02 03 04

1999

01 02 03 04

1998

01 02 03 04

1997

01 02 03 04

1996

01 02 03 04

1995

01 02 03 04

1994

01 02 03 04

1993

01 02 03 04

1992

01 02 03 04

1991

01 02 03 04

1990

01 02 03 04

1989

01 02 03 04

1988

01 02 03 04

1987

01 02 03 04

1986

1985

1984

1983

1982

1981

1980

[0]

Abstract:

15230| 9

Cover-Table of Contents-Back Cover[0]

Abstract:

9491| 0

Cover-Table of Contents-Back Cover[0]

Abstract:

8961| 0

Cover-Table of Contents-Back Cover[0]

Abstract:

8447| 0

A Survey on Genetic Programming to Image Analysis[3-13]

Bi Ying,Xue Bing,Zhang Mengjie

Doi:10.13705/j.issn.1671-6833.2018.06.018

Abstract: As an evolutionary computation (EC) technique, Genetic programming (GP) has been widely applied to image analysis in recent decades. However, there was no comprehensive and systematic literature review in this area. To provide guidelines for the state-of-the-art research, this paper presented a survey of the literature in recent years on GP for image analysis, including feature extraction, image classification, edge detection, and image segmentation. In addition, this paper summarised the current issues and challenges, such as computationally expensive, generalisation ability and transfer learning, on GP forimage analysis, and pointd out promising research directions for future work.

8279| 4591

Cover-Table of Contents-Back Cover[0]

Abstract:

7398| 0

A New Cryptophane A-coated SAW Methane Gas Sensor[6-23]

Wang Wen1,Hu Haoliang1,He Shitang1,Pan Yong2,Zhang Caihong3

Doi:10.13705/j.issn.1671-6833.2016.03.026

Abstract: In view of the current situation that the traditional methane sensor technology is difficult to imple-ment the field detection and monitor on methane gas, a novel room-temperature SAW methane gas sensor coa-ted with cryptophane-A sensing interface is proposed by utilizing the supermolecular compound cryptophane-A’ s specific clathration to methane molecules. The sensor was composed of differential resonator-oscillators with excellent frequency stability, a supra-molecular CrypA coated along the acoustic propagation path, and a frequency acquisition module. The supramolecular CrypA was synthesized from vanillyl alcohol using a three-step method and deposited onto the surface of the sensing resonators via dropping method. Fast response and excellent repeatability were observed in gas sensing experiment, and the estimated detection limit and meas-ured sensitivity in gas dynamic range of 0 . 2% ~5% was evaluated as ~0 . 05 % and ~184 Hz/%, respec-tively. The measured results indicated the SAW sensor was promising for under-mine methane gas detection and monitor.

7276| 4432

Cover-Table of Contents-Back Cover[0]

Abstract:

7237| 0

Urban Optimal Capital Structure Analysis based on Random Forest and Multi-objective Particle Swarm Optimization[14]

Li Yanyan 1,Yang Haotian 2,Zeng Yufan 3

Doi:10.13705/j.issn.1671-6833.2019.04.028

Abstract: Urban capital structure was a complex?problem affected by multi-factors and multi-objective particle.This paper attempt ed to explore a scientific and appropriate d algorithm to construct the optimal capital structure model under the influence of multi-objective and multi-factors to analyze the situation of urban capital structure.First, the data in history could find the relationship among features of the data in history by using the regression characteristics of random forest. Then, the multi-objective particle swarm optimization algorithm was used to find values of the features that achieve the best results according to the existing relationship features. Then finding the most correlate data from the historical data based on the best eigenvalues of these effects. Therefore, the cities and the years with relatively better capital structure allocations are analyzed. We could play a good role in the reference and development of each city by continuously learning these superior structural configurations

7195| 3726

Numerical Simulation of Multi-phase Flow Mixing in Stiring Tank Based on EDEM-FLUENT Coupling[79-84]

Wang Jianming; Qiu Qinyu; He Xunchao

Doi:10.13705/j.issn.1671-6833.2018.05.015

Abstract: By means of EDEM-FLUENT simulation and VOF(Volume of Fluid) method and Euler-Lagrangian model, a mixture model of discrete solid, continuous liquid and gas phase was constructed to simulate the three-phase flow with solid-liquid-gas in a stirring tank. The effect of the moving state of solid particles in stirring tank and free liquid level were explored. The gas-liquid continuous phase modeling based on VOF method using FLUENT software could capture gas-liquid interface well and the model was closer to the actual working condition. Based on the Discrete Element Method(DEM), the discrete element modeling of solid particles was established and its position information in the tank was simulated intuitively by the joint simulation of the two software. The dispersion of solid particles was consistent with the results obtained by Euler method.

6983| 3672

Cover-Table of Contents-Back Cover[0]

Abstract:

6864| 0

Semi-coupled Dictionary Learning Super-resolution Reconstruction Model with Detail Constraint Factor[59]

Huang Yuda; Wang Yanran; Niu Sijie;

Doi:10.13705/j.issn.1671-6833.2021.03.010

Abstract: In order to improve the super-resolution reconstruction quality of single image, an improved learning based super-resolution approach was proposed in this paper. To tackle the problem of low details of semi-coupled dictionary learning super-resolution algorithm, the paper presented learning strategy where detail constraint factor and semi-coupled dictionary learning were performed in turn. In reconstruction stage, detail constraint factor was designed by the gradient in both horizontal and vertical direction. Combined with semi-coupled dictionary learning, detail constraint factor was used to further improve the super-resolution reconstruction quality. In order to improve the contribution of detail constraint factor on preserving boundary information, the adaptive regular parameter was explored via the approximate Laplacian distribution of edge difference. Compared with the semi coupled dictionary learning super-resolution algorithm, the peak signal-to-noise ratio of this method was increased by 1.5% on average. Experiments demonstrated that the proposed method could achieve better reconstruction effect in both subjective and objective evaluation and improve the quality of super-resolution.

6820| 1306

Analysis on Ultimate Bearing Capacity of Ground Foundation Adjacent to Slope Based on Bilateral Asymmetry Failure Mode[85-91]

Jiang Yang1,Guo Jiankun 1，Wang Xiaomou 2，Hou Chaoqun 3

Doi:10.13705/j.issn.1671-6833.2018.06.016

Abstract: In the field of engineering construction, foundations were often placed adjacent to slopes. In the present research work, the evaluation of the maximum bearing capacity of slope foundations lacked a sufficientrate method. A bilateral asymmetry slip failure model for ground foundation adjacent to slope was develthe strength of soil on the side of flat ground was reduced and this is characterized by a mobilization factor. Base on limit equilibrium method and superposition principle, three bearing capacity factors were ex-pressed. The upper bound bearing capacity for ground foundation adjacent to slope was deduced based on limitanalysis approach. Centrifugal model tests were used to verify the theoretical analysis results; and thetion and failure characteristics of these foundations were studied. In addition the influence of variousuch as the contact conditions of the foundation, the location of the foundation, and the height of slope on themaximum bearing capacity of these foundation

6499| 3570

Research on Speech Recognition Based on Improved LSTM Deep Neural Network[63-67]

Zhao Shufang, Dong Xiaoyu

Doi:10.13705/j.issn.1671-6833.2018.02.004

Abstract: The language model based on neural network LSTM structure, the LSTM structure used in the hidden layer unit, the structure unit comprises a memory unit which can store the information for a long time, which has a good memory function for the historical information. But the LSTM in the current input information state9 does not affect the final output information of the output gate, get less historical information. To solve the above problems, this paper puts forward based on improved LSTM (long short-term memory) modeling method of network model. The model increases the connection from the current input gate to the output gate, and simultaneously combines the oblivious gate and the input gate into a single update. The door keeper input and forgotten past and present memory consolidation, can choose to forget before the accumulation of information, the improved LSTM model can learn the long history of information, solve the drawback of the LSTM method is morerobust. This paper uses the neural network languag LSTM model based on the inproved model on TIMIT data sets show that the axxuracy of test. The results illustrate that the improved LSTM identification error rate is 5
% lower than the standard LSTM identification error rate.

6492| 3329

Study on Numerical Simulation of The Steam Turbine Blade Aerodynamic steam turbine increases.Performance Optimization Based on ANSYS CFX[1-6]

Zhou Junjie, Wang Pu, Zhou Jinfang

Doi:10.13705/j.issn.1671-6833.2017.01.008

Abstract: The analysis was held with the 125MW axial flow steam turbine impulse stage blade.The three-dimensional numerical simulation and optimization were conducted by using the commercial software ANSYS CFX.The results showed that the pressure distribution of blade surface reduced,and the radial secondary flow loses was controlled effectively,with optimizing the structure geometric parameters such as ellipticity of the leading edge and trailing edge,relative pitch,inter-stage ratio,and so on.Isentropic efficiency increased by 0.43％,the total pressure loss coefficiency decreased about 0.005.After the optimization,the aerodynamic performance of the blade increased,and the energy loss in the blade decreased and the efficiency of steam turbine increased.

6467| 4173

Review on Modeling and Control of Proton Exchange Membrane Fuel Cell[61]

Han Chuang, Wu Lili

Doi:10.3969/j.issn.1671-6833.2015.06.012

Abstract: For the modeling and control of proton exchange membrane fuel cells, the empirical model and mechanism model based on polarization curve and parameter dimension are summarized, the electrochemical steady-state model and dynamic model based on electrochemical reaction, temperature, pressure and other factors are analyzed, and the intelligent method model based on neural network identification, swarm intelligence algorithm and support vector machine is introduced.The existing intelligent control strategies of proton exchange membrane fuel cells are summarized. Finally, it is pointed out that it will be a development direction of modeling to optimize the model parameters and environmental parameters of proton exchange membrane fuel cells by using swarm intelligence algorithm. The generalized Hamilton theory can also be tried to be used in the modeling of proton exchange membrane fuel cells.At the same time, the intelligent control strategy combining the new algorithm will become the research trend of proton exchange membrane fuel cell control.

6466| 5322

Preparation and applications of molybdenum disulfide nanoplate based nanocomposites[18]

CHEN Deliang,DONG Huina,ZHANG Rui

Doi:10.3969/j.issn.1671-6833.2015.06.005

Abstract: Molybdenum disulfide ( MoS2 ) with a typical layered structure easily forms few-layered MoS2 nanosheets，and has a wealth of optical，electrical and catalytic performance with wide application potentials in areas such as photo-electrical and energy conversion． The preparation of few-layered MoS2 nanocrystals and MoS2-based nanocomposites using molybdenum-containing chemicals as starting materials by wet-chemical and vapor-deposition methods are the cutting-edge focuses of recent research． However，the synthesis of MoS2 nanocrystals from chemical reagents with a long route is not low-carbon and environment friendly． Molybdenite is a typical layered mineral and composed of layered MoS2 units． The amount of molybdenite in China is huge and it is a green and low-carbon way to prepare few-layered MoS2 nanomaterials via the intercalation-exfoliation strategy using the purified molybdenite as the direct raw materials．

6404| 9056

Preparation of Zirconium Oxide / Graphene Composites and their Adsorption Properties for PO43-[23-28]

Shi Chunyan1,Fan Bingbing1,Li Yaya1,Hu Yongbao1,Zhang Rui2

Doi:10.13705/j.issn.1671-6833.2017.01.009

Abstract: In this work,graphene oxide (GO) was prepared by an improved Hummers method.Zirconia/graphene composites (ZrO2/rGO) were rapidly synthesized by hydrothermal method with Zr(OH)4/rGO as precursor prepared by ultrasound-stirred-coprecipitation.The adsorption capacity of Zr (OH) 4/rGO and ZrO2/rGO composites decreased with the increase of pH value and increased with the increase of phosphate concentration and the solution temperature.The maximum adsorption capacities of Zr (OH)4/rGO and ZrO2/rGO composites were 81.84 mg/g and 63.58 mg/g respectively at pH 2.0.The adsorption kinetics of these two adsorbents accorded with the pseudo-second-order model and isothermal adsorption complied with the Langmuir isotherm equation.The results of its recycling properties showed the adsorption capacity decreased for the Zr (OH) 4/rGO samples,while ZrO2/rGO samples were almost the same as the initial adsorption performance.

6373| 4442

Optimized Echo State Network on the Basis of Mutual Information and Just-In-Time and its application[1-6]

Zhang Heng, Wang Heshan

Doi:10.13705/j.issn.1671-6833.2017.05.018

Abstract: To improve the adaptability of echo state network (ESN),an optimization method based on mutual information (MI) and Just-In-Time (JIT) learning was proposed in this paper to optimize the input scaling and the output layer of ESN.The method was named as MI-JIT optimization method and the obtained new network was MI-JIT-ESN.The optimization method mainly consists of two parts.Firstly,the scaling parameters of multiple inputs were adjusted on the basis of MI between the network inputs and outputs.Secondly,based on JIT learning,a partial model of output layer was established.The new partial model could make the regression results more accurate.Further,a multi-input multi-output MI-JIT-ESN model was developed for the fed-batch penicillin fermentation process.The experimental results showed that the obtained MI-JIT-ESN model performed well,and that it had better adaptability than ESN model without optimization and other neural network models.

6323| 4455

Moth-flame Optimization Algorithm Based on Chaotic Initialization and Gaussian Mutation[53]

Liu Qian; Feng Yanhong; Chen Yingying;

Doi:10.13705/j.issn.1671-6833.2021.03.009

Abstract: Moth-flame optimization algorithm (MFO) has some drawbacks in solving optimization problems, such as low precision and high possibility of being trapped in local optimum. A modified MFO algorithm based on chaotic initialization and Gaussian mutation is proposed. Firstly, the cube chaotic map is used to initialize the moth population, which makes the moth more evenly distributed in the search space. Then, Gaussian mutation is adopted to disturb a few poor individuals to enhance the ability of escaping the local optimum. Finally, Archimedes curve is introduced to expand the search scope and strength the exploration ability in the unknown field. A series of experiments are carried out on CEC14 test function set and 21 extensible Benchmark functions. Compared with standard moth-flame optimization algorithm, genetic algorithm, artificial bee colony algorithm, particle swarm algorithm, differential evolution algorithm, flower pollination algorithm, and butterfly optimization algorithm, the results demonstrate that the proposed algorithm is strengthened in obtaining solutions with better quality and convergence.

6214| 1883

Design of a Cooker Anti-overheating System based on Non-contact Temperature Measurement[18-22]

Li Yifeng, Mao Xiaobo, Yang Yihang, Zhu Feng

Doi:10.13705/j.issn.1671-6833.2017.05.021

Abstract: In order to prevent the serious safety problem caused by the dry pot burning and stove explosion and firing,an anti-overheating system was designed.The system of infrared temperature sensor MLX90614 on the bottom of the pot was used to realize the non-contact real-time temperature monitoring.The real-time temperature data was collected and processed by the STM32 microcontroller and SMBus.When the temperature of the bottom of the boiler was beyond the normal heating range,the temperature monitoring module could send a voice alarm.When the threshold value of the dry burning temperature was reached,the gas circuit could be cut off by the control circuit serially connected in the thermocouple temperature detection circuit.Experimental results showed that the proposed system could cut off the gas path once the preset temperature reached and prevent the dry pot burning effectively.

6194| 5579

The Haze Plate Recognition System Based on PSO-RBF Neural Network[46-50]

Mao Xiaobo, Zhang Qun,Liang Jing, Liu Yanhong

Doi:10.3969/j.issn.1671-6833.2017.04.002

Abstract: In this paper,a new algorithm of license plate recognition in the hazy weather was designed.Firstly,defogging operation was introduced for license plate image in the environment of hazy by using improved dark channel prior.Then after the pretreatment,positioning,segmentation and extraction,coarse grid characteristic matrix is obtained.Finally,radial basis function (RBF) neural network,which was optimized by particle swarm algorithm in advance,was used to identify the character.The experiment results showed that the improved algorithm not only had a good effect on haze removal,but also reduced the duration of defogging,which effectively improve the license plate recognition speed and accuracy in fog and haze weather.

6186| 4347

System for automatic recognition and renewal of driving license[13-17,22]

Maling1,Jiang Huiqin1,Liu Yumin2

Doi:10.13705/j.issn.1671-6833.2017.05.013

Abstract: In order to meet the practical requirements of automatic application and renewal of driver’s license,a high speed system for automatic recognition of driver’s licenser was designed and implemented.The hardware was designed to capture the image of the driver’s license that contained the smallest identifiable features.Because of the complex background such as the shadow line and so on in the driver’s license images,the existing recognition algorithms had the low recognition accuracy,universality and robustness problems.This paper first solved the segmentation difficulties for uneven illumination,noise,tilt and shadow line character by combined adaptive binarization and morphological processing.Then,the Blob analysis was used to extract the important local features of the driver’s license,and the recognition accuracy was further improved by using the prior information and the correlation matching algorithm.The experimental results showed that not only the false recognition rate was 0,but also the practical products was developed,and the better social effects were achieved.

6150| 4240

Piles Kind Optimal and Sheet Pile Adaptability Analysis in Highway Extension Projects[44-49]

Li Haibin1,Ke Shengwang2,Shen Yanjun2

Doi:10.13705/j.issn.1671-6833.2017.05.006

Abstract: With the increasing of highway extension projects and widely use of sheet piles in railway construction,the mechanical behavior of extension embankment was analyzed through simulating different kinds of pile and load of different positions.Then the optimal pile kind and the most unfavorable load position were proposed.Through continuous observing of settlement in sheet pile section and CFG pile section,the optimal adaptability of sheet pile was showed in extension projects.The analysis results showed that the effect on settlement of PTC pile,CFG pile and cement mixing pile was gradually decreased.The PTC pile and CFG pile should be firstly selected from the options of controlling settlement.The most unfavorable load position was in new embankment and its quality was the key control point in construction.The effect on decreasing differential settlement was appeared in process of semi-rigid base construction,and it would be even obvious in pavement construction.The sheet pile was an effective supplement to traditional soft soil treatment methods.It had better adaptability and foreground in highway extension projects.

6131| 4071

A Survey of Evolutionary Algorithms for Large Scale Optimization Problem[15-21]

Liang Jing1,Liu Rui1,Qu Boyang2,Yue Caitong1

Doi:10.13705/j.issn.1671-6833.2017.06.016

Abstract: Based on the characterisities of large-scale problems, lager-scale optimization were grossly analyzed. This paper introduced some methods for lager-scale problems.The methods included the initialization method, decomposition strategy, updating strategy and so on. This paper mainly focued on the search strategy, update strategy, mutation strategy and cooperative coevolution. Meanwhile, the characteristics of lager-scale optimization algorithm testing function set and evaluation method were listed. Finally, the future research directions were given.

6119| 4774

Probabilistic Conditional Preference Network Assisted Interactive Genetic Algorithm and Its Application[1-5]

Sun Xiaoyan, Zhu Lixia, Chen Yang

Doi:10.13705/j.issn.1671-6833.2017.06.001

Abstract: Interactive evolutionary algorithms with user preference implicitly extracted from interactions of user are more powerful in alleviating user fatigue and improving the exploration in personalized search or recommendation. However, the uncertainties existing in user interactions and preferences have not been considered in the previous research, which will greatly impact the reliability of the extracted preference model, as well as the effective exploration of the evolution with that model. Therefore, an interactive genetic algorithm with probabilistic conditional preference networks (PCP-nets)is proposed , in which, the uncertainties are further figured out according to the interactions, and a PCP-net is designed to depict user preference model with higher accuracy by involving those uncertainties. First, the interaction time is adopted to mathematically describe the relationship between the interactions and user preference, and the reliability of the interaction time is further defined to reflect the interactive uncertainty.The preference function with evaluation uncertainty is established with the reliability of interaction time. Second, the preference weights on each interacted object are assigned on the basis of preference function and reliability. With these weights, the PCP-nets are designed and updated by involving the uncertainties into the preference model to improve the approximation. Third, a more accurate fitness function is delivered to assign fitness for the individuals. Last, the proposed algorithm is applied to a personalized book search and its superiority in exploration and feasibility is experimentally demonstrated.

6110| 4717

Effect of particle diameter and particle axial distribution on heat and mass transfer inside adsorption heat transformer[17-22]

Sheng Zunrong1,Xue Bing1,Liu Zhouming1,Wei Xinli2

Doi:10.3969/j.issn.1671-6833.2017.01.011

Abstract: A direct-contact method of zeolite adsorption liquid water was adopted to enhance heat and mass transfer rate within adsorption heat transformer.Hot water was recycled to generate superheated steam directly,and then saturated zeolite would be regenerated by drying gas.The reactor with was filled spherical zeolite with same mass and different diameters.The mass of steam generated by small particle packed bed was 64.89％ higher than that generated by big particle packed bed.The maximum steam temperature and gross temperature life had increased by about 37C.Experiments of two kinds of packed types in double layer reactor (finecoarse bed and coarse-fine bed) have shown that small particle played a more effective role for the heating of steam and packed bed;the mean maximum temperature of the steam at the top of fine-coarse bed is 37.23％ higher than that of coarse-fine bed and the lasting time of the maximum temperature is decreased by 14.25％.The steam generation rate of fine-coarse bed was 16.18％ higher than that of coarse-fine bed,which is more efficient in steam generation.In regeneration process,drying time of upper reactor was 25.03％ shorter than coarse-fine bed.It concluded that fine-coarse bed was more effective for zeolite regeneration.

6076| 4323

Research on control of permanent magnet synchronous linear motor based on energy forming[1-5]

JIAO Liu-cheng,YAO Tao

Doi:10.3969/j.issn.1671-6833.2015.03.001

Abstract: In view of the speed control problem of the linear permanent magnet synchronous motor ( L.PMSM) ,which is viewed as an energy-transformation device,from the viewpoint of energy shaping,applying port-con-trolled Hamultonian with dissipation and passivty-based control theory,the port-controlled Hamltonan modelof LPMSM is deduced. Based on the Hamiltonian structure,the desired Hamiltonian function of the closed-loop system is given,and the speed controller is designed by using the method of interconnection and dampingassignment. In the design,the Hamiltonian function is used directly as the storage function,and the systemcan achieve the required performance and bring more definite physical meaning on the condition of satisfyingpassivity. The simulation results show that the closed-loop control system can respond quickly to changes inload resistance and has good robustness.

6052| 4181

Electric field analysis in electrospinning based on COMSOL Multiphysics[46]

LIU Zhenghua1, WANG Jing2,DU Haiying’1,2

Doi:10.13705/j.issn.1671-6833.2016.03.010

Abstract: In order to solve the problem that electrospinning process is hard to control，FEA tool softwareCOMSOL Multiphysics was used to simulate the the electric field orientation within the electrospinning. Basedon the vector maps and contour lines, the electric fields distribution was analyzed. Which includes single-nee-dle electrospinning device，electrospinning device with circle and orparallel auxiliary electrodes. Experimentwith parallel auxiliary electrodes was conducted，and the deposition area with the ellipse shape matched thesimulation result.

6039| 3569

The filtration method of cliff point cloud vegetation based on the surface orthographic projection[77-82]

Li Cailin, Chen Wenhe, Wang Jiangmei, Tian Pengyan, Yao Jili

Doi:10.13705/j.issn.1671-6833.2016.06.005

Abstract: Cliff and steep slope are important landscape elements of topographic map, and these elements play a very important role in the construction of the ecological environment and prevention of geological disasters, etc. However, it is unfavorable to observe and process data because of vegetation occlusion on cliff. In this paper, we present a cliff vegetation filtration method based on the principle of surface orthographic projection. Firstly, transform the original three dimensional point cloud of cliff to the spatial cartesian coordinate system, whose xy plane is the cliff face and z-axis is perpendicular to the direction of the cliff surface. Then the grid on the xy plane is divided to establish local grid Digital Terrain Model ( DTM) by fitting surface, and the vegeta-tion points can be extracted through setting a reasonable distance threshold. Finally, after inverse projection transformation, cliff rocky points preserved are mapped to the original spatial coordinate system. The experi-mental analysis using actual cliff point cloud data shows that the cliff point cloud vegetation filtering method based on the surface orthographic projection is feasible and effective.

6038| 3809

Preparation of Supported Phosphotungstic Acid Catalyst andlts Application in the Synthesis of BEA[49-53]

WAN Ya-zhen,LIU Ya-nan,CHEN Di

Doi:10.3969/j.issn.1671-6833.2015.04.012

Abstract: PTA supported catalyst was prepared by dip roasting method for the synthesis of 2-(4’ -ethyl benzoyl) benzoic acid (BEA) from phthalic anhydride and ethyl benzene as raw materials and chlorobenzene as solvent.The experimental results showed that when the load of PTA was 30%(mass fraction) and the roasting temperature was 300℃, the catalytic activity of PTA was more than doubled with SiO2 as the carrier.The effects of XRD on loading capacity and NH3-TPD on calcination temperature were analyzed. Ft-ir and BET were used to characterize PTA/SiO2 catalysts.The reuse performance of PTA/SiO2 catalyst was investigated, and the results showed that the original catalytic activity of PTA/SiO2 was still maintained after repeated use.

6024| 3994

Effects Evaluation of the Impact on Carbon Emission Intensity by Consumption of Main Types of Energy in China Based on LSDV Estimation[90-94]

Wei Ran

Doi:10.13705/j.issn.1671-6833.2019.02.010

Abstract: Impact effects on carbon emissions intensity by population, per capita GDP, and main types of energy in China were evaluated with the fixed effect model based on LSDV estimation with reasons of the results of Likelihood Ratio Test and Hausman Test. The traditional model of STIRPAT was improved by adding Carbon Emission Intensity and Energy Consumption Variables, which included consumptions of coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, and natural gas, except population and per capita GDP. The results show that consumptions of different types of energy have different impacts on carbon emissions intensity from 2004 to 2016 in China. Five variables of energy consumption, which were corresponding to coal, coke, gasoline, diesel oil, and natural gas, had played positive effects on carbon emission intensity from the data of China Statistical Yearbook and China Energy Statistical Yearbook of 200 5 to 201 7. Other variables of crude oil consumption, fuel oil consumption, and kerosene consumption took opposite impact on carbon emission intensity. Moreover, change of population had the most significant favorable influence on carbon emission intensity in all studied variables. Unfortunately, per capita GDP and coal consumption contributed to the increasing of carbon emission intensity in China in the studied period.

5988| 2567

Application of Information Fusion in Welding based on Arc and Ultrasonic sensor[28-31,38]

Liu Guangrui; Zhou Wenbo; Tian Xin; Guo Kefu

Doi:10.13705/j.issn.1671-6833.2017.02.025

Abstract: BP neural network for effectively fusioning the information obtained by arc sensor and ultrasonic sensor and information of welding parameters such as welding current,welding speed,welding groove and so on was used to obtain the prediction model of weld penetration depth.Simulation results showed that:the prediction model of weld penetration depth could measure the weld penetration quickly,accurately and in real time.For the precise control of weld penetration,parameters self-tuning fuzzy PID controller was desing,which combined with the advantages of traditional PID controller and fuzzy controller.Smulation results showed that compared with traditional PID controller,parameters self-tuning fuzzy PID controller had a significant advantage in the performance of the system.

5985| 4526

The Analysis and Improved Design of a New AGV Drive Unit Based on Differential Driving[73-78]

Zhao Huadong, Jiangnan, Lei Chaofan

Doi:10.13705/j.issn.1671-6833.2018.05.012

Abstract: Commercial automayic guided vehicles (AGV) usually used chain transmission mechanism power transmission, and the fixed structure of the wheel could be considered as cantilever structure. Therefore, the problem of wheels "tilting" and start-stop "shocking" easily occurs, which limited the accurate movement of the AGV during frequent and rapid acceleration or deceleration. In this paper, AGV designed by a company was taken as an example. Though repeated tests and numerical simulations, the structure and force analysis were used to find out the reasons for this phenomeno. The larger stress was caused by the "L"-shaped suspension mechanism, which magnified the contact gaps of each component; the uses of the chain transmission mechanism could make it easy for the AGV to form gaps between the sprocket and the chain when the AGV started, stopped, moved forward, backward frequently. Then a new drive unit structure was put forward from the engineering point of view, which could solves the above problems, at the same time-greatly could reduced the stress in the mechanism, could improve the transmission precision, and could provide a more practical and optimized driving structure for the design of AGV.

5967| 3614

Improved AFSA Optimization of SVM in The Application of Magnetic EAS Acoustic Signal Detection[35-38,83]

Deng Jicai, Geng Yanan

Doi:10.13705/j.issn.1671-6833.2017.04.001

Abstract: In order to improve the detection rate of the acoustic magnetic EAS system,and enhance the antiinterference performance,the paper studied a new label detection algorithm that was the combination of the improved artificial fish swarm algorithm (IAFSA) and the support vector machine (SVM).An improved scheme was proposed after analyzing the strengths and weaknesses of the traditional AFSA and SVM.The experimentalresults showed that the IASFA had the faster rate of convergence and the higher accuracy than AFSA,the genetic algorithm and the particle swarm algorithm;The IASFA-SVM had the higher detection rate,the longer detective distance and the lower rate of false than the traditional magnetic label detection algorithm,and the IASFA-SVM also could meet the requirements of real-time detection.

5966| 3644

Normalized PCA Algorithm Based on Spark[7-12]

Dong Chee-hwa1,Wang Guoyin2,Yongxi3,Shi Xiaoyu2,Li Qingliang4

Doi:10.13705/j.issn.1671-6833.2017.05.001

Abstract: Principal Component Analysis (PCA) is a well known model for dimensionality reduction in data mining,it transforms the original variables into a few comprehensive indices.In this paper,we study the principle of PCA,the distributed architecture of Spark and PCA algorithm of distributed matrix from spark’s ML-lib,then improved the design and present a new algorithm named SNPCA (Spark’s Normalized Principal Component Analysis),this SNPCA algorithm computes principal components together with data normalization process.We carried out benchmarking on multicore CPUs and the results demonstrate the effectiveness of SNPCA.

5963| 5105

[0]

Abstract:

5952| 1

Maximum Power Point Tracking of Permanent Magnet Direct Drive Wind Power System Based on Sliding Mode Controller[1-5]

Liu Yanhong, Zhao Jinglong

Doi:10.13705/j.issn.1671-6833.2016.04.001

Abstract: A high-order non-singular terminal sliding mode control strategy is proposed to address the issue of achieving maximum wind energy capture in permanent magnet direct drive wind power generation systems. Based on the nonlinear model of the permanent magnet direct drive wind power generation system, a maximum power point tracking method based on optimal torque tracking is proposed, Applying high-order non-singular terminal sliding mode control to the design of torque controller and current controller for permanent magnet synchronous generator (PMSG), achieving fast tracking and stable control of the maximum power point of the permanent magnet direct drive wind power generation system without wind speed sensors. Simulation results verify the effectiveness of the proposed control scheme

5940| 3798

Sintering Furnace System Identification Based on Particle Swarm Algorithm and Neural Network Control[39-43]

Cao Ben, Yuan Zhong, Yu Liu Hong

Doi:10.13705/j.issn.1671-6833.2017.02.022

Abstract: During heating process of sintering furnace,the model parameters were easy to change,and traditional PID control was difficult to achieve the desired control effect.This paper used particle swarm optimization algorithm to identify the mathematical model of sintering furnace,for sintering furnace with high inertia,time-variation and strong time delay etc,a method of supervision and control based on RBF neural network,which combined PID control with neural network control.When temperature or parameters changed greatly,PID control played a major role.neural network played a regulatory role and compensated the shortage of PID control.The simulation results of MATLAB software showed that this method could improve the control precision of sintering furnace,which had a certain practicality.

5940| 4259

Algorithm of Target Tracking Based on Mean Shift with ELM[1-5]

Mao Xiaobo, Hao Xiangdong, Liang Jing

Doi:10.3969/j.issn.1671-6833.201503043

Abstract: In view of the problem of object deviation when occlusions occur during the target tracking, a new algorithm using Mean Shift with ELM is proposed. According to the formal information of the object’ s loca-tion, current possible location was predicted by ELM, the iteration was started from the possible location in-stead of formal location, and the object’ s real center is calculated by mean shift algorithm. The simulation re-sults show that proposed algorithm can track precisely target occluded, operation time and number of iteration are reduced so that efficiency and robustness are improved.

5937| 4325

Research and Application of HHT-Based Power Load Combination Forecasting[1-5]

JIANG Jian-dong1 ,ZHANG Hao-jie1 ,WANG Jing2

Doi:10.3969/j.issn.1671-6833.2015.04.001

Abstract: To further improve the accuracy of power load forecasting，on the basis of the analysis of affectingfactors of power load, a combination prediction model based on HHT is proposed. This model uses EMD algo-rithm to decompose the original load sequence. Thus， a stationary sequence of different frequencies，which ismore predictable than the original load sequence，can be obtained. Based on the components of different fre-quencies，according to the characteristics of the different frequency of subsequence ，the RBF neural network ,BP neural network and time series model are selected to forecast while considering the influence of temperatureon the load. Then，a new combined model can be achieved. The experiment shows that the proposed modelcan effectively improve the accuracy of load forecasting.

5914| 4547

Multi-objective Evolutionary Algorithm and Its Application in Electric Power Environment Economic Dispatch[1-9]

Xiao Junming, Zhou Qian, Qu Boyang, Wei Xuehui

Doi:Multi-objective Evolutionary Algorithm and Its Ap

Abstract: The energy supply of power system is very important to modern society, and the scientific and effective solution to the problem of environmental economic dispatch of power system is the guarantee of energy supply. The multi-objective evolutionary algorithm has unique advantages in solving the problem of environmental economic dispatch of power system. This paper presses In chronological order, the multi-objective evolutionary algorithm is first introduced, and then the application of the multi-objective evolutionary algorithm in the power system environmental economic dispatching problem is discussed. The direction of development is prospected.

5894| 3949

Experimental Study on Low Compression Ratio Screw Extruder of Straw[1]

FANGShuqi1,2,HELiping1,ZHANGLonglong1,CHANGChun1,2,BAI Jing1,2,CHENJunying1，

Doi:10.3969/j.issn.1671-6833.2015.06.001

Abstract: The effects of processing variables,such as screw speed,initial moisture content and the length ofthe straw plug pipe of extrusion process on the dewatering rate,handling capacity,output per kW h etc.were experimentally studied using a low CR screw straw extruder. And the response surface optimization exper-imental results showed the extruder can run efficiently,stably and continuously with considerate dewateringrate,handling capacity and output per kW ·h under the conditions that moisture content is 85% ,screw speed50.8 r/min,length of the straw plug pipe is 26.91 mm.

5885| 4784

Effects of nano-ZrO2 powder addition on properties, phase composition and microstructure of Al-Si incorporated Al2O3-C materials[15]

LIU Zhi-fang,LIU Xin-hong,HUANG Ya-lei,MA Teng

Doi:10.3969/j.issn.1671-6833.2015.05.004

Abstract: The effects of nano-ZR02 powder on the properties, composition and structure of Al-Si composite Al203-C were studied by using plate corundum aggregate and fine powder, Al powder, Si powder, graphite and nano-Zro2 powder as raw materials and phenolic resin as binder.The results show that the introduction of nano-ZRO2 powder has little effect on the strength of the sample at room temperature and high temperature, but it is beneficial to improve the molding density and oxidation resistance of the sample, and can significantly improve the thermal shock resistance of the sample.The reason for the increase of sample density is that the nano-cobalt oxide has a good filling effect and helps sintering.Nano-zro2 can promote the reaction of Al and Si to generate more non-oxide whiskers, and form a cross-linked network structure in the sample, and the toughening of nano-powder and the phase transition toughening of ZrO2 are conducive to improving the thermal shock resistance of the sample.

5863| 3291

Impact of Triangle Leakage on Comprehensive Performanceof Heat Exchangers with Helical Baffles[1-4]

LIU Min-shan，XU Wei-feng ，JIN Zun-long，WANG Yong-qing，WANG Dan

Doi:10.3969/j.issn.1671-6833.2014.06.001

Abstract: A numerical simulation of trisection-ellipse heat exchangers with helical baffles is carried out， andthe helix angles are 15° and 20° respectively ， and we studied the impact of triangle leakage between continu-ously overlapped and adjacent baffles on heat transfer and resistance performance of heat exchangers.Throughthe comparative analysis about the simulation results of existing triangle leakage and that of blocking trianglearea without leakage , the results show :triangle leakage makes a more serious short circuit flow for the shell-si-ded fluid;Triangle leakage makes heat transfer coefficient，shell-sided pressure drop and comprehensive per-formance of heat exchanger reduce. When triangle leakage is blocked，heat transfer coefficient increases by8.5% ~ 11% , shell-sided pressure drop increases marginally , comprehensive performance increases by 8.1 %~11 . 1 % .

5810| 4196

Biodegradation of Toluene in a Biofilter under Low Concentration[12-16]

ZHU Yazhong，LI Shunyi，LUO Yimeng ，MA Hongye，WANG Yan

Doi:10.13705/j.issn.1671-6833.2017.01.010

Abstract: Self-made biological fillers embedded with Pseudomonas putida were used as biofilter packing materials for treating toluene.The effects of inlet loading rate (ILR) and empty bed residence time (EBRT) were evaluated.Changes in micro-organisms before and after the shut down period and its effect on biofilter performance were investigated.Results indicated that,no need for hanging film,activities of micro-organisms were high,capacity to eliminate toluene was strong.Optimal EBRT was 74.2 s,and removal efficiency ranged from 49.3 to 97.3 ％;maximum elimination capacity,16.97 g · (m3 · h)-1 was occurred at ILR of 22.11 g · (m3 · h)-1.The recovery time needed for achieving constant state,after biofilter shut down for 3 d,7 d and 30 d,were 5,21 and 45 h,respectively.Microbial counts after recovery were significantly higher than the 30d shut-down period,and lower layer had the highest microbial population.

5810| 3892

Hydrothermal Synthesis of CuO Micro/Nanoparticles and Their Application in Gas Sensors[10-14]

Hu Xiaobing, Xie Zhenfang, Xie Ji, Xie Lili, Zhu Zhigang

Doi:10.13705/j.issn.1671-6833.2016.03.022

Abstract: Micro/Nano-particles of CuO were prepared with hexamethylenetetramine template. The composi-tion and morphology of the product were characterized by SEM and X-ray diffraction. The synthetic powder was prepared as sensitive membrane, and its gas sensitivity was studied with a static gas distribution method. The results indicated that the uniform copper oxide powders was synthesized at the 110℃, and the molar ratio be-tween copper nitrate and hexamethylenetetramine was 1∶45. The spindle structure was around 1~2 μm, and was composed of 100 nm nanoplates. The sensor had better selectivity with CH3 COCH3 and H2 S. Copper ox-ide showed good selectivity to hydrogen sulfide and its sensitivity had a certain degree of improvement after fur-ther doping 0. 25% ~1. 25% noble metal catalyst Pt.

5802| 4036

A Distributed Clustering Mechanism Based on Energy Balancein Wireless Sensor Networks[6-10]

FENG Dong-qing,XING Kai-li

Doi:10.3969/j.issn.1671-6833.2015.03.002

Abstract: Focusing on the target tracking problem in resource-constrained wireless sensor networks,a novelenergy-balanced optimal distributed clustering mechanism is adopted by introducing an energy-balanced indexbased on the standard deviation of residual energv of nodes. Then,it is transformed into a multi-obijective con-strained optimization problem,and a binary particle swarm optimization algorithm is employed to solve thisproblem. Simulation results in Matlab environment show that the energy-balanced optimal distributed clustering mechanism guarantees energy balance and tracking accuracy comparing with the clustering mechanisms respec-tively based on the energy consumption and the extended Kalman filter,and that it improves the network life-time of nearly 2-fold,effectively prolonging the network lifetime.

5773| 3323

Research on performance improvement of crowdsourcing based on task pricing[15]

Deng Shaohong 1,Li Ling 1Guibin 2

Doi:10.13705/j.issn.1671-6833.2019.04.019

Abstract: First, according to the theory of space crowdsourcing, the concept of equivalent task representative points is proposed, and the relationship between the original task pricing law and task density, membership density, member average credibility and nearest neighbor reach distance is studied. On this basis, from the perspectives of the contractor, the platform and the contractor, According to the four steps of completing the task, a task pricing model based on multi-objective programming , a member dynamic grab order model, a task allocation model and a task completion probability prediction model are respectively established. Furthermore, the TOPSIS method is used to calculate the comprehensive evaluation index of different pricing schemes, and then choose the optimal task pricing scheme by the ranking result of the comprehensive evaluation index. Finally, the optimized scheme is compared with the original scheme. Under the condition that the total cost of the contractor is as low as possible, the platform task completion rate, the average individual member income and the unit reputation value conversion rewards are significantly improved, that is, the crowdsourcing performance are improved. The result verifies the feasibility and effectiveness of the model and provides reference for the task pricing of the crowdsourcing platform

5733| 3248

The HoMM-UKF Algorithm of SINS’ Attitude Estimation Model[39-45]

Ding Guoqiang1Zhang Duo1Xiong Ming1Zhou Weidong2

Doi:10.13705/j.issn.1671-6833.2017.01.017

Abstract: In order to improve the precision requirement about the attitude control of the strap-down inertial navigation system,the high order moment matching UKF (Higher-order Moment Matching UKF,HoMMUKF) algorithm was proposed,that is to estimate the SINS’ attitude parameters of based on its quaternion error model.In the recursive calculation process,for accurately approximating computational purposes,it uses high order moment matching method to calculate the average skewness value and peak value of the predicted sampling points set and their weights of the system state parameters in the view of the probability distribution.Making use of attitude quaternion method,then onlinear quaternion error model was constructed,in which model the systemnoise vector depends on system state vector,meanwhile construct its measure equation whose measurement noise vector depends on quaternion measurement vector by pseudo observation vector method was constructed,the weighted average of estimated quaternion with Lagrangian operator was calculated,the system noise variance calculation with the system noise separation algorithm was carried out,and finallyconstruct the SINS’ attitude estimation HoMM-UKF algorithms simulation on SINS attitude experiment platform was designed.It can be seen that HoMM-UKF algorithm’s calculation accuracy is higher than others and has better numerical stability,comparison of the UKF,and CDKF algorithms,and so the HoMM-UKF algorithm’s feasibility and calculation accuracy is verified.

5729| 3754

Overview of Recent Progress in Low-resource Few-shot Continuous Speech Recognition[1-9]

QU Dan, YANG Xukui, YAN Honggang, CHEN Yaqi, NIU Tong

Doi:10.13705/j.issn.1671-6833.2023.04.014

Abstract: Low-resource few-shot speech recognition is an urgent technical demand faced by the speech recognition industry. The framework technology for few-shot speech recognition is first briefly discussed in this article. The research progress of several important low resource speech technologies, including feature extraction, acoustic model, and resource expansion, is then highlighted. The latest advancements in deep learning technologies, such as generative adversarial networks, self-supervised representation learning, deep reinforcement learning, and meta-learning, are then focused on in order to address few-shot speech recognition on the basis of the development of continuous speech recognition framework technology. On that basis, the problems of limited complementarity, unbalanced task and model deployment faced by this technology are analyzed for the subsequent development. Finally, a summary and prospect of few-shot continuous speech recognition are given.

1805| 9344

Preparation and applications of molybdenum disulfide nanoplate based nanocomposites[18]

CHEN Deliang,DONG Huina,ZHANG Rui

Doi:10.3969/j.issn.1671-6833.2015.06.005

6404| 9056

Experimental Research on Hysteretic Behavior ofHRB500 Reinforced Concrete Piers[74-77]

RONG Xian,SONG Peng,ZHANG Jianxin,etc;

Doi:10.3969/j.issn.1671-6833.2014.06.018

Abstract: Based on the quasi-static test study of seismic performance of HRB500 reinforced concrete piers ,influence law about steel strength ，the spacing，the axial compression ratio on seismic behavior was obtainedaccording to the analysis of its failure characteristics, hysteresis curves，skeleton curves，stiffness degradationunder low eyclic loads. The results show that increasing steel strength can improve components’ bearing ca-pacity and deformation capacity obviously , stirrup ratio can not influence members’ bearing capacity and de-formation capacity ,axial compression ratio can improve components’bearing capacity , but on the other hand,it is useless to improve components’deformation capacity.

4905| 8171

2020 Issue02[97]

Abstract:

934| 8141

Design of a Cooker Anti-overheating System based on Non-contact Temperature Measurement[18-22]

Li Yifeng, Mao Xiaobo, Yang Yihang, Zhu Feng

Doi:10.13705/j.issn.1671-6833.2017.05.021

6194| 5579

Research Progress of Multimodal Named Entity Recognition[60-71]

WANG Hairong, XU Xi, WANG Tong, JING Boxiang

Doi:10.13705/j.issn.1671-6833.2024.02.001

Abstract: In order to solve the problems in studies of multimodal named entity recognition, such as the lack of text feature semantics, the lack of visual feature semantics, and the difficulty of graphic feature fusion, a series of multimodal named entity recognition methods were proposed. Firstly, the overall framework of multi modal named entity recognition methods and common technologies in each part were examined, and classified into BilSTM-based MNER method and Transformer based MNER method. Furthermore, according to the model structure, it was further divided into four model structures, including pre-fusion model, post-fusion model, Transformer single-task model and Transformer multi-task model. Then, experiments were carried out on two data sets of Twitter-2015 and Twitter2017 for these two types of methods respectively. The experimental results showed that multi-feature cooperative representation could enhance the semantics of each modal feature. In addition, multi-task learning could promote modal feature fusion or result fusion, so as to improve the accuracy of MNER. Finally, in the future research of MNER, it was suggested to focus on enhancing modal semantics through multi-feature cooperative representation, and promoting model feature fusion or result fusion by multi-task learning.

1344| 5502

Zoom Camera Self-calibration Method Based on Two Vanishing Points(PD)[91-96]

Zhao Shujun, Duan Shaoli, Zhang Xiaofang, Li Lei, Liu Xiaomin

Doi:10.13705/j.issn.1671-6833.2016.05.018

Abstract: The calibration method of the zoom camera is studied. The self-calibration method based on the two vanishing points is used to calibrate the general parameters of the zoom camera under two fixed focal lengths. By comparing with Zhang Zhengyou’s calibration method and the results of the machine vision software Halcon calibration, the results are verified. The feasibility and robustness of this method are verified. In order to better reflect the zoom characteristics of the zoom camera, a thick lens model that can more accurately describe the zoom camera is established. The author performs SIFT feature matching on the zoom image, and according to the matching point pair The linear equations are established, and the least square method is used to estimate the zoom center of the zoom image. In addition, the optical center displacement between different focal lengths is also calculated. The experimental results show that there is an obvious gap between the optical center displacement and the focal length, which shows that The thick lens model is more suitable for describing the zoom lens of the camera.

5302| 5458

Cover - Table of Contents - Back Cover[0]

Abstract:

4145| 5433

Multi-Objective Evolutionary Algorithm Based on Decomposition for Engineering Optimization[38]

ZHANG Chunjiang1,2，TAN Kay Chen2，GAO Liang1, wU qing3

Doi:10.3969/j.issn.1671-6833.2015.06.008

Abstract: In order for effective application of Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D) in engineering optimization，normalization of the range of objective values is needed. A self-a-daptive s constrained Differential Evolution ( gDE) algorithm is proposed to obtain the minimum and maximumvalues of each objective on the Pareto Front ( PF). After normalization，MOEA/D can then be effectively ap-plied. In addition ,the self-adaptive s constraint method is combined with MOEA/D for constraint handling. Abenchmark problem and a weld bean design problem are used to evaluate the performance of the algorithm a-gainst two other normalization methods. One main advantage of the proposed method is the selective concen-trated optimization on some regions on the Pareto front which allows handling of problems where regions of Pa-reto front are difficult to be optimized.

5694| 5427

The Method of Degradation Feature Extraction of Rolling Bearing Based on MEMD and Multivariate Multiscale Entropy[86-91]

Li Lingjun, Jin Bingma, Yanli Han, Jie Hao, Wang body

Doi:10.13705/j.issn.1671-6833.2018.01.004

Abstract: The method of extracting degradation features was proposed based on MEMD and MMSE to solve the problem that non-stationarity of fault signals of roller bearing and degradation condition, which was characteristic of non-ststionarity and hard to recognize. The character of MEMD was adopted to catch different scales of signals effectively during the process of multiscalization, which made complexity of different degradation condition distinguished better than other methods. Firstly, multichannel signals corresponding to various degradation condition of roller bearing were decomposed adaptively using MEMD, then, the reconstructed signals by multiscale IMF was dealt with MSE analysis. The results showed that the proposed method could efficiently evaluate the degradation trend of roller bearing by handing the experimental signals.

5011| 5356

Review on Modeling and Control of Proton Exchange Membrane Fuel Cell[61]

Han Chuang, Wu Lili

Doi:10.3969/j.issn.1671-6833.2015.06.012

6466| 5322

Reinforcement Learning Autonomous Driving Trajectory Prediction Based on Directed Graph[53-61]

CUI Jianming¹, LIN Fanrong¹, ZHANG Di¹ , ZHANG Luning¹, LIU Ming²

Doi:10.13705/j.issn.1671-6833.2023.05.002

Abstract: As an important part of autonomous driving, trajectory prediction aimed to forcast the vehicle′s driving path, so that the vehicle could make path planning according to the driving estimation, so as to make safe and accurate decisions. Firstly, in order to improve the accuracy of vehicle trajectory prediction, the directed graph method was used to construct a high-definition driving scene map, and the directed graph method vectorized the map information to effectively extract the map topology. Secondly, GAIL was used to learn the driving strategy of the dataset through the confrontation game between the generator and the discriminator, so as to adopt the corresponding driving behavior according to the current state. Finally, the multimodal prediction trajectory scheme was obtained by sampling traversal. Simulation was carried out on the nuScenes motion prediction dataset. The quantitative results showed that compared with other methods, when K = 5, the minimum final displacement error MinFDE5 was increased by 10. 8%; when K = 10, the minimum fianl displacement error MinFDE10 increased by 17. 53%, the minimum average displacement error MinADE10 increased by 9. 52%, and the error rate MissRate10 decreased by 28. 26%. The evaluation showed that the generated trajectories were multimodal, could conform to the basic structure of the scene, with improved accuracy.

1909| 5314

A Review: Pre-warning system of oil-drilling engineering[70-73]

Li Guang1, Zhang Heng2, Wang Jie2, Zhu Xiaodong2, Yue Caitong2

Doi:10.13705/j.issn.1671-6833.2017.06.010

Abstract: Warning technology of drilling engineering was the key technolog of drilling safety protection. Through the monitoring of real-time well site drilling process parameters, huge amounts of drilling data mining and intelligent learning, abnormal state modeling and optimization, abnormal state modeling and optimization, abnormal characteristics of the early warning model online judging process, achieved the goal of oil drilling abnormal state arly warning, and prevention of drilling engineering accidents. This paper reviewed the development course of early warning technology, introduced the drilling engieering warning technology architecture, and also introduced the early warning teachnology in detail and compared their characteristics, finally depicted the development of future early warning system for drilling engineering.

5515| 5311

A Multi-objective Estimation of Distribution Algorithm for the Fingertip Localization[12]

Liu Ke 1;Gong Dunwei 2

Doi:10.13705/j.issn.1671-6833.2019.04.011

Abstract: In the human-computer interaction system based on fingertip, the position of fingertip center is very important. By solving the multi-objective optimization model for the fingertip localization, several fingertip center positions can be obtained. The fingertip pixels distribute around the fingertip centers, so the optimal solution components of this optimization model have the above distribution law. Using the estimation of distribution algorithm with the distribution law to solve this optimization model, can obtain accurate results. This paper discusses the estimation of distribution algorithm for the fingertip localization. It proposes that the decision variable dimension, population size, maximum sampling variance, and minimum sampling variance are the main parameters of this estimation of distribution algorithm. The experimental results show that each main parameter has its best value; when their values are their best values, the fingertip center positions obtained by the proposed method excel the results of the existing methods.

4885| 5218

Research on the Method for 3D Tolerance Analysis Considering Tolerance Principle[39-44]

Zhao Fengxia , Jin Shaobo , Li Jifeng

Doi:10.13705/j.issn.1671-6833.2017.06.007

Abstract: A method of considering tolerance principle for three dimensional tolerance analysis was put forward. Based on small displacement torsor (SDT) theory and modal interval arithmetic, the tolerance models of size tolerance and geometrical tolerance of the feature of size apply independent principle, envelope requirement, maximum material requirement, least material requirement or reciprocity requirement, were established respectively. By using the space vector to represent 3D dimension chain, a mathematical model is built to calculate the closed loop tolerance based on space vector loop stack principle. The application of the proposed method is illustrated through presenting an example, the tolerance analysis steps are given, and the availability of the proposed method was proved successfully.

5504| 5189

Comparative Study on Cooling Channel for Electric Vehicle Drive Motor Based on CFD[41-45]

Shen Chao1,Yu Peng1,Yang Jianzhong1,Zhang Dongwei2,Wei Xinli2

Doi:10.13705/j.issn.1671-6833.2018.01.003

Abstract: Based on the cooling characteristics of the electric vehicle drive motor, a novel cooling structure the circumferential multi spiral structure, was proposed. The three dimensional numerical model of fluid flow and heat transfer in the shell was established. The flow field and temperature field of different water cooling schemes were calculated based on CFD technology. The numerical results showed that the temperature uniformity and cooling performance of Circumferential "Z" structure is better than the circumferential multi spiral structure; and the circumferential "Z" structure was suitable for the cooling of 135KW electric vehicle drive motor under the condition of inlet water temperature was 65ºC, with the optimal water flow rate 9.8L/min. However, the circumferential multi spiral structure could be used for higher power density of the motor cooling for the better performance of pressure resistance. The research provided a theoretical basis for cooling design and optimization of the small size and high power density motor.

5148| 5114

Normalized PCA Algorithm Based on Spark[7-12]

Dong Chee-hwa1,Wang Guoyin2,Yongxi3,Shi Xiaoyu2,Li Qingliang4

Doi:10.13705/j.issn.1671-6833.2017.05.001

5963| 5105

Swarm intelligence optimization algorithm[1-2]

Cheng Shi 1,Wang Rui 2,Wu Guohua 3,Guo Yinan 4,Malembo 5,Shi Yuhui 6

Doi:10.13705/j.issn.1671-6833.2018.06.024

Abstract: The core idea of swarmintelligence (swarmintelligence) is that several simple individuals form a group, through cooperation, competition, interaction and learning mechanisms to show advanced and complex functions, in the absence of local information and models, still able to complete the solution of complex problems.The solution process is to initialize the variable randomly, and calculate the output value of the objective function after iterative solution.Swarm intelligent optimization algorithm is not dependent on gradient information, and it is not continuous and derivable to solve problems, which makes it suitable for both continuous numerical optimization and discrete combinational optimization.At the same time, the potential parallelism and distributed characteristics of swarm intelligence optimization algorithm make it have significant advantages in dealing with big data.

5466| 5046

Research on Glass Fiber Fabric Defect Detection Method Based on Blob Analysis[90]

WANG Qinghai1，ZHAO Fengxia2,Ll Jifeng2，JIN Shaobo2

Doi:10.3969/j.issn.1671-6833.2015.06.018

Abstract: In order to solve the problems，such as low efficiency，poor real-time performance and so on，in on－line detection of glass fiber fabric，a new method of fabric defect detection based on Blob analysis is proposed． Firstly，the image is smoothed by using mean filter，and the noises and the fabric textures are weakened． Then，the Otsu algorithm is used to find the best threshold to segment the image into Blob and background pixels． The shape of the Blob region is adjusted by using morphological processing． Finally，the connectivity analysis and feature extraction of the image are carried out． The number and size of the defects are obtained by using the least square fitting of the Blob region． Experimental results show that the method is simple，reliable and robust．

5220| 4974

Study on preparation and properties of epoxy asphalt for pavement of concrete bridge deck[78-81]

Cong PeiLIANG,LIU Jianfei,ZHAO Zhiqiang,etc;

Doi:10.3969/j.issn.1671-6833.2014.06.019

Abstract: Aiming at the application of epoxy asphalt in concrete bridge pavement, epoxy asphalt was prepared. The effects of different resin content on viscosity, high and low temperature properties of epoxy asphalt bond, mechanical properties, low temperature crack resistance and high temperature stability of epoxy asphalt mixture were studied.The results show that the addition of epoxy resin can improve the road performance and mechanical properties of asphalt mixture. With the increase of the addition amount, the curing reaction process of epoxy asphalt is accelerated, the high temperature performance and fatigue resistance are enhanced, the stiffness modulus is increased, the creep rate is decreased, the low temperature crack resistance is decreased, and the fatigue resistance and high temperature stability of asphalt mixture are improved.By comprehensive analysis,30% is the best dosage of epoxy resin.

4834| 4938

Analysis of the Influence of Soil Electrical Parameters Frequency-dependence Characteristics on the Effective Length of the Grounding Electrode[29-34]

Li Jingli, He Pengwei, Qiu Zaisen, Li Yuanbo, Guo Liying

Doi:10.13705/j.issn.1671-6833.2017.01.016

Abstract: Impulse charactersitic of grounding devices was the important factor of lightning withstand level and lightning trip-out rate of transmission line.Based on HIFREQ program and FFTSES program in grounding power system analysis software CDEGS,this paper presented a grounding system impulse characteristic modeling considered soil frequency-dependence,especially,the Visacro-Alipio soil frequency-dependence formula has been introduced.The impact of the soil frequency-dependence on the effective length of the grounding device in different initial soil resistivity and different impulse current waveform was analyzed.The calculating results showed that when considering soil frequency-dependence,the impulse effective length would be shorter,especially for the grounding devices buried in high resistivity soil.

5585| 4788

Experimental Study on Low Compression Ratio Screw Extruder of Straw[1]

FANGShuqi1,2,HELiping1,ZHANGLonglong1,CHANGChun1,2,BAI Jing1,2,CHENJunying1，

Doi:10.3969/j.issn.1671-6833.2015.06.001

5885| 4784

A Survey of Evolutionary Algorithms for Large Scale Optimization Problem[15-21]

Liang Jing1,Liu Rui1,Qu Boyang2,Yue Caitong1

Doi:10.13705/j.issn.1671-6833.2017.06.016

6119| 4774

The Study of Dynamic Deflection Temperature Correction of Asphalt Pavementunder Effect of Thermal-structure Coupling Field[1-5]

ZHENG Yuanxun ，YANG Peibing

Doi:10.3969/j.issn.1671-6833.2014.02.001

Abstract: In order to study the influence of asphalt pavement temperature on pavement deflection, a finite element coupling model of asphalt pavement was established considering the temperature sensitivity of road material parameters.Based on the numerical model, the variation of pavement deflection under FWD dynamic loading under different temperature conditions and the influence of temperature on the maximum deflection of asphalt pavement with different thickness are analyzed.At the same time, the influence of asphalt pavement structure and material parameters on the dynamic bending temperature correction coefficient is analyzed. Finally, the dynamic bending temperature correction coefficient of asphalt pavement is studied based on the coupling model and compared with the test results.The results show that the pavement thickness and base modulus have great influence on the temperature correction coefficient. The temperature correction coefficient of asphalt pavement deflection established based on the finite element model is in good agreement with the temperature correction coefficient established through the experimental research, and can be used as an effective supplement to the experimental research.

4154| 4751

Design of Fuzzy Classification System Based on Fireworks Optimization and Differential Evolution Algorithm[47]

ZHU Xiaodong,LIU Chong,GUO Yamo

Doi:10.3969/j.issn.1671-6833.2015.06.009

Abstract: A novel approach to construct accurate and interpretable fuzzy classification system based on fire-works optimization algorithm(FOA) combined with differential evolution operators is proposed.It is the firsttime to apply FOA in fuzzy modeling.The fireworks optimization algorithm is a novel swarm intelligent algo-rithm based on simulating the explosion process of fireworks,which can optimize the construction and parame-ters of fuzzy system with good convergence speed and optimization accuracy.To improve the diversity of theswarm and avoid being trapped in local optima too early ,the differential evolution is performed to further opti-mize the model at each iteration.The proposed approach is applied to the lris benchmark classification prob-lem,and the results prove its validity.

5313| 4724

Probabilistic Conditional Preference Network Assisted Interactive Genetic Algorithm and Its Application[1-5]

Sun Xiaoyan, Zhu Lixia, Chen Yang

Doi:10.13705/j.issn.1671-6833.2017.06.001

6110| 4717

A Review of Machine Learning-Based Methods for Database Tuning[1-11]

SHI Lei, LI Tian, GAO Yufei, WEI Lin, LI Cuixia, TAO Yongcai

Doi:10.13705/j.issn.1671-6833.2024.01.008

Abstract: Knobs tuning is a key technology that affects the performance and adaptability of databases. However, traditional tuning methods have difficulty in finding the optimal configuration in high-dimensional continuous parameter spaces. The development of machine learning could bring new opportunities to solve this problem. By summarizing and analyzing relevant work, existing work was classified according to development time and characteristics, including expert decision-making, static rules, heuristic algorithms, traditional machine learning methods, and deep reinforcement learning methods. The database tuning problem was defined, and the limitations of heuristic algorithms in tuning problems were discussed. Traditional machine learning-based tuning methods were introduced, including random forest, support vector machine, decision tree, etc. The general process of using machine learning methods to solve tuning problems was described, and specific implementations were provided. The shortcomings of traditional machine learning models in adaptability and tuning capabilities were also discussed. The principles of deep reinforcement learning models were emphasized, and the mapping relationship between tuning problems and deep reinforcement learning models was defined. Recent relevant work on improving database performance, time consumption and model characteristics was introduced, and the process of building and training agents based on deep neural networks was described. Finally, the characteristics of existing work were summarized, and the research hotspots and development directions of machine learning in database tuning were outlined. Distributed scenarios, multi-granularity tuning, adaptive algorithms and self-maintenance capabilities were identified as future research trends

1772| 4708

Time Series Prediction Method of Dissolved Gas Content in Transformer Oilased on ARIMA Model[67-72]

Jia Rubin,Gao Jinfeng

Doi:10.13705/j.issn.1671-6833.2020.03.010

Abstract: The dissolved gas content in transformer oil is an important index to measure the operation status of transformers. The differential autoregressive moving average model (ARIMA) is used to predict the gas content in transformer oil. This method uses the time corresponding to the gas content value as an index to input the prediction model through python programming. The original non-stationary time series is converted into a stationary time series by means of difference processing, and then several sets of models are obtained by using the autocorrelation function and partial autocorrelation function parameter selection principles, and are used in the process of optimizing several sets of models. A set of optimal models were obtained by Chichi information, Bayesian information, and Hannan-Quine criteria. Finally, the residuals of the optimal models were tested by correlation testing methods, and the gas content was predicted using the models that met the residual requirements. Experiments show that the proposed prediction method has high prediction accuracy, which can provide a valuable reference for rationally arranging the condition-based maintenance of transformers.

3515| 4595

A Survey on Genetic Programming to Image Analysis[3-13]

Bi Ying,Xue Bing,Zhang Mengjie

Doi:10.13705/j.issn.1671-6833.2018.06.018

8279| 4591

Research and Application of HHT-Based Power Load Combination Forecasting[1-5]

JIANG Jian-dong1 ,ZHANG Hao-jie1 ,WANG Jing2

Doi:10.3969/j.issn.1671-6833.2015.04.001

5914| 4547

Application of Information Fusion in Welding based on Arc and Ultrasonic sensor[28-31,38]

Liu Guangrui; Zhou Wenbo; Tian Xin; Guo Kefu

Doi:10.13705/j.issn.1671-6833.2017.02.025

5985| 4526

Effect of material formulation and formed time on the morphology of Poly (vinyl alcohol)/chitosan composite fiber[1-5]

Dai Pinqiang1,Song Lairui2,Cui Zhixiang3,Wang Qianting3

Doi:10.13705/j.issn.1671-6833.2016.03.023

Abstract: Chitosan ( CS)/poly ( vinyl alcohol) ( PVA) composite fibers were fabricated by electrospinning in this study. The influences of material formulation and formed time on the viscosity,electrical conductivity and the morphology, average diameter, diameter distribution of CS/PVA composite fiber were investigated. The re-sults showed that, the introduction of CS could increase the viscosity,electrical conductivity of CS/PVA blend solution. And the viscosity of blend solution decreased with the increase of formed time. In addition, the more CS content was, the smaller diameter of CS/PVA composite fiber would be. The fiber-forming capacity of CS/PVA blend solution decreased dramatically as the solution formed time increased.

5643| 4515

Effects analysis of CTE on the PCC Pavement Design[6-9]

WANG Peng , FAN Lei，CUI Can

Doi:10.3969/j.issn.1671-6833.2014.02.002

Abstract: In order to research the effects of coefficient of thermal expansion ( CTE) on the PCC pavement de-sign，some efforts have been done. According to the PCC pavement design standard in our country ，the influ-ence of CTE on the temperature stress is analyzed , and the M-E design method is used to analyses the influ-ence of CTE on transverse joint faulting. The conclusion is drawn that CTE has a great impact on the designingof pavement thickness especially on the joint load transfer and the warping of slab corner. So introducing theparameter of CTE to the PCC pavement design is of great significance.

4901| 4506

Optimized Echo State Network on the Basis of Mutual Information and Just-In-Time and its application[1-6]

Zhang Heng, Wang Heshan

Doi:10.13705/j.issn.1671-6833.2017.05.018

6323| 4455

Preparation of Zirconium Oxide / Graphene Composites and their Adsorption Properties for PO43-[23-28]

Shi Chunyan1,Fan Bingbing1,Li Yaya1,Hu Yongbao1,Zhang Rui2

Doi:10.13705/j.issn.1671-6833.2017.01.009

6373| 4442

A New Cryptophane A-coated SAW Methane Gas Sensor[6-23]

Wang Wen1,Hu Haoliang1,He Shitang1,Pan Yong2,Zhang Caihong3

Doi:10.13705/j.issn.1671-6833.2016.03.026

7276| 4432

Study of Face Orientation Recognition Based on Development Network[23-27]

Wang Dongshu, Tan Dapei, Wei Xiaoqin

Doi:10.13705/j.issn.1671-6833.2017.02.023

Abstract: Based on the characteristic of face orientation,position and the light background in face recognition,a new method of face orientation recognition based on development network is proposed.The characteristic of human’s eye was very prominent,so the position of eyes was chosen as the face orientation feature vector.And the deveiopment network model was used to recognize human’s face orientation in the different light background images.The result showed that this method could effectively solve the difficult problem of face orientation recognition under varying illumination conditions by comparing with the test results of other methods,which was fast,stable and effective.The recognition rate was as high as 100％.

5567| 4361

The Morphology and Mechanical Properties of Poly(Lactic Acid) Stereocomplex Crystal[83-87]

Liu Yanping Wei Hanghang, Li Qian

Doi:10.13705/j.issn.1671-6833.2017.06.017

Abstract: The surface morphology and the different mechanical properties between crystalline region and amorphous region of the stereocomplex crystal were studied in this paper. The same mass ratio of Poly (L-lactic acid) (PLLA) and poly (D-lactic acid) (PDLA) stereocomplex was prepared by solution blending. Differential Scanning Calorimetry, Polarizing Microscope, Atomic Force Microscopy, Confocal Laser Scanning Microscope and Nano Indentation Tester were used to list the surface morpholigy of PLA stereocomplex crystal and the diversification of mechanical properties. The result showed that a high degree of stereo-tacticity of PLLA/PDLA blend could be achieved from the mass ratio of 1/1 for sample.The research also showed that obviously depression phenomenon on the surface of crystal was formed due to the contraction of the molecular chain. Furthermore, the hardness and modulus of crystalline region were improved compared to the amorphous region.

4994| 4356

The Haze Plate Recognition System Based on PSO-RBF Neural Network[46-50]

Mao Xiaobo, Zhang Qun,Liang Jing, Liu Yanhong

Doi:10.3969/j.issn.1671-6833.2017.04.002

6186| 4347

Algorithm of Target Tracking Based on Mean Shift with ELM[1-5]

Mao Xiaobo, Hao Xiangdong, Liang Jing

Doi:10.3969/j.issn.1671-6833.201503043

5937| 4325

Effect of particle diameter and particle axial distribution on heat and mass transfer inside adsorption heat transformer[17-22]

Sheng Zunrong1,Xue Bing1,Liu Zhouming1,Wei Xinli2

Doi:10.3969/j.issn.1671-6833.2017.01.011

6076| 4323

Double-Buffered Queue-Based Parallel Processing Algorithmfor Massive Terrain Dataset[6]

CHEN Xiaopan1 ,QU Jiantao1,2,ZHAO Yameng2, WANG Peng1, 2 , CHEN Yulin1

Doi:10.13705/j.issn.1671-6833.2016.03.002

Abstract: When dealing with massive terrain data ，the advantage of hardware performance can’t be fully uti-lized. This has become a bottleneck，which restricts the speed of massive terrain tiles rendering. This paperanalyzes the key factors that affect large-scale terrain rendering speed，and proposes a parallel algorithm formassive terrain data processing. The algorithm adopts double buffer queues and divides large scale terrain ren-dering into two parallel processing which includes data processing and rendering. The two buffer queues areresponsible for data reading and writing operations in turn. The loading priority of terrain tiles is consideredand tasks are allocated based on the priority. The experimental results show that this approach improves thespeed of rendering massive terrain tiles greatly.

5361| 4322

Design and Implementation of Cyberspace War Situation Visualization System for Joint Operations.[45-51]

Zhang Yang; Si Guangya; Wang Yanzheng

Doi:10.13705/j.issn.1671-6833.2018.05.004

Abstract: In this paper, the system function analysis was carried out based on the capability demand of the joint operation for Cyberspace War Situation Visualization System(CWSVS). Then, a distributed system architecture based on HLA was constructed based on the capability perspective, service perspective and operational perspective, and the scenario generating architecture and real-time running architecture were designed. The component-based rapid and customizable situation driven technology and the map-based multi-layered dynamic fusion visualization technology were taken as the key technology examples. At last, the cyberspace Offensive and defensive operations against the C4ISR system was given as an typical example, and the utility of the system was demonstrated

5247| 4307

Intrusion Detection Model Based on CNN and BiGRU Fused Neural Network[37-43]

ZHANG Anlin1, ZHANG Qikun2, HUANG Daoying2, LIU Jianghao2, LI Jianchun2, CHEN Xiaowen2

Doi:10.13705/j.issn.1671-6833.2022.03.003

Abstract: Aiming at the problems of unbalanced data types and incomplete feature learning in deep learning intrusion detection, a neural network intrusion detection model based on the fusion of convolutional neural networks(CNN)and bidirectional gated recurrent unit(BiGRU)was proposed.The SMOTE-Tomek algorithm was used to balance the data set, the feature importance algorithm based on mean decrease impurity was used to realize feature selection; the CNN and BiGRU models used for feature fusion and attention mechanism was introduced for feature extraction, so as to improve the overall detection performance of the model.The intrusion detection data set CSE-CIC-IDS2018 was used for multi classification experiments, the model was compared with the classical single deep learning models.The experimental results showed that, firstly, in terms of data set balance, after being processed by SMOTE-Tomek algorithm, the recognition accuracy of DoS attacks-Slow HTTP Test class was improved from 0 to 34.66%, that of SQL Injection class was improved from 0 to 100%, and DDoS attack-LOIC-UDP, Brute Force-Web and Brute Force-XSS classes were improved by 5.22 percentage points, 6.55 percentage points and 35.71 percentage points respectively.It was proved that the balanced data set improved the recognition accuracy of a few classes significantly compared with the unprocessed data set.Secondly, in terms of the overall detection performance of the model, in the comparison of multi classification experiments, the overall classification accuracy, recall and F1 value of the model in this study were higher than those of several other single neural network models.The overall evaluation accuracy of each attack traffic category was about 2.10 percentage points higher than that of the highest LSTM model.The recall rate of the overall evaluation was about 1.50 percentage points higher than that of the highest LSTM model.Compared with the highest GRU model, the overall F1 value increased by about 1.97 percentage points.It was proved that the model had better detection effect.

3090| 4263

Sintering Furnace System Identification Based on Particle Swarm Algorithm and Neural Network Control[39-43]

Cao Ben, Yuan Zhong, Yu Liu Hong

Doi:10.13705/j.issn.1671-6833.2017.02.022

5940| 4259

System for automatic recognition and renewal of driving license[13-17,22]

Maling1,Jiang Huiqin1,Liu Yumin2

Doi:10.13705/j.issn.1671-6833.2017.05.013

6150| 4240

Power Load Characteristic Classification Technology Research Based on An Improved Fuzzy C-means Clustering Algorithm[56]

ZHAO Guosheng1,NIU Zhenzhen1，LIU Yongguang2, SUN Chaoliang2

Doi:10.3969/j.issn.1671-6833.2015.06.011

Abstract: In view of the disadvantages of the traditional Fuy C-means clustering algorithm， the author pro-poses an adaptive FCM algorithm. This algorithm is based on two clustering results evaluation index of withinthe class distance MIA and between the class distance MDC. The ratio of MDC and MIA，defined as l, is anadaptive function to determine the clustering number c of FCM algorithm. At the same time，according to thefuzy decision method，we use the objective function and partition entropy of FCM algorithm together to deter-mine the value of optimal fuzzy weighted m. ’This algorithm not only overcomes the FCM algorithm disadvan-tage of not being able to determine the clustering number automatically and fuzzy weighted index needs to begiven by experience,but also the clustering result is optimal. Finally,the correctness and effectiveness of thealgorithm were proved through example analysis.

5059| 4237

Design of Splitters in Flue of SCR System for a 6U Mw Coal-fired Boiler[37-40]

WANG Wei-shu1, SHAGN GUAN Shan-shan1,LU Tong1 ,YANG Zhi-feng2 ,ZHENG Chun-xiong1,CHEN Gang1

Doi:10.3969/j.issn.1671-6833.2015.04.009

Abstract: Based on FLUENT6.3 software，the simulation and analysis were applied to the optimization designof splitters in Selective Catalytic Reduction ( SCR ) denitrification system for a 600MW coal-fired boiler. Theresults show that because of variable cross-section and deflection in flue,flow field in the system appears non-uniform severely when there is no splitters. The velocity difference on two sides of the section at AlG lowerreaches is up to 15 m/s，the velocity deviations in the reactor inlet is 31.25% , and the flow field is poor intail flue，which affects the operation of downstream equipments.Reasonable splitters can improve flow field influe effectively. After equipping the transition pipe of the inlet with two group of longitudinal splitters, the ve-locity deviations in section at AIG lower reaches is reduced to 15% and in the reactor inlet is reduced to 13% .Splitters in elbow is equipped with an extension for guiding，which have a good effect on homogenization of thedownstream flow field.

5439| 4232

Impact of Triangle Leakage on Comprehensive Performanceof Heat Exchangers with Helical Baffles[1-4]

LIU Min-shan，XU Wei-feng ，JIN Zun-long，WANG Yong-qing，WANG Dan

Doi:10.3969/j.issn.1671-6833.2014.06.001

5810| 4196

Research on control of permanent magnet synchronous linear motor based on energy forming[1-5]

JIAO Liu-cheng,YAO Tao

Doi:10.3969/j.issn.1671-6833.2015.03.001

6052| 4181

Journal Information

Bimonthly(Started in 1980)

Administrated by:
The Education Department of Henan Province
Sponsored by: Zhengzhou University
Edited & Published by:
Editorial Office of Journal of Zhengzhou University( Engineering Sciences)
E-mail: gxb@zzu.edu.cn
Website: http://gxb.zzu.edu.cn/
Address: No.100 Science Avenue,100,
Zhengzhou 450001,China
Telephone: (0371) 67781276, 67781277
Chief Editor: ZHENG Suxia
Executive Chief Editor: XIANG Sa
Printed by: Shanxi Tongfang Knowledge Network Printing Co.,Ltd.
Distributed by: Office of Postal Distribution of Henan Proince
Distributed Abroad by: Publishing Trading Corporation,P.O.B.782, Beijing100011, China
Publication Scope: Public Publication
Periodicity:Bimonthly
Founded in:1980
Code of Domestic Distribution: 36-232
Code of Overseas Distribution: BM2642
ISSN:1671-6833
CN:41-1339/T
CODEN:ZDXGAN

For Authors

MORE+