检索结果-内蒙古大学图书馆

Penetration quality prediction of asymmetrical fillet root welding based on optimized BP neural network

JOURNAL OF MANUFACTURING PROCESSES 2020年 50卷 247-254页

作者： Chang, Yushuo Yue, Jianfeng Guo, Rui Liu, Wenji Li, Liangyu Tianjin Polytech Univ Sch Mech Engn Tianjin Peoples R China

Penetration morphology has an important influence on the weld quality. Due to the nonlinear and strong coupling characteristics of welding process, neural network is often used to predict weld formation and quality. However, the prediction method of fillet weld penetration needs further exploration due to the difficulty in quality evaluation. More intricately, fillet welds of the medium-thickness plate with one-side V-groove are structurally asymmetrical, which makes the penetration quality difficult to guarantee. In this study, the penetration quality of asymmetrical fillet welds is depicted by two characteristic quantities: penetration depth and penetration deflection. The penetration deflection can be reflected by leg length on both sides. After correlation analysis, a back-propagation neural network(BPNN) optimized by mind evolutionary algorithm (MEA) is proposed. The welding current, welding speed, torch work angle and real-time molten pool width were chosen as input parameters, and the penetration of blunt edge and the leg length on both sides of the weld were chosen as output parameters. The results demonstrated that it is feasible and reasonable to predict the penetration of asymmetrical fillet welds by this model. Experimental comparison showed that the optimized model has a significant improvement in predictive performance. The prediction error of blunt edge penetration is controlled within 0.1 mm, and the prediction error rate of leg length is less than 7 %. It satisfies the accurate prediction of penetration quality of asymmetrical fillet welds, and lays the foundation for the study of penetration morphology control of asymmetrical fillet automatic welding.

关键词： Asymmetrical fillet weld Penetration forming BPNN mind evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Aging prognosis model of proton exchange membrane fuel cell in different operating conditions

引用

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第20期45卷 11761-11772页

作者： Chen, Kui Laghrouche, Salah Djerdir, Abdesslem CNRS UMR 6174 FEMTO ST F-90000 Paris France Univ Bourgogne Franche Comte CNRS FR 3539 FCLABBelfort UTBM F-90000 Paris France

The aging prognosis model of Proton Exchange Membrane Fuel Cell (PEMFC) can predict the aging state of PEMFC to develop an effective prognostic maintenance plan. This paper proposes an aging prognosis model of PEMFC in different operating conditions based on the Backpropagation (BP) neural network and evolutionary algorithm. The influence of PEMFC current, hydrogen pressure, temperature, and relative humidity on the aging of PEMFC can be considered by the proposed method. Firstly, the aging prognosis model of PEMFC is built by the BP neural network. Then, the evolutionary algorithm including mind evolutionary algorithm (MEA), Particle Swarm Optimization (PSO), and Genetic algorithm (GA) is used to optimize the parameters of the established aging prognosis model of PEMFC. Finally, the accuracy of the proposed aging prognosis model is validated by 3 PEMFC aging experiments in different operating conditions. The results show that MEA, GA, and PSO can greatly improve the accuracy of the aging prognosis model of PEMFC. The MEA improves the accuracy by 10 times, while the computing time increases by 0.085s. The proposed MEA-BP that has a very short computing time can be applied to online applications. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Proton exchange membrane fuel cell Aging prognosis model mind evolutionary algorithm Particle swarm optimization Genetic algorithm Backpropagation neural network

来源：评论

学校读者我要写书评

暂无评论

Established prediction models of thermal conductivity of hybrid nanofluids based on artificial neural network (ANN) models in waste heat system

引用

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2020年 110卷 104444-104444页

作者： Wang, Jiang Zhai, Yuling Yao, Peitao Ma, Mingyan Wang, Hua Kunming Univ Sci & Technol State Key Lab Complex Nonferrous Met Resources Cl Kunming 650093 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Met & Energy Engn Kunming 650093 Yunnan Peoples R China

The properties of water (W)/ethylene glycol (EG) mixtures vary significantly with the proportion of EG and temperature, so it is suitable to use such fluids as exchange heat mediums in a waste heat system with temperature fluctuations. The experiments were conducted with 1.0 wt% Cu/Al2O3- EG/W hybrid nanofluids at temperatures ranging from 20 to 50 degrees C, where the base fluid (EG/W) mixture ratio was varied from 20:80 to 80:20. To search individuals which contain optimal weights and thresholds, a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) coupled with a back-propagation neural network (GA-BPNN and MEA-BPNN, respectively) were used to improve the accuracy in the predicted thermal conductivity. The results show that the thermal conductivity increases nonlinearly with the ratio of water to ethylene glycol and temperature, due to the higher thermal conductivity of water and stronger collision frequency between molecular and nanoparticles. Binary Polynomial Regression (BPR) was fit with (coefficient of determination) R-2 = 0.9984 as functions of temperature and mixture ratio. Comparisons of the prediction performance and capability of BPR, the performance of R-2 increases by 0.11% and 0.13% for GA-BPNN and MEA-BPNN. It indicates that the combined BPNNs both predicate more accurately, particularly MEA-BPNN has the highest prediction accuracy.

关键词： Hybrid nanofluids Thermal conductivity Artificial neural network Genetic algorithm mind evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

A machining error tracing method based on MEA-BP neural network for quality improvement of gear hubs

引用

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年第7期37卷 852-865页

作者： Liu, Yongsheng Zhang, Xinhui Ding, Kai Hui, Jizhuang Zhao, Jin Chan, Felix T. S. Changan Univ Inst Smart Mfg Syst Xian Peoples R China Changan Univ Key Lab Rd Construct Technol & Equipment MOE Xian Peoples R China Xian XD High Voltage Apparat Co Ltd Dept Elect Measurement & Control Xian Peoples R China Macau Univ Sci & Technol Dept Decis Sci Macau Peoples R China

The machining quality of workpieces is greatly influenced by the performance of an equipment. Furthermore, it is difficult to establish an error tracing model with high tracing accuracy using a mathematical method. In this study, the machining quality of gear hubs for an automobile synchronizer produced on an intelligent manufacturing line was evaluated. The main sources of machining errors were analyzed, and the machining error tracing model for the gear hub was established through a back propagation (BP) neural network. To improve the performance of the error tracing model, the weights and thresholds of the BP neural network were optimized using the mind evolutionary algorithm (MEA). The MEA-BP error tracing model was trained and tested using online measurement results and historical data of the production line. The results showed that the average tracing accuracy of the MEA-BP method was 97.4%, which was 12.1% higher than that of the BP method. The average running time of the MEA-BP was far less than that of a genetic algorithm (GA) improved BP method. These comparisons prove that the proposed MEA-BP error tracing method is both feasible and effective. The proposed method can improve the machining quality and error tracing in intelligent manufacturing applications.

关键词： Machining quality error tracing mind evolutionary algorithm back propagation neural network online measurement intelligent manufacturing

来源：评论

学校读者我要写书评

暂无评论

Geomagnetic Positioning-Aided Wi-Fi FTM Localization algorithm for NLOS Environments

引用

IEEE COMMUNICATIONS LETTERS 2022年第5期26卷 1022-1026页

作者： Sun, Meng Wang, Yunjia Huang, Lu Jia, Haonan Bi, Jingxue Joseph, Wout Plets, David China Univ Min & Technol Sch Environm & Spatial Informat Key Lab Land Environm & Disaster Monitoring MNR Xuzhou 221116 Jiangsu Peoples R China State Key Lab Satellite Nav Syst & Equipment Tech Shijiazhuang 050081 Hebei Peoples R China Shandong Jianzhu Univ Coll Surveying & Geoinformat Jinan 250101 Peoples R China Univ Ghent Dept Informat Technol WAVES Grp B-9052 Ghent Belgium

WiFi-based indoor localization using the fine time measurement (FTM) protocol has become a popular technique. However, in harsh Non-Line-of-Sight (NLoS) environments, WiFi FTM positioning (WFP) suffers from poor performance. In this letter, a novel WiFi FTM localization method with the assistance of geomagnetic positioning (GP) is proposed. To ensure the accuracy of GP, an enhanced mind evolutionary algorithm (EMEA) is designed. A fine-grained WiFi position estimation method using the overlapping searching area (OSA) and the coincident points selection strategy is proposed. Experimental results show that the EMEA-based GP improves the localization performance of WFP in NLOS environments, the mean localization error (ME) and root-mean-square error (RMSE) of the GP-aided WFP are 1.82 m and 2.08 m, respectively. Compared with the classic WFP using the weighted least square (WLS) method, the ME and RMSE are reduced by 51.7% and 52.4%, respectively.

关键词： Wireless fidelity Location awareness Distance measurement Statistics Sociology Estimation evolutionary computation Indoor localization WiFi FTM magnetic positioning NLOS mind evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Introduce canopy temperature to evaluate actual evapotranspiration of green peppers using optimized ENN models

引用

JOURNAL OF HYDROLOGY 2020年 590卷 125437-125437页

作者： Liu, Jingran Meng, Xiankuan Ma, Yingjie Liu, Xin Hebei Univ Engn Sch Water Conservancy & Hydroelect Power Handan 056038 Peoples R China Xinjiang Agr Univ Urumqi 830052 Peoples R China

Canopy temperature (T-c) was introduced using the Elman Neural Network (ENN) optimized by mind evolutionary algorithm (MEA) and Genetic algorithm (GA) methods to improve the predicting accuracy of actual evapotranspiration (ET) of green peppers. The improving planting methods, rainwater harvesting partial plastic film mulching and regulated deficit irrigation (MFR-RDI) with straw mulling, had been employed to save irrigation water. The results indicated that MEA-Elman1 (RMSE = 0.349 mm/d, MAE = 0.303 mm/d, NS = 0.951) and GA-Elman1 (RMSE = 0.487 mm/d, MAE = 0.404 mm/d, NS = 0.913) models can accurately estimate the ET of green peppers, indicating that the T-c can well improve the accuracy of both MEA-Elman and GA-Elman models in predicting ET under the condition of MFR-RDI. Furthermore, the results showed that the performance of MEA-Elman model was better than GA-Elman model under the same factors. Based on the above results, different input factors in different growth periods of crops has been adopted using the MEA-Elman4 model to further improve the predicting accuracy of ET. Indeed, the MEA-Elman4 model exerted the best performance (RMSE = 0.339 mm/d, MAE = 0.285 mm/d, NS = 0.954), which should be given priority to predict the ET of green peppers.

关键词： Green peppers Evapotranspiration Canopy temperature Elman Neural Network mind evolutionary algorithm Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Estimation of the SOC of Energy-Storage Lithium Batteries Based on the Voltage Increment

引用

IEEE ACCESS 2020年 8卷 198706-198713页

作者： Zhao, Bo Hu, Juan Xu, Shouping Wang, Jiangzhao Zhu, Yanqing Zhang, Li Gao, Chaofei Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China China Elect Power Res Inst State Key Lab Operat & Control Renewable Energy & Beijing 100192 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China

State of charge (SOC) estimations are an important part of lithium-ion battery management systems. Aiming at existing SOC estimation algorithms based on neural networks, the voltage increment is proposed in this paper as a new input feature for estimation of the SOC of lithium-ion batteries. In this method, the port voltage, current and voltage increment are taken as inputs and the current SOC is used as output to train a neural network. Different from the adaptive filtering algorithm, which requires complex equivalent circuit parameter identification, this algorithm uses the voltage increment instead of the open circuit voltage (OCV);hence, the complexity of the SOC estimation algorithm is reduced, and the problem of inaccurate estimation caused by neural network algorithms without considering the internal structure of the battery is avoided. The experimental results show that compared with the traditional neural network algorithm, the neural network SOC estimation algorithm based on the voltage increment could improve the accuracy of SOC estimation.

关键词： State of charge Estimation Batteries Neural networks Machine learning algorithms Filtering Autoregressive processes Lithium battery state of charge neural network mind evolutionary algorithm voltage increment

来源：评论

学校读者我要写书评

暂无评论

Prediction method of primary frequency modulation capability of power system based on MEA-BP algorithm

引用

ENERGY REPORTS 2023年 9卷 111-118页

作者： Zhang, Yudong Tang, Fan Liang, Xiaobin Sun, Jian Li, Hongxun Ru, Hang State Grid Corp China Southwest Branch 299 shuxiu West Rd Chengdu 610000 Peoples R China Southwest Jiaotong Univ Sch Elect Engn 999 Xian Rd Chengdu 610000 Peoples R China

In order to accurately predict the primary frequency modulation capability of power system when frequency deviation occurs, a method for predicting primary frequency modulation performance of power system based on improved neural network with thought evolution algorithm (MEA) is presented. Nine-dimensional parameters, such as frequency deviation before and after disturbance, system backup capacity, load level, mechanical power and electrical power, are selected as input eigenvalues, and the output is the system power change curve. This paper takes CEPRI36v8 system as an example of simulation, obtains the primary frequency modulation curve of the system by setting the shear load disturbance with PSASP, uses the disturbance data and the primary frequency modulation response data as training set, test set and validation set data, trains the prediction model of primary frequency modulation capability based on MEA-BP, and finds the prediction error is about 0.5% compared with the actual data, which proves that the proposed method can accurately and quickly predict the primary frequency modulation capability after the frequency deviation of the power network. This method can assist power dispatcher to analyze the primary frequency modulation response of power grid after power disturbance.(c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Primary frequency Prediction analysis Power system disturbance BP neural network mind evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Feed intake prediction model for group fish using the MEA-BP neural network in intensive aquaculture

引用

Information Processing in Agriculture 2020年第2期7卷 261-271页

作者： Lan Chen Xinting Yang Chuanheng Sun Yizhong Wang Daming Xu Chao Zhou Beijing Research Center for Information Technology in Agriculture Beijing 100097China Tianjin University of Science and Technology Tianjin 300222China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China National Engineering Laboratory for Agri-product Quality Traceability Beijing 100097China

In aquaculture,the accurate prediction of feed intake for group fish is considered to be crucial to any feeding *** studies mainly used mathematical statistics to establish the mapping relationship between feed intake and influencing *** result was easily influenced by subjective *** solve the above issues,this paper proposed a feed intake prediction model for group fish using the back-propagation neural network(BPNN)and mind evolutionary algorithm(MEA).Firstly,four factors,including water temperature,dissolved oxygen,the average fish weight and the number of fish were selected as the input of the BPNN ***,the initial weight and threshold of the BPNN were optimized by the MEA to improve the matching ***,the prediction model was achieved after *** results showed that the correlation coefficient between the predicted and measured values reached *** the root mean squared error,mean square error,mean absolute error,mean absolute percent error of the model was 6.89,47.53,6.17 and 0.04,*** addition,the proposed method also had the better nonlinear fitting ability than BPNN and *** using an intelligent optimization algorithm,the mapping relationship between fish intake and environmental factors was automatically established,thus avoiding the subjectivity of traditional ***,it can lay a theoretical foundation for the development of intelligent feeding equipment and meet the needs of the smart fishery.

关键词： BP neural network Feed intake prediction Group fish mind evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Detection of Foreign Matter in Transfusion Solution Based on Gaussian Background Modeling and an Optimized BP Neural Network

引用

SENSORS 2014年第11期14卷 19945-19962页

作者： Zhou, Fuqiang Su, Zhen Chai, Xinghua Chen, Lipeng Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100191 Peoples R China

This paper proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP) neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA) is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory.

关键词： detection of foreign matter Gaussian background model mind evolutionary algorithm BP neural network ReliefF feature selection

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：