Abstract:

Abstract: Promotion strategy is an important part of gas station business, and data-driven promotion strategy has become an urgent demand for gas stations to achieve precise marketing. A deep learning model was proposed for forecasting gasoline sales based on historical gas station data, promotion strategies and key features, and a promotion strategy formulation method based on sales forecasting models. Due to the historical data characteristics of gas stations, a multi-level network structure was designed to process data of different types, and combine promotion strategy information to perform oil sales forecasts. In addition, by introducing key features, the accuracy of the sales forecast model was improved; by inputting different promotion strategies, the automatic selection of gas station marketing strategies was realized. The results of experiments conducted on a data set constructed from real gas station data showed that the sales forecast model proposed had lower forecast errors than other mainstream methods

Abstract: Aiming at the long-term prediction problem of wireless communication network traffic, a base station network traffic prediction method was proposed based on Wide & Deep learning. Firstly, S-H-ESD (seasonal hybrid extreme studentized deviate test) algorithm and data smoothing method were used to preprocess the network traffic data, and to reduce the impact of noise data on the prediction. Then, the network flow was input into the deep part (neural network) of the Wide & Deep model, the radio resource control (RRC) and physical resource block (PRB) were input into the wide part (linear model) of the Wide & Deep model, and the deep and wide parts were combined to predict the network traffic. The method established one prediction model for the network traffic of all base stations. The root mean squared logarithmic error (RMSLE) of prediction results was 0.985, which was significantly better than that of the traditional seasonal autoregressive integrated moving average model (RMSLE was 2.095) and that of the long short-term memory network model (RMSLE was 3.281). Experimental results showed that the Wide & Deep model could better solve the problem of long-term prediction of wireless network traffic via combining the memory ability of the linear model and the generalization ability of the depth model.

Abstract: It is expensive to obtain labeled data in actual oil logging, and a large amount of cheap unlabeled data are not used. How to use limited labeled samples and a large number of unlabeled samples to obtain accurate oil layer distribution remains to be solved. The semi-supervised learning methods were widely used because they could obtain good classification models using both a small number of labeled samples and a large number of unlabeled samples. Therefore, based on a semi-supervised support vector machine (S3VM), an improved semi-supervised support vector machine based on co-training and quantum-behaved particle swarm optimization algorithm (QPSO-CS3VM) was proposed for oil layer recognition. Firstly, the multi-view-based co-training strategy combined with S3VM was used to construct two independent initial classifiers, and then exchanged and labelled unlabeled samples to improve the overall oil layer recognition accuracy. Secondly, in order to improve the initial classification accuracy of original classifiers, the quantum behavioral particle swarm algorithm was introduced to optimize S3VM. Finally, a newly nearest neighbor data editing approach was used to predict the confidence of the pseudo-labelling of unlabeled data to reduce the deterioration of model perfor-mance caused by misclassification of data. The improved co-training semi-supervised SVM proposed in this paper improved the classification accuracy by 5.00% and 3.12% compared to the traditional co-training algorithm by performing oil layer recognition on the logging data of the two wells. The algorithm proposed in this paper had high accuracy in oil layer recognition and had practical application.

Abstract: Infrared target detection algorithms suffered from problems such as poor adaptability and high computational complexity. An improved Ghost-YOLOv5 infrared target detection algorithm was proposed based on feature distillation to solve the above problems. Firstly, GhostNet block was used for backbone pruning. Se-condly, two effective data enhancement methods including Mosaic and Copy-paste were used, together with feature distillation to improve the accuracy in object detection. Furthermore, an infrared image dataset that contained a variety of scenarios with pedestrians, motor vehicles, and non motorized targets was constructed. The test experimental results on the above dataset showed that the model parameters obtained by the algorithm proposed in this paper using GhostNet module were only 1.9M, and the accuracy of the small model on the infrared dataset were improved by 6.6% through feature distillation and data enhancement. And the overall mAP value reached 90.1%. The detection speed of the model could reach 25 frames per second and the average detection accuracy could reach 90.2% when measured empirically on Hisi, all achieving higher detection accuracy compared to a variety of common models portable to this platform.

Abstract: Due to the impact of buried environment and medium transported, oil pipelines will be gradually corroded with the increasing of service life. Traditional methods for calculating the residual strength of corroded pipelines included formula calculation and finite element analysis, etc. Aiming at the problems of low calculation accuracy of formulas and too complicated finite element analysis (FEA) in the prediction of the residual strength of corroded pipelines, an improved particle swarm optimization neural network model (IPSO-BPNN) was proposed to predict the residual strength of corroded pipelines. Firstly, based on the traditional particle swarm optimization, a new nonlinear decreasing inertia weight was proposed to update the velocity and location of elements quickly, and the genetic crossover operator was introduced to increase the diversity of particles, then form an improved particle swarm optimization algorithm (IPSO). Secondly, the IPSO algorithm was used to optimize the weights and thresholds of the neural network, and initialize the neural network with optimized weights and thresholds to establish the IPSO-BPNN model. Finally, the linear regression(LR),FEA, back-propagation neural network(BPNN), particle swarm optimization back-propagation neural network(PSO-BPNN) and IPSO-BPNN model were experimented on real pipelines test blasting data sets to predict the residual strength of the corroded pipelines. MAE, RMSE and MAPE were used as indicators to evaluate the predic-tability of the models. The results on the test set of two data sets showed that the MAE of the IPSO-BPNN model was 0.525 4, 0.718 5; the MAPE was 3.77%, 2.68%; the RMSE was 0.672 6, 0.947 2, respectively. The three indicators were significantly improved compared to LR, FEA, BPNN and PSO-BPNN. It showed that this method could improve the accuracy of predicting the residual strength of corroded pipelines, and could provide a more accurate basis for pipeline inspection.

Abstract: To solve the problems of fine granularity of butterfly classification, low recognition efficiency and poor accuracy of existing models in the field environment, aim at the automatic recognition of butterfly species in the field, an embedded channel attention MultSE1D recognition network was proposed to improve the backbone network of YOLOv3 model on the basis of self-built hybrid data set. The network used multiscale to extract high-dimensional features, so that the network had a variety of receptive fields, and paid more attention to the local subtle differences between many subclasses of butterflies and the surrounding environment. One dimensional convolution was used to replace the compressed excitation layer to avoid dimensionality reduction of channel features, to reduce the model parameters and to improve the operation efficiency of the model. According to the above method, the final mean average precision (mAP) of the model was 83.2%. The results showed that the improved recognition network could effectively improve the accuracy of the original model to extract the butterfly image features and the learning ability of the detail features, and could provide an effective solution to the problem of identifying the species of butterfly digital images in the wild.

Abstract: At present, the ant colony loop phenomenon and uneven energy distribution of nodes in the wireless sensor networks could cause nodes to go dormant prematurely and shorten the network lifetime. To improve the accuracy of the pheromone update formula and further balance nodes energy consumption, the following improvements could be made based on the original ant colony algorithm: add data into ant data packet, such as the sequence number of the forward ant data packet, the source address on the packet of the forward ant, number of packet path nodes, energy consumed by relay nodes, length of path, survival time of the ant data packet, initial energy and average remaining energy of packet. The adaptive evaporation coefficient could be introduced into the pheromone update formula whose number of routing hops was altered to the energy consumption of multiple hops; the pheromone increment formula could be improved, the number of nodes visited by packets was redefined as the node energy loss function. In experimental results, there was a 5.7 percent reduce in the shortest path. It was obvious that this algorithm could effectively mitigate the ant loop effect, ba-lance nodes energy consumption and extend the network lifetime.

Abstract: Switched reluctance motor (SRM) had the advantages of simple structure, low cost and large star-ting torque, but it also had the disadvantage of large torque ripple. Direct torque control could reduce torque ripple of SRM, but there was torque runaway during commutation. In order to avoid it, it was necessary to optimize the direct torque control of SRM. The proposed paper combined the cubic torque sharing function with direct torque control, which aimed at synthesizing constant instantaneous torque and distributed the desired torque of each phase in different positions. Through torque hysteresis control, the synthetic instantaneous torque was tracked to the command torque output by the speed closed-loop controller to achieve balanced phase commutation and thus suppressed torque ripple, and subdivided six sectors of traditional direct torque control. Switch tables were optimized by adding transition voltage vectors, and torque runaway was avoided by combining magnetic link and torque double hysteresis control methods. Finally, the control method was verified by digital simulation and experiment. The results showed that when the motor speed reached 3 000 r/min and the load torque was 1 N·m, the torque ripple was reduced from 98.56% to 21.24%; When the load torque was 1.5 N·m, the torque ripple was reduced from 58.71% to 19.05%. The torque ripple of SRM was less than that of direct torque control.

Abstract: Using the two-phase flow VOF model in the ANSYS Fluent software, a three-dimensional dynamic model of the droplet in the microfluidic chip passing through the symmetric Y-type bifurcation microchannel was established, and the dynamic simulation of the droplet flow and the simulation result of the droplet interface evolution were realized. It was consistent with the experimental results to verify the effectiveness of the model. Through this model, the splitting behavior of the droplet passing through the symmetric Y-type bifurcation microchannel and the evolution process of the pressure field and velocity field during the droplet splitting process were studied, and the breaking mechanism of the droplet under four different flow patterns and diffe-rent stages was revealed. The relationship between the flow pattern of the droplet and the capillary number Ca and the droplet size was obtained; according to the various flow pattern data obtained by the simulation, the prediction of the droplet rupture and non-rupture boundary under the model was obtained by fitting in MATLAB equation. This research not only provided a simple and effective method for droplet splitting, but also had potential application value in the fields of biomedicine, energy chemical industry and food engineering.

Abstract: A wrist rehabilitation device driven by bending pneumatic muscles was designed and implemented to assist patients in completing wrist extension/flexion and adduction/abduction rehabilitation training. The design and modeling of the flexible drive, the design of the exoskeleton rehabilitation glove, the design of the control system and the safety design have been completed. The structure and main production process of the bending and contracting pneumatic artificial muscle were elaborated, and its kinematics characteristics and output force characteristics were tested. The kinematics model of the bending and contracting pneumatic artificial muscle was established by analytical methods, laying the foundation for the precise control of the bending and contracting pneumatic artificial muscle. The wrist rehabilitation device was designed and implemented, and simulated rehabilitation training was carried out. The results showed that the angle error was within 10%, and the wrist could be effectively driven by the device for rehabilitation training.

Abstract: In view of the differences in the identification of disease scales by different technicians in the current process of stone arch bridge technical condition assessment, a method for assessing the technical condition of stone arch bridges based on the entropy method-cloud model was proposed, which based on the existing assessment standards for stone arch bridges. Firstly, the method converted the comment set and evaluation data into a cloud model. Then it used the entropy method to adjust the weights of the stone arch bridge components and introduced the combined fuzzy pasting schedule method calculating the similarity to obtain the evaluation results. Finally, taking a stone arch bridge as an example, the applicability of this method in the evaluation of the technical condition of stone arch bridges was explored, and the results showed that: the evaluation results of the stone arch bridge were classified into 4 categories and the evaluation results of the cloud model were consistent with the results obtained by the “Highway Bridge Technical Condition Evaluation Standards” method, which were in line with the on-site inspection results. The method took into account the subjective randomness of the disease scale determination and the actual difference of the engineering project, which could provide a more scientific and reasonable decision-making basis for the maintenance of stone arch bridges.

Abstract: Due to the complexity of the traditional force controlled method in analyzing the deformation process of the ground and the existing tunnels caused by the orthogonal under-crossing of the tunnel, the displacement controlled method was adopted in this article to analyze the influence of tunnel excavation on the ground settlement and the longitudinal deformation of the arch bottom of the existing tunnels based on Park’s convergence model that described more realistic tunnel boundary deformation. Firstly, through the comparison of the mea-suring data with the calculation results of the method in this paper and the force controlled method, the feasibility of the displacement control method under newly built tunnels under-crossing existing tunnel was verified, and then the four tunnel convergence modes of Park were used as the boundary conditions on the new tunnel for further analysis. The comparison between the calculated results and the actual measurements showed that the results of the displacement control method for natural site settlement were very close to the measured data. The calculation results showed that, given the displacement of the tunnel vault g, there were certain differences in the shape and value of the ground settlement trough and the deformation of tunnel arch bottom caused by Park’s four convergence models. With the same soil loss and g value, the effects of convergence mode on surface settlement and the deformation of tunnel arch bottom were circular non-uniform convergence, elliptical convergence, elliptic non-uniform convergence, and uniform convergence in descending order. Among them, the effect of elliptical convergence and non-uniform convergence of ellipse was relatively close, while the uniform convergence and the actual measurements had the worst fitting effect. Surface settlement caused by tunnel construction and the deformation mode of existing tunnel was different, the surface settlement appeared as a Gaussian curve while the tunnel deformation may appear as a “W” shape when the distance between the tunnels was constant.

Abstract: Considering the heterogeneity of micro-structure of recycled aggregate concrete (RAC), a five-phase model was established, which included new and old interface transition zones (ITZ), new mortar, old adherent mortar, and natural aggregate. Based on the Monte Carlo theory and MATLAB software platform, a real convex polygon coarse aggregate analysis model of RAC was developed. The experimental results were simi-lar to the model calculation results, which proved that the five-phase convex aggregate model could better predict the chloride permeation behavior in RAC. Furthermore, the influence of the characteristics of recycled aggregate concrete on chloride ion penetration was studied. The results showed that: when the content of recycled coarse aggregate (RCA) increased from 0 to 50%, the chloride ion diffusion coefficient and diffusion depth increasd by 102.0% and 23.9%, respectively. When the ITZ thickness increased from 25 μm to 100 μm, the chloride ion diffusion depth and diffusion coefficient increased by 13% and 37.0%, respectively. When the RCA content was large, the increase of old adherent mortar would significantly reduce the chloride ion permeability of RAC. When the bonding rate of old mortar increased from 0.1 to 0.4, and the chloride ion diffusion coefficient increased by 18.2%. New and old ITZ and old adherent mortar were the fundamental factors affec-ting the chloride penetration resistance of RAC.

Abstract: In recent years, the damages of network attacks such as launched by APT has become more and more serious. Although existing studies based on signal game theory could simulate the APT attack and defense process to a certain extent, they ignored the phenomenon of unequal benefits between the two sides in the process of attack and defense and the multi-stage confrontation process, resulting in the lack of universality of the model and method. In this paper, a proactive defense model based on non-zero-sum signal game was proposed. First of all, the attack and defense game tree was built based on the signal game theory and the analysis of network attack and defense multi-stage confrontation process. Secondly, the non-zero-sum method and discount factor were used to build the multi-stage income of model in the process of offensive and defensive based on the situation of unequal income. On this basis, a quantitative method was proposed for network attack and defense characteristics, and the current optimal defense strategy algorithm was obtained based on the Nash equilibrium and refined Bayesian equilibrium existing in the analysis model. Finally, the model and method were verified by simulation experiments. The results showed the feasibility and effectiveness of the proposed model and method.

Abstract: The increasing number of nonlinear devices increased the harmonic content of grid voltage background and led to the grid-connected current distortion of inverter. In order to suppress grid-connected current harmonics, an improved grid voltage proportional-feedforward control strategy was proposed. The influence of inverter side inductance and filter capacitance on grid voltage proportional-feedforward control strategy of LCL grid-connected inverter was analyzed, and the optimal selection method of inverter side inductance and filter capacitor was obtained. That was to say, according to the performance requirements and equipment safety requirements of the inverter, the range of inductance and filter capacitor on the inverter side of LCL grid-connected inverter was determined, and the appropriate inductance value was determined accordingly. Then the harmonic content of grid-connected current was reduced by selecting a smaller capacitance value. Finally, the effectiveness of the control strategy and optimization method was verified in the prototype of LCL grid-connected inverter. The experimental results showed that the total harmonic distortion rate of grid-connected current of LCL grid-connected inverter was reduced from 4.04% to 2.58%, and the third and fifth harmonics with higher harmonic content were reduced from 2% to less than 1%, which improved the performance of the inverter.

Abstract: A secure data sharing mechanism based on blockchain was proposed aiming at the security and privacy issues of vehicle sharing information in the Internet of Vehicles. In this mechanism, the shared message and reputation value were stored through the blockchain based on the DAG structure, and the storage burden of the vehicle would be reduced. The malicious information in the network was excluded through the reputation mechanism. In addition, a pseudonym substitution strategy based on user privacy needs was introduced, which parameterize vehicle driving goals and driver privacy protection needed. A calculation method for the replacement interval of pseudonyms was proposed, which redefined the frequency of pseudonym replacement. Experiments showed that when the malicious nodes in the network reached 30%, the accuracy of the vehicle recei-ving information was higher than 91%, the mechanism could effectively improve the safety and reliability of information sharing in the Internet of Vehicles.