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International Conference on Wireless Power Transfer (ICWPT)

作者： Wang, ChuangXin Chen, Zhong Changsha Univ Technol Sch Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9789819708796;9789819708772;9789819708765

The disorderly charging tactics of large-scale electric vehicle (EV) will lead to the increase of peak-valley load difference of the power grid and destroy the stability of the power system. In order to solve this problem, considering the peak adjustment demand of the grid side and the different demands of the users on the charging amount and charging cost, this paper proposes a hierarchical optimization scheduling method based on the dynamic TOU time segment model. This method updates the load curve according to the load information of each EV when it is connected to the grid. Meanwhile, the dynamic time-of-use electricity price model proposed in this paper is used to dynamically update the peak-valley electricity price of the EV. Among them, the upper layer model takes the minimum variance of the total load of the grid as the objective function, and the lower layer model takes the minimum deviation of the agent scheduling plan, the maximum amount of charging and the minimum cost of charging as the objective function, and adopts the improved PSO algorithm to optimize the charging (discharging) tactics of each EV. Finally, the simulation analysis is carried out with a concrete example. The results show that this method can effectively reduce the peak-valley difference of load demand curve on the premise of ensuring the travel demand and economic benefits of EV owners.

关键词： particle swarm optimization algorithm user responsiveness electric vehicle orderly charge and discharge

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The Mechanism of the Role of Big Data Knowledge Management in the Development of Enterprise Innovation

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第6期14卷 1364-1372页

作者： Yan, Guangyu Ma, Rui Chengdu Polytech Dept Sci Res Chengdu 610041 Peoples R China Chengdu Univ Tradit Chinese Med Hlth & Rehabil Coll Chengdu 611137 Peoples R China

The effectiveness and efficiency of enterprise knowledge management depends on the effectiveness and efficiency of the enterprise's implementation of knowledge management. Big data technology can collect, analyse and apply the massive amount of data in an organisation to support the implementation of knowledge management. Therefore, exploring the role of big-data knowledge management in the development of enterprise innovation will help enterprises to better implement knowledge management. Based on this, the study aims to propose a model for predicting big data knowledge management and enterprise innovation development for high-tech enterprises in China. The study firstly used Principal Component Analysis (PCA) to decrease the dimensionality of the model, and then used the particle swarm algorithm to optimize BP neural network (PSO-BP). Network (PSO-BP) was used to evaluate enterprise knowledge management and enterprise innovation development. The results of the study show that the absolute values of the relative errors of the pre-processed model do not exceed the 5% threshold, and only the relative errors of some indicators are relatively large, such as X5 and X7, with values of 4.5% and -3.8%, indicating that the model has a good performance in predicting the innovation effect of enterprises.

关键词： Big data knowledge management BP neural network algorithm enterprise innovation development principal component analysis particle swarm optimization algorithm correlation analysis

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Selective Maintenance of Multistate Systems Considering the Random Uncertainty of the System Mission Period and Mission Breaks

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第5期48卷 7059-7075页

作者： Wang, Hai-Peng Duan, Fu-Hai Wang, Xiao-Long He, Yu-Ling North China Elect Power Univ Dept Mech Engn Baoding 071003 Hebei Peoples R China North China Elect Power Univ Hebei Key Lab Elect Machinery Hlth Maintenance Fa Baoding 071003 Hebei Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Liaoning Peoples R China

A selective maintenance model for multistate systems that simultaneously considers the random uncertainty of the system mission period and mission breaks and the requirements of different system performance levels is proposed. Unlike traditional studies, the proposed model not only considers the random uncertainty of the mission period and mission breaks and the requirements of different system performance levels but also effectively manages the selective maintenance problems of multistate systems by using a heuristic algorithm. In this model, random variables that conform to a verified distribution are employed to characterize the randomness of the mission period and mission breaks, while the service age reduction model is utilized to describe the effective age of the system components after maintenance. To construct this method, the quantitative relationships between the system maintenance cost and the service age reduction factor and between the maintenance time and the service age reduction factor are established. Then, based on the multistate system reliability and general generating function technology, the mission completion rate models of the components and the system are established. Finally, the solution approach for making selective maintenance decisions for multistate systems based on the particle swarm optimization (PSO) algorithm is presented. To validate the effectiveness of the proposed model as well as the solution algorithm, a coal transportation system at a thermal power station is studied, and satisfactory results are obtained.

关键词： Selective maintenance Multistate system Random uncertainty particle swarm optimization algorithm

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Blockchain-Based Power Transaction Method for Active Distribution Network

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Energy Engineering 2023年第5期120卷 1067-1080页

作者： Fei Zeng Zhinong Wei Haiteng Han Yang Chen College of Energy and Electrical Engineering Hohai UniversityNanjing211100China School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213164China

A blockchain-based power transaction method is proposed for Active Distribution Network(ADN),considering the poor security and high cost of a centralized power trading ***,the decentralized blockchain structure of the ADN power transaction is built and the transaction information is kept in ***,considering the transaction needs between users and power suppliers in ADN,an energy request mechanism is proposed,and the optimization objective function is designed by integrating cost aware requests and storage aware ***,the particle swarm optimization algorithm is used for multi-objective optimal search to find the power trading scheme with the minimum power purchase cost of users and the maximum power sold by power *** experimental demonstration of the proposed method based on the experimental platform shows that when the number of participants is no more than 10,the transaction delay time is 0.2 s,and the transaction cost fluctuates at 200,000 yuan,which is better than other comparison methods.

关键词： Blockchain active distribution network power transaction energy request mechanism particle swarm optimization algorithm

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AGRICULTURAL UAV CROP SPRAYING PATH PLANNING BASED ON PSO-A* algorithm

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INMATEH-AGRICULTURAL ENGINEERING 2023年第3期71卷 625-636页

作者： Fan, Lijuan Xinxiang Vocat & Tech Coll Xinxiang 453006 Henan Peoples R China

Currently, drones have been gradually applied in the field of agriculture, and have been widely used in various types of agricultural aerial operations such as precision sowing, pesticide spraying, and vegetation detection. The use of agricultural UAVs for pesticide spraying has become an important task in the agricultural plant protection process. However, in the crop spraying process of agricultural UAVs, it is necessary to traverse multiple spray points and plan obstacle avoidance paths, which greatly affects the efficiency of agricultural UAV crop spraying operations. To address the above issues, traditional particle swarm optimization (PSO) algorithms have strong solving capabilities, but they are prone to falling into local optima. Therefore, this study proposes an improved PSO algorithm combined with the A* algorithm, which introduces a nonlinear convergence factor balancing algorithm for global search and local development capabilities in the traditional PSO algorithm, and adopts population initialization to enhance population diversity, so that the improved PSO algorithm has stronger model solving capabilities. This study designs two scenarios for agricultural UAV crop spraying path planning: one without obstacles and one with obstacles. Experimental simulation results show that using the PSO algorithm to solve the obstacle-free problem and then using the A* algorithm to correct the path obstructed by obstacles in the obstacle scenario, the agricultural UAV crop spraying trajectory planning based on the PSO-A* algorithm is real and effective. This research can provide theoretical basis for agricultural plant protection and solve the premise of autonomous operation of UAVs.

关键词： particle swarm optimization algorithm A* algorithm UAV path planning crop spraying operations

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A new two-axis solar tracker based on the online optimization method:Experimental investigation and neural network modeling

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Energy and AI 2023年第4期14卷 341-359页

作者： Amir Pouya Masoumi Vahid Bagherian Ali Reza Tavakolpour-Saleh Elham Masoomi Department of Mechanical and Aerospace Engineering Shiraz University of TechnologyShirazIran Department of Mechanical Engineering Shiraz UniversityShirazIran Department of Computer Engineering Qazvin BranchIslamic Azad UniversityQazvinIran

This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement *** this research,four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles,which determine the position of the *** magnitude of the sunray is considered as the cost function of all ***,several experiments are carried out to find the best optimization algorithm with optimal population size,number of iterations,and also the best initialization *** initialization leads to faster convergence compared to random *** results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms,with a convergence time of less than 40 *** average fitness value or voltage received by the tracker is 2.4 Volts in this method,which is higher than other *** also performs well with a population size of 15 and 7 ***,the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from *** of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network *** performance of the proposed ANN model is evaluated using regression plots,demonstrating a strong correlation between predicted and target ***,the outcomes reveal the feasibility of using online optimization algorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.

关键词： Intelligent two-axis solar tracker optimization algorithm particle swarm optimization algorithm Online optimization method Artificial neural network

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Research on the collaborative mechanism between site selection optimization model and control of green space planning under the background of urban renewal

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DISCOVER SUSTAINABILITY 2025年第1期6卷 1-22页

作者： Li, Xiangyun Dai, Desheng Dong, Jingwen Hu, Kaili Nanjing Municipal Design & Res Inst Co Ltd Nanjing 210018 Peoples R China Nanjing Inst Technol Sch Civil Engn & Architecture Nanjing 211167 Peoples R China Natl Engn Res Ctr Water Resources Efficient Utiliz Nanjing 210024 Peoples R China

As a part of integrated urban ecosystems, green space is an important target of urban renewal planning. In the process of urban renewal of green space, it is difficult to control the special planning for green space systems, and the scale of urban public space is insufficient. From the perspective of spatial governance, it is of great practical importance to study the relationship between site selection optimization and the control of special planning for green space systems. This paper proposes a method of green space evaluation and site selection optimization based on Geographic Information System (GIS) modelling and particle swarm optimization algorithm. It optimizes the green space allocation by using the model constraints. Taking the special planning for green space systems in Gaochun District, Nanjing City as an example, it analyzes the constraints of the special planning for green space systems in the site selection optimization model from two perspectives: indicator conditional constraints and space requirement constraints. The GIS model of optimal site selection is introduced into the special planning for green space systems' adjustment process in the context of urban renewal. This approach can bring a variety of positive benefits to meet the needs of green space sharing and people's well-being.

关键词： Green space planning Site selection optimization Urban renewal particle swarm optimization algorithm

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Gene Regulatory Network Construction Based on a particle swarm optimization of a Long Short-term Memory Network

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CURRENT BIOINFORMATICS 2020年第7期15卷 713-724页

作者： Tang, Zhenhao Chai, Xiangying Wang, Yu Cao, Shengxian Northeast Elect Power Univ Sch Automat Engn Jilin Jilin Peoples R China

Background: The Gene Regulatory Network (GRN) is a model for studying the function and behavior of genes by treating the genome as a whole, which can reveal the gene expression mechanism. However, due to the dynamics, nonlinearity, and complexity of gene expression data, it is a challenging task to construct a GRN precisely. And in the circulating cooling water system, the Slime-Forming Bacteria (SFB) is one of the bacteria that helps to form dirt. In order to explore the microbial fouling mechanism of SFB, constructing a GRN for the fouling-forming genes of SFB is significant. Objective: Propose an effective GRN construction method and construct a GRN for the fouling-forming genes of SFB. Methods: In this paper, a combination method of Long Short-Term Memory Network (LSTM) and Mean Impact Value (MIV) was applied for GRN reconstruction. Firstly, LSTM was employed to establish a gene expression prediction model. To improve the performance of LSTM, a particle swarm optimization (PSO) was introduced to optimize the weight and learning rate. Then, the MIV was used to infer the regulation among genes. In view of the fouling-forming problem of SFB, we have designed electromagnetic field experiments and transcriptome sequencing experiments to locate the fouling-forming genes and obtain gene expression data. Result: In order to test the proposed approach, the proposed method was applied to three datasets: a simulated dataset and two real biology datasets. By comparing with other methods, the experimental results indicate that the proposed method has higher modeling accuracy and it can be used to effectively construct a GRN. And at last, a GRN for fouling-forming genes of SFB was constructed using the proposed approach. Conclusion: The experiments indicated that the proposed approach can reconstruct a GRN precisely, and compared with other approaches, the proposed approach performs better in extracting the regulations among genes.

关键词： Gene regulatory network long short-term memory particle swarm optimization algorithm mean impact value slime-forming bacteria yeast gene data machine learning

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Hybrid Noise Reduction Filter Using the Gaining–Sharing Knowledge-Based optimization and the Whale optimization algorithms

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SN Computer Science 2024年第4期5卷 417页

作者： Nabahat, Mehrdad Khiyabani, Farzin Modarres Navmipour, Nima Jafari Department of Mathematics Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran

Noise reduction is one of the main challenges for researchers. Classical image de-noising methods reduce the image noise but sometimes lose image quality and information, such as blurring the edges of the image. To solve this challenge, this work proposes two optimal filters based on a generalized Cauchy (GC) distribution and two different nature-inspired algorithms that preserve image information while decreasing the noise. The generalized Cauchy filter and the bilateral filter are two parameter-based filters that significantly remove image noise. Parameter-based filters require proper parameter selection to remove the noise and maintain the edge details. To this end, two filters are considered. In the previous works, the parameters of the mask that was made with the GC function were optimized and the mask size was considered fixed. By studying different noisy images, we find that the selected mask size significantly impacts the designed filter performance. Therefore in this paper, a mask is designed using the GC function to formulate the first filter, and despite the optimization of the filter parameters, the selected mask size is also optimized using the peak signal-to-noise ratio (PSNR) as a fitness function. In most metaheuristic-based bilateral filters, only the domain and range parameters, which are based on Gaussian distribution, are optimized and the neighboring radius is a constant value. Filter results on different noisy images show that the neighboring radius has a major effect on the filter performance. Since the filter designed with the GC function causes significant noise removal, this function is effective, and on the other hand, it’s almost similar behavior with the Gaussian function has caused it to be combined with the bilateral filter to design the second filter in this paper. The kernel of the domain and range is considered to be the GC function instead of the Gaussian function. The domain and range parameters and the neighboring radius are opti

关键词： Bilateral filter Gaining sharing knowledge-based optimization algorithm Generalized Cauchy filter Image de-noising Image noise particle swarm optimization algorithm Whale optimization algorithm

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A Improved particle swarm optimization algorithm for the Application with Boundary-layer Problems of Power-law Fluids

A Improved Particle Swarm Optimization Algorithm for the App...

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2013 International Conference on Mechanical Engineering and Instrumentation(ICMEI 2013)

作者： Jianhui Zhou Lixi Yue Yannan Lu College of Qian'an Hebei United University

In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suction/injection as well as a reverse flow boundary conditions. A improved particle swarm optimization algorithm(ISPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

关键词： particle swarm optimization algorithm Ordinary differential equations Parameter estimation boundary-layer problems

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