2025 volumne 46 Issue 05
ZHANG Zhen1, XIAO Zongrong2, LI Youhao3, HUANG Weitao3
Abstract: To address the safety risks posed by construction vehicles operations in highrisk areas near natural gas pipelines, particularly the physical impacts and environmental disturbances caused by heavy vehicles, in this study an improved YOLOv7-based construction vehicles recognition algorithm was proposed. Six common types of construction vehicles including dump trucks, rollers, mixers, forklifts, excavators, and loaders were selected as the research objects. A custom dataset, containing images captured in various environments and angles, was used to train the model, ensuring its performance. Firstly, the CBAM attention mechanism was introduced into the YOLOv7 head, and an improved GAM attention mechanism was added to the max pooling layer to enhance the model′s focus on key image features and improve detection accuracy. Secondly, the DySample dynamic upsampling module replaced the nearest neighbor interpolation, boosting precision. Finally, an improved SPPCSPC module was designed to enhance feature extraction efficiency, reduce computational costs, and accelerate inference. These modifications could enable the model to maintain high detection accuracy even in challenging scenarios such as low-quality images or distant targets. Experimental results demonstrated that the proposed algorithm achieved a precision P of 97.7%, recall R of 94.7%, mAP@0.5 of 98.6%, and mAP@0.5∶0.95 of 90.4%. Compared to the original YOLOv7 algorithm, these metrics improved by 1.3, 1.4, 1.4, and 3.7 percentage points, respectively.
WEI Mingjun1,2, CHEN Xiaoru1, LIU Ming1, LIU Yazhi1,2, LI Hui1
Abstract: Camouflaged object detection (COD) is facing significant challenges due to the high similarity between target objects and their background, such as blurred edge predictions, incomplete detection results, and interference caused by the insufficient use of edge and texture information. To address the issues of current COD, a novel edge-texture guided enhancement network (ETGENet) was proposed to further improve the performance of COD through explicit and sufficient edge-texture guidance strategies. Firstly, a key feature guided enhancement module (FGEM) was used in ETGENet, which could use parallel feature refinement branches to process and enhance object features. The guide branch could obtain object features by guiding attention correlation with edge and texture cues to enhance the network′s understanding of object details and suppress noise interference. While the self-enhancement branch could use the self-attention mechanism to refine the characteristics of camouflaged objects from a global perspective. Secondly, a feature interaction fusion module (FIFM) was also proposed to progressively fuse adjacent features. FIFM could utilize the attention interaction mechanism and weighted fusion strategy to learn complementary information between features to generate more complete predicted map. Experiments on three public datasets CAMO, COD10K, and NC4K demonstrate that the proposed network outperformed state-of-the-art methods in the field across metrics such as structure measure S, adaptive enhanced matching measure E, weighted F-measure, and mean absolute error M. Notably, on the largest test set, NC4K, the weighted F-measure surpassed the best-performing method among the 12 advanced COD methods, FSPNet, by 2.2 percentage points.
LIU Zhaoying1, CHEN Zhiyuan1, ZHANG Ting1, SHI Yanan2, CHEN Yingchun3
Abstract: Aiming at the problem of limited resources and low contrast of surface defect images in industrial scenarios, an improved YOLOv5 industrial product surface defect detection method was proposed. This method first introduced a receptive field enhancement module in the backbone network to extract richer visual features from different levels of receptive fields. Secondly, a shuffle attention module was added to the feature fusion network to more effectively fuse feature maps of different dimensions. Finally, a task decoupling detection head was adopted, allowing the classification and regression tasks to use independent networks for prediction, reducing mutual interference and improving detection accuracy. The experimental results showed that the parameter and computational complexity of this network were lower than models such as YOLOX, YOLOv7, and deformable DETR. On the pipeline Digital Ray (DR) defect image dataset and NEU-DET dataset, the mAP@0.5 were increased by 2.23 percentage points and 2.99 percentage points respectively, balancing the requirements for real-time and accurate defect detection in industrial scenarios.
CHEN Liwei, PENG Yifei, YU Renping, SUN Yuancheng
Abstract: Aiming at the problem in existing methods of automatic segmentation of hippocampus image, which can not make good use of the context information, might lead to the difficulty in improving the segmentation accuracy and large memory consumption in the process of training and detection, a new model called MVF-2.5D U-Net based on multi-view fusion and 2.5D U-Net was introduced. Firstly, this model improved the 2D U-Net by incorporating a Triplet Attention module and adjusting the depth of the network. Secondly, the traditional single-slice input was replaced by a three-channel 2.5D image composed of adjacent slices. Finally, a volume fusion network was constructed to replace the conventional majority voting scheme. This study was validated by cross-validation on the HarP dataset. The experimental results showed that the average Dice coefficient and Hausdorff distance of the model on the hippocampus image segmentation task were 0.902 and 3.02, respectively, the accuracy and stability was better than the traditional U-Net model and comparison algorithm, and it was also suitable for the resource-constrained situation, which proved that the proposed model could achieve hippocampus segmentation on MRI more effectively.
CAO Yangjie, WANG Weiping, LI Zhenqiang, XIE Jun, LYU Runfeng
Abstract: To address the issues of excessive reliance on annotated data and high computational complexity in 3D scene editing algorithms, in this study a multimodal scene editing method named CLIP2Gaussian was proposed, which integrated CLIP with 3D Gaussian. Firstly, the algorithm employed SAM to extract target masks from multiview images and introduced a bidirectional propagation strategy to ensure mask consistency across different views. Secondly, the extracted masks were assigned semantic labels using CLIP and mapped to 3D Gaussian points to enable semantic embedding in the 3D scene. Finally, a differentiable rendering mechanism was used to optimize the parameters of the 3D Gaussians, and a spatial consistency regularization strategy was introduced by applying clustering to enhance the consistency and stability of semantic labels in 3D space. Experimental results showed that CLIP2Gaussian achieved 61.23% IoU on the LERF dataset and a per-query response time of 0.57 seconds in semantic segmentation tasks, improving the speed by 54 times compared to LERF while achieving superior accuracy and efficiency. Ablation studies further verified that the proposed method enabled precisely editing of target regions with minimal disturbance to the original scene.
ZHANG Haibin1, WEI Hongji1, WANG Chao2, XIANG Changbo3, YANG Mingyang3, LI Xiaolong4
Abstract: In complex electromagnetic environments, interference signals can severely degrade the detection and recognition performance of frequency-hopping signals. To address issues of false detection, missed detection, and over-detection in traditional methods, in this study an improved time-frequency diagram-based signal detection and recognition algorithm was proposed by modifying the YOLOv5s network. Firstly, a composite dataset containing frequency hopping signals + interference signals was constructed, comprising 4 modulation types of frequency hopping signals and 6 interference types, with 300 high-resolution time-frequency diagram samples generated for each combination (totaling 7 200 groups). Secondly, considering the similar features between interference and signals in time-frequency diagrams, and recognizing that the frequency variation pattern of hopping signals could make background information around signals crucial for differentiation, a context hierarchy module was proposed to hierarchically process background information. This module employed depthwise separable convolution to extract surrounding background features and utilized a gated aggregation mechanism to perform weighted fusion of background information and signal features, thereby generating more discriminative composite features. Finally, the backbone network of YOLOv5s was modified by integrating the context hierarchy module and gated aggregation mechanism to develop an improved frequency hopping signal detector. Simulation results showed that compared with the original YOLOv5s network, the proposed method achieved 15.9 percentage points improvement in recall rate R, 8.9 percentage points enhancement in mean average precision mAP@0.5∶0.95, and 9 percentage points increase in F1, while significantly reducing false and missed detection occurrences.
HAN Chenchen, LU Xiankai, WANG Zhicheng, XIONG Xiaozhou
Abstract: In response to the challenges of maintaining structural and temporal consistency between video frames in video prediction tasks, an object-centric video prediction algorithm based on dynamic memory and motion information was proposed. Firstly, by introducing an object-centric model, the objects in the scene were decoupled to ensure the consistency and stability of long-term dynamic modeling of video objects, to effectively maintain the structural consistency of video objects. Secondly, an object dynamic memory module was designed to capture the longterm dependencies of videos and model object dynamics, to overcome the shortcomings of existing video prediction methods in predicting dynamic interactions between objects and enhancing the temporal consistency of video objects. Thirdly, the feature similarity matrix of adjacent frames was used to capture the motion information between frames and model the spatiotemporal relationships of the video sequence, further strengthened the temporal consistency of video objects. Finally, a cross-attention mechanism was utilized to integrate the temporal and structural information of video objects, further improved the video prediction performance. Experiments on video prediction were conducted on the Obj3D and CLEVRER datasets with complex object interactions. The results showed that compared to the state-of-the-art object-centric video prediction algorithms, the proposed algorithm increased performance on the PSNR and SSIM metrics by 4.5% and 1.4%, respectively, and also achieved a 20% reduction in the LPIPS metric.
DONG Weiyu1, LIU Pengkun2, LIU Chunling1, TANG Yonghe1, MA Yupu2
Abstract: In the field of automated penetration testing, most existing attack path decision algorithms are based on partially observable Markov decision processes (POMDP), with problems such as high algorithm complexity, slow convergence speed, and susceptibility to getting stuck in local optima. In this study a reinforcement learning algorithm NoisyNet-A3C was proposed based on Markov Decision Process (MDP). And it was applied to the field of automated penetration testing. This algorithm trained actor-critic through multiple threads, and the operation results of each thread were fed back to the main neural network. At the same time, the latest parameter updates were 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 increased the randomness of the behavior strategy, facilitated faster exploration of effective paths, reduced the impact of data disturbances, and enhanced the robustness of the algorithm. The experimental results showed that compared with A3C, Q-learning, DQN, and NDSPI-DQN algorithms, the NoisyNet-A3C algorithm converged more than 30% faster, verifying that the algorithm proposed in this study converged faster.
HAN Huijian, XING Huaiyu, ZHANG Yunfeng, ZHANG Rui
Abstract: Addressing the challenges posed by the varying scales of steel surface defects and the limited multi-scale feature processing capabilitied and accuracy of existing detection algorithms, in this study a steel surface defect detection method that integrates hybrid sampling and multi-attention collaboration was proposed. Firstly, an efficient channel feature extraction backbone was constructed to emphasize defect feature extraction against the complex background of steel surfaces. Secondly, a dual-attention collaborative feature pyramid was introduced to expand the network′s receptive field, thereby enhancing the capture of multi-scale defect features and improving the detection performance for small targets. Finally, a Transformer-based hybrid sampling strategy was designed to dynamically perceive defect regions, thereby boosting the overall detection performance of the model. Experimental comparisons on the NEU-DET dataset revealed that, compared to the baseline DETR algorithm, the improved algorithm achieved a 6.1 percentage point increase in mean average precision, reaching 81.4%, thereby enhancing the model′s accuracy in detecting steel surface defects. Additionally, with a detection speed of 44.2 frame/s, the proposed algorithm strikes a commendable balance between detection speed and performance.
LIU Kai, WANG Jiaqin, LI Hantao
Abstract: Vehicle trajectory prediction (VTP) was a significant research subject in the transportation technology field. Traditional VTP methods require extensive feature engineering and struggle to adapt to complex and dynamic environments in real-time. Deep learning (DL) overcomes the limitations of traditional methods by achieving efficient data representation through multi-layer neural networks. Therefore, in this study a comprehensive review of DL-based VTP methods was carried out to explore their applications and performance in VTP. Firstly, the traditional VTP and DL-based VTP methods were explored, and the main consideration problems and problem formulations in VTP were introduced. Secondly, various VTP schemes, including input data, output results and prediction methods were analyzed and compared. Subsequently, commonly used evaluation metrics was introduced, and the experimental results of these VTP approaches were compared, the applications of VTP were analyzed, and the superior performance of DL in VTP were demonstrated. Finally, future research directions of VTP are discussed in terms of datasets, modeling approaches, and computational efficiency. It identifies that vehicle interaction collaborative modeling, model generalization, and multimodal fusion would constitute the primary challenges and research frontiers in the field.
QIN Dongchen, LUO Qingzhou, YANG Junjie, CHEN Jiangyi, WU Hongxia
Abstract: The issues of low charging speed, rapid temperature rise, lithium plating, and overcharging in lithiumion batteries were addressed in this study. A model predictive control (MPC) charging strategy based on an improved particle swarm optimization (PSO) algorithm was proposed.Firstly,an equivalent circuit-thermal-electrochemical-aging coupled model was established, combining the advantages of equivalent circuit and electrochemical models to accurately predict terminal voltage, temperature variations, and aging mechanisms (e. g., SEI film growth, active material loss, and capacity loss from lithium plating).Secondly,the coupled model was discretized to build a state-space model, with added safety constraints to prevent thermal runaway, lithium plating, and overcharging. Based on the state-space model, future battery states were predicted, and a cost function for charging time and energy loss was formulated.Finally, the improved PSO algorithm was used to solve for the optimal charging current sequence, enabling real-time charging optimization.MATLAB/Simulink simulations showed the strategy significantly reduced charging time while effectively controlling battery temperature, terminal voltage, and lithium plating overpotential, avoiding issues like thermal runaway, lithium plating and overcharging. Experimental comparisons with traditional strategies showed a reduction in charging time by 17.3% to 61.1% and capacity decay by 7.6% to 36%. This research provided a new direction for lithium-ion battery charging optimization.
LIU Haibo1,2, LIANG Peng1, LIU Ziqian1
Abstract: In view of the time-delay caused by signal transmission in the large-scale interconnected power systems, the robust stability of power systems with stochastic time-delay was investigated. Firstly, by considering the stochastic characteristics of time-delay and uncertain factors in practical systems, and assuming that the probability of timedelay obeyed Bernoulli distribution, a new system model was established. Secondly, to introduce more probability distribution information of stochastic time-delay, a new augmented vector and L-K functional with more state information and multiple integral terms were constructed. Then, by utilizing the generalized free weight matrix inequality (GFWMI) to handle the quadratic integral term, more accurate upper bound was obtained. As a consequence, a less conservative mean square asymptotic stability criterion was derived by using Schur lemma and linear matrix inequality method. Simulation results show that the proposed method improves the time-delay stability margin of the system by 73% and reduces the time to achieve stability by 40% under parameter disturbances. The conservatism of the results was significantly reduced in comparison with existing literature.
LI Qing, LI Hong, DONG Haiying, WANG Hao
Abstract: To solve the issues of high computational and communication costs, as well as the difficulty in achieving fully distributed control within multi-distributed power generation optimization scheduling, a source load collaborative scheduling method based on the supernode collaborative consistency algorithm was proposed in this study, which combined frequency modulation control to enhance the consistency algorithm. Firstly, the original distributed power network structure′s topology was reconstructed based on the single-hop sampling method. Super nodes were chosen for solving of the decision variables, while the local set of ordinary nodes within the partition only communicated with these supernodes. Secondly, an adaptive global correction coefficient frequency modulation control method for improving the discrete consistency algorithm was introduced to better adapt to real-time scheduling and fully distributed control of active distribution networks. This method was utilized to address the system′s minimum operating cost optimization scheduling model. Finally, the effectiveness of the proposed scheduling method in dealing with topology switching and source load mutation scenarios was verified through MATLAB simulation. The simulation results showed that rapid convergence of consistency incremental cost could be achieved by the suggested scheduling algorithm, effectively controlling each DG′s incremental cost below 8.95 yuan when the system underwent sudden changes. Without sudden changes in the system, 197 iterations were needed by the algorithm to achieve scheduling allocation, offering an effective solution for the scheduling department.
ZHOU Enze1, HUANG Daochun2, WANG Lei1, PENG Tianhao2, LIU Shuqin1, WANG Hao1, CHEN Chao1
Abstract: In the complex background of transmission line corridors, traditional early warning methods for detecting wildfires showed poor performance, slow detection speeds, and high rates of false positives and missed detections in image recognition. In this study a wildfire detection method was introduced for transmission corridor based on an improved YOLOv8s model. Firstly, through network collection and screening of existing datasets, a wildfire image dataset featuring wilderness backgrounds was obtained, providing a more suitable match for the target background. Secondly, the ODConv module was introduced, replacing the original C2f module with the C2f_OD module for feature extraction in the Backbone and Neck sections of the baseline model, thereby enhancing the model′s detection performance for flames and smoke. Secondly, the Head section was replaced with the DyHead module, integrating three attention mechanisms scale, spatial, and task to further improve detection accuracy. The WIoU loss function was employed to focus detection frame regression on prediction boxes of ordinary quality, enhancing the model′s generalization performance in complex backgrounds. Finally, three ablation experiments and one comparative experiment were designed. The results demonstrated that, compared to the original YOLOv8s model, the proposed algorithm achieved 5.6% increase in mAP@0.5, 4.51% increase in P, 5.41% increase in R, and a detection speed of 34.9 frames per second, meeting the requirements for accurate wildfire detection along transmission corridor.
ZHANG Bei, XU Shuo, ZHONG Yanhui, CAI Hongjian, ZANG Quansheng, LI Xiaolong
Abstract: To address the issues of low detection accuracy and speed of ground-penetrating radar for loose defects under complex environmental conditions, a loose defect recognition method based on an improved YOLOv8 algorithm (YOLOv8-DN) was proposed. A DN module was designed and replaced C2f module, integrating a dynamic deformable convolution module and a multi-scale feature fusion module. The receptive fields of the dynamic deformable convolution kernels were adapted to accommodate the morphological complexity of defect features, while the multi-scale feature fusion path was employed to enhance the model′s ability to capture small and blurred defect regions. By replacing the original C2f module with the DN module, the recognition capability for complex defects was significantly improved, and computational overhead was effectively reduced. It was shown by experimental results that compared with the original YOLOv8 algorithm, the improved algorithm achieved a 5.29 percentage point increase in mAP, a 5.2 percentage point reduction in missed detection rate, and a 4.9 frame/ms improvement in inference speed. In addition, the integrity and accuracy of the detected mask regions were significantly enhanced, which validated the effectiveness and feasibility of the proposed algorithm and provided a novel solution for the rapid and precise detection of loose defects in the semi-rigid base layers of asphalt pavements.
BAO Tengfei1, CHENG Jianyue1, XING Yu2, ZHOU Xiwu3, CHEN Yuting1, ZHAO Xiangyu1
Abstract: In view of the difficulty of balancing convergence speed and nonlinear regression accuracy of existing inversion methods, a new method combining gradient descent (GD) and particle swarm optimization (PSO) for inversion analysis of initial in-situ stress field was proposed. In this method, the gravity field and five tectonic stress fields that affects the initial in-situ stress field were considered as eight basic boundary conditions. The stress values of measuring points with each boundary condition were calculated by using finite element software. The measured in-situ stress values were taken as the target values, and the influence coefficients of each boundary condition were obtained by regression analysis using GD-PSO algorithm. The regression stress values of each point of the model were calculated and input into the 3D finite element model as the initial stress field to balance the in-situ stress. The example analysis showed that compared with the calculation results of PSO algorithm, the cubic regression polynomial obtained by GD-PSO algorithm had the highest accuracy, and the mean square error was 0.579. The regression results fit well with the measured ground stress values. After the in-situ stress balance, except for the vertical stress value, the difference between the calculated in-situ stress value at the measurement point and the measured value was relatively small, and the directional displacement of surrounding rock was basically zero, and the maximum displacement was only 5.26 mm.
FANG Wei1,2,3,4, WANG Haoxi1
Abstract: Addressing the limitations of existing deep learning-based radar echo extrapolation algorithms in spatialtemporal feature extraction and long-term dependency modeling, a novel SRU-Former model that integrated spatialtemporal reconstruction unit (SRU) and Transformer was proposed for radar echo extrapolation. Firstly, a newly designed SRU was introduced into the model′s encoder and decoder to extract fine-grained spatiotemporal features from radar images via separation, transformation, and reconstruction. Secondly, a variant architecture model of Transformer, Poolformer, was introduced between the encoder and decoder, using global average pooling to replace the self-attention mechanism, thereby assisting the model in modeling highly dynamic radar sequences. Finally, SRU-Former was trained and tested on two meteorological radar datasets from Jiangsu Province and Shanghai City, respectively, and compared with current mainstream deep learning models. In the 2-hour extrapolation task, SRUFormer achieved optimal values in four metrics: CSI, FAR, MSE, and SSIM. Specifically, CSI was improved by 0.020 on the Jiangsu Province dataset and by 0.048 on the Shanghai City dataset. Experimental results showed that SRU-Former effectively improved model prediction accuracy, with more precise capture of strong echo regions and clearer detail textures in later extrapolation stages.
Pre-publication   
QIN Dongchen, ZHANG Wencan, WANG Tingting, CHEN Jiangyi
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.
ZHAO Dong, LI Yarui, WANG Wenxiang, SONG Wei
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.
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Abstract:
Bi Ying,Xue Bing,Zhang Mengjie
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.
Wang Wen1,Hu Haoliang1,He Shitang1,Pan Yong2,Zhang Caihong3
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.
Li Yanyan 1,Yang Haotian 2,Zeng Yufan 3
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
Wang Jianming; Qiu Qinyu; He Xunchao
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.
Huang Yuda; Wang Yanran; Niu Sijie;
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.
Han Chuang, Wu Lili
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.
Shi Chunyan1,Fan Bingbing1,Li Yaya1,Hu Yongbao1,Zhang Rui2
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.
Zhou Junjie, Wang Pu, Zhou Jinfang
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.
Jiang Yang1,Guo Jiankun 1,Wang Xiaomou 2,Hou Chaoqun 3
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

CHEN Deliang,DONG Huina,ZHANG Rui
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.
Zhao Shufang, Dong Xiaoyu
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. 
Zhang Heng, Wang Heshan
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.
Li Yifeng, Mao Xiaobo, Yang Yihang, Zhu Feng
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.
Sheng Zunrong1,Xue Bing1,Liu Zhouming1,Wei Xinli2
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.
Mao Xiaobo, Zhang Qun,Liang Jing, Liu Yanhong
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.
Li Haibin1,Ke Shengwang2,Shen Yanjun2
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.
Maling1,Jiang Huiqin1,Liu Yumin2
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.
Sun Xiaoyan, Zhu Lixia, Chen Yang
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.
Liang Jing1,Liu Rui1,Qu Boyang2,Yue Caitong1
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.
Liu Qian; Feng Yanhong; Chen Yingying;
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.
Liu Guangrui; Zhou Wenbo; Tian Xin; Guo Kefu
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.
LIU Zhenghua1, WANG Jing2,DU Haiying’1,2
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.
FANGShuqi1,2,HELiping1,ZHANGLonglong1,CHANGChun1,2,BAI Jing1,2,CHENJunying1,
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.
Li Cailin, Chen Wenhe, Wang Jiangmei, Tian Pengyan, Yao Jili
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.
JIANG Jian-dong1 ,ZHANG Hao-jie1 ,WANG Jing2
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.
JIAO Liu-cheng,YAO Tao
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.
Deng Jicai, Geng Yanan
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.
Wei Ran
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.
Cao Ben, Yuan Zhong, Yu Liu Hong
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.
Mao Xiaobo, Hao Xiangdong, Liang Jing
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.
Xiao Junming, Zhou Qian, Qu Boyang, Wei Xuehui
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.
Zhao Huadong, Jiangnan, Lei Chaofan
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.
ZHANG Chunjiang1,2,TAN Kay Chen2,GAO Liang1, wU qing3
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.
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Abstract:
LIU Min-shan,XU Wei-feng ,JIN Zun-long,WANG Yong-qing,WANG Dan
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 % .
Dong Chee-hwa1,Wang Guoyin2,Yongxi3,Shi Xiaoyu2,Li Qingliang4
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.
WAN Ya-zhen,LIU Ya-nan,CHEN Di
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.
Liu Yanhong, Zhao Jinglong
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
Hu Xiaobing, Xie Zhenfang, Xie Ji, Xie Lili, Zhu Zhigang
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.
FENG Dong-qing,XING Kai-li
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.
ZHU Yazhong,LI Shunyi,LUO Yimeng ,MA Hongye,WANG Yan
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.
QU Dan, YANG Xukui, YAN Honggang, CHEN Yaqi, NIU Tong
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.
Abstract:
SHI Lei, LI Tian, GAO Yufei, WEI Lin, LI Cuixia, TAO Yongcai
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
CHEN Deliang,DONG Huina,ZHANG Rui
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.
RONG Xian,SONG Peng,ZHANG Jianxin,etc;
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.
CUI Jianming1, LIN Fanrong1, ZHANG Di1 , ZHANG Luning1, LIU Ming2
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.
WANG Hairong, XU Xi, WANG Tong, JING Boxiang
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.
ZHANG Chunjiang1,2,TAN Kay Chen2,GAO Liang1, wU qing3
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.
Li Yifeng, Mao Xiaobo, Yang Yihang, Zhu Feng
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.
FANGShuqi1,2,HELiping1,ZHANGLonglong1,CHANGChun1,2,BAI Jing1,2,CHENJunying1,
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.
Li Lingjun, Jin Bingma, Yanli Han, Jie Hao, Wang body
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.
YU Kunjie, YANG Zhenyu, QIAO Kangjia, LIANG Jing, YUE Caitong
Abstract: To address the difficulties of slow convergence and difficulty in finding feasible solutions when solving large-scale constrained multi-objective optimization problems, an adaptive two-stage large-scale constrained multiobjective evolutionary algorithm was proposed. In the first stage, the algorithm adaptively selected some variables for optimization according to the nature of the decision variables, without considering any constraint to make the population quickly cross the infeasible region and approach the unconstrained Pareto front. In the second stage, the algorithm considered all the constraints and optimizes the variables as a whole using the ε constraint-handling technique. At the same time, the feasible and non-dominated solutions obtained in the evolutionary process were saved and updated using archive to continuously improve the convergence and diversity of the population. Finally, the proposed algorithm was experimentally compared with the other six algorithms on 37 test functions, and the results showed that the proposed algorithm could achieved the best results on 25 functions and outperforms the comparison algorithm on at least 31 functions, respectively; meanwhile, the feasibility rate of the proposed algorithm in more than 90% of the functions could reach 100%, which could effectively solve large-scale constrained multi-objective optimization problems.
Li Guang1, Zhang Heng2, Wang Jie2, Zhu Xiaodong2, Yue Caitong2
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.
Han Chuang, Wu Lili
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.
Zhao Shujun, Duan Shaoli, Zhang Xiaofang, Li Lei, Liu Xiaomin
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.
WANG Dingbiao, WANG Shuai, ZHANG Haoran, WU Qitao, YANG Chongrui, WANG Guanghui
Abstract: Fluid topology optimization is a breakthrough technology, which has broad application prospects in aerospace, automotive, electronic chips and other fields, however, the design of complex structure is difficult to process through the traditional manufacturing technology. With the development of additive manufacturing (3D printing) technology, it could provide an effective way to further expand the application and research of fluid topology optimization, which would of great significance for realizing the structural lightweight, dynamic optimization, safety optimization and performance improvement of related industrial equipment, and implementing the national strategy of “energy conservation and consumption reduction, carbon peak and carbon neutralization”. With the help of the literature metrology tool VOSviewer, were classified and summarized the literature related to fluid topology optimization in the Web of Science database were classified, comprehensively and the theoretical system, solution methods, optimization methods, and engineering applications of fluid topology optimization were expounded systematically, and the related problems were discussed. First of all, compared with solid topology optimization, fluid topology optimization involved more fields, more diverse flow regime characteristics, and more complex mathematical models, so it was more difficult to solve, took longer to calculate, and required more computing resources, which was the main factor restricting the engineering application of fluid topology optimization. Secondly, the three links and key technologies of fluid topology optimization were systematically described: representation method of design variable, CFD model and solution method, topology optimization model and solution method, and the characteristics and application scenarios of existing technologies were analyzed. At the same time, several application scenarios of fluid topology optimization, such as electronic chip heat sink, aircraft, automobile and heat exchanger, were briefly described. Finally, the development trend of fluid topology optimization was predicted and summarized. It was suggested that the multidisciplinary topology optimization research on turbulence, conjugate heat transfer, fluid-solid-heat coupling, fluid-solid-heat-mass coupling should be further strengthened; the research of topology optimization based on multi-objective function should be expanded; the deep combination with artificial intelligence should be further strengthened, more robust and mature intelligent CFD solver and intelligent optimization solver, and even intelligent software of fluid topology optimization should be developed.
Liu Ke 1;Gong Dunwei 2
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.
LIU Na 1,2 , ZHENG Guofeng 1,2 , XU Zhenshun 1,2 , LIN Lingde 1,2 , LI Chen 1,2 , YANG Jie 1,2
Abstract: Few-shot spoken language understanding ( SLU) is one of the urgent problems in dialogue artificial intelligence (DAI) . The relevant literature on SLU task, combining the latest research trends both domestic and foreign was systematically reviewed. The classic methods for SLU task modeling in non-few-shot scenarios were briefly introduced, including single modeling, implicit joint modeling, explicit joint modeling, and pre-trained paradigms. The latest studies in few-shot SLU were introduced, which included three kinds of few-shot learning methods based on model fine-tuning, data augmentation and metric learning. Representative models such as ULMFiT, prototypical network, and induction network were discussed. On this basis, the semantic understanding ability, interpretability, generalization ability and other performances of different methods were analyzed and compared. Finally, the challenges and future development directions of SLU tasks were discussed, it was pointed out that zero-shot SLU, Chinese SLU, open-domain SLU, and cross-lingual SLU would be the research difficulties in this field
Zhao Fengxia , Jin Shaobo , Li Jifeng
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.
Shen Chao1,Yu Peng1,Yang Jianzhong1,Zhang Dongwei2,Wei Xinli2
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.
Fu Zhen1,Shen Wanqing1,Kong Zhifeng2,Zhang Chao2
Abstract: With the fact that plasticizers were used successfully in plastic products to improve the low-temperature flexibility of asphalt binder,two kinds of plasticizer are selected in this paper to study the impact of two plasticizers on asphalt.In this paper,4 different dosages of the two plasticizer totally 8 dosages were put into asphalt to study the performance of asphalt binders by several routine tests including the penetration,softening point,ductility,viscosity,measuring-stress ductility and elasticity resuming.And the modification effect was evaluated in the aspect of temperature sensitivity,high temperature and low temperature,elastic recovery and aging.The test results showed that the plasticizers did help significantly in the low-temperature performance of the modified asphalt binders,also in the facts of temperature sensitivity,anti-aging ability and elasticity resuming,but not in high-temperature performance.In general,the plasticizer DOM was better than DOP in improving the properties of asphalt binders.
Jia Rubin,Gao Jinfeng
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.
ZHANG Anlin1, ZHANG Qikun2, HUANG Daoying2, LIU Jianghao2, LI Jianchun2, CHEN Xiaowen2
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.
Dai Pinqiang1,Song Lairui2,Cui Zhixiang3,Wang Qianting3
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.
Cheng Shi 1,Wang Rui 2,Wu Guohua 3,Guo Yinan 4,Malembo 5,Shi Yuhui 6
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.
WANG Qinghai1,ZHAO Fengxia2,Ll Jifeng2,JIN Shaobo2
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.
Guo Yinan 1,Cheng Wei 1,Yang Huan 1,Yang Fan 1,2,Lu hope 1
Abstract: As the key equipment of tunneling a roadway, controlling the anchor-hole drills mainly depends on the operator’s experience. Improper rotary speed of an anchor-hole drill generally results in sticking or breaking pipes, which reduces the drilling efficiency. Especially, the nonlinearities and time-varying parameters, as well as the disturbances resulted from various factors in the anchor-hole drill rotary system shall be taken into consideration. A novel optimal active-disturbance-rejection controller is proposed in the paper. The set value of the rotary speed is dynamically estimated in terms of the geological condition of surrounding rocks. Brain storm optimization algorithm is employed to find the optimal parameters of the controller, which have the best dynamic and steady control performances. Based on the simulation platform composed of AMESim and Matlab, the experimental results for a single surrounding rock with or without the external disturbance show that the proposed ADRC controller has better dynamic and steady performances and stronger robustness than the optimal PID controller.
Liang Jing1,Liu Rui1,Qu Boyang2,Yue Caitong1
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.
Shen Xianzhang, Liu Xiaolan, Wu Tianfu, Minzun South
Abstract: This article analyzes the working principles of SNIh to estimate compensation control and sampling PI control, and compares the two control algorithms through simulation.
CHEN Xiaopan1 ,QU Jiantao1,2,ZHAO Yameng2, WANG Peng1, 2 , CHEN Yulin1
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.
Ding Guoqiang1Zhang Duo1Xiong Ming1Zhou Weidong2
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.
Dong Chee-hwa1,Wang Guoyin2,Yongxi3,Shi Xiaoyu2,Li Qingliang4
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.
Bi Ying,Xue Bing,Zhang Mengjie
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.
Cong PeiLIANG,LIU Jianfei,ZHAO Zhiqiang,etc;
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.
Sun Xiaoyan, Zhu Lixia, Chen Yang
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.
Li Jingli, He Pengwei, Qiu Zaisen, Li Yuanbo, Guo Liying
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.
ZHENG Yuanxun ,YANG Peibing
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.
WANG Shenwen1,2, ZHANG Jiaxing1,2, CHU Xiaokai1,2, LIU Hong3, WANG Hui4
Abstract: In multimodal multi-objective optimization problem, the same position of Pareto front often corresponded to multiple Pareto optimal solutions in decision space. However, the existing multi-objective optimization algorithms could only obtain one of the Pareto optimal solutions. Therefore, in this paper, a two-stage search multimodal multi-objective differential evolution algorithm was proposed, which divided the optimization process into two stages: elite search and partition search. In the elite search stage, elite mutation strategy was used to generate high-quality individuals to ensure the search accuracy and efficiency of the population. In the stage of partition search, the decision space was divided into several subspaces, and the detected population was used to explore each subspace in depth, so as to reduce the complexity of the problem and to improve the expansion and uniformity of the population in the decision space. The performance of the algorithm was compared with five classical algorithms NSGAII、MO_Ring_PSO_SCD、DN-NSGAII、Omni-Optimizer、MMODE on 18 multimodal and multi-objective optimization test functions, such as MMF1. Experimental results showed that there were 16 test functions in the performance index of Pareto approximation (PSP) of the proposed algorithm, which were better than the other five comparison algorithms.
Liu Guangrui; Zhou Wenbo; Tian Xin; Guo Kefu
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.
Mao Xiaobo, Zhang Qun,Liang Jing, Liu Yanhong
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.
ZHU Xiaodong,LIU Chong,GUO Yamo
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.
Zhang Heng, Wang Heshan
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.
Wang Wen1,Hu Haoliang1,He Shitang1,Pan Yong2,Zhang Caihong3
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.
Shi Chunyan1,Fan Bingbing1,Li Yaya1,Hu Yongbao1,Zhang Rui2
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.
JIANG Jian-dong1 ,ZHANG Hao-jie1 ,WANG Jing2
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.
Wang Dongshu, Tan Dapei, Wei Xiaoqin
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%.
Sheng Zunrong1,Xue Bing1,Liu Zhouming1,Wei Xinli2
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.
Maling1,Jiang Huiqin1,Liu Yumin2
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.
Liu Yanping Wei Hanghang, Li Qian
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.
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Bimonthly(Started in 1980)
Administrated by:
The Education Department of Henan Province
Sponsored by: Zhengzhou University
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Periodicity:Bimonthly
Founded in:1980
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ISSN:1671-6833
CN:41-1339/T
CODEN:ZDXGAN

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