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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Jiang, Zhengsong Tian, Guohui Cui, Yongcheng Liu, Tiantian Gu, Yu Wang, Yifei School of Control Science and Engineering Shandong University Jinan China School of Information University of California Berkeley United States

ISBN: (纸本)9781665430654

In order to improve the task execution capability of home service robot, and to cope with the problem that purely physical robot platforms cannot sense the environment and make decisions online, a method for building digital twin system for home service robot based on motion simulation is proposed. A reliable mapping of the home service robot and its working environment from physical space to digital space is achieved in three dimensions: geometric, physical and functional. In this system, a digital space-oriented URDF file parser is designed and implemented for the automatic construction of the robot geometric model. Next, the physical model is constructed from the kinematic equations of the robot and an improved particle swarm optimization algorithm is proposed for the inverse kinematic solution. In addition, to adapt to the home environment, functional attributes are used to describe household objects, thus improving the semantic description of the digital space for the real home environment. Finally, through geometric model consistency verification, physical model validity verification and virtual-reality consistency verification, it shows that the digital twin system designed in this paper can construct the robot geometric model accurately and completely, complete the operation of household objects successfully, and the digital twin system is effective and practical. © 2023 IEEE.

关键词： Digital Twin Motion Simulation particle swarm optimization algorithm Service Robot

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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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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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Fault Diagnosis of Chemical Processes Based on PSO-GCN

Fault Diagnosis of Chemical Processes Based on PSO-GCN

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2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Luo, Chen Yang, Jin School of Aeronautical Engineering Civil Aviation University of China China School of Transportation Science and Engineering Civil Aviation University of China China

ISBN: (纸本)9798331520816

As industrial production advances, ensuring process safety and minimizing failure-related losses is crucial. Detecting abnormal conditions efficiently is key. To address the complexity, high dimensionality, nonlinearity, and time-varying nature of chemical process data, this paper proposes a fault diagnosis method based on particle swarm optimization (PSO) and Graph Convolutional Networks (GCN). The method denoises the data, constructs a graph using a sliding window, and uses PSO to optimize the diagnostic model's parameters and extract features. These features are then classified via a fully connected layer and classifier. Experimental results show that the method outperforms other machine learning and deep learning approaches in accuracy and precision, and excels at detecting minor faults in chemical processes. © 2024 IEEE.

关键词： Chemical Processes Fault Diagnosis Graph Convolutional Networks particle swarm optimization algorithm

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MECHANICAL PROPERTY PREDICTION MODEL OF STRIP BASED ON PSO-BP NEURAL NETWORK

MECHANICAL PROPERTY PREDICTION MODEL OF STRIP BASED ON PSO-B...

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2007中国宁波国际计算机科学与技术学术大会

作者： Wang Xiaolin Wang Pengfei Liu Hongshen Huang Zhenyi Department of Computer Science & Technology Anhui University of Technology Department of Materials Science & Engineering Anhui University of Technology

ISBN: (纸本)9780972147903

Mechanic property prediction of hot rolled strip has enormous economic benefits and vast applied *** neural network is used almost in Mechanic property prediction of hot rolled strip in *** BP has local infinitesimal *** with PSO(particle swarm optimization) global optimization algorithm,a new PSO-BP neural network is *** PSO-BP algorithm takes on advantages of global optimization ability,the rapid constringency of BP rapid training algorithm,the ability of nonlinear approach of multilayer feedforward network,and improves the performance of Neural ***-BP neural network model has good application foreground.

关键词： Mechanical property prediction particle swarm optimization algorithm BP neural network PSO-BP

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Improving performance of open-pit mine production scheduling problem under grade uncertainty by hybrid algorithms

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Journal of Central South University 2020年第9期27卷 2479-2493页

作者： Kamyar TOLOUEI Ehsan MOOSAVI Amir Hossein BANGIAN TABRIZI Peyman AFZAL Abbas AGHAJANI BAZZAZI Department of Petroleum and Mining Engineering South Tehran BranchIslamic Azad UniversityTehranIran Department of Mining Engineering University of KashanKashanIran

One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s *** the mining industry,one of the most significant difficulties is long-term production scheduling(LTPS)of the open-pit *** and uncertainty-based approaches are identified as the main strategies,which have been widely used to cope with this *** the last few years,many researchers have highly considered a new computational type,which is less costly,i.e.,meta-heuristic methods,so as to solve the mine design and production scheduling *** the optimality of the final solution cannot be guaranteed,they are able to produce sufficiently good solutions with relatively less computational *** the present paper,two hybrid models between augmented Lagrangian relaxation(ALR)and a particle swarm optimization(PSO)and ALR and bat algorithm(BA)are suggested so that the LTPS problem is solved under the condition of grade *** is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the ***,the Lagrangian coefficients are updated by using PSO and *** presented models have been compared with the outcomes of the ALR-genetic algorithm,the ALR-traditional sub-gradient method,and the conventional method without using the Lagrangian *** results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid ***,it is more effectual than the conventional ***,the time and cost of computation are diminished by the proposed hybrid *** CPU time using the ALR-BA method is about 7.4%higher than the ALR-PSO approach.

关键词： open-pit mine long-term production scheduling grade uncertainty augmented Lagrangian relaxation particle swarm optimization algorithm bat algorithm

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Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling

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Journal of Central South University 2021年第8期28卷 2333-2344页

作者： JI Ya-feng SONG Le-bao SUN Jie PENG Wen LI Hua-ying MA Li-feng School of Mechanical Engineering Taiyuan University of Science and TechnologyTaiyuan 030024China State Key Laboratory of Rolling and Automation Northeastern UniversityShenyang 110819China School of Materials Science and Engineering Taiyuan University of Science and TechnologyTaiyuan 030024China

To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip ***,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple ***,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a ***,the prediction performance comparisons of the three models are ***,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence ***,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μ***,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.

关键词： strip crown support vector machine principal component analysis cuckoo search algorithm particle swarm optimization algorithm

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Optimal Scheduling Method for Cogeneration System Based on Heat Storage Device

Optimal Scheduling Method for Cogeneration System Based on H...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhang Xu Pang Xinfu Zhao Wenjing Liu Li Zhang Yifan Mou Zurong Shenyang Inst Engn Key Lab Energy Saving & Controlling Power Syst Li Shenyang 110136 Peoples R China Shenyang Inst Engn Sch Elect Power Shenyang 110136 Peoples R China Shenyang Inst Engn Sch Automat Shenyang 110136 Peoples R China State Grid Liaocheng Elect Power Supply Co Liaocheng 252000 Shandong Peoples R China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158556

In the north of China, the problem of wind abandonment is very serious. In order to improve the ability of wind power consumption, a cogeneration system with an electric boiler is proposed. First, the mathematical model of the minimum power generation cost of the traditional unit and the optimized model are established. Second, the model is solved by using the particle swarm optimization algorithm. In addition, a power structure of combined heat and power system (CHP) is constructed for simulation experiments. Through experimental analysis, proving the feasibility of the model. Finally, Simulation results show that making the electric boiler work in the period of wind abandonment can effectively alleviate the wind abandonment phenomenon. It also can provide more space for the wind power and enhance the wind power consumption.

关键词： Combined heat and power generation Wind abandonment Electric boiler Wind power particle swarm optimization algorithm

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swarm intelligence optimization and its application in geophysical data inversion

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Applied Geophysics 2009年第2期6卷 166-174页

作者： Yuan Sanyi Wang Shangxu Tian Nan CNPC Key Laboratory of Geophysical Exploration Key Laboratory of Earth Prospecting and Information Technology China University of Petroleum Beijing 102249 China

The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food. In this article, first the particle swarm optimization algorithm was described in detail, and ant colony algorithm improved. Then the methods were applied to three different kinds of geophysical inversion problems：（1） a linear problem which is sensitive to noise, （2） a synchronous inversion of linear and nonlinear problems, and （3） a nonlinear problem. The results validate their feasibility and efficiency. Compared with the conventional genetic algorithm and simulated annealing, they have the advantages of higher convergence speed and accuracy. Compared with the quasi-Newton method and Levenberg-Marquardt method, they work better with the ability to overcome the locally optimal solutions.

关键词： swarm intelligence optimization geophysical inversion multimodal particle swarm optimization algorithm

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Micro-grid Dynamic Modeling Based on RBF Artificial Neural Network

Micro-grid Dynamic Modeling Based on RBF Artificial Neural N...

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International Conference on Power System Technology (PowerCon)

作者： Cai Changchun Wu Min Deng Lihua Deng Zhixiang Zhang Jianyong Hohai Univ Changzhou Jiangsu Peoples R China Hohai Univ Coll IOT Engn Changzhou Jiangsu Peoples R China Hohai Univ Dept Mathes & Phys Engn Changzhou Jiangsu Peoples R China

ISBN: (纸本)9781479950324

A simplified equivalent model of microgrid, based on the RBF Artificial Neural Network, is present in this paper. The proposed model is suitable for the dynamic studies of microgrids. Nonlinear mapping of RBF neural network describes the dynamic characteristics of the Point of Common Couple(PCC) of micro-grid under the connected mode. The development model is evaluated using the voltage, current and power of the PCC as the input and output of the RBF neural network in the train process. The PSO algorithm is used for the parameter optimization of RBF and improved the generalization of the dynamic model. The simulation results show the proposed modeling method in this paper is suitable and effective, and the RBF neural network based dynamic model can describe the dynamic characteristics of micro-grid accurately.

关键词： RBF neural network Equivalent Modeling particle swarm optimization algorithm Micro-grid

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