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Improved Spectral Clustering Clothing Image Segmentation algorithm Based on Sparrow Search algorithm

Journal of Donghua University(English Edition) 2022年第4期39卷 340-344页

作者： HUANG Wenan QIAN Suqin College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile&Fashion Technology Ministry of EducationDonghua UniversityShanghai 201620China

In the process of clothing image researching,how to segment the clothing quickly and accurately and retain the clothing style details as much as possible is the basis of subsequent image *** clustering clothing image segmentation algorithm is a common method in the process of clothing image ***,the traditional model requires high computing power and is easily affected by the initial center of *** often falls into local *** at the above two points,an improved spectral clustering clothing image segmentation algorithm is proposed in this *** Nystrom approximation strategy is introduced into the spectral mapping process to reduce the computational *** the clustering stage,this algorithm uses the global optimization advantage of the particle swarm optimization algorithm and selects the sparrow search algorithm to search the optimal initial clustering point,to effectively avoid the occurrence of local *** the end,the effectiveness of this algorithm is verified on clothing images in each environment.

关键词： clothing segmentation spectral clustering particle swarm optimization algorithm intelligent fashion design

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Optimum Distribution of Aseismic Engineering Investment for Gas Pipeline Network System Based on Improved particle swarm algorithm

Optimum Distribution of Aseismic Engineering Investment for ...

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2nd International Conference on Intelligent Computation Technology and Automation

作者： Liu Wu Liu Yi Gu Xue-qin Wang Deng-hai Southwest Petr Univ Sch Petr Engn Chengdu Peoples R China Xian Changqing Technol Engn Co LTD Xian Peoples R China Southwest Petr Univ Sch Electron & Informat Engn Chengdu Peoples R China

ISBN: (纸本)9780769538044

Under the limit of engineering investment, the aseismic optimum design of pipeline system is used to minimize the sum of aseismic cost. Since optimal problem is an nonlinear hybrid model which involves dual "curse of dimensionality" during the mathematical programming and system reliability calculation, traditional optimal techniques can hardly solve the problems of distribution of aseismic engineering investment for lifeline network system. Evolutionary-based particle swarm algorithm has been investigated and the improved design of inertia weight has been fulfilled in this paper. A new hybrid particle swarm algorithm makes use of the ergodicity of chaos to improve the capability of precise search and keep the balance between the global search and the local search. Combining with Boolean cubical matrix disjoint calculation method used as aseismic reliability analysis tool of network system, the improved algorithm is applied to the optimization solution of investment distribution for gas pipeline network The analysis of an example in a certain pipeline network system demonstrates that the established model and algorithm can achieve better convergent performance and speed.

关键词： gas pipeline network aseismic optimum design distribution of investment particle swarm optimization algorithm inertia weight chaos

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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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Improved PSO-BPNN Multi-Parameter Identification Method and Its Application in Battery Thermal Network Model 2

Improved PSO-BPNN Multi-Parameter Identification Method and ...

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

作者： Jiang, Tingzhi Ma, Mingyao Hu, Qiong Song, Qiwei Hefei University of Technology National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology Hefei China Sungrow Power Supply Co. Ltd Power Generation Sys Hefei China

ISBN: (纸本)9798350313765

Traditional battery thermal models either lack accuracy or have high computational costs, making it difficult for battery management systems to monitor battery temperature online. In this study, a three-dimensional thermal network model was developed specifically for a 120Ah square lithium-ion battery, significantly reducing the computational burden while ensuring relatively accurate outputs at multiple temperature points. Firstly, the heat generation rate of the entire battery during charging and discharging processes was obtained using an accelerated adiabatic calorimeter (ARC). Then, based on the positions of the electrode tabs and temperature measurement points, the battery was divided, and a three-dimensional thermal network model architecture was constructed. Special particle swarm algorithms and neural networks were used to fit the thermal parameters of the thermal network model. Finally, discharge temperature experiments were conducted to validate the feasibility of the thermal network model. © 2023 IEEE.

关键词： BP neural network particle swarm optimization algorithm Square lithium-ion battery Three-dimensional battery thermal network model

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Implementation of Demand Side Management Using PSO algorithm

Implementation of Demand Side Management Using PSO Algorithm

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Vardhan, Reddi Vivek Vaishnavi, Jonnalagadda Shyamala, Pachila Krishna Sai Siri, Gadi Sree Anand, R. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798350308167

In the present-day scenario, the management of grids plays a vital role. Demand-side management here acts as an important tool in developing trends. to meet the total power demand during peak loads, like reducing the total power demand in those situations. This paper focuses on the optimization of load consumption curves in smart grids using load-shifting techniques. And also, this paper discusses the optimization of load consumption through a PSO algorithmic approach, which in turn focuses on day-ahead load schedules concerning DSM, which is responsible for a low cost. © 2023 IEEE.

关键词： day ahead load scheduling Demand Side Management particle swarm optimization algorithm

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optimization algorithm for E-commerce Order Delivery Route 2

Optimization Algorithm for E-commerce Order Delivery Route

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

作者： Dong, Dandan Shaanxi Fashion Engineering University School of Economics and Management Xi'an China

ISBN: (纸本)9798350317558

With the rapid development of e-commerce, logistics distribution has become an important means for e-commerce enterprises to improve their competitiveness. This article focuses on the optimization problem of e-commerce order delivery routes, using particle swarm optimization algorithm for research, aiming to improve delivery efficiency, reduce costs, and enhance customer satisfaction. This article compares the time for solving the optimal path before and after optimization. The experimental results show that after optimizing with the particle swarm optimization algorithm, the average solving time is reduced from 67.12s to 27.49s. The research results indicate that the particle swarm optimization algorithm is superior to traditional algorithms in terms of solving efficiency and quality, providing strong support for e-commerce enterprises in fierce market competition. © 2024 IEEE.

关键词： E-commerce Order Experimental Testing Order Delivery particle swarm optimization algorithm Route optimization

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Application Research of Wireless Sensor Network in Structural Health Monitoring of Civil Engineering 2

Application Research of Wireless Sensor Network in Structura...

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2nd IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Li, Songtao Gao, Yanlong Wang, Baolin Tian, Ruxue Zhengzhou Railway Vocational & Technical College College of Railway Engineering Zhengzhou China Henan Jiaoyuan Engineering Technology Group Co. Ltd Zhengzhou China Zhengzhou Road Inspection Engineering Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9798350368208

Aiming at the needs of structural health monitoring in civil engineering, this paper deeply studies the application of wireless sensor networks in it, and focuses on the design and optimization of intelligent sensor networks. First, this paper outlines the important role of wireless sensor networks in civil structural health monitoring, including real- time data acquisition, remote monitoring and fault warning. Subsequently, a structural health monitoring method based on modal recognition technology is introduced in detail, which evaluates the health status of the structure by analyzing its dynamic response. This paper introduces a particle swarm optimization algorithm to optimize the layout and parameter settings of the sensor network to improve the accuracy and reliability of the monitoring system. The effectiveness of the proposed method is verified through system simulation. The simulation results show that the optimized sensor network can more accurately capture the abnormal behavior of the structure and maintain stable performance under various working conditions. The research results of this paper provide an efficient technical solution for the structural health monitoring of civil engineering, which has important theoretical significance and practical application value. © 2024 IEEE.

关键词： Civil structure health monitoring intelligent sensor network modal recognition particle swarm optimization algorithm

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Design of beamforming metasurface based on fully connected neural networks 14

Design of beamforming metasurface based on fully connected n...

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14th International Symposium on Antennas Propagation and EM Theory

作者： Li, Shi Sheng, Lili Wen, Jinfang Cao, Weiping Lu, Lucong Guilin Univ Elect Technol Sch Informat & Commun Guilin Peoples R China

ISBN: (纸本)9798350353129;9798350353136

In order to solve the problem that the period of designing metasurface with specific functions is too long by using electromagnetic simulation software, this paper proposes a metasurface design method based on artificial neural network, which can accurately predict the far-field pattern. In the training process, the root-mean-square error decreases with the increase of the number of iterations, the minimum root-mean-square error is 0.01, and the training time is 21 s. After the training is completed, the model can quickly predict the far-field pattern according to the coding matrix. The single prediction time of the neural network is 0.0065 s, while the single simulation time of the full-wave simulation software is 4 min, which is far more than that of the neural network. The accuracy of the predicted data is verified by comparing the predicted results with the results of the full-wave simulation. By combining the neural network with particle swarm optimization algorithm, the metasurface coding matrix corresponding to the target pattern can be designed. Compared with the traditional metasurface design method, this design method greatly reduces the design time and solves the problems of time consuming and cost in the traditional design method.

关键词： Neural network particle swarm optimization algorithm Beamforming metasurface Coding matrix Far-field pattern

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Digital Twin System for Home Service Robot Based on Motion Simulation

Digital Twin System for Home Service Robot Based on Motion S...

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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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