检索结果-内蒙古大学图书馆

Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization

EGYPTIAN INFORMATICS JOURNAL 2025年 29卷

作者： Wang, Liang Sun, Nan Southeast Univ Sch Mech Engn Nanjing 211189 Jiangsu Peoples R China Changchun Humanities & Sci Coll Changchun 130117 Jilin Peoples R China

This study presents an innovative approach to optimize energy management in residential microgrids, in light of the rising demand for energy and mounting environmental concerns. The research underscores the vital role of efficient energy management and responsive load control to improve energy efficiency and reduce consumer costs. To this end, a framework is proposed in which a power aggregator operates within a microgrid to manage residential electricity consumption. The primary goal of this framework is to minimize energy costs while considering subscriber preferences and the capacity limitations of the distribution network. The improved particle swarm optimization (IPSO) algorithm is employed to optimize energy management, resolve convergence challenges, and ensure user requirements are effectively prioritized. Integrating emergency, economic, and planned strategies provides cost savings, ensures grid stability, and enhances user satisfaction. The incorporation of Internet of Things (IoT) technology enables seamless communication, precise device control, and data-driven decision-making, empowering households to manage their energy loads more effectively and contribute to grid efficiency. Through scenario analysis, this research demonstrates the IPSO algorithm's potential for significant cost reductions and improved grid stability. In Scenario 1, focused exclusively on affordability, numerical analyses present the total cost of electricity under different load conditions over three months. Scenario 2, also prioritizing affordability, highlights the impact of economic considerations on electricity expenses. Furthermore, Scenario 3 (80 % emergency + 20 % affordable) and Scenario 4 (50 % emergency + 20 % affordable + 30 % planned) showcase the potential for cost reduction through various priority combinations. These insights reflect the effectiveness of load management strategies facilitated by IoT technology. This comprehensive energy management approach lays a stro

关键词： Consumption Management Utilization Priority Improved Particle Swarm Optimization algorithm Smart Building

来源：评论

学校读者我要写书评

暂无评论

A new algorithm to optimize thermodynamic parameters: Application to the Ag-Pd and La-C systems

引用

CALPHAD-COMPUTER COUPLING OF PHASE DIAGRAMS AND THERMOCHEMISTRY 2025年 89卷

作者： Zhang, Xinyi Zhang, Liang Fu, Taibai Wang, Jiangxing Wang, Bo Xie, Ziqing Liu, Yuling Du, Yong Hunan Normal Univ Key Lab Comp & Stochast Math Minist Educ Changsha 410081 Peoples R China Cent South Univ State Key Lab Powder Met Changsha 410083 Peoples R China

A new algorithm for optimizing thermodynamic parameters of binary systems is introduced. Unlike traditional algorithms that are commonly single-objective and need extensive manual testing, this algorithm constructs a multi-objective optimization for different types of experimental data. Then, by using weighted sum method, the multi-objective optimization problem is transformed into a single-objective optimization, which is solved by Barzilai-Borwein method. The key advantage of this algorithm is that no restrictions on the selection of initial values are needed. Finally, this algorithm is applied to optimize the thermodynamic parameters in the Ag-Pd and La-C systems. The experimental phase diagrams and thermodynamic properties in these two systems are satisfactorily reproduced by the present calculation.

关键词： CALPHAD Optimization methods algorithm Ag-Pd La-C

来源：评论

学校读者我要写书评

暂无评论

NONCONVEX BI-OBJECTIVE OPTIMIZATION MODEL WITH STABILITY GUARANTEES AND ITERATIVE algorithm FOR S-TYPE BIOCHEMICAL SYSTEMS

引用

JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第5期21卷 3441-3464页

作者： Yue, Meibo Xu, Gongxian Feng, Shuang Bohai Univ Sch Math Sci Jinzhou 121013 Peoples R China

In this paper, the steady-state optimization problem of S-type biochemical systems is investigated. A mathematical model of the S-type biochemical system is given. A nonconvex bi-objective optimization model with stability guarantees is constructed with the optimization objectives of maximizing the product yield and minimizing the sum of metabolite concentrations, which can ensure that the S-type biochemical system operates at an optimal and stable steady state. The nonconvex bi-objective optimization model is transformed into a nonconvex single-objective optimization problem based on the normal-boundary intersection (NBI) method. In order to efficiently solve the transformed nonconvex single-objective problem, a geometric programming algorithm is proposed based on the equivalence transformation and monomial compensation strategy, and the convergence analysis of the algorithm is given. An iterative solution algorithm for nonconvex bi-objective optimization model with stability guarantees is given based on this algorithm. The Pareto optimal solutions and Pareto optimal fronts of three different nonconvex bi-objective optimization models are obtained by computing three examples of S-type biochemical systems.

关键词： S-type biochemical system bi-objective optimization stability guar- antees nonconvex problem algorithm geometric programming

来源：评论

学校读者我要写书评

暂无评论

A hybrid approach for MRI brain image segmentation using KIFECM-IPSO algorithm

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Verma, Deepa Verma, Hanuman Tiwari, Pawan Kumar Inst Engn & Technol Comp Sci & Engn Dept Lucknow 226021 Uttar Pradesh India Bareilly Coll Dept Math Bareilly 243005 Uttar Pradesh India Dr APJ Abdul Kalam Tech Univ Fac Engn & Technol Lucknow 226031 Uttar Pradesh India

A hybrid approach for brain magnetic resonance imaging (MRI) image segmentation is proposed, that combines the intuitionistic particle swarm optimization (IPSO) algorithm and the kernel intuitionistic fuzzy entropy cmeans (KIFECM) to overcome the local minima trapping problem of KIFECM. In terms of intuitionistic fuzzification, intuitionistic PSO (IPSO) is a global optimization technique that investigates random search with candidate solution. The hybrid KIFECM-IPSO technique combines the best aspects of IPSO and KIFECM algorithms to improve clustering outcomes and prevent the local minima entrapment issue. The performance of the algorithm can be evaluated using various measures such as the similarity measure, average similarity measure, jaccard coefficient, false negative ratio and false positive ratio on the real brain data and simulated brain data. The results have been compared with KIFECM, PSO-KIFCM, FEC, PSO and FCM-PSO algorithms to determine the effectiveness of the proposed algorithm and the experimental results shows that the proposed KIFECM-IPSO perform better. Friedman's statistical test is also carried out to demonstrate that the performance of the proposed algorithm is not only better but the performance difference is statistically significant also.

关键词： Image segmentation Intuitionistic fuzzy sets Kernel intuitionistic fuzzy entropy c -means algorithm Kernel methods Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Analytical-experimental based inversion algorithm of stratum thermal properties for medium-depth coaxial borehole

引用

RENEWABLE ENERGY 2025年 240卷

作者： Dong, Shihao Yu, Yuelong Li, Bingxue Kosonen, Risto Ni, Long Harbin Inst Technol Sch Architecture & Design Key Lab Cold Reg Urban & Rural Human Settlement En Minist Ind & Informat Technol Harbin 150090 Peoples R China BMW Brilliance Automot Ltd Shenyang 110143 Peoples R China Aalto Univ Sch Engn FI-00076 Aalto Finland

Energy saving and decarbonization become consensus of the world. Medium-depth ground source heat pumps employing coaxial borehole heat exchangers have emerged as a pivotal choice for low-carbon heating. Heat transfer performance of coaxial borehole is vital for design, whose fundamental determinant is stratum thermal properties. However, direct measurement of these properties proves challenging for the complex stratum. Inversion methods, though avoiding this limitation, lack applicability to medium-depth boreholes. Therefore, a stratum thermophysical inversion algorithm was established based on an analytical model coupling the spatialtemporal dynamics of in-borehole fluid and stratum. Notable for its independence from initial values and the absence of parameter fitting, this algorithm offers a user-friendly solution. To validate the algorithm's accuracy and investigate heat extraction characteristics in Songliao Basin, an inaugural thermal response test at depth of 2500m was conducted in Shenyang, Liaoning. The maximum mean relative error is 9.4 % or the maximum absolute temperature error is +/- 0.94 degrees C. Correlation analysis suggests using the initial 0-13 h of data for volume heat capacity inversion and subsequent data for effective thermal conductivity inversion to enhance precision. Furthermore, a recommended minimum test duration of 80 h ensures robust results.

关键词： Coaxial borehole heat exchanger Thermal response test Thermal conductivity Stratum thermal properties algorithm Nomenclature E average relative error

来源：评论

学校读者我要写书评

暂无评论

Irradiance sensorless PSO-based Integral Backstepping and Immersion & invariance algorithm for robust MPPT control with real-climatic microcontroller-in-the-loop experimental validation

引用

COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Chen, Jian Harrison, Ambe Alombah, Njimboh Henry Mbasso, Wulfran Fendzi Molu, Reagan Jean Jacques Alharbi, Abdullah M. Jangir, Pradeep Fuzhou Polytech Sch Intelligent Engn Fuzhou 350108 Peoples R China Univ Buea Coll Technol COT Dept Elect & Elect Engn POB 63 Buea Cameroon Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India Univ Bamenda Coll Technol Dept Elect & Elect Engn Bambili POB 39 Bamenda North West Cameroon Univ Douala Technol & Appl Sci Lab UIT Douala POB 8689 Douala Cameroon Prince Sattam bin Abdulaziz Univ Dept Elect Engn Coll Engn Wadi Addawasir Wadi Addawasir 11991 Saudi Arabia Chandigarh Univ Gharuan Univ Ctr Res & Dev Mohali 140413 India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Graph Era Deemed be Univ Dept CSE Dehra Dun 248002 Uttarakhand India Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India

This paper presents a novel irradiance sensorless Maximum Power Point Tracking (MPPT) controller for photovoltaic (PV) systems using a Particle Swarm Optimization (PSO)-based Integral Backstepping (IBSC) and Immersion & Invariance (I&I) algorithm. The proposed controller addresses the limitations of traditional and contemporary MPPT methods, such as the need for costly irradiance sensors and suboptimal performance under dynamic environmental conditions. The integration of a higher-order sliding mode differentiator (HOSMD) with the IBSC enhances transient response by completely eliminating overshoots, achieving a 0 % overshoot compared to 4.8 % with the conventional IBSC under standard test conditions. The system exhibits rapid tracking convergence with a significantly reduced tracking time of 0.4 ms, approximately seven times faster than the traditional Perturb and Observe (P&O) algorithm's 3 ms. Under real-world conditions, the proposed system's irradiance estimator maintains a mean absolute error below 15 W/m(2), with a maximum error of 69 W/m(2) at high irradiance levels. The system achieves an operating efficiency of 99.99 % with peak-to-peak power ripples of just 0.17 % under standard conditions, outperforming eight state-of-the-art MPPT techniques. This robust and efficient MPPT solution is validated through extensive simulations and real-climatic conditions. Additionally, real-climatic experimental implementations are carried out using Microcontroller-in-the-loop (MIL) integration. The acquired experimental results do not only corroborate the simulation outcomes but also endorses the reliability and practical robustness of the proposed MPPT controller.

关键词： MPPT algorithm Particle swarm optimization (PSO) Integral-backstepping controller (IBSC) Higher-order sliding mode differentiator (HOSMD) Real experimental environmental conditions Real experimental environmental conditions Pv systems, immersion and invariance (I&I)

来源：评论

学校读者我要写书评

暂无评论

Interval-oriented eigensystem realization algorithm and its modification for structural modal parameter identification with bounded uncertainties

引用

JOURNAL OF SOUND AND VIBRATION 2025年 601卷

作者： Yang, Chen Xu, Xinhuan Wang, Xiaohan Fan, Ziyao Beihang Univ Sch Astronaut Beijing 100191 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Structural modal parameter identification is the initial step in modeling, monitoring and controlling dynamic systems, which can determine the accuracy of dynamics and control research. However, the uncertainty of dynamic systems is difficult to quantify, which will lead to deviations in structural modal parameter identification. Aiming to identify modal parameters under the influence of structural uncertainty parameters, this study proposed a novel interval-oriented eigensystem realization algorithm (ERA) and its modification with bounded uncertainties, which is particularly suitable for the case where structural uncertainty samples are scarce. The uncertain structures are quantified as interval uncertain parameters, which can reduce the need for quantification of uncertainty parameters without loss of accuracy. The first and second-order interval-oriented singular value decomposition (SVD) is developed, which is regarded as an important tool to solve the interval Hankel matrix. The conventional modal parameter identification method of ERA and ERA/DC are extended into the interval framework using first and second-order interval perturbation with a detailed derivation process, and the identified bounds of frequency and damping ratio can be accurately estimated using both interval-oriented ERA and ERA/DC in conjunction with first and second-order interval perturbation SVD. Finally, two numerical examples and one experimental verification are used to assess the proposed method.

关键词： Structural modal parameter identification Non-probabilistic theory Interval-oriented eigensystem realization algorithm Interval-oriented singular value decomposition (SVD) Interval Hankel matrix

来源：评论

学校读者我要写书评

暂无评论

A novel Hybrid PSO-Heuristic algorithm with Combinatorial Benders' Cuts for maximal evacuation planning in wildfire disasters

引用

APPLIED MATHEMATICAL MODELLING 2025年 145卷

作者： Bigdellou, Saeide Chen, Qian Beheshti, Saeed Univ British Columbia Fac Appl Sci Sch Engn Okanagan Campus Kelowna V1V BC Canada Toronto Metropolitan Univ Dept Mech & Ind Engn Toronto ON Canada

Natural disasters, such as floods and fires, affect various regions of the world every year. One of the most critical aspects of disaster management and planning is facilitating the evacuation of people. Therefore, this study develops a mathematical model for emergency evacuation, taking into account the constraints and limitations of transferring individuals to shelters. The model, called the maximal bus evacuation planning, considers constraints including road blockages, vehicle fuel, and passenger capacities. Given the Nondeterministic Polynomial-time hard complexity of this problem, a Hybrid Particle Swarm Optimization-Heuristic algorithm, which combines the Particle Swarm Optimization algorithm with a Heuristic algorithm, is used to solve the nonlinear model. Furthermore, we linearize the model and solve it using the developed Combinatorial Benders' Cuts approach in three versions. Numerical computations are examined under various scenarios, and the outputs of the Hybrid Particle Swarm Optimization - Heuristic algorithm reach suitable solutions within a short timeframe. Additionally, the results from solving the linearized model using the Combinatorial Benders' Cuts show that, in most cases, the execution time of the second version is better than the other versions.

关键词： Bus evacuation planning Combinatorial Benders' Cuts Particle Swarm Optimization - Heuristic algorithm Vehicle Routing Problem Multiple time windows

来源：评论

学校读者我要写书评

暂无评论

A new feature selection algorithm combining genetic algorithm, exponential decay function, and machine learning to realize hyperspectral estimation of winter wheat leaf area index

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 230卷

作者： Yang, Chenbo Bai, Juan Sun, Hui Bi, Rutian Song, Lifang Muhammad, Amjad Wang, Chao Zhao, Yu Yang, Wude Xiao, Lujie Zhang, Meijun Song, Xiaoyan Feng, Meichen Shanxi Agr Univ Coll Agr Taigu 030801 Shanxi Peoples R China Shanxi Agr Univ Coll Resources & Environm Taigu 030801 Shanxi Peoples R China Yuncheng Univ Yuncheng 044000 Shanxi Peoples R China

The leaf area index (LAI) is an important parameter that can reflect the growth status of the winter wheat population. Using hyperspectral technology for rapid and non-destructive estimation of LAI can provide some reference for adjusting field management measures to promote healthy growth of winter wheat. Screening hyperspectral information is an important direction in hyperspectral research. Genetic algorithm (GA) is a commonly used variable selection method. However, traditional GA cannot effectively control the results of variable screening. Meanwhile, it is expected to further improve the variable screening effect by using more machine learning algorithms as fitness evaluation functions. In this regard, this study combined the exponential decay function in competitive adaptive reweighted sampling algorithm (CARS) and the GA, and combined multiple machine learning algorithms as fitness evaluation functions to propose the continuous reweighted decay genetic algorithm (CRDGA), to better screen the important bands of winter wheat LAI. Finally, hyperspectral estimation models for winter wheat LAI were constructed using multiple machine learning algorithms. The main results were as follows: Compared to the number of bands screened by CARS and GA, the number of bands screened by CRDGA was fewer, with 44, 32, 24, 10, 7, 19, and 197 bands, respectively. Using partial least squares regression, support vector regression (SVR), K-nearest neighbor regression (KNNR), and gaussian process regression (GPR) as fitness evaluation functions all had a faster convergence rate, other fitness evaluation functions all had a slower convergence rate. However, only SVR, KNNR, and GPR as fitness evaluation functions had fast running rates in both GA and CRDGA. When using the same fitness evaluation function to run GA and CRDGA to achieve the optimal fitness value, the fitness value obtained by the CRDGA algorithm was lower than that obtained by the GA algorithm. Among all the constructed

关键词： Hyperspectral Leaf area index Competitive adaptive reweighted sampling Genetic algorithm Continuous reweighted decay genetic algorithm Machine learning

来源：评论

学校读者我要写书评

暂无评论

An improved version of firebug swarm optimization algorithm for optimizing Alex/ELM network kidney stone detection

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 99卷

作者： Ding, Huan Huang, Qirui Razmjooy, Navid Nanyang Inst Technol Sch Informat Engn Nanyang 473004 He Nan Sheng Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran SIMATS Saveetha Sch Engn Dept Comp Sci & Engn Div Res & Innovat Chennai 602105 Tamil Nadu India Islamic Univ Coll Tech Engn Najaf Iraq

The use of CT scan to diagnose kidney stones is among the most accurate ways to confirm the presence of kidney stones in patients. The scan takes photographs inside the body using a computer and an X-ray. The present study proposes a new automatic methodology using an integrated Alexnet and ELM (Extreme Learning Machine) network to deliver more useful outcomes of detection for kidney stone. Afterward, the network is optimized on the basis of a newly improved version of firebug swarm optimization algorithm. The designed network is applied to the "CT Kidney Dataset", and its outcomes are then verified by some different advanced procedures. The final results indicated that the proposed approach has better performance than the other methods.

关键词： Kidney stone Diagnosis Alexnet Extreme learning machine Improved firebug swarm optimization algorithm

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：