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Research on Grounding Grid Corrosion Diagnosis Based on Genetic k-means algorithm

ENERGY SCIENCE & ENGINEERING 2025年第6期13卷 3074-3087页

作者： Huang, Longsheng Xiao, Xianghui Huang, Mingxian Zhang, Zhenshan Song, Yunhao Guan, Luchang Foshan Univ Sch Mechatron Engn & Automat Foshan Peoples R China Foshan Univ Guangdong Prov Key Lab Ind Intelligent Inspect Tec Foshan Peoples R China

Grounding grid corrosion is one of the main reasons that affect the stable operation of electrical equipment in substations and endanger personal safety. After many years of operation, the grounding conductors will be eroded by soil. It may even cause major accidents and economic losses. Therefore, it is of great significance to diagnose the corrosion faults of the grounding grid and find out the corroded conductors. In this paper, the genetic k-means algorithm (GkA) is proposed to solve the mathematical model and judge the corrosion of grounding conductors. This algorithm combines GA's global searching ability and k-means's local searching ability, which improves the diagnosis result. In the simulation experiment, compared with the single GA's diagnosis, the diagnosis results of GkA were improved, and the number of misdiagnosed branches decreased by 66.7%. The simulation results show that the proposed algorithm takes less time to run, can eliminate the misdiagnosed branches commendably, and improve the accuracy of diagnosis. The proposed method provides a new idea to evaluate the corrosion degree of the grounding grid. The clustering algorithm is used to classify branches with similar corrosion degrees to achieve the purpose of corrosion diagnosis.

关键词： corrosion diagnosis electrical network theory genetic algorithm grounding grid k-means clustering algorithm

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A Low-Voltage Distribution Network Line Loss Calculation Method Based on Enhanced k-means clustering and BP Neural Network 3

A Low-Voltage Distribution Network Line Loss Calculation Met...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Cong, Rili Man, Xiangyang Wang, Xiaoyan Fan, Jiaqi Liu, Jianfeng Economic and Technological Research Institute State Grid Inner Mongolia Eastern Electric Power Co. Ltd. Hohhot010020 China

ISBN: (纸本)9798350382426

As the energy revolution progresses and efforts to achieve the 'double carbon' target continue, the calculation of distribution network line losses as an important technical indicator will effectively reflect the economic stability and operational capacity of the network. This paper presents an effective approach to computing the wire loss in photovoltaic integrated low-voltage electrical substation systems, addressing issues such as precision and pace of computation. First, the wire loss indicators presented in this article are extracted using hierarchical analysis, and the enhanced k-means clustering method is then used to classify the wire loss data in accordance with the resulting metrics. Subsequently, a BP neural network optimized by LM is applied for the classification and training of the model data, and the correlation is fitted between the wire loss rate and exponent. Simulation calculations using sample data from a PV-integrated low-voltage distribution network in a region are undertaken to prove the feasibility of the proposal. © 2023 IEEE.

关键词： BP network k-means clustering algorithm low-voltage region power line loss

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Bus travel feature inference with small samples based on multi-clustering topic model over Internet of Things

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 163卷

作者： Liu, Hongjie Shi, Haotian Yuan, Tengfei Fu, Sicheng Ran, Bin Xi'an Jiaotong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Univ Wisconsin Madison Civil & Environm Engn Madison WI 53706 USA Shanghai Univ SILC Business Sch Shanghai 201800 Peoples R China

With the widespread application of Internet of Things (IoT) technology, there has been a shift from a broad-brush to a more refined approach in traffic optimization. An increasing amount of IoT data is being utilized in trajectory mining and inference, offering more precise characteristic information for optimizing public transportation. Services that optimize public transit based on inferred travel characteristics can enhance the appeal of public transport, increase its likelihood as a travel choice, alleviate traffic congestion, and reduce carbon emissions. However, the inherent complexities of disorganized and unstructured public transportation data pose significant challenges to extracting travel features. This study explores the enhancement of bus travel by integrating advanced technologies like positioning systems, IoT, and AI to infer features in public transportation data. It introduces the Mk-LDA (meanshift kmeans Latent Dirichlet Allocation), a novel thematic modeling technique for deducing characteristics of public transit travel using limited travel trajectory data. The model employs a segmented inference methodology, initially leveraging the Mean-shift clustering algorithm to create POI seeds, followed by the P-k-means algorithm for discerning patterns in user travel behavior and extracting travel modalities. Additionally, a P-LDA (POI-Latent Dirichlet Allocation) inference algorithm is proposed to examine the interplay between travel characteristics and behaviors, specifically targeting attributes significantly correlated with public transit usage, including age, occupation, gender, activity levels, cost, safety, and personality traits. Empirical validation highlights the efficacy of this thematic modeling-based inference technique in identifying and predicting travel characteristics and patterns, boasting enhanced interpretability and outperforming conventional benchmarks.

关键词： Internet of Things Point of interest k-means clustering algorithm Latent dirichlet allocation Feature inference Intelligent transportation

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Mapping global human thermal comfort: Spatiotemporal variability of UTCI regions under climate change

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SUSTAINABLE CITIES AND SOCIETY 2025年 119卷

作者： Zheng, Jing Du, Yaxing Zhu, Xian Dong, Wenjie Sun Yat Sen Univ Sch Atmospher Sci Key Lab Trop Atmosphere Ocean Syst Southern Marine Sci & Engn Guangdong Lab ZhuhaiMi Zhuhai 519082 Peoples R China

Human Thermal Comfort (HTC) is directly related to human health, quality of life, and sustainable urban planning. However, the global evaluation of outdoor thermal comfort via Universal Thermal Climate Index (UTCI) is still inadequate. This study systematically analyzed global HTC, via UTCI between 1951 and 2100 based on the Coupled Model Intercomparison Project Phase 6, covering historical trends and four Shared Socioeconomic Pathways (SSPs) future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). The following key findings were presented: (1) The global average UTCI exhibits a latitudinal gradient distribution and a clear, consistent increase over time. (2) Ten characteristic thermal regions were identified by combining Empirical Orthogonal Function (EOF) and kmeans clustering. (3) EOF analysis revealed significant global warming and complex regional contrasts. (4) Low-latitude thermal regions expand poleward in all scenarios, with SSP5-8.5 causing major thermal property shifts and region redistributions. These results underscore a significant impact of changes inhuman thermal comfort under climate change, offering recommendations for interventions and policy guidance to address the challenges of climate change.

关键词： Human thermal comfort (HTC) Universal Thermal Climate Index (UTCI) k-means clustering algorithm Empirical orthogonal function (EOF) analysis

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Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

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Computers, Materials & Continua 2024年第6期79卷 4659-4681页

作者： Xin Xiong Jing Zhang Sanli Yi Chunwu Wang Ruixiang Liu Jianfeng He Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China College of Physics and Electronic Engineering Hanshan Normal UniversityChaozhou521041China Department of Clinical Psychology Second People’s Hospital of YunnanKunming650021China

The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical *** methods such as Atomic Agglomerative Hierarchical clustering(AAHC),k-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature *** these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy k-means clustering *** innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG *** this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,*** findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task *** imagined movement,microstate A was significantly *** with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of ***,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual *** utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a *** results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EE

关键词： EEG microstate motor imagery k-means clustering algorithm gaus sian kernel function shannon entropy Lempel-Ziv complexity

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Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved clustering algorithm

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IEEE ACCESS 2024年 12卷 76634-76647页

作者： Zhang, Ning Zhu, Liang Heilongjiang Univ Technol Sch Elect & Informat Engn Jixi 158100 Peoples R China

With the application and development of new technologies such as the Internet of Things and big data in the energy field, smart grids have gradually become the main trend for the future development of the power system. However, frequent communication between various participants in the smart grid can lead to higher privacy leakage risks, and malicious attackers can cause economic losses and security accidents to the smart grid by injecting abnormal power data. To address the issues of data privacy protection and anomaly detection in smart grids, this study proposed a matrix completion based smart grid data privacy protection scheme and an unsupervised learning based smart grid anomaly data detection scheme. A matrix completion based smart grid data privacy protection scheme was proposed to address the issue of data privacy protection between different trust domains in the smart grid. This scheme repaired missing elements in the relevant matrix through matrix completion, and increased random perturbations by adding noise with the same statistical characteristics as the original data to achieve data privacy protection. To address the issue of missing power data labels and inaccurate principal component analysis caused by abnormal data, an improved k-means clustering algorithm was used to remove outliers and optimize the results, to achieve accurate detection of abnormal data. The experimental results showed that the proposed algorithm has an accuracy of 0.9, an F1 value of 0.7, and a Bayesian detection rate of 0.61, all of which are higher than other algorithms. The AUC value of the proposed method was 0.79, indicating that the scheme established in this chapter has certain advantages in detecting abnormal electricity consumption.

关键词： Smart grids Electricity Data privacy Protection clustering algorithms Security Quantum computing Internet of Things Matrix completion k-means clustering algorithm smart grid data privacy monitor

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kM-Mask RCNN: A Lightweight Instance Segmentation algorithm for Strawberries With Multiple Growth Cycles

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IEEE ACCESS 2024年 12卷 169291-169309页

作者： Cong, Peichao Xu, Yutao Li, Tianheng Li, Shanda Feng, Hao Zhang, Xin Guangxi Univ Sci & Technol Sch Mech & Automot Engn Liuzhou 545006 Peoples R China

Accurate and efficient detection of multi-growth-cycle strawberry fruits can improve automated harvesting. However, the small size and unbalanced distribution of strawberry fruits make accurate identification of multi-growth-cycle strawberries difficult using the existing detection models. Herein, local enhancement technology is adopted for preprocessing when compiling the dataset to ensure the balance of the number of samples in each category to solve the above problems. Second, a new instance segmentation algorithm called kM-Mask RCNN is developed, which optimally adjusts the size of the anchor frame and the anchor ratio based on the k-means clustering algorithm to improve the recognition accuracy of the algorithm on small targets and uses MobileNet V3 to replace the Resnet50 structure in the Mask RCNN backbone network to reduce the complexity of the algorithm and realize lightweight operation. Finally, the experimental results reveal five strawberry growth stages in the homemade dataset(based on StrawDI_Db1 database): 'Green ripe stage', 'White ripe stage', 'Turning stage', 'Mature', and 'Deformed', for which the kM-Mask RCNN yields mAPs of 91.19%, 88.09%, 93.70%, 93.19%, and 87.13%, respectively. The P, R, and F1-score values of this algorithm are 93.9%, 94.2%, and 94.05%, respectively. Additionally, the number of parameters, FLOPs, and fps of this algorithm are 27M, 12G, and 22.32, respectively, satisfying the real-time requirements for strawberry detection. The findings provide important theoretical support for the automated harvesting of strawberries with multiple growth cycles.

关键词： Feature extraction Accuracy Residual neural networks Robots clustering algorithms Classification algorithms Transformers Prediction algorithms Instance segmentation Manuals Convolutional neural networks k-means clustering algorithm lightweighting mask RCNN strawberry

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Toward ecological environmental risk for spoil ground group management in mega projects

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第9期31卷 3706-3726页

作者： Li, Yulong Yao, Ziwen Wu, Jing Zeng, Saixing Wu, Guobin Cent Univ Finance & Econ Sch Management Sci & Engn Beijing Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai Peoples R China

PurposeThe numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental ***/methodology/approachBased on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and k-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed *** results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive ***/valueThis paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

关键词： Spoil grounds Ecological environment Risk assessment Categorization Projection pursuit clustering method k-means clustering algorithm

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kBMP: kubernetes-Orchestrated IoT Online Battery Monitoring Platform

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IEEE INTERNET OF THINGS JOURNAL 2024年第14期11卷 25358-25370页

作者： Chen, Qingyang He, Yinghui Yu, Guanding Xu, Chong Liu, Mingyang Li, Zhenming Zhejiang Univ Coll Informat & Elect Engn Hangzhou 310027 Zhejiang Peoples R China China Elect Power Res Inst Co Ltd Energy Storage & Novel Technol Elect Engn Dept Beijing 100192 Peoples R China

The rise in renewable energy has driven the widespread use of large-scale energy storage batteries, which makes the risk of overheating more threatening. To ensure battery safety, it is essential to build a monitoring system with a comprehensive evaluation of large quantities of batteries. However, existing battery management systems exhibit significant limitations in terms of monitoring scope, analytical precision, and transmission efficiency. As an applicable solution, cloud-edge technology is an advanced integrated method that provides low-latency data access, accurate analysis capabilities, and adjustable monitoring ranges. In this work, the kubernetes-orchestrated battery monitoring platform (kBMP), which integrates kubernetes and cloud-edge technology, is proposed to provide comprehensive battery management. Specifically, kubernetes is used to ensure low latency in data transmission and analysis, while the k-means clustering algorithm is applied to provide accurate thermal runaway (TR) warnings. To validate the performance of kBMP, four sets of real battery TR data are fed to test its accuracy and latency. The experimental findings reveal that kBMP is capable of providing battery TR warnings in advance within 30 min. Additionally, the platform concurrently decreases data transmission latency by up to 20% and reduces replica scaling latency by 50% compared to the platform without integrating kubernetes.

关键词： Batteries Monitoring Cloud computing Data visualization Servers Data analysis Data communication Battery monitoring platform cloud-edge technology k-means clustering algorithm kubernetes thermal runaway (TR) prediction

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A Molecular Communication Perspective on Synchronization of Coupled Microfluidic-Spectroscopy

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2024年第3期23卷 458-471页

作者： Qian, Xuewen Angerbauer, Stefan Egan, Malcolm Di Renzo, Marco Haselmayr, Werner Univ Paris Saclay Lab Signauxet Syst CNRS Cent Supelec F-91192 Gif Sur Yvette France Johannes Kepler Univ Linz Inst Commun Engn & RF Syst A-4040 Linz Austria INSA Lyon UR3720 CITI Inria F-69621 Villeurbanne France

A challenge for real-time monitoring of biochemical processes, such as cells, is detection of biologically relevant molecules. This is due to the fact that spectroscopy methods for detection may perturb the cellular environment. One approach to overcome this problem is coupled microfluidic-spectroscopy, where a microfluidic output channel is introduced in order to observe biologically relevant molecules. This approach allows for non-passive spectroscopy methods, such as mass spectrometry, to identify the structure of molecules released by the cell. Due to the non-negligible length of the microfluidic channel, when a sequence of stimuli are applied to a cell it is not straightforward to determine which spectroscopy samples correspond to a given stimulus. In this paper, we propose a solution to this problem by taking a molecular communication (MC) perspective on the coupled microfluidic-spectroscopy system. In particular, assignment of samples to a stimulus is viewed as a synchronization problem. We develop two new algorithms for synchronization in this context and carry out a detailed theoretical and numerical study of their performance. Our results show improvements over maximum-likelihood synchronization algorithms in terms of detection performance when there are uncertainties in the composition of the microfluidic channel.

关键词： Molecular communication synchronization Skellam distribution k-means clustering algorithm

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