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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Dong, Dandan Shaanxi Fashion Engineering University School of Economics and Management Xi'an China

ISBN: (纸本)9798350317558

With the rapid development of e-commerce, logistics distribution has become an important means for e-commerce enterprises to improve their competitiveness. This article focuses on the optimization problem of e-commerce order delivery routes, using particle swarm optimization algorithm for research, aiming to improve delivery efficiency, reduce costs, and enhance customer satisfaction. This article compares the time for solving the optimal path before and after optimization. The experimental results show that after optimizing with the particle swarm optimization algorithm, the average solving time is reduced from 67.12s to 27.49s. The research results indicate that the particle swarm optimization algorithm is superior to traditional algorithms in terms of solving efficiency and quality, providing strong support for e-commerce enterprises in fierce market competition. © 2024 IEEE.

关键词： E-commerce Order Experimental Testing Order Delivery particle swarm optimization algorithm Route optimization

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Dynamic performance analysis and optimization of pulp-molding machine frames

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第1期122卷 209-217页

作者： Lou, Junbin Gong, Yaping Zhejiang Ocean Univ Sch Marine Engn Equipment Zhoushan 316000 Peoples R China Jiaxing Univ Coll Informat Sci & Engn Jiaxing 314001 Zhejiang Peoples R China Zhejiang Ocean Univ Maritime Sch Zhoushan 316000 Peoples R China

The pulp-molding product is a new degradable and pollution-free packaging material replacing traditional packaging materials. Countries are strongly recommending that pulp-molding machines are mainly used to produce pulp-molding products. The stability, efficiency, and rapidity of its products' molding and processing quality are largely controlled by the structural dynamic mechanics of the molding machine frame. Its equipment's dynamic performance is measured by the 1st-order natural frequency. The pulp-molding machine frame was taken as an example for optimizing the design and investigating the dynamic performance in the work. Based on finite elements, the beam structure was divided into finite elements, and the global independent generalized displacement coordinates of equipment were extracted using multi-point constraint elements. The global independent generalized displacement coordinates and the Lagrange equation were used to establish the device's elastic dynamic model. Besides, the correctness of the theoretical model was verified by the finite element method. Finally, the first natural frequency was taken as the optimization design goal, and the particle swarm optimization algorithm was utilized to optimize the section size of the beam in equipment. The results could optimize the molding machine's design and analyze the dynamic performance at the pre-design stage.

关键词： Pulp-molding machine Elastodynamic modeling Natural frequency particle swarm optimization algorithm

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optimization of Multi-Objective Unequal Area Facility Layout

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IEEE ACCESS 2022年 10卷 38870-38884页

作者： Tang, Hongtao Ren, Senli Jiang, Weiguang Chen, Qingfeng Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China

The unequal area facility layout problem (UA-FLP) refers to the reasonable arrangement of a certain number of facilities in a given layout area. The facility layout should satisfy given layout constraints and optimize given optimization objectives as far as possible. In this paper, a method combining improved lowest horizontal line method and particle swarm optimization algorithm is proposed to solve UA-FLP, which achieves multi-objective optimization of material handling cost, the adjacent value and the utilization rate of floor shop. On the one hand, the algorithm formulates facility packing rules through the improved lowest horizontal line method, which simplifies the legalization of facility layout. On the other hand, a modified particle swarm optimization (PSO) algorithm combining objective space division method (OSD) and niche technology is used for multi-objective optimization. The proposed algorithm overcomes the shortcomings of previous facility layout methods such as complex overlapping interference process, large amount of calculation and long time for multi-objective optimization. Compared with the comparison methods, the results show that material handling cost is reduced by 1%-6% and the utilization ratio of floor shop is increased by 2%-7%.

关键词： Layout optimization particle swarm optimization Conferences Costs Materials handling Interference Facility layout lowest horizontal line particle swarm optimization algorithm niche technology

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Degradation prediction model of PEMFC based on multi-reservoir echo state network with mini reservoir

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第94期47卷 40026-40040页

作者： Zhang, Shaojie Chen, Tao Xiao, Fei Zhang, Rufeng Wuhan Univ Technol Sch Mech & Elect Engn 122 Luoshi Rd Wuhan 430070 Peoples R China

The durability of the proton exchange membrane fuel cell (PEMFC) has always been a major obstacle in its commercialization process and effective degradation prediction can improve this problem to a certain extent. Data-driven degradation prediction model is one of the most effective prediction methods available, which is able to ignore the structure of the PEMFC itself and rely solely on the data to make predictions, greatly simplifying the pre-diction process. Echo state network (ESN), as one of the data-driven methods, has received much attention for its low computational complexity and fast convergence in the degra-dation prediction of PEMFC. In this paper, the multi-reservoir echo state network with mini reservoir (MRM) degradation prediction model of PEMFC is proposed. The structure of MRM is that the main reservoirs are stacked in a layer and the mini reservoir is in the next level to collect and organize the main reservoir states. In addition, in order to improve the prediction accuracy, this paper firstly uses Savitzky-Golay (SG) filter to process the original data, and then investigates the influence of two important parameters, the number of main reservoirs and the number of main reservoir neurons, on the prediction accuracy and finds the optimal number of main reservoirs and main reservoir neurons for this model using particle swarm optimization (PSO) algorithm. Finally, the effectiveness of the model is verified on different lengths of training sets under both static and dynamic conditions. The results show that the model has higher accuracy and better robustness in the PEMFC degradation prediction compared with other models. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Degradation prediction Main reservoir Savitzky-golay filter Multi-reservoir echo state network with mini reservoir particle swarm optimization algorithm

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Time Series Prediction of Transformer Oil Chromatography Based on Attention-PSO-GRU Model

Time Series Prediction of Transformer Oil Chromatography Bas...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Teng, Yuanxin Wang, Guan Su, Xiaomeng Wu, Meiying Shandong Univ Sch Elect Engn Jinan Peoples R China Guangdong Power Grid Guangzhou Power Supply Bur Guangzhou Peoples R China

ISBN: (纸本)9798350339345

The content of dissolved gas in transformer oil is an important characteristic to reflect the operating condition of transformers. To overcome the problems that the traditional transformer oil chromatography gas prediction model cannot effectively use the older transformer oil chromatography data and it is difficult to track the transformer oil chromatography data under abnormal conditions and it can only rely on experience to adjust the model parameters, we introduce Gated Recurrent Unit (GRU), Attention Mechanism (APM) and particle swarm optimization (PSO) to construct the Attention-PSO-GRU prediction model. Firstly, the transformer oil chromatography monitoring data are pre-processed, and the attention mechanism is introduced into the time channel to realize the redistribution of the input data weights. Then, the hyperparameters in the GRU model are optimality-seeking iterated by PSO to realize the adaptive tuning of the model. Finally, the optimized GRU neural network is used to extract spatiotemporal features from transformer oil chromatography monitoring data and construct the Attention-PSO-GRU transformer oil chromatography gas prediction model. The experimental results show that the Attention-PSO-GRU model can reach 97.9% accuracy in predicting the normal transformer oil chromatography data and has a better tracking ability for the abnormal transformer oil chromatography data. Therefore, the Attention-PSO-GRU model proposed in this paper can effectively improve the accuracy of transformer oil chromatography gas prediction, which has practical significance for preventing transformer faults and ensuring the safe and stable operation of power systems.

关键词： oil chromatography gas content prediction attention mechanism particle swarm optimization algorithm Gated recurrent neural network

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Application Research of Wireless Sensor Network in Structural Health Monitoring of Civil Engineering 2

Application Research of Wireless Sensor Network in Structura...

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2nd IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Li, Songtao Gao, Yanlong Wang, Baolin Tian, Ruxue Zhengzhou Railway Vocational & Technical College College of Railway Engineering Zhengzhou China Henan Jiaoyuan Engineering Technology Group Co. Ltd Zhengzhou China Zhengzhou Road Inspection Engineering Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9798350368208

Aiming at the needs of structural health monitoring in civil engineering, this paper deeply studies the application of wireless sensor networks in it, and focuses on the design and optimization of intelligent sensor networks. First, this paper outlines the important role of wireless sensor networks in civil structural health monitoring, including real- time data acquisition, remote monitoring and fault warning. Subsequently, a structural health monitoring method based on modal recognition technology is introduced in detail, which evaluates the health status of the structure by analyzing its dynamic response. This paper introduces a particle swarm optimization algorithm to optimize the layout and parameter settings of the sensor network to improve the accuracy and reliability of the monitoring system. The effectiveness of the proposed method is verified through system simulation. The simulation results show that the optimized sensor network can more accurately capture the abnormal behavior of the structure and maintain stable performance under various working conditions. The research results of this paper provide an efficient technical solution for the structural health monitoring of civil engineering, which has important theoretical significance and practical application value. © 2024 IEEE.

关键词： Civil structure health monitoring intelligent sensor network modal recognition particle swarm optimization algorithm

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Numerical Research on Suppression of Thermally Induced Wavefront Distortion of Solid-state Laser Based on Neural Network

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CURRENT OPTICS AND PHOTONICS 2022年第5期6卷 479-488页

作者： Liu, Hang He, Ping Wang, Juntao Wang, Dan Shang, Jianli China Acad Engn Phys Inst Appl Elect Mianyang 621900 Sichuan Peoples R China China Acad Engn Phys Grad Sch Beijing 100088 Peoples R China China Acad Engn Phys Key Lab Sci & Technol High Energy Laser Mianyang 621900 Sichuan Peoples R China

To account for the internal thermal effects of solid-state lasers, a method using a back propagation (BP) neural network integrated with a particle swarm optimization (PSO) algorithm is developed, which is a new wavefront distortion correction technique. In particular, by using a slab laser model, a series of fiber pumped sources are employed to form a controlled array to pump the gain medium, allowing the internal temperature field of the gain medium to be designed by altering the power of each pump source. Furthermore, the BP artificial neural network is employed to construct a nonlinear mapping relationship between the power matrix of the pump array and the thermally induced wavefront aberration. Lastly, the suppression of thermally induced wavefront distortion can be achieved by changing the power matrix of the pump array and obtaining the optimal pump light intensity distribution combined using the PSO algorithm. The minimal beam quality beta can be obtained by optimally distributing the pumping light. Compared with the method of designing uniform pumping light into the gain medium, the theoretically computed single pass beam quality beta value is optimized from 5.34 to 1.28. In this numerical analysis, experiments are conducted to validate the relationship between the thermally generated wavefront and

关键词： Back propagation neural network particle swarm optimization algorithm Pump array Lasers diode -pumped

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Micro-amplitude vibration suppression of a bistable nonlinear energy sink constructed by a buckling beam

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NONLINEAR DYNAMICS 2022年第4期108卷 3185-3207页

作者： Zeng, You-cheng Ding, Hu Du, Rong-Hua Chen, Li-Qun Shanghai Univ Sch Mech & Engn Sci Shanghai Inst Appl Math & Mech Shanghai Key Lab Mech Energy Engn Shanghai 200444 Peoples R China Changsha Univ Sci & Technol Coll Automot & Mech Engn Changsha 410114 Peoples R China

Effective reduction of micro-amplitude vibration has always been a serious challenge. The nonlinear energy sink (NES) has been proven to be able to reduce vibration at a wide frequency. However, the energy threshold of the NES prevents it from suppressing micro-vibrations. In this paper, a bistable nonlinear energy sink (BNES) based on a buckling beam oscillator is constructed. The threshold of the NES is lowered by the nonlinear dynamic behavior of jumping between wells of the bistable oscillator. The motion equations of the discrete-continuous system are derived by using Hamilton's principle. The approximate analytical solution is obtained and verified numerically. The results show that even when the primary system has a micro-amplitude resonance, the nonlinear cross-well vibration of the BNES can still reduce the vibration. The robustness of the BNES is stronger than that of the tuned mass damper (TMD). The optimal parameters of the BNES are obtained with particle swarm optimization (PSO) algorithm. The result of parameter optimization shows that the energy threshold of the nonlinear energy sink can be effectively lowered. In short, the method based on nonlinear dynamics in this paper provides an effective reduction strategy for micro-vibration in engineering. [GRAPHICS]

关键词： Buckling beam Bistable Micro-amplitude vibration particle swarm optimization algorithm Harmonic balance method

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Real-time implementation and evaluation of gorilla troops optimization-based control strategy for autonomous microgrids

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IET RENEWABLE POWER GENERATION 2022年第14期16卷 3071-3091页

作者： Maged, Nourhan A. Hasanien, Hany M. Ebrahim, Essamudin A. Tostado-Veliz, Marcos Jurado, Francisco Elect Res Inst Power Elect & Energy Convers Dept Cairo Egypt Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo 11517 Egypt Univ Jaen Super Polytech Sch Linares Dept Elect Engn Linares Spain

The present work develops a novel gorilla troops optimization algorithm (GTO) for optimal control of an autonomous microgrid. The proposed proportional-integral controller parameters are properly designed by the GTO algorithm. It is based on the integral squared error to minimize the system objective function. The suggested islanded microgrid system operates with several decentralized, electronically interfaced energy resources and their local loads. The new optimal controller is applied to regulate its voltage at the different points of common coupling and its frequency despite the load variation. The performance of the new proposal is compared with the PI controller that was designed by a particle swarm optimization algorithm. It is observed that the microgrid functioning operation is improved even more by applying a GTO-based than the other. The control strategy is thoroughly tested using a Matlab/Simulink simpower-system environment under various microgrid operating situations. It is also implemented with OPAL 4510 real-time hardware in the loop and a rapid control prototyping module. The different benchmark experiments serve to show the superiority of the new optimizer.

关键词： PI control power engineering computing power generation control distributed power generation islanded microgrid system real-time implementation PI controller optimal controller microgrid functioning operation GTO algorithm power system simulation optimal control particle swarm optimisation proportional-integral controller parameters rapid control prototyping module autonomous microgrid particle swarm optimization algorithm power distribution control power distribution faults integral squared error load variation OPAL 4510 real-time hardware gorilla troops optimization-based control strategy system objective function voltage control interfaced energy resources Matlab-Simulink simpower-system environment

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Design of beamforming metasurface based on fully connected neural networks 14

Design of beamforming metasurface based on fully connected n...

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14th International Symposium on Antennas Propagation and EM Theory

作者： Li, Shi Sheng, Lili Wen, Jinfang Cao, Weiping Lu, Lucong Guilin Univ Elect Technol Sch Informat & Commun Guilin Peoples R China

ISBN: (纸本)9798350353129;9798350353136

In order to solve the problem that the period of designing metasurface with specific functions is too long by using electromagnetic simulation software, this paper proposes a metasurface design method based on artificial neural network, which can accurately predict the far-field pattern. In the training process, the root-mean-square error decreases with the increase of the number of iterations, the minimum root-mean-square error is 0.01, and the training time is 21 s. After the training is completed, the model can quickly predict the far-field pattern according to the coding matrix. The single prediction time of the neural network is 0.0065 s, while the single simulation time of the full-wave simulation software is 4 min, which is far more than that of the neural network. The accuracy of the predicted data is verified by comparing the predicted results with the results of the full-wave simulation. By combining the neural network with particle swarm optimization algorithm, the metasurface coding matrix corresponding to the target pattern can be designed. Compared with the traditional metasurface design method, this design method greatly reduces the design time and solves the problems of time consuming and cost in the traditional design method.

关键词： Neural network particle swarm optimization algorithm Beamforming metasurface Coding matrix Far-field pattern

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