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Intelligent Innovation and Entrepreneurship Platform Based on pso algorithm + Big Data 1

Intelligent Innovation and Entrepreneurship Platform Based o...

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Fan, Zhaohong Shandong Polytechnic Vocational College Shandong Jining China

ISBN: (纸本)9798350316469

Traditional innovation and entrepreneurship platforms face problems such as low resource utilization efficiency and inaccurate decision-making. In order to solve these problems, this paper adopts the combination of pso (Particle Swarm Optimization) algorithm and big data to improve the efficiency, accuracy and intelligence level of the algorithm. This article describes how to apply the pso algorithm to the innovation and entrepreneurship platform to perform task optimization and decision support in the big data environment, and also optimize the convergence speed of the pso algorithm, and effectively control the error. By optimizing resource utilization, the overall efficiency and cost-effectiveness of the platform is improved. The improved platform resource utilization rate in this paper has reached 79% -89%, and the intelligent innovation and entrepreneurship platform based on pso algorithm and big data has achieved significant improvements in algorithm efficiency, accuracy and resource utilization rate. This provides innovative entrepreneurs with a more efficient, accurate and intelligent platform. © 2023 IEEE.

关键词： Big Data Analysis Entrepreneurship Platform Intelligent Recommendation pso algorithm

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Construction of an Intelligent Decision System for Building Construction Management Risk Based on pso algorithm

Construction of an Intelligent Decision System for Building ...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Wu, Shulong Zhou, Zhiqiu Wu, Shubin Northwest Institute of Nuclear Technology Shaanxi Province Xi'an China Gansu Tianlin Real Estate Development Group Co. Gansu Province Tianshui China

ISBN: (纸本)9798350315455

With the increasing severity of construction safety issues, the risk factors during the construction process have also become more and more significant. How to strengthen the monitoring of risk factors in the construction process, timely identify and deal with safety hazards during the construction process is an urgent problem to be solved. Building an intelligent decision-making system for construction risks based on the pso (Ppaper Swarm Optimization) algorithm and real-time monitoring and warning of construction risks can provide relevant managers with a platform for timely and accurate acquisition of danger signals and intelligent decision-making analysis. Finally, it was found through experiments that the pso algorithm could improve the accuracy of decision-making from 87.70% to 95.17%. © 2023 IEEE.

关键词： construction of buildings decision-making system pso algorithm risk management

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APS Modulation of High-Frequency Partial Paralleled DAB Converter Using Improved pso algorithm and Online Optimization algorithm 2

APS Modulation of High-Frequency Partial Paralleled DAB Conv...

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2nd IEEE International Power Electronics and Application Symposium, PEAS 2023

作者： Chen, Zhaoyang Zhang, Zhe Fu, Xingyu Liu, Yuan Guo, Xiaoqiang Hebei University of Technology Department of Electrical Engineering Tianjin China School of Electrical Engineering Yanshan University Qinhuangdao China

ISBN: (纸本)9798350313765

For the partial paralleled dual active bridge (PPDAB) converter, in order to find out the two phase-shift angles which can minimize RMS current of the inductor under different load conditions, two algorithms are proposed in this paper. One is the improved particle swarm optimization (pso) algorithm, and the other is the online optimization algorithm. In the improved pso algorithm, the LTspice is called to avoid the calculation errors and the influence of parasitic parameters in high switching frequency. The other is the online optimization algorithm, which can improve the optimization accuracy in actual circuit. Additionally, a simple fuzzy algorithm is proposed to guarantee continuous output power. Furthermore, for eliminating the error of the delivered power in the actual circuit caused by the parasitic parameters of the devices, an PI control is proposed, which can be called in the improved pso algorithm and the online optimization algorithm. Finally, the proposed pso algorithm and the adaptive PI control are verified by simulation. © 2023 IEEE.

关键词： fuzzy algorithm online optimization PI control pso algorithm

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The Performance of a Grid-Connected Induction Motor during Unbalanced Voltage Sags and Harmonic Reduction Utilizing a Floating DC Link Converter based on the pso algorithm

The Performance of a Grid-Connected Induction Motor during U...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Siva Prasad, D. Jayalakshmi, V. Bharath Institute of Higher Education and Research Electrical and Electronics Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350361537

In the present research, the particle swarm optimisation (pso) algorithm is proposed as a basic modern algorithm approach for induction motors (IMs) with enhanced dynamic performance, unbalanced voltage sags and regulation of floating DC link converter that is maintained floating capacitors in charging and discharging states. The basic three-phase H-bridge converter has the ability to produce high-frequency harmonics in the converters for the purpose of compensating for voltage sag. Electromagnetic interference (EMI) could be occurred during switching. To satisfy regulatory standards, filters and other EMI mitigation techniques can be necessary. However, as a consequence of the floating capacitance voltage amplitude of the converter the conventional strategies of predictive torque control (PTC), direct torque control (DTC), model predictive control (MPC) and Artificial Bee Colony Optimization (ABC) algorithm result in a high torque ripple in the steady state and slow dynamics in the transient state. The present research proposes a three-leg, two-level IGBT with a floating capacitor. This difficulty is avoided by the proposed converter architecture with pso algorithm and it further eliminates the need for extra energy storage products, transformers, power filters to lessen THD and ameliorate uneven voltage sags. © 2024 IEEE.

关键词： Dc link converters Electromagnetic interference Induction motor drives pso algorithm Voltage regulation

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Simulation of CEDnet Reliability Evaluation Model Based on Improved pso algorithm 3

Simulation of CEDnet Reliability Evaluation Model Based on I...

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3rd International Conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Zhao, Jincheng Xu, Huichen Mei, Zengyang Li, Pei Yin, Junjie State Grid Jiangsu Electric Power Company Information and Telecommunication Branch Jiangsu Nanjing210024 China

ISBN: (纸本)9798350318180

With the growth of CEDnet, a large quantity of Electrical Power System (EPS) services need to be transmitted through CEDnet. EPS's dependence on the communication network is constantly increasing, and the impact of communication network failures on EPS is also becoming increasingly severe. Studying the operation mode of CEDnet is an effective means to improve the reliable and secure operation of CEDnet, and is the main content of running network information reliability. It not only adds effective measures for program management such as secure operation and maintenance of communication networks, but also ensures the efficient growth of various communication operations, thereby achieving the optimal configuration of communication resources. In this article, a CEDnet optimization method based on improved particle swarm optimization (pso) algorithm is proposed. On the basis of the minimum construction cost, considering the factors of network reliability and service distribution, a problem model of CEDnet line optimization is designed, and its reliability is evaluated through simulation experiments. After simulation verification, this method can improve the flexibility and comprehensiveness of the planning scheme to a certain extent, providing an effective route optimization scheme for CEDnet. © 2023 IEEE.

关键词： CEDnet pso algorithm Reliability Safe operation

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Optimal Allocation of Innovation and Entrepreneurship Education Resources in Colleges and Universities Based on pso algorithm 2

Optimal Allocation of Innovation and Entrepreneurship Educat...

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

作者： Jue, Liu Yunnan College of Business Management Yunnan Kunming China

ISBN: (纸本)9798350317558

Under the background of knowledge economy and globalization, entrepreneurship education in colleges and universities has become an important force to promote economic development and social progress. Optimizing the allocation of educational resources and stimulating students' innovative spirit and practical ability are the key directions of current higher education reform. This paper will discuss how to cultivate innovative talents to meet the needs of the future society through entrepreneurship education and optimizing resource allocation in colleges and universities. © 2024 IEEE.

关键词： Allocation of educational resources innovation and entrepreneurship education in colleges and universities multi-objective optimization pso algorithm

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PM2.5 Measuring Method Using RBF Neural Network Combined With pso algorithm

PM2.5 Measuring Method Using RBF Neural Network Combined Wit...

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第27届中国控制与决策会议

作者： XU Lin GUAN Tian-yi LIYan-nong ZHENG Wen-jing GUO Jing-yi College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781479970186

Considering the disadvantage of PM2.5 detection devices,there are many problems such as the low automation,poor test repeatability,and dielectric material loss *** paper presents a new PM2.5 detection system using laser diffraction technique based Fraunhefor diffraction *** Radial Basis Function(RBF) neural network with the inputs of multiple laser diffraction signals is used to be the micro particles calculating model to improve the detecting *** tackle the problems in the training algorithms,the Particle Swarm Optimization(pso)algorithm is employed to optimize the key parameters of the RBF neural network(RBFNN).The simulation and experiment results show that the new PM2.5 detection system satisfies the detection requirements with the high calculating precision,and effectively overcomes the problems in the conventional detection system.

关键词： PM25 detection Laser diffraction RBF neural network pso algorithm

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Research of MPPT Control Method Based on pso algorithm

Research of MPPT Control Method Based on PSO Algorithm

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4th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Wang Yunliang Bian Nan Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Tianjin Peoples R China Tianjin Univ Technol Sch Control Engn Tianjin Peoples R China

ISBN: (纸本)9781467381734

Because the output characteristics of photovoltaic (PV) array is nonlinear and output power changes as circumstances change, it is necessary to perform Maximum Power Point Tracking (MPPT) control in order to make the PV array working at Maximum Power Point (MPP). When the photovoltaic cells are in partial shade condition, the traditional MPPT method will fail. So this article uses the improved pso algorithm to implement MPPT of the shaded photovoltaic, carry on the modeling and simulation with matlab/simulink. The simulation results show that using pso can track the location of the maximum power point quickly and accurately and get a good control effect.

关键词： photovoltaic cells partial shade maximum power point tracking pso algorithm

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Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and pso algorithm

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2022年 9卷 817723页

作者： Liu, Ying Jiang, Du Yun, Juntong Sun, Ying Li, Cuiqiao Jiang, Guozhang Kong, Jianyi Tao, Bo Fang, Zifan Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Peoples R China Wuhan Univ Sci & Technol Res Ctr Biomimet Robot & Intelligent Measurement Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmission & Mfg Engn Wuhan Peoples R China Wuhan Univ Sci & Technol Precis Mfg Res Inst Wuhan Peoples R China Three Gorges Univ Hubei Key Lab HydroElect Machinery Design & Maint Yichang Peoples R China

With the manipulator performs fixed-point tasks, it becomes adversely affected by external disturbances, parameter variations, and random noise. Therefore, it is essential to improve the robust and accuracy of the controller. In this article, a self-tuning particle swarm optimization (pso) fuzzy PID positioning controller is designed based on fuzzy PID control. The quantization and scaling factors in the fuzzy PID algorithm are optimized by pso in order to achieve high robustness and high accuracy of the manipulator. First of all, a mathematical model of the manipulator is developed, and the manipulator positioning controller is designed. A PD control strategy with compensation for gravity is used for the positioning control system. Then, the PID controller parameters dynamically are minute-tuned by the fuzzy controller 1. Through a closed-loop control loop to adjust the magnitude of the quantization factors-proportionality factors online. Correction values are outputted by the modified fuzzy controller 2. A quantization factor-proportion factor online self-tuning strategy is achieved to find the optimal parameters for the controller. Finally, the control performance of the improved controller is verified by the simulation environment. The results show that the transient response speed, tracking accuracy, and follower characteristics of the system are significantly improved.

关键词： manipulator pso algorithm quantization factor-proportion factor position control fuzzy-PID control

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Bayesian network structure learning based on HC-pso algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第2期46卷 4347-4359页

作者： Gao, Wenlong Zhi, Minqian Ke, Yongsong Wang, Xiaolong Zhuo, Yun Liu, Anping Yang, Yi Lanzhou Univ Sch Publ Hlth Inst Hlth Stat & Intelligent Anal Lanzhou Gansu Peoples R China Lanzhou Univ Sch Publ Hlth Dept Epidemiol & Hlth Stat Lanzhou Gansu Peoples R China Lanzhou Univ Sch Math & Stat Lanzhou Gansu Peoples R China

Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-pso combining the HC (Hill Climbing) algorithm and pso (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses pso algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-pso algorithm is superior in feasibility and accuracy.

关键词： Bayesian network structure learning HC algorithm pso algorithm DE algorithm

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