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Iet International Conference on Wireless Sensor Network 2010(2010年物联网应用技术国际会议 IET-WSN 2010)

作者： Ronglin Hu Xiangping Gu Ying Jin Jingjie Yan School of Computer Engineering Huaiyin Institute of Technology Huai'an Jiangsu Province China School of Information Science and Engineering Southeast University Nanjing Jiangsu Province China

ISBN: (纸本)9781617825057

Coverage in wireless sensor networks (WSNs) is treated as a key technology for many monitoring applications.A novel sensor node's model is presented based on the neural network because the exiting models are not feasible in the special coal mine *** addition, we provide an improved intelligent algorithm GNG-Like(Growing Neural GasLike), taking into consideration application-special requirements and energy-conservation characteristics, which is applied into the coal mine to study the dynamic coverage in the inspection *** GNGLike, threshold is set and corresponding parameters are modified to achieve fast response and coverage for the changing environment in coal *** simulate GNG-Like algorithm in the Java platform and compare it to GNG intelligent *** results show that, compared with the exiting GNG, GNG-Like has a faster reaction rate, achieves optimize dynamic coverage and prolongs the network lifetime.

关键词： wireless sensor networks coverage GNG-like intelligent algorithm coal mine

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Quality of Services Multicast Routing by Using intelligent algorithm

Quality of Services Multicast Routing by Using Intelligent A...

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2019 International Conference on Applied Mathematics, Modeling, Simulation and Optimization(AMMSO 2019)

作者： Shu-qing LIN School of Information Engineering Guangzhou College of Commerce

This paper introduces quality of services multicast routing by using intelligent algorithm. Its main objective is to construct a multicast tree that optimizes a multi objective function with respect to performance-related constraints(Short path, delay, packet loss). The proposed algorithm can be divided into two steps: firstly, the multicast tree selection algorithm is based on shortest path;secondly, to find routes with two or more QoS parameters.

关键词： intelligent algorithm Multicast routing QoS

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An intelligent algorithm with interactive learning mechanism for high-dimensional optimization problem based on improved animal migration optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第12期34卷 e5774-e5774页

作者： Lai Zhaolin Hu Xiaochun Jiang Caoqing Guangxi Univ Finance & Econ Dept Comp Sci Nanning 530003 Peoples R China

Recently, a new intelligent algorithm called animal migration optimization (AMO) is proposed. The AMO algorithm, which is inspired by the behavior of animal migration, exhibits good performance on a series of benchmark functions whose dimensionality is less than or equal to 30. However, its performance deteriorates evidently when the dimensionality is more than 30. To overcome this drawback, we proposed an improved AMO (IAMO) with interactive learning mechanism in this article. First, an interactive learning mechanism is designed to improve the intelligence of AMO. During the the search process, individuals learn from each other by the exchange of information, and the search mode is adjusted dynamically. Second, a refined search method is adopted to enhance the search ability of AMO. Individuals will search around the current solutions in order to improve the quality of solutions. Third, a birth-and-death strategy is introduced to avoid local optimum. In addition, the property convergence of IAMO is analyzed theoretically. The proposed algorithm is validated on several standard benchmark functions whose dimensionality is 50, 100, and 150, respectively. The empirical results demonstrate that the performance of our proposed algorithm is promising.

关键词： high dimension intelligent algorithm interactive learning migration optimization

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Multi Constrained Optimization model of Supply Chain Based on intelligent algorithm

Multi Constrained Optimization model of Supply Chain Based o...

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2016 5th International Conference on Advanced Materials and Computer Science(ICAMCS 2016)

作者： Han Juan School of Management Shanghai University

In order to research the quality coordination mechanism of logistics service supply chain in condition of bounded rationality,based on research method of logistics supply chain optimization model of evolutionary game theory of intelligent algorithm,evolutionary game model for quality coordination of logistics service supply chain is built and also *** research results indicate:the factors affecting quality coordination of logistics service supply chain are coordination cost,extraneous income,default cost,excess earning,complaint probability,reputation loss and loss sharing *** adjusting the values of these parameters,the quality coordination of logistics service supply chain can be effectively facilitated.

关键词： Supply Chain intelligent algorithm Logistics

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Advances in the Application of intelligent algorithms to the Optimization and Control of Hydrodynamic Noise: Improve Energy Efficiency and System Optimization

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APPLIED SCIENCES-BASEL 2025年第4期15卷 2084-2084页

作者： Xu, Maosen Fan, Bokai Lin, Renyong Lin, Rong Wu, Xian Zheng, Shuihua Gu, Yunqing Mou, Jiegang China Jiliang Univ Coll Metrol Measurement & Instrument Hangzhou 310018 Peoples R China Leo Grp Pump Zhejiang Co Ltd Wenling 317500 Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Peoples R China Zhejiang Univ Coll Energy Engn Hangzhou 310012 Peoples R China

Hydrodynamic noise is induced by hydrodynamic phenomena, such as pressure fluctuations, shear layers, and eddy currents, which have a significant impact on ship performance, pumping equipment efficiency, detection accuracy, and the living environment of marine organisms. Specifically, hydrodynamic noise increases fluid resistance around the hull, reduces speed and fuel efficiency, and affects the stealthiness of military vessels;whereas, in pumping equipment, noise generation is usually accompanied by energy loss and mechanical vibration, resulting in reduced efficiency and accelerated wear and tear of the equipment. Traditional physical experiments, theoretical modeling, and numerical simulation methods occupy a key position in hydrodynamic noise research, but each have their own limitations: physical experiments are limited by experimental conditions, which make it difficult to comprehensively reproduce the characteristics of the complex flow field;theoretical modeling appears to be simplified and idealized to cope with the multiscale noise mechanism;and numerical simulation methods, although accurate, are deficient in the sense that they are computationally expensive and difficult to adapt to complex boundary conditions. In recent years, intelligent algorithms represented by data-driven algorithms and heuristic algorithms have gradually emerged, showing great potential for development in hydrodynamic noise optimization applications. To this end, this paper systematically reviews progress in the application of intelligent algorithms in hydrodynamic noise research, focusing on their advantages in the optimal design of noise sources, noise prediction, and control strategy optimization. Meanwhile, this paper analyzes the problems of data scarcity, computational efficiency, and model interpretability faced in the current research, and looks forward to the possible improvements brought by hybrid methods, including physical information neural networks, in future researc

关键词： hydrodynamic noise numerical simulation intelligent algorithm energy efficiency optimization and control

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Failure mode and intelligent prediction method of thin-layered rock mass tunnel

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ENVIRONMENTAL EARTH SCIENCES 2025年第3期84卷 1-17页

作者： Niu, Wen-jing Li, Kun-guang Liu, Xu-feng Guangxi Univ Sch Resources Environm & Mat Nanning 530000 Peoples R China Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Peoples R China

Different modes of disasters will occur in the process of tunnel construction in thin-layered rock mass. The key to tunnel safety control is to analyze the main control factors affecting the failure mode of thin-layered rock mass tunnel and predict the potential failure mode. The failure characteristics of thin-layered rock mass were analyzed and classified based on 22 tunnel projects. In addition, the influence factors of different failure modes are quantitatively analyzed. Taking the influencing factors as the prediction index, combined with the actual case, the initial database is constructed. The Fisher-Freeman-Halton exact test method was used to calculate the main control factors of the failure mode. The initial database samples were pretreated by augmentation and CRITIC method weighting, and the failure mode prediction model of thin-layered rock mass tunnel was established by random forest. The evaluation metrics and validation index of the model is tested by practical engineering case, and the results are compared with RF, AdaBoost and SVM algorithms. The findings show that the failure modes of thin-layered rock mass tunnel can be divided into four types, including squeeze bending failure, buckling bending failure, along-layer slip failure, and falling block failure. The angle between the maximum stress and the rock strata and the degree of joint development are the main control factors affecting the failure mode. The accuracy, precision, recall and F1-Score of the CRITIC-RF are 81.8%, 81.7%, 80.2%, 0.809, and higher than AdaBoost and SVM. The Oob-Score of the CRITIC-RF is 0.824 and higher than RF. The reliability of the prediction results of the prediction model is ensured. The research results can provide the basis and reference for the failure mode prediction and active control of thin-layered rock mass tunnel. The research results can provide scientific basis for the prediction and precise control of engineering disasters in thin-layered rock masses.

关键词： Thin-layered rock mass Prediction model Failure mode intelligent algorithm Rock mechanics

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Application and Comparison of Three intelligent algorithms in 2D Otsu Segmentation algorithm 5

Application and Comparison of Three Intelligent Algorithms i...

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5th International Conference on Swarm Intelligence (ICSI)

作者： Cao, Lianlian Ding, Sheng Fu, Xiaowei Chen, Li Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9783319118970;9783319118963

2D Otsu thresholding algorithm has been proposed based on Otsu algorithm, it is more effective in image segmentation. However, the computational burden of finding optimal threshold vector is very large for 2D Otsu method. In this paper, three kinds of intelligent algorithm are applied to improve and compare the efficiency of search. Experimental results show that these methods can not only obtain the ideal segmentation results but also greatly reduce the launch time. Moreover, it is proved that the quantum particle swarm optimization (QPSO) algorithm has the highest efficiency.

关键词： 2D Otsu image segmentation intelligent algorithm QPSO

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Summary of the novel MPPT (maximum power point tracking) algorithm based on few intelligent algorithms specialized on tracking the GMPP (global maximum power point) for photovoltaic systems under partially shaded conditions 14

Summary of the novel MPPT (maximum power point tracking) alg...

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IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Ma, Youjie Zhou, Xuesong Gao, Zhiqiang Bai, Tianqi Tianjin Univ Technol 391 Binshui Xidao Tianjin 300384 Peoples R China

ISBN: (纸本)9781509067596

Energy efficient conversion is always a hot topic in the field of photovoltaic power generation. Contract to traditional algorithm, the lasted research showed intelligent algorithms have remarkable advantages in multiple-peak output power characteristics affected by partial shading. This article presents some popular intelligent algorithms such as particle swarm optimization(PSO), genetic algorithm(GA), ant colony optimization (ACO),etc. When PV systems are affected by partial shading, these GMPPT (global maximum power point tracking) algorithm is required to increase the energy harvesting capability of the system. By analyze and discuss the search efficiency and convergence property of several intelligent optimization methods mentioned above, multiple problem still exist and the GMPP can not usually be tracked exactly and quickly. Thus, this article point out it is great significance that several hybrid intelligent algorithms are put forward to compensate the deficiency of single algorithms, and modified the optimization is also required.

关键词： Photovoltaic generation systems (PGS) Maximum power point tracking (MPPT) intelligent algorithm Partially shaded conditions Global MPPT(GMPPT)

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Barabasi-albert model-enhanced genetic algorithm for optimizing LGBM in ship power grid fault diagnosis

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MEASUREMENT 2025年 249卷

作者： Huang, Kangzheng Li, Weibo Gao, Feng Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China Northwest Minzu Univ Coll Elect Engn Lanzhou 730124 Gansu Peoples R China

The reliability of the electrical systems of ships, as the main carriers of goods worldwide, is critical to maritime safety. Equipment malfunctions, if not detected and addressed promptly, it may lead to serious safety accidents. While various fault diagnosis methods exist, they often struggle with insufficient accuracy and weak noise immunity in handling ship multivariate time series data. This study aims to develop a more accurate fault diagnosis method for ship power grids. To achieve this objective, this study proposes a novel approach combining complex network, intelligent algorithm and machine learning (Barabasi-albert model-enhanced genetic algorithm for optimizing LGBM, BGL) for ship electrical grid fault diagnosis. The methodology consists of three main steps: feature extraction using wavelet transform (WT), feature subset selection through complex networks combined with intelligent algorithms and node importance algorithm, and finally, fault diagnosis implementation using a machine learning model. The experimental validation is carried out on a simulation dataset containing normal and multi-class fault state ship power grids. The results show that BGL can accurately identify faulty and normal states and distinguish different fault types. When compared with 14 benchmark algorithms and 3 LGBM-based improved algorithms, BGL improves the accuracy, precision, recall, and F1-Score by at least 0.72 %, 0.68 %, 0.90 %, and 0.90 %, respectively, in a noise-free environment, and by at least 1.86 %, 2.09 %, 2.08 %, and 2.06 % in a noisy environment. It is shown that BGL can effectively capture the characteristic patterns of different fault types with strong noise resistance and generalization ability, and provides a general framework for fault diagnosis of multivariate time-series data in ship power grids.

关键词： Ship power grid Fault diagnosis Machine learning Complex network intelligent algorithm

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Prediction of Dissolved Alcohol Concentrations in Transformer Oil and Aging Evaluation of Cellulose Insulation

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2025年第2期32卷 1238-1245页

作者： Zhang, Heng Zhang, Yuan Jiang, Zaijun Fan, Xianhao Li, Wen Liu, Chuying Zhang, Enze Wu, Thomas Liu, Jiefeng Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530004 Peoples R China State Grid Jining Power Supply Co Jining 272000 Shandong Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Alcohol (methanol and ethanol) markers in transformer oil have progressively played a crucial role in assessing the aging state of cellulose paper. There is a lack of reported studies on the prediction of alcohol concentration in transformer oil. Given this, this work presents an approach that employs an atomic orbit search optimized support vector machine (AOS-SVM) to predict the alcohol concentration. Initially, oil-paper insulation samples are prepared, and the alcohol concentrations are determined. Furthermore, methanol and ethanol concentrations in oil were predicted using AOS-SVM, and the MSE of the prediction results reached 0.0428 and 0.0064, respectively. Eventually, a quantitative model combining methanol, ethanol, and degree of polymerization (DP) was constructed to evaluate the aging of insulating paper, which verified the reliability of the proposed model. In this regard, the proposed method exhibits reliable predictive performance in terms of alcohol concentration in oil, thereby contributing to the effective evaluation of the aging state of insulating paper.

关键词： Aging Oils Electrons Methanol Ethanol Power transformer insulation Oil insulation Prediction algorithms Support vector machines Predictive models Aging evaluation concentration prediction intelligent algorithm oil-paper insulation power transformer

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