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JOURNAL OF MEASUREMENTS IN ENGINEERING 2023年第4期11卷 437-452页

作者： Dong, Xue Zibo Vocat Inst Int Sch Zibo Peoples R China

The deformation detection of large machinery is usually achieved using three-dimensional displacement measurement. Binocular stereo vision measurement technology, as a commonly used digital image correlation method, has received widespread attention in the academic community. Binocular stereo vision achieves the goal of three-dimensional displacement measurement by simulating the working mode of the human eyes, but the measurement is easily affected by light refraction. Based on this, the study introduces particle swarm optimization algorithm for target displacement measurement on Canon imaging dataset, and introduces backpropagation neural network for mutation processing of particles in particle swarm algorithm to generate fusion algorithm. It combines the four coordinate systems of world, pixel, physics, and camera to establish connections. Taking into account environmental factors and lens errors, the camera parameters and deformation coefficients were revised by shooting a black and white checkerboard. Finally, the study first conducted error analysis on binocular stereo vision technology in three dimensions, and the relative error remained stable at 1 % within about 60 seconds. At the same time, three algorithms, including the spotted hyena algorithm, were introduced to conduct performance comparison experiments using particle swarm optimization and backpropagation network algorithms. The experiment shows that the three-dimensional error of the fusion algorithm gradually stabilizes within the range of [-0.5 %, 0.5 %] over time, while the two-dimensional error generally hovers around 0 value. Its performance is significantly superior to other algorithms, so the binocular stereo vision of this fusion algorithm can achieve good measurement results.

关键词： binocular stereo vision digital image correlation method backpropagation neural network particle swarm optimization algorithm displacement measurement

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Grid Task Scheduling using Mutation particle swarm algorithm

Grid Task Scheduling using Mutation Particle Swarm Algorithm

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2011 International Conference on Intelligent Computing and Integrated Systems(ICISS 2011)

作者： Renhua Li, Wenming Huang, Qing Yuan School of Computer Science & Engineering Guilin University of Electronic Technology Guilin 541004, China

Computing grids utilize Internet or special networks to access computing resources which are geographically widespread, in order to solve complex problems more effectively. Task scheduling in grid plays an important role in grid system. This paper introduces mutation into particle swarm algorithm. The method makes the algorithm jump out local optimization and search for the global optimal solution in other areas. To some extent, it overcomes the inherent flaw of PSO that falling into local optimization. Using this method in grid task scheduling can not only generate relevant scheme dynamically, and also make the complete time minimum. The experiment shows that the algorithm achieves a better result in task scheduling.

关键词： particle swarm optimization algorithm grid task scheduling

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Supply China Production-distribution Cost optimization under Grey Fuzzy Uncertainty

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东华大学学报（英文版） 2008年第1期25卷 41-47页

作者： LIU Dong-bo CHEN Yu-juan HUANG Dao TIAN Yu College of Mechanical and Electronic Engineering Shanghai Normal University Shanghai 201418 China Research Institute of Automation East China University of Science and Technology Shanghai 200237 China

Most supply chain programming problems are restricted to the deterministic situations or stochastic environments. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved particle swarm optimization (PSO) algorithm based on the Differential Evolution (DE) algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.

关键词： supply chain optimization grey fuzzy uncertainty neural network particle swarm optimization algorithm differential evolution algorithm

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Techno-economic Unified OPF Modeling for VSC-HVDC Converter Installation

ELECTRICA

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ELECTRICA 2021年第3期21卷 352-+页

作者： Agrawal, Sunilkumar Kundu, Prasanta SV Natl Inst Technol Dept Elect Engn Surat India

This paper demonstrates the analysis of novel methodology developed to select optimal buses for installation of convertor stations in the hybrid voltage source converter based high voltage direct current system. Here, a modified unified optimal power flow model is developed for the optimal power flow problem and solved using the particle swarm optimization technique for the voltage source converter based high voltage direct current network. The analysis has been performed for optimizing the various techno-economic objective functions, including generation cost, voltage deviation, and total power system losses, for better power system operation. The developed unified optimal power flow model's effectiveness and methodology for deciding the high voltage direct current converter's optimal location are examined, with several tests performed with modified five bus and IEEE-30 bus system. The impact of high voltage direct current line replacement is decided based on optimal results obtained for selected techno-economic objective functions by replacing each AC line with high voltage direct current independently. The obtained results have proved the voltage source converter high voltage direct current controller's impact on optimization of generation cost, voltage deviation, and total power system losses.

关键词： Optimal power flow particle swarm optimization algorithm power system optimization unified optimal power flow modeling voltage source converter based high voltage direct current

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Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid optimization algorithm

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algorithmS 2017年第1期10卷 19-19页

作者： Wang, Ru Tan, Chao Xu, Jing Wang, Zhongbin Jin, Jingfei Man, Yiqiao China Univ Min & Technol Sch Mechatron Engn 1 Daxue Rd Xuzhou 221116 Peoples R China CRRC Tangshan Corp Ltd 3 Changqian Rd Fengrun Dist 063000 Tangshan Peoples R China

In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO) and genetic algorithm (GA). A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.

关键词： self-tuning PID controller genetic algorithm particle swarm optimization algorithm pressure control

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A Novel Approach to Task Scheduling using The PSO algorithm based Probability Model in Cloud Computing

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第11期9卷 309-321页

作者： Li Ruizhi Gao Jue Gao Honghao Bian Minjie Xu Huahu Shanghai Univ Sch Engn & Comp Sci Shanghai 200444 Peoples R China Shanghai Shang Da Hai Run Informat Syst Co Ltd Shanghai Peoples R China Shanghai Univ Ctr Comp Shanghai 200444 Peoples R China

With the development of cloud computing technology, people not only want to pursue the shortest time to complete the tasks by using cloud computing, but also hope to take into the running costs of machines. Existing task scheduling algorithm in the cloud computing environment has been unable to meet people's needs. As an extension and generalization of the model checking theory, probability model checking is also used in many fields, such as random distributed algorithm and other areas. The task scheduling algorithm based on the particle swarm optimization algorithm combined with probability model is proposed in this paper. The algorithm defines the fitness functions of the time cost and the running cost. The fitness functions can improve the efficiency of the cloud computing platform. At the same time, the probability model can be used to analyze the running states of machines and the computing capability of the nodes in the cloud cluster. The probability, which is calculated by the probability model, provides the basis for changing particle swarm algorithm's the inertia factor and the learning factor, so as to solve the drawback that the inertia factor and the learning factor solely depend on the fixed value.

关键词： particle swarm optimization algorithm Probability Model Inertia Factor Learning Factor Auto-correcting

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A Distributed Approach to Solving Hybrid Flow-shop Scheduling Problem

A Distributed Approach to Solving Hybrid Flow-shop Schedulin...

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21st Chinese Control and Decision Conference

作者： Zou Feng-xing Zeng Ling-li Gao Zheng Liu Feng Natl Univ Def Technol Dept Automat Control Coll Mechatron & Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781424427239

In this paper, a mathematical programming model is established for hybrid flow-shop scheduling problem,with the minimum of the makespan as the objective function. Based on the particle swarm optimization algorithm, a distributed approach according to the process is presented to solve the global problem. Compared with the references, the experimental results indicate that the distributed approach performs better on improving computing and searching speed and being feasible and effective on global optimum.

关键词： Hybrid Flow-shop Scheduling Distributed Approach particle swarm optimization algorithm Time Consumption

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Research on Environmental Economic Dispatch of Power System Including Wind Farm

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Physics Procedia 2012年 24卷 107-113页

作者： Zhong Jia-qing Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province Yanshan University Qinhuangdao China 066004

In power market environment with energy conservation and emission reduction, clean energy power has an increasingly important position because of its low cost and environmental pollution. This paper researches on power system dynamic economy dispatch including wind system. The model of environment economic dispatch including wind power is established with the lowest generating cost of total power system as the objective function. The various constraint conditions are considered with conventional thermal power units and wind power. According to actual load data in a certain area, simulation test is completed using particle swarm optimization algorithm which has advantage of strength searching capability and fast optimizing. Simulation results show that the mathematical model is correct and the optimization algorithm is effective. Meanwhile, the application of the relative entropy balance theory is on evaluation and selection to the decision results.

关键词： Environmental economic dispatch Generation cost Emission cost particle swarm optimization algorithm Relative entropy balance

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Application of Two Modified Autonomous Development algorithms in Robot Obstacle Avoidance

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电脑学刊 2017年第3期28卷 235-250页

作者： Hong-Ge Ren Rui Yin Tao Shi Fu-Jin Li

In view of high dimension, the difficulty of training, the problem of slow learning speed in the application of BP neural network in mobile robot path planning, an algorithm of reinforcement Q learning based on online sequential extreme learning machine (Q-OSELM algorithm) was proposed in this paper. And then, due to the random selection of weight and threshold parameters, it also proposes an extreme learning machine algorithm optimized by particle swarm (PSO-ELM algorithm) in this paper. Firstly, Q-OSELM algorithm obtains current environment and the status information of the robot through the characteristic of reinforcement learning, which combines dynamic network with supervised learning. After that, the online sequential extreme learning machine is used to approximate the function of the current status to get the rewards and punishments of the current status; Secondly, it is used to solve the problem of slow training speed by the characteristic of less parameter settings and better generalization performance. PSO-ELM algorithm is used to optimize the input weights and the hidden layer bias of the extreme learning machine which have been seen as the particle of particle swarm optimization algorithm to improve the network structure of the extreme learning machine. It could overcome inaccuracy of traditional extreme learning machine through particle swarm optimization algorithm. Finally, the performance of two learning algorithms is verified. The simulation experimental results show that the Q-OSELM learning algorithm improves the initiative of machine learning. And compared with the Q-OSELM algorithm, the PSO-ELM algorithm has better generalization ability and higher training precision. Simulation experiments are carried out to verify the stability and convergence of the two algorithms.

关键词： autonomous learning obstacle avoidance online sequential extreme learning machine particle swarm optimization algorithm reinforcement Q learning

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Design and optimization of Personalized Education System Based on Intelligent algorithms

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Procedia Computer Science 2024年 243卷 514-522页

作者： Jie Zhu Yahong Liao Danni Lu Wuhan Railway Vocational College of Technology Wuhan 430205 Hubei China Xiamen Jin Shang Middle School Xiamen 361000 Fujian China Hubei Vocational College of Railway Transportation Wuhan 430070 Hubei China

The education industry, as the top priority of social operation, is constantly emerging with education systems or online education platforms based on internet technology. However, most of them are facing problems of rigidity, stiff, or resource scarcity. Therefore, this article aimed to establish a personalized education system to solve this problem and optimize the system based on intelligent algorithms. At the end of this article, a comparison was made on the algorithm performance of the decision tree algorithm in the intelligent algorithm. Compared with the original algorithm of the system, the accuracy increased from 70.35% to 75.68%. The system based on the intelligent algorithm also helped the students in the experimental class improve their grades, and even cleared the score record below 40 points, helping to improve the overall performance of the entire class.

关键词： Intelligent algorithm Decision Tree algorithm Personalized Education System particle swarm optimization algorithm

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