检索结果-内蒙古大学图书馆

Intelligent optimization of finite element model parameters for large-span bridges based on MA-info algorithm

STRUCTURES 2024年 70卷

作者： Ding, Jiaxuan Gao, Liang Shi, Shunwei Zhang, Yanan Yang, Mingmei Beijing Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Track Engn Beijing 100044 Peoples R China

This study proposes a bridge Finite Element (FE) model updating method based on the Metamodel Assisted weIghted meaN oF vectOrs (MA-info) algorithm, which improves the simulation accuracy for large-span bridge FE models. The first step of this method is to perform sensitivity analysis, with the aim of selecting the parameters involved in the update. Subsequently, a Radial Basis Function Neural Network (RBFNN) metamodel is constructed to replace time-consuming FE analysis. Combining the measurement data of static and dynamic responses, the info algorithm is introduced to update material and boundary condition parameters. Finally, A comparative analysis is conducted with other advanced optimization algorithms. Results demonstrate that the info outperforms multiple popular algorithms in terms of accuracy and convergence speed. After 18 iterations, the maximum relative error of natural frequency decreased from 12.8 % to 1.84 %, and the relative error of maximum deflection at mid-span decreased from 36.85 % to 3.39 %. Additionally, the updated model can accurately simulate static and dynamic responses, matching well with measurement results. The proposed MAinfo offers a promising approach for accurate parameter estimation in large-span bridge FE models.

关键词： Finite element model updating Metamodel Neural network info algorithm High-speed railway Suspension bridge

来源：评论

学校读者我要写书评

暂无评论

Optimization of cable connection layout of unattended AC/DC joint construction station based on info algorithm

引用

DISCOVER APPLIED SCIENCES 2025年第6期7卷 1-14页

作者： Hu, Long Yu, Zhiwei Liang, Minxin Chen, Xiaoying Chen, Lixia Huang, Guohui Guangzhou Power Supply Bureau Guangdong Power Grid Guangzhou 510620 Peoples R China

In order to improve the comprehensive evaluation performance of the optimization of cable connection layout for unattended AC and DC joint construction stations, and avoid falling into local optimal solutions in the optimization process. An optimization method based on info algorithm for cable connection layout of unattended AC-DC joint construction station is studied. The cable topology of the unattended AC/DC combination station is built according to the cable connection principle of an unattended AC/DC combination station. The info method is an effective means for optimizing the cable connection layout of unmanned AC/DC co construction stations. It is a population-based optimization algorithm that performs search optimization by calculating the weighted average of vectors. Based on the cable connection principle of the unattended AC/DC common station, the cable topology is constructed, and the optimization model is established with the objective function of minimizing the total cost of laying cables, and the constraints that the cable laying capacity is smaller than the channel capacity. Using the info algorithm, the feasible solutions of the model are formed into a search domain and solved, and finally the optimal cable connection layout results are output. The experimental results show that the channel capacity occupancy rate is significantly reduced, and the cable material reduction is more than 10% by using this method to optimize the cable connection layout of the unattended AC/DC composite station. It effectively improves the comprehensive evaluation performance of the cable connection layout and provides strong support for the stable operation of the power system and the construction of smart grid.

关键词： info algorithm Unattended AC and DC Joint construction station Cable connection Layout optimization

来源：评论

学校读者我要写书评

暂无评论

Thermal management for EV power batteries based on info-ADRC algorithm

引用

INTERNATIONAL JOURNAL OF GREEN ENERGY 2025年

作者： Zhou, Shuwen Zhao, Siwen Duan, Xinrui Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Liaoning Peoples R China

During high-rate discharge, power batteries generate a considerable amount of heat. If this thermal energy is not dissipated effectively, the resulting rapid temperature rise can significantly impact the operational lifespan of the battery. Consequently, building a thermal control system that can keep the battery temperature status in an acceptable range and increase the homogeneity is vital. To this purpose, this study proposes an ADRC method that combines a BP neural network and an info algorithm to regulate the temperature environment suitable for the battery cluster's operation. The findings confirm the capability of the optimization algorithm to effectively reduce the battery's temperature fluctuation under different operating conditions. After optimization, the total temperature variation decreases by over 24%. In addition, info-ADRC technology meets the current requirements of the electric vehicle industry by keeping peak temperature deviations within 1 degrees C within the battery clusters.

关键词： Battery thermal management system Liquid-cooled thermal model BP neural network info algorithm ADRC

来源：评论

学校读者我要写书评

暂无评论

Developing a strategy based on weighted mean of vectors (info) optimizer for optimal power flow considering uncertainty of renewable energy generation

引用

NEURAL COMPUTING & APPLICATIONS 2023年第19期35卷 13955-13981页

作者： Farhat, Mohamed Kamel, Salah Atallah, Ahmed M. Abdelaziz, Almoataz Y. Tostado-Veliz, Marcos Ain Shams Univ Fac Engn Elect Power & Machines Engn Dept Cairo 11517 Egypt Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Univ Jaen Elect Engn Dept EPS Linares 23700 Spain

In recent years, more efforts have been exerted to increase the level of renewable energy sources (RESs) in the energy mix in many countries to mitigate the dangerous effects of greenhouse gases emissions. However, because of their stochastic nature, most RESs pose some operational and planning challenges to power systems. One of these challenges is the complexity of solving the optimal power flow (OPF) problem in existing RESs. This study proposes an OPF model that has three different sources of renewable energy: wind, solar, and combined solar and small-hydro sources in addition to the conventional thermal power. Three probability density functions (PDF), namely lognormal, Weibull, and Gumbel, are employed to determine available solar, wind, and small-hydro output powers, respectively. Many meta-heuristic optimization algorithms have been applied for solving OPF problem in the presence of RESs. In this work, a new meta-heuristic algorithm, weighted mean of vectors (info), is employed for solving the OPF problem in two adjusted standard IEEE power systems (30 and 57 buses). It is simulated by MATLAB software in different theoretical and practical cases to test its validity in solving the OPF problem of the adjusted power systems. The results of the applied simulation cases in this work show that info has better performance results in minimizing total generation cost and reducing convergence time among other algorithms.

关键词： Optimal power flow Renewable energy sources Uncertainty modeling info algorithm

来源：评论

学校读者我要写书评

暂无评论

Robust kernel extreme learning machines with weighted mean of vectors and variational mode decomposition for forecasting total dissolved solids

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Chen, Huiling Ahmadianfar, Iman Liang, Guoxi Heidari, Ali Asghar Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325000 Peoples R China Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Thi Qar 64001 Iraq Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran

A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (info) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELMinfo. info is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. info, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-info model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELMSMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices.

关键词： Water quality Optimization Variational mode decomposition Multi-criteria decision making Boruta-XGBoost feature selection Weighted mean of vectors optimizer info info algorithm

来源：评论

学校读者我要写书评

暂无评论

Parameter estimation of ECM model for Li-Ion battery using the weighted mean of vectors algorithm

引用

JOURNAL OF ENERGY STORAGE 2024年 76卷

作者： Merrouche, Walid Lekouaghet, Badis Bouguenna, Elouahab Himeur, Yassine Renewable Energy Dev Ctr CDER Bouzareah 16340 Algeria Res Ctr Ind Technol CRTI POB 64 Algiers 16014 Algeria Univ Dubai Coll Engn & Informat Technol Dubai U Arab Emirates

Accurate parameter estimation of the equivalent circuit model (ECM) for Li-Ion batteries (LiBs) allows for better behavior modeling and understanding. This is crucial for various applications, such as battery management systems (BMSs) and renewable energy systems, as it enables more precise performance prediction and optimization. This article introduces a novel artificial intelligence (AI) approach to estimate the ECM parameters of LiBs. The proposed method leverages the innovative weIghted meaN oF vectOrs (info) algorithm to optimize ECM parameter values. info combines multiple vectors, considering their relative importance, to generate a single vector. The algorithm minimizes the disparity between the ECM's estimated voltage and the battery's measured voltage. The effectiveness of the proposed method is assessed using a test profile based on real-world driving data. Furthermore, A comparative analysis is conducted with other state-of-the-art optimization algorithms, including Artificial Ecosystem Optimizer (AEO), Grey Wolf Optimizer (GWO), Artificial Hummingbird algorithm (AHA), Harris Hawks Optimization (HHO), Leader Harris Hawks Optimization (LHHO), Particle Swarm Optimization (PSO), Genetic algorithm (GA) and Adolescent Identity Search algorithm (AISA). Results demonstrate that the info outperforms these methods in terms of accuracy and convergence speed. The proposed info algorithm offers a promising approach for accurate parameter estimation in LIB modeling, contributing to improved BMS and enhanced performance prediction.

关键词： Artificial intelligence Equivalent circuit model Lithium-ion battery info algorithm Parameters extraction

来源：评论

学校读者我要写书评

暂无评论

Research and Evaluation of a New Structural Damage Identification Method Based on a Refined Genetic algorithm

引用

APPLIED SCIENCES-BASEL 2024年第22期14卷 10454页

作者： Qin, Yuantian Yin, Zhehang Ma, Jiahao Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China

In order to solve the problem of structural damage location and degree identification, the weighted mean of vectors algorithm (info), a high-performance optimization algorithm, was first introduced to identify structural damage. By comparison with the refined genetic algorithm (RGA), the accuracy and advantages of info are analyzed and evaluated. An objective function is constructed by combining the dynamic response transfer ratio without modal analysis. The info and RGA algorithms are used to optimize the objective function for damage identification. The simulation results verify the effectiveness of the three damage identification methods. The results show that the identification effect of the info algorithm can reach nearly 100% without noise influence, and the anti-noise ability is the strongest. Among the three algorithms, the damage identification accuracy of the info algorithm is the highest, followed by the RGA algorithm and the GA algorithm.

关键词： damage identification RGA info algorithm dynamic response transfer ratio

来源：评论

学校读者我要写书评

暂无评论

Precise parameter estimation of PEM fuel cell via weighted mean of vectors optimizer

引用

JOURNAL OF COMPUTATIONAL ELECTRONICS 2024年第5期23卷 1039-1048页

作者： Lekouaghet, Badis Khelifa, Mohammed Amin Boukabou, Abdelkrim Res Ctr Ind Technol CRTI POB 64 Algiers 16014 Algeria Univ Jijel BP 98 Ouled Aissa Jijel 18000 Algeria Univ Tizi Ouzou Fac Elect & Comp Engn Dept Elect BP 17 Tizi Ouzou 15000 Algeria

This paper deals with the determination of the optimal values to be given for the seven unknown parameters of the proton exchange membrane fuel cell (PEMFC). To this end, the weighted mean of vectors optimizer (info) metaheuristic algorithm is applied to estimate these parameters by minimizing the sum of squared errors (SSEs) between the measured and calculated voltages of the PEMFC. Three commercial types of PEMFCs are investigated: (i) BCS 500 W Stack, (ii) NedStack PS6 Stack, and (iii) Horizon 500 W Stack. The accuracy of the applied info algorithm is verified by comparing the estimated voltage-current (I-V)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(I-V)$$\end{document} characteristics with the measured data. Furthermore, the estimated parameters of electrical PEMFCs, the minimum reached SSE, and the standard deviation Std values achieved by info are compared with the results obtained using other competitive metaheuristic optimization algorithms such as Honey badger algorithm, Gradient-based optimizer, Harris hawks optimization, and others. From the obtained results, the convergence curves show that the unknown parameters of the three PEMFCs are better estimated using the proposed info than other algorithms.

关键词： Fuel cell (FC) Parameter estimation Modeling Optimization Metaheuristics info algorithm

来源：评论

学校读者我要写书评

暂无评论

PM2.5 concentration forecasting: Development of integrated multivariate variational mode decomposition with kernel Ridge regression and weighted mean of vectors optimization

引用

ATMOSPHERIC POLLUTION RESEARCH 2024年第6期15卷

作者： Tao, Hai Ahmadianfar, Iman Goliatt, Leonardo Kazmi, Syed Shabi Ul Hassan Yassin, Mohamed A. Oudah, Atheer Y. Homod, Raad Z. Togun, Hussein Yaseen, Zaher Mundher Nanchang Inst Sci & Technol Sch Informat & Artificial Intelligence Nanchang Peoples R China Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran Univ Fed Juiz de Fora Computat Modeling Program Juiz De Fora MG Brazil Shantou Univ Guangdong Prov Key Lab Marine Disaster Predict & P Shantou 515063 Peoples R China Shantou Univ Guangdong Prov Key Lab Marine Biotechnol Shantou 515063 Peoples R China King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Membranes & Water Secur Dhahran 31261 Saudi Arabia Univ Thi Qar Coll Educ Pure Sci Dept Comp Sci Nasiriyah 64001 Iraq Al Ayen Univ Sci Res Ctr Informat & Commun Technol Res Grp Thi Qar Iraq Basrah Univ Oil & Gas Dept Oil & Gas Engn Basra Iraq Univ Baghdad Coll Engn Dept Mech Engn Baghdad Iraq King Fahd Univ Petr & Minerals Civil & Environm Engn Dept Dhahran 31261 Saudi Arabia

The accurate prediction of the PM 2.5 air quality parameter within industrial and urban settings is the most pressing issue examined by the researchers because it has high health implications. However, accurately forecasting PM 2.5 levels is crucial. The traditional machine learning (ML) models are incapable, as the indices fluctuate daily. To successfully manage this problem, a new ML framework is proposed that incorporates various techniques, such as LGBM feature selection (light gradient-boosting machine), MVMD (multivariate variational mode decomposition), KRidge (kernel Ridge regression), and info (weighted mean of vectors). The proposed framework is used to estimate PM 2.5 pollution at specific stations in China for a one-time prediction. The LGBM feature selection technique is the first step of the pre-processing, which performs to select the most important variables. Next, the MVMD splits the initial signal into intrinsic mode functions (IMFs), accommodating the signal ' s non-stationary multivariate. Following that, the KRidge approach is applied to each sub-component using the best input feature, and the resulting predictions are summed up to get the PM 2.5 levels. To assess the validity of the proposed MVMD-KRidge-info model, the categories of gaussian-process-regression (GPR), locally-weighted-linear-regression (LWLR), and multivariate adaptive regression splines (MARS) are analyzed in individual and hybrid moldes. As observed from the research results, MVMD-KRidge-info performs optimally at forecasting PM 2.5 levels at Huairou and Shunyi, as evidenced by R, RMSE, MAPE, I A , MdAE, and U 95% metrics.

关键词： Air quality forecasting PM2.5 Kernel ridge regression MVMD info algorithm LGBM 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：