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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Zhang, Zhiqiang Wang, Le Hu, Jiongsong Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou Peoples R China Univ South China Sch Comp Sci Hengyang Peoples R China

ISBN: (纸本)9781665423144

With the increasing size and complexity of modern industrial production, the requirements for control systems are becoming higher and higher. The particle swarm optimization algorithm is a population-based stochastic optimization method. This paper first introduces the origin, principle and implementation steps of PSO algorithm, and then introduces the application of particle swarm algorithm in control system. In this paper, the fuzzy control strategy based on particle swarm optimization is proposed to improve the performance of fuzzy control. The design idea and implementation of the control strategy are described in detail. Finally, the system simulation is carried out with Matlab, and the simulation results are compared and analyzed. The results show that the control strategy can effectively improve the dynamic quality and steady-state accuracy of the control system, and has a good practical application prospect.

关键词： particle swarm optimization algorithm principle chaotic control system system simulation

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Response-Compensated DMPC-Based Frequency Control Strategy for AGC with Large-Scale Wind Power

Response-Compensated DMPC-Based Frequency Control Strategy f...

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2023 IEEE Sustainable Power and Energy Conference, iSPEC 2023

作者： Zhang, Peng Mo, Zhonghua Mastoi, Muhammad Shahid Yang, Yinghai Cai, Shaorong Hassan, Mannan Wang, Delin The Southwest Branch of State Grid Corporation Sichuan Chengdu610102 China School of Electrical Engineering Southwest Jiaotong University Sichuan Chengdu611756 China

ISBN: (纸本)9798350322699

This paper proposes an AGC frequency control strategy using response-compensated distributed model predictive control (DMPC) when large-scale wind power is incorporated into the automatic generation control (AGC) of power systems to optimize the response speed of frequency modulation control systems based on model predictive control. It examines the influence of AGC system parameters on wind power penetration rate based on frequency control models for conventional units and doubly-fed wind turbines. A response compensation link is added to DMPC, and the particle swarm optimization algorithm is used to set the control parameters of the optimization link and analyze the stability of the system after the link is added. A frequency control strategy based on response compensation DMPC is then proposed based on system frequency deviation. Simulations are conducted to verify the AGC model of a regional interconnected power system with large-scale wind power. Results indicate that the proposed control strategy can improve system frequency stability and speed up frequency control response times. © 2023 IEEE.

关键词： automatic generation control frequency stability large-scale wind power model predictive control particle swarm optimization algorithm

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UAV Localization Model Based on Multi-Dimensional Eigenvalue-Kalman Filter 6

UAV Localization Model Based on Multi-Dimensional Eigenvalue...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Tian, Mengxian Shandong University of Science and Technology Tai'an China

ISBN: (纸本)9798350305623

With the rapid development of science and technology, drone technology is gradually becoming an indispensable part of human life. With the increasing demand for complex tasks of UAVs from all walks of life, a single UAVs will not be able to effectively complete the task, and UAVs cluster gradually comes into people's perspective, and the resulting positioning of UAVs cluster has become the focus of current research. Azimuth-only passive positioning is a method that can adjust the position of UAVs and maintain formation. Several drones in the team send signals, and the rest of the drones passively receive signals to obtain direction information for positioning, and then adjust the position of the drones. This paper establishes Kalman filter algorithm model based on multidimensional eigenvalues, path planning model based on Bayesian estimation, and uses triangulation positioning method, particle swarm optimization algorithm and other methods to optimize azimuthing-only passive positioning of UAVs in formation flight. This method effectively improves the accuracy of UAV positioning and plays an important role in solving practical problems. © 2023 IEEE.

关键词： Bayesian estimation path planning model Kalman filter algorithm particle swarm optimization algorithm triangulation positioning method

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Maintenance Scheduling of Semiconductor Production Equipment Based on particle swarm optimization 11

Maintenance Scheduling of Semiconductor Production Equipment...

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11th IEEE International Conference on Software Engineering and Service Science (IEEE ICSESS)

作者： Zhao, Chengwei Zhang, Hesheng Fan, Chenghao Gao, Zhiyuan Zhu, Xiaojing Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

ISBN: (纸本)9781728165790

In this paper, we study the problem of wafer production equipment maintenance scheduling. In order to shorten the maintenance time and ensure the continuity of the production, we proposed an improved particle swarm optimization (PSO) algorithm. The proposed hybrid algorithm is based on standard PSO, and integrated PSO with Simulated Annealing (SA) with probabilistic jump ability to avoid PSO trapping in local optimum. Besides, adaptive adjustment method of inertia weight is used, and add disturbance to the velocity update formula of the algorithm to ensure the fast optimization of particles in the early stage and avoid premature convergence. Experiment results indicate that the proposed algorithm is significant in terms of the maintenance time and downtime loss compared to conventional approaches.

关键词： Equipment maintenance particle swarm optimization algorithm scheduling

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Design of Intelligent Monitoring System for Blast Furnace Hearth Erosion 41

Design of Intelligent Monitoring System for Blast Furnace He...

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第41届中国控制会议

作者： Shuqiao Wang Yan Yuan Jingjie Liang Zhuofu Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper takes the iron-making blast furnace in operation of a steel plant as the specific object,analyzes the erosion status and characteristics of different parts of the furnace hearth and bottom combined with iron-making process and erosion mechanism of the blast furnace,designs an intelligent monitoring system for erosion of the furnace hearth lining,visualizes the temperature measurement *** erosion calculation model is established by using the heat transfer mechanism to quickly solve the erosion boundary for the area with low erosion degree and rate,and performs a fast solution of the erosion *** areas with high erosion degree and rate,the particle swarm optimization algorithm is selected by using its adaptive *** solves the erosion boundary accurately from the rough solution of the mechanism model,and the residual thickness of furnace lining is output and displayed,which helps ironmakers optimize the blast furnace production operation,maintenance strategy,improve production efficiency and reduce energy consumption.

关键词： Blast furnace lining erosion particle swarm optimization algorithm Intelligent monitoring system

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Short-Term Subway Inbound Passenger Flow Prediction Based on AFC Data and PSO-LSTM Optimized Model

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URBAN RAIL TRANSIT 2022年第1期8卷 56-66页

作者： Liu, Jiaxin Jiang, Rui Zhu, Dan Zhao, Jiandong Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Minist Transport Key Lab Transport Ind Big Data Applicat Technol C Beijing 100044 Peoples R China

Making accurate predictions of subway passenger flow is conducive to optimizing operation plans. This study aims to analyze the regularity of subway passenger flow and combine the modeling skills of deep learning with transportation knowledge to predict the short-term subway passenger flow in the scenarios of workdays and holidays. The processed data were collected from two months of Automated Fare Collection (AFC) data from Xizhimen station of Beijing metro. The data were first cleaned by the established cleansing rules to delete malformed and abnormal logic data. The cleaned data were used to analyze the spatial characteristics in passenger flow. Second, a short-term subway passenger flow prediction model was built on the basis of long short-term memory (LSTM). Determining that the error will be relatively high in peak hours, we proposed gradual optimizations from data input by dividing one whole day into different time periods, and then used particle swarm optimization (PSO) to search for the optimal hyperparameters setting. Finally, inbound passenger flow of Beijing Xizhimen subway station in 2018 was selected for numerical experiments. Predictions of the LSTM-based model had higher accuracy than the traditional machine learning support vector regression (SVR) model, with mean absolute percentage error (MAPE) of 21.97% and 4.80% in the scenarios of workdays and holidays, respectively, which are both lower than those of the SVR model. The optimized PSO-LSTM model has been verified for its effectiveness and accurateness by the AFC data.

关键词： Short-term subway passenger flow prediction Deep learning Long short-term memory Automated Fare Collection (AFC) data particle swarm optimization algorithm

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Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions

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IEEE ACCESS 2025年 13卷 89248-89265页

作者： Yang, Cheng-Hong Lee, Borcy Lin, Yu-Da Tainan Univ Technol Dept Informat Management Tainan 710 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung 811 Taiwan Kaohsiung Med Univ Dept Biomed Engn Kaohsiung 807 Taiwan Kaohsiung Med Univ Sch Dent Kaohsiung 807 Taiwan Kaohsiung Med Univ Drug Dev & Value Creat Res Ctr Kaohsiung 807 Taiwan Natl Penghu Univ Sci & Technol Dept Comp Sci & Informat Engn Penghu 880011 Taiwan

Research on the study of houses, condominiums and buildings in Taiwan's metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors-the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index-significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced b

关键词： Indexes Market research Clustering algorithms Stock markets Government Autoencoders Economics Accuracy Forecasting Economic indicators Machine learning real estate particle swarm optimization algorithm economy autoencoder deep learning

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Global optimization algorithm for Cloud Service Composition

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2021年第10期E104D卷 1580-1591页

作者： Yang, Hongwei Xue, Fucheng Liu, Dan Li, Li Feng, Jiahui Changchun Univ Sci & Technol Sch Comp Sci & Technol Changchun Peoples R China

Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.

关键词： web service composition beetle antennae search algorithm particle swarm optimization algorithm global optimization algorithm

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An effective analysis of online education model based on artificial intelligence

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期40卷 3535-3546页

作者： Peng, Lihua Langfang Normal Univ Langfang 065000 Hebei Peoples R China

With the development of artificial intelligence in education, online education has been recognized by the society as a new teaching method. It can make full use of the advantages of the network across regions, and make full use of the advantages of network technology to share the resources of colleges and universities, which is a promising educational method. In response to the demand of online education for learner information, this paper proposes the learner model Neighbor Mean Variation Multi-Objective particle swarm optimization-Genetic algorithm (NMVMOPSO-GA). This model includes the learner's learning interest sub-model, the learner's cognitive ability sub-model and the learner's knowledge sub-model. The modelling techniques of the three sub-models are discussed separately, and their status and role in the online education system are analyzed. At the same time, for the knowledge model that reflects the learner's learning progress and knowledge mastery, a learner knowledge sub-model constructed with Bayesian networks is proposed. The neighbor mean mutation operator is introduced to optimize the multi-objective particle swarm optimization algorithm and improve the convergence performance and stability of the multi-objective particle swarm optimization algorithm. We study the application of multi-objective particle swarm optimization algorithm in online course resource generation service. Through simulation experiments, it is verified that the multi-objective particle swarm optimization algorithm can improve the performance and stability of online course resource generation.

关键词： Online education artificial intelligence intelligent learning particle swarm optimization algorithm

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Measurement of gas volume fraction in gas-liquid two-phase flow using arrayed fiber-optic probes combined with the PSO-BP-AdaBoost algorithm

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OPTICAL FIBER TECHNOLOGY 2025年 93卷

作者： Xing, Wenju Gao, Hong Qiao, Xueguang Xian Shiyou Univ Shaanxi Prov Oil & Gas Resource Fiber Opt Detect E Sch Sci Xian 710065 Peoples R China Northwest Univ Sch Phys Xian 710069 Peoples R China

For the measurement of gas volume fraction in natural gas wells, a strategy based on the fusion of arrayed fiberoptic probes (AFOP) and artificial intelligence algorithms is proposed to enhance the precision and efficiency of gas volume fraction monitoring. As a key front-end component for obtaining gas phase information, AFOP determines the optimal structure by analyzing its performance metrics in bubble capture and its interference with fluid flow. A back-end gas volume fraction prediction model was constructed using a machine learning algorithm. The model first uses a particle swarm optimization (PSO) algorithm to enhance the backpropagation (BP) neural network as a weak predictor and then integrates multiple weak predictors through the adaptive boosting (AdaBoost) algorithm to create a strong predictor. The experimental results show that compared with the support vector machine (SVM), BP neural network, and PSO-BP neural network, the PSO-BP-AdaBoost algorithm has advantages in prediction precision, with a maximum relative error of only 0.14 %, providing a more effective solution for research and application in related fields.

关键词： Arrayed fiber-optic probes Gas volume fraction Backpropagation neural network particle swarm optimization algorithm Adaptive boosting algorithm

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