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Sports image detection based on FPGA hardware system and particle swarm algorithm

MICROPROCESSORS AND MICROSYSTEMS 2021年 80卷 103348-103348页

作者： Hu Jing Xing Xiaoqiong Anhui Finance & Trade Vocat Coll Publ Educ Dept Hefei 230601 Anhui Peoples R China Anhui Hefei 168 Senior High Sch Hefei 230601 Anhui Peoples R China

The action of the opposition is conveyed on a fundamental level. Moreover, it finds image detection related information on contenders and football sports Image, which can be realized in the outside image. From the force situation, one can see that image advancement is up. Therefore, this assessment development image as a thing to ponder the usage of image acknowledgment development. Football sports detection is dealing with the whole system. The structure configuration relies upon hardware, including a Field Programmable Gate Array (FPGA). This new computation particle swarm algorithm estimation is implemented to edge recognizable proof, grayscale planning, object get, target affirmation, image area development, etc., which are consolidated into the genuine need of the game video to achieve the various essentials of development image acknowledgment. All the while, it has set itself up as a demonstrating ground to test the suitability of the investigation framework that sees the affirmation of contenders, games affirmation, sports lead judgment, etc. Football sports Image detection results of the relevant investigations have revealed that the existence of the solution.

关键词： Field Programmable Gate Array (FPGA) particle swarm algorithm Video Grayscale planning Edge recognizable proofI

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A Multi-Objective Optimization Approach for Elevator Group Control Systems Based on particle swarm algorithm

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第8期33卷 2450138-2450138页

作者： Zhang, Yuting Cui, Wei Hebei Vocat Univ Ind & Technol Intelligent Mfg Inst 626 Hongqi Ave Shijiazhuang Hebei Peoples R China Zhengzhou Tech Coll Software Engn Dept 81 Zhengshang Rd Zhengzhou Henan Peoples R China UCSI Univ 1 Jalan Menara Gading Kuala Lumpur 56000 Malaysia

Currently, elevator group control systems containing multiple elevators are used in high-rise buildings, where elevator groups are centrally dispatched according to a set dispatching plan to provide vertical transportation services for the passengers. However, if these elevators work independently, once passengers send out a call signal, multiple elevators will respond to the same request at the same time, and the elevator group will repeatedly go back and forth, which seriously affects the system operation efficiency and also increases energy consumption, so it is a key issue to solve the dispatching problem in the elevator group control system. Elevator group control scheduling is an NP-hard problem with explosive combination characteristics. In this paper, a multi-objective model is formulated based on the criteria of average passenger travel time, average waiting time and system energy consumption for the scheduling problem. Then the particle swarm optimization algorithm is proposed to solve the scheduling problem. Finally, the performance of the proposed algorithm is compared with the genetic algorithm-based elevator group control scheduling, the simulation results show that the proposed algorithm with fast convergence while the waiting time of passengers is significantly decreased.

关键词： Multi-objective optimization elevator group control particle swarm algorithm

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Optimized Design of Cylindrical Uniform-Field Coil System With particle swarm algorithm for Calibrating Induction Magnetometer

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Ding, Zhongya Huang, Ziyuan Han, Bangcheng Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Beihang Univ Res Inst Frontier Sci Beijing 100191 Peoples R China

To calibrate induction magnetometer with high precision, a cylindrical uniform-field coil system design method is proposed, which combines forward method with reverse design method in this article. The optimized cost function of coil design is set to the average field error within the target region to minimize the error of induction magnetometer calibration caused by uniform-field coil system. The particle swarm algorithm is applied to solving each group of coil's position and number of turns in the coil optimized design. According to the finite element analysis, the average error of the optimized coil in the target area is 0.009%, which is reduced by eight times than that of the Lee-Whiting coil system (0.075%). The experimental results of coil constant and magnetic fields along the axial were in good agreement with the simulation results. In addition, calibration experiments of three sets of induction magnetometers were carried out, whose experimental results show that the error of calibration using the optimized coil are all less than those of the Lee-Whiting coil system.

关键词： Magnetic fields Magnetometers Magnetic shielding Magnetic field measurement Calibration Magnetic noise Magnetic resonance imaging Calibration of induction magnetometer cylindrical uniform-field coil system particle swarm algorithm

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Improved particle swarm algorithm Using Rubik's Cube Topology for Bilevel Building Energy Transaction

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JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME 2022年第7期144卷 070910-070910页

作者： Feng, Xiaochun Chen, Yang Zhang, Jian Cho, Heejin Shi, Xin Northwest A&F Univ Sch Econ & Management Yangling 712100 Shaanxi Peoples R China Oak Ridge Natl Lab Div Environm Sci Oak Ridge TN 37830 USA Univ Wisconsin Green Bay Richard J Resch Sch Engn Green Bay WI 54311 USA Mississippi State Univ Dept Mech Engn Mississippi State MS 39762 USA Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA

Following the rapid growth of distributed energy resources (e.g., renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as reducing power loss and stabilizing the main power grid. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. In addition, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush-Kuhn-Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence-based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubik's Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubik's Cube topology-based particle swarm algorithm. The

关键词： particle swarm algorithm cubic topology bilevel energy transaction local energy market

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A New particle swarm algorithm for Online Trading Customer Classification

Frontiers in Economics and Management

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Frontiers in Economics and Management 2022年第11期3卷 43-51页

作者： Hankun Ye

Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. Aiming at the shortages of the existing particle swarm and K-means algorithm for customer classification, this paper advances a new customer classification algorithm through improving the existing particle swarm algorithm and combining it with K-means algorithm. First the paper designs 21 customer classification indicators based on consumer characteristics and behaviors analysis, including customer characteristics type variables and customer behaviors type variables; Second, limitation of particle swarm algorithm and K-means algorithm are analyzed; Then corresponding improvements for particle swarm algorithm are advanced including improvement of the speed update formula of particle and , improvement of balancing the development and detection capability of particle of the algorithm; Thirdly, the online trading customer classification algorithm combining the improved particle swarm algorithm and K-means algorithm is advanced. Finally the experimental results verify that the new algorithm can improve effectiveness and validity of customer classification when used for classifying network trading customers practically.

关键词： particle swarm algorithm K_means algorithm Electronic Marketing Customer Classification

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A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved particle swarm algorithm

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2022年 10卷 832829页

作者： Zhao, Guojun Jiang, Du Liu, Xin Tong, Xiliang Sun, Ying Tao, Bo Kong, Jianyi Yun, Juntong Liu, Ying Fang, Zifan Wuhan Univ Sci & Technol Key Lab Met Equipment Control Technol Minist Educ 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 Transmiss & Mfg Engn Wuhan Peoples R China Wuhan Univ Sci & Technol Precis Mfg Res Inst Wuhan Peoples R China China Three Gorges Univ Hubei Key Lab Hydroelect Machinery Design & Mainte Yichang Peoples R China

The analysis of robot inverse kinematic solutions is the basis of robot control and path planning, and is of great importance for research. Due to the limitations of the analytical and geometric methods, intelligent algorithms are more advantageous because they can obtain approximate solutions directly from the robot's positive kinematic equations, saving a large number of computational steps. particle swarm algorithm (PSO), as one of the intelligent algorithms, is widely used due to its simple principle and excellent performance. In this paper, we propose an improved particle swarm algorithm for robot inverse kinematics solving. Since the setting of weights affects the global and local search ability of the algorithm, this paper proposes an adaptive weight adjustment strategy for improving the search ability. Considering the running time of the algorithm, this paper proposes a condition setting based on the limit joints, and introduces the position coefficient k in the velocity factor. Meanwhile, an exponential product form modeling method (POE) based on spinor theory is chosen. Compared with the traditional DH modeling method, the spinor approach describes the motion of a rigid body as a whole and avoids the singularities that arise when described by a local coordinate system. In order to illustrate the advantages of the algorithm in terms of accuracy, time, convergence and adaptability, three experiments were conducted with a general six-degree-of-freedom industrial robotic arm, a PUMA560 robotic arm and a seven-degree-of-freedom robotic arm as the research objects. In all three experiments, the parameters of the robot arm, the range of joint angles, and the initial attitude and position of the end-effector of the robot arm are given, and the attitude and position of the impact point of the end-effector are set to verify whether the joint angles found by the algorithm can reach the specified positions. In Experiments 2 and 3, the algorithm proposed in this paper

关键词： particle swarm algorithm joint limiting adaptive strategy spinor theory robot inverse kinematics solution

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Hydraulic Performance Optimization of Pump Impeller Based on a Joint of particle swarm algorithm and Least-Squares Support Vector Regression

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IEEE ACCESS 2020年 8卷 203645-203654页

作者： Fang, Hua Ma, Jianfeng Zhang, Wei Yang, Hui Chen, Feng Li, Xiaojun Zhejiang Sci Tech Univ Natl Prov Joint Engn Lab Fluid Transmiss Syst Tec Hangzhou 310018 Peoples R China ZheJiang Fuchunjiang Hydropower Equipment Co Ltd Hangzhou 311501 Peoples R China Zhejiang Inst Mech & Elect Engn Dept Mech Technol Hangzhou 310053 Peoples R China

The hydraulic performance of centrifugal pumps is considerably affected by the impeller, and an effective optimization method for centrifugal pump impeller has been developed in the current study. A combination of the least-squares support vector regression machine (LSSVR) and particle swarm optimization algorithm (PSO) is proposed to redesign the impeller and improve the hydraulic performance. In the case study, four key geometric parameters of the impeller, namely, inlet angle, outlet angle, wrap angle and number of blades are selected for optimization. Maximum efficiency and constant head are selected as the optimization targets. During the optimization design, the required database for the LSSVR agent model is designed according to design of experiments. The optimal solution is then found in the established agent model space by the particle swarm algorithm and then verified by computational fluid dynamics. Ultimately, an improved impeller structure with an improved efficiency is provided. Numerical results show that the optimized impeller's efficiency is increased by 1.29% under the condition that the head is essentially unchanged. Then, the reason for the improvement of impeller hydraulic efficiency is explained by the entropy production method. The conclusions show that the PSO-LSSVR method can be used to optimize the pump impeller and achieve higher pump performance.

关键词： Centrifugal pump design optimization least-squares support vector machine particle swarm algorithm

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Comparative Study on Adjustable Parameter Interface of TSP based on Ant Colony algorithm and particle swarm algorithm

International Core Journal of Engineering

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International Core Journal of Engineering 2021年第8期7卷 34-41页

作者： Liqian Wang Geng Liang

With the continuous development of intelligent algorithms, new algorithms emerge one after another, each has its own advantages and disadvantages. At the same time, the performance comparison of different algorithms has become an important work to test the algorithm. In this paper, the problem of travel agent (TSP) is studied by using the visualization and operability of GUI in MATLAB. The simulation of the interface of adjustable parameters is carried out for ant colony algorithm and particle swarm algorithm respectively. Through the running program, it can be concluded that ant colony algorithm has positive feedback of pheromone, particle swarm optimization has fast and global convergence, and the advantages of both can be complementary.

关键词： TSP Problem Ant Colony algorithm particle swarm algorithm Adjustable Parameter Interfacee

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Forecast of agricultural water resources demand based on particle swarm algorithm

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Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 2022年第1期72卷 30-42页

作者： Wenzhou Yi Information Engineering College Guangdong Polytechnic of Engineering Guangzhou People’s Republic of China

ABSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** planning and management of water resources are becoming more and more important, and the forecast of water demand as the prerequisite and foundation of the entire planning has become a very important task in agricultural development. This paper combines the particle swarm algorithm to construct the agricultural water resource demand forecasting model, analyzes the shortcomings of the traditional particle swarm algorithm, and makes appropriate improvements to the quantum particle swarm algorithm. Moreover, this paper constructs the functional structure of the agricultural water resource demand forecast model based on the forecast demand of water resources, and analyzes the application process of the particle swarm algorithm in the system of this paper. After the model is constructed, the performance of the model is verified, and the simulation test is designed to evaluate the effect of system forecast with actual data. At the same time, this paper uses the model constructed in this paper to analyze the factors affecting water resources forecast demand. From the results of the experimental analysis, it can be seen that the model constructed in this paper is more effective in the forecast of water resources demand.

关键词： particle swarm algorithm agricultural water resources demand forecast machine learning

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Optimization of Operational Route in AS/RS Based on particle swarm algorithm

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Journal of Southwest Jiaotong University(English Edition) 2008年第1期16卷 92-94页

作者：黄学飞刘云霞 Department of Mechanical and Electronic Engineering Panyu Polytechnic School of Economics and Management Zhejiang University of Science and Technology

Optimization of the operational route in the automated storage/retrieval system （AS/RS） is transformed into the traveling salesman problem, To make the moving distance of the storage/retrieval machine shortest, we carry out a group of tests where 20 goods locations are chosed. Using PSO for operational route of AS/RS, the operation time can be shortened by about 11%. The experiments indicate that under the same conditions, the more the goods locations are, the higher the operation efficiency of the storage/retrieval machine is.

关键词： particle swarm algorithm Traveling salesman problem Automated storage/retrieval system

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