检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Design of Intelligent Monitoring System for Blast Furnace Hearth Erosion 41

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

引用

ENERGIES 2021年第20期14卷 6514-6514页

作者： Yi, Min Xie, Wei Mo, Li Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.

关键词： electricity price forecast maximum information number Pearson coefficient BP neural network particle swarm optimization algorithm simulated annealing algorithm

来源：评论

学校读者我要写书评

暂无评论

An Efficient optimization Design for 1 MHz Ultrasonic Transmitting Transducer

引用

IEEE SENSORS JOURNAL 2021年第6期21卷 7420-7427页

作者： Chen, Dongdong Zhao, Jianxin Fei, Chunlong Li, Di Liu, Yusang Chen, Qiang Lou, Lifei Feng, Wei Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Columbia Univ Sch Engn & Appl Sci New York NY 11101 USA

Due to the low efficiency of traditional design method, an efficient optimization design method is developed to design and fabricate high-performance ultrasonic transmitting transducer (UTT) based on PiezoCAD software and particle swarm optimization (PSO) algorithm. Based on the data of design and performance parameters of UTT obtained by PiezoCAD software, the genetic algorithm-based back-propagation neural networks (GA-BPNNs) are established to describe their mapping relationship. The optimality criterions are established based on the performance parameters including center frequency (CF) and maximum echo amplitude (MEA). Then, based on the established GA-BPNNs and optimality criterions, the optimization design method of UTT is developed under the framework of PSO algorithm. According to the desired performance, the design parameters of UTT, including the thickness and diameter of PZT-4 (Pb(ZrxTi1-x)O-3), the acoustic impedance of backing, are optimized by the developed method, and they are 2 mm, 30 mm and 3 MRayl, respectively. Based on the optimized design parameters, the CF and MEA simulated by PiezoCAD are 1.05 MHz and -45.1 dB, which are in good agreement with the designed ones (1 MHz and -45 dB). In addition, the CF of the fabricated UTT is 1.07 MHz, and its insertion loss is -7.2 dB at 1.075MHz, which implies that the fabricated UTT has excellent performance. Therefore, the developed optimization design method is effective to fabricate high-performance UTT.

关键词： optimization Acoustics Design methodology Software Impedance Transducers Genetic algorithms Ultrasonic transmitting transducer back-propagation neural network optimization design particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Equipment maintenance task prediction model analysis based on BAS-PSO hybrid optimization algorithm

引用

SYSTEMS SCIENCE & CONTROL ENGINEERING 2021年第1期9卷 455-466页

作者： Song, Weixing Wu, Jingjing Chang, Huiqiang Xu, Guihua Army Engn Univ PLA Shijiazhuang Hebei Peoples R China Western Theater Army Dept Logist Lanzhou Peoples R China Unite 31682 PLA Lanzhou Peoples R China Unite 32272 PLA Lanzhou Peoples R China

Aiming at the maintenance task prediction problem of armored forces, the macro model and micro model are established to analyze the constraint conditions, and the equipment maintenance task prediction model is established in order to meet the motor hours echelon storage. Under the condition of meeting the balance of annual motor hours payments, the motor hours consumed by equipment are allocated according to the annual training tasks, and a hybrid optimization algorithm of improved particle swarm optimization is designed to solve the model, and a case study is carried out on a few vehicles in a certain army. The simulation results show that the model can effectively solve the problem of equipment maintenance task prediction, and a provide reference value for troops to make the maintenance plans.

关键词： Maintenance tasks motor hours balance of payments echelon storage equipment utilization beetle antennae search algorithm particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Design and key technology of the energy consumption management system for the liquid cooling data center

引用

ENERGY SCIENCE & ENGINEERING 2023年第3期11卷 1284-1293页

作者： Lai, Yibo Fang, Xiang Han, Rongjie Xuan, Yi Huang, Jiabin Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou 310009 Peoples R China

In view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.

关键词： energy consumption management system energy consumption sampling circuit liquid cooling data center particle swarm optimization algorithm resource configurator

来源：评论

学校读者我要写书评

暂无评论

algorithms for solving assembly sequence planning problems

引用

NEURAL COMPUTING & APPLICATIONS 2021年第2期33卷 525-534页

作者： Su, Yingying Mao, Haixu Tang, Xianzhao Shenyang Univ Sch Mech Engn Shenyang 110044 Liaoning Peoples R China

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy-particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly

关键词： Assembly sequence planning particle swarm optimization algorithm Various population strategy Hybrid algorithm

来源：评论

学校读者我要写书评

暂无评论

Discrete particle swarm optimization algorithm for Lot-streaming No-wait Flow Shop Scheduling Problem

引用

2nd International Conference on Advanced Engineering Materials and Technology (AEMT)

作者： Yang, Lin Pan, Yuxia Hainan Univ Sanya Coll Sanya 572022 Hainan Peoples R China

ISBN: (纸本)9783037854471

This paper proposed discrete particle swarm optimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding scheme based on job permutation and newly-designed methods were adopted to produce new individuals. After the DPSO-based exploration, a efficient fast local search based on swap neighborhood structure is used to enhance the exploitation capability. Simulation results show the effectiveness of the proposed algorithms.

关键词： particle swarm optimization algorithm lot-streaming no-wait flow shop scheduling problem local search

来源：评论

学校读者我要写书评

暂无评论

Localization Technology Based on Quantum-behaved particle swarm optimization algorithm for Wireless Sensor Network

Localization Technology Based on Quantum-behaved Particle Sw...

引用

2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Zhao, Ji Fu, Yi Wang, Han-bo Wuxi City Coll Vocat Technol Dept Elect Informat Engn Wuxi 214000 Jiangsu Peoples R China

ISBN: (纸本)9783037855034

This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references for the rest of nodes to localize. The ranging-based localization problem is formulated as a multidimensional optimization issue, and the quantum-behaved particle swarm optimization algorithm (QPSO) is used to exploit their quick convergence to quality solutions. Finally, the simulation results compared with the particle swarm optimization algorithm (PSO) algorithm show that QPSO outperforms the PSO and improve the node position accuracy, which prove the validity of the presented method.

关键词： quantum-behaved particle swarm optimization algorithm particle swarm optimization algorithm location wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

A hybrid SVR-PSO model to predict concentration of sediment in typical and debris floods

引用

EARTH SCIENCE INFORMATICS 2021年第1期14卷 365-376页

作者： Kazemi, Mahsa Sheikh Banihabib, Mohammad Ebrarim Soltani, Jaber Univ Tehran Coll Aburaihan Dept Water Engn Tehran Iran

Since sediment concentration is an effective factor on increasing debris flood's peak flow and damages from floods, developing new models to predict the sediment concentration of debris floods has crucial importance. In this study, a hybrid SVR-PSO model was proposed to predict the concentration of sediment in typical and debris floods, and it was examined in three basins located in Gilan, Mazandaran, and Tehran Provinces, Iran. Mean elevation and slope of the basin, the area of the basin, current day's rainfall, the rainfall of previous days (1-3 days before flood) for all rain-gauge stations of the basins, as well as the discharge of the previous day, were used as the input variables of the model. Then, various combinations of variables were tested to assess the factors influencing the concentration of sediment in typical and debris floods in order to find the best variable combination with a high performance in predicting the concentration of sediment in the studied floods. The results showed that basin elevation, current day's rainfall, previous day's discharge, rainfall of the previous day, basin area, rainfall of the previous two days, basin slope, and rainfall of the previous three days were the key factors influencing the concentration of sediment in typical and debris floods, respectively. Coefficient of determination, root mean square error, and mean absolute percentage error were estimated 0.96, 0.003, and 14.38% for the proposed model at the testing phase, respectively. This implies model's good performance for predicting the concentration of sediment in typical and debris floods so that the present model can provide reliable predictions of flood character in basins.

关键词： Concentration of sediment Debris flood particle swarm optimization algorithm Prediction Support vector regression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：