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kBMP: kubernetes-Orchestrated IoT Online Battery Monitoring Platform

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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Flexible DC distribution line protection principle based on distance metric of clustering centres

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第18期16卷 3661-3672页

作者： Dai, Zhihui Liu, Shanshan Chen, Siqi Zhao, Jian Wu, Tong North China Elect Power Univ Sch Elect Engn Baoding Hebei Peoples R China

Aiming at the issue of unsatisfactory selectivity of single-terminal protection, and poor speed and high requirements for data synchronization of double-terminal protection in flexible DC distribution networks, an artificial intelligence (AI)-based method is proposed to obtain the best clustering centres through unsupervised clustering analysis of historical data to realize the non-deterministic flexible DC distribution line protection principle. The method forms historical data samples by simulating different types of short-circuit faults at different locations of the line in advance, combining them with the actual faults, and collecting and processing the post-fault currents. The k-means clustering algorithm is then used to find the best clustering centres corresponding to different fault types, and fault identification and pole selection are realized by comparing the distance between real-time data and each clustering centre. The process relies merely on single-terminal current as the characteristic quantity, and it does not need complicated feature extraction and calculation. Thus, the cumbersome threshold setting in conventional current protections can be avoided. Finally, the case studies are carried out in PSCAD/EMTDC, and the results show that the proposed protection has good selectivity and rapidity, and the tolerance to fault resistance is improved compared with the conventional local-current-based protections.

关键词： short-circuit faults fault identification fault location EMTP fault currents pole selection pattern clustering fault diagnosis artificial intelligence-based method single-terminal protection k-means clustering algorithm power distribution protection fault resistance DC distribution networks power distribution faults data synchronization PSCAD-EMTDC simulation power system CAD cumbersome threshold setting DC distribution line protection principle power distribution lines unsupervised clustering analysis local-current-based protection

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GPR Diffraction Separation by Incorporating Multilevel Wavelet Transform and Multiple Singular Spectrum Analysis

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APPLIED SCIENCES-BASEL 2025年第6期15卷 3204-3204页

作者： Wang, Haolin Wang, Honghua Hou, Zhiyang Zhou, Fei Guilin Univ Technol Coll Earth Sci Guilin 541004 Peoples R China

By leveraging amplitude differences between reflected and diffracted signals in Ground Penetrating Radar (GPR) data, multiple singular spectrum analysis (MSSA) is considered an attractive approach to separate diffraction, which has identified great potential in their detectability of small-scale geological structures. However, conventional MSSA encounters difficulties in pinpointing the singular value threshold that corresponds to reflection, diffraction, and noise within the singular spectrum, leading to a resolution loss of the extracted diffraction profile. To address this issue, this paper develops a new technique that incorporates multilevel wavelet transform (MWT) and MSSA to separate GPR diffraction. By first implementing the MWT on GPR data decompose, the strategy can obtain various approximate detailed coefficients of multiple transformation levels for the subsequent inverse MWT to construct the corresponding coefficient profile. The issue of coefficient profiles that depict reflections often contains residual diffractions is also addressed by performing multiple singular spectrum SVDs based on the Hankel matrix within the dominant frequency domain. Building upon this, the k-means clustering algorithm is introduced to perform MSSA for classifying singular values into k categories. The diffraction wavefield is rebuilt by combining these outcomes with the coefficient profiles that depict diffractions at various transformation levels. Numerical tests showcase that the biorthogonal wavelet basis function bior4.4 provides remarkably efficient GPR diffraction separation performance, and the number of clusters in the k-means clustering algorithm typically ranges from 9 to 15, accounting for the complexity of the wave components. Compared to plane wave deconstruction (PWD), the proposed MWT-MSSA approach reduces energy loss at the diffraction vertex, decreases residual diffraction energy within the reflection profile, and enhances computational efficiency by approx

关键词： ground penetrating radar (GPR) multilevel wavelet transform (MWT) multiple singular spectrum analysis (MSSA) k-means clustering algorithm diffraction separation

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Improved landslide susceptibility assessment: A new negative sample collection strategy and a comparative analysis of zoning methods

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ECOLOGICAL INDICATORS 2024年 169卷

作者： Wang, Jiani Wang, Yunqi Li, Manyi Qi, Zihan Li, Cheng Qi, Haimei Zhang, Xiaoming Beijing Forestry Univ Sch Soil & Water Conservat Beijing 100083 Peoples R China Three Gorges Reservoir Area Chongqing Forest Ecosy Chongqing 400700 Peoples R China Chongqing Inst Geol & Mineral Resources Observat & Res Stn Ecol Restorat Chongqing Typ Min Minist Nat Resources Chongqing 401120 Peoples R China China Inst Water Resources & Hydropower Res Beijing 100048 Peoples R China

Landslide susceptibility assessment (LSA) aims to determine the spatial probability of landslides, reducing the loss caused by future landslides. In order to assess the impact of various negative sample collection strategies on the prediction accuracy of the landslide susceptibility assessment (LSA) model, and to investigate the effectiveness of landslide susceptibility zoning methods. Taking Fengjie County, Chongqing City, China as the study area, this study proposes three negative sample collection strategies based on slope unit, buffer zone, and information value, and combines them with C5.0 decision tree (DT) model respectively to construct an LSA model. Concurrently, the landslide susceptibility indexes (LSIs) were divided using the k-means clustering algorithm and contrasted with the natural breakpoint classification (NBC), quantile classification (QC), equal interval classification (EIC), and geometric interval classification (GIC) methods. The results show that: (1) Rainfall, elevation, and water system are the primary conditioning factors of landslide development in the study area. (2) The accuracy of the negative sample collection strategy based on the slope units on the model training subset and the test subset reached 97.78 % and 92.99 %, respectively, and the AUC values were 0.978 and 0.930, indicating high model accuracy. (3) The zoning effect based on the k-means clustering algorithm was the best, and the predicted very-high and high susceptibility areas were 805.73 km2 and 567.66 km2, respectively, accounting for 19.59 % and 13.80 % of Fengjie County. The very-high and high susceptibility areas had maximum FR values of 3.963 and 1.432, respectively, when compared to other zoning methods. This study can provide a more objective and scientific method for LSA, and the findings can offer more precise decision assistance for risk management and geological disaster prevention.

关键词： Landslide susceptibility assessment Negative sample collection strategy C5.0 decision tree k-means clustering algorithm Landslide susceptibility zoning

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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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Battery Swapping Dispatch for Self-Sustained Highway Energy System Based on Spatiotemporal Deep-Learning Traffic Flow Prediction

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第1期60卷 1058-1070页

作者： Sun, Yuxin Li, Yiqun Borozan, Stefan Wang, Guibin Qiu, Jing Strbac, Goran Univ Liverpool Dept Elect & Elect Engn Liverpool L69 7ZX England Shenzhen Urban Transport Planning Ctr Shenzhen 518060 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Due to the complexity of traffic flow and the stochastic swapping behavior of electric vehicles (EVs), efficient battery dispatch is challenging. Therefore, the battery swapping dispatch framework based on traffic flow prediction is proposed to overcome this inconvenience. The framework is solved by minimizing the total transportation cost and satisfying the EV battery swapping requirement. Naturally, precise traffic flow prediction plays a vital role in efficient battery dispatch. Therefore, this article designs a deep learning prediction framework by leveraging the graph convolutional network (GCN) and the temporal convolutional network (TCN), named Spatiotemporal traffic flow network (STFNet). GCN is applied to learn the topology characteristic of the daily spatiotemporal traffic, which enables STFNet to capture the spatial feature. TCN is developed to acquire the daily traffic flow temporal dependence. Additionally, a pre-partition method based on k-means clustering is applied to improve the effectiveness of the battery dispatch framework. The experimental results indicate that the proposed battery dispatch framework is skillful. Due to the precise prediction of STFNet, the battery swapping dispatch based on STFNet prediction is the most economical, achieving a minimum of 25.82% reduction in the total transportation cost compared to benchmark models. Furthermore, the impact of the pre-partition method has been proven in the case of studies with a huge routing distance declining, dramatically reducing the total transportation cost and making the dispatch more reasonable.

关键词： Batteries Costs Road transportation Transportation Spatiotemporal phenomena Predictive models Biological system modeling Battery swapping daily traffic flow prediction deep learning k-means clustering algorithm vehicle routing problem

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The comprehensive evaluation of innovative education quality from the perspective of balanced and stable development

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INTERNATIONAL JOURNAL OF INNOVATION AND SUSTAINABLE DEVELOPMENT 2025年第1期19卷 43-57页

作者： Feng, Xiaoping Hunan City Univ Coll Humanities Yiyang 413000 Hunan Peoples R China

In order to overcome the problems of long data collection time, high error rate of index weight calculation and low accuracy of traditional evaluation methods, a comprehensive evaluation method of innovative education quality from the perspective of balanced and stable development is proposed. Firstly, the comprehensive evaluation index of innovative education quality under the background of balanced and stable development is preliminarily determined. The k-means clustering algorithm is used to collect education data, and the data are fused and reduced. Then, the comprehensive evaluation system of innovative education quality is established. Finally, the weight of evaluation index is calculated by AHP, and the comprehensive evaluation of education quality is realised by three-level fuzzy comprehensive evaluation model. The experimental results show that the average data acquisition time of this method is 0.63 s, the maximum error rate of evaluation index weight calculation is 2%, and the evaluation accuracy is above 94%.

关键词： balanced and stable development innovative education comprehensive quality evaluation k-means clustering algorithm reduction processing three-level fuzzy comprehensive evaluation

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Research on Urban Storm Flood Simulation by Coupling k-means Machine Learning algorithm and GIS Spatial Analysis Technology into SWMM Model

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WATER RESOURCES MANAGEMENT 2024年第6期38卷 2059-2078页

作者： Liu, Chengshuai Hu, Caihong Zhao, Chenchen Sun, Yue Xie, Tianning Wang, Huiliang Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China

Accurate flood simulation has significant practical implications for urban flood management. The focus of this study is to develop a new flood model (k-SWMMG) based on the Storm Water Management Model (SWMM), which innovatively couples the k-means clustering machine learning algorithm and GIS spatial analysis techniques. The k-means clustering machine learning algorithm is used to determine the uncertain parameters of the SWMM model, while GIS spatial analysis techniques enhance the two-dimensional realism of flood simulation. We applied the k-SWMMG model to six historical observed flood events in a specific catchment area in Zhengzhou City, using rainfall and flow data. The study shows that: 1) k-SWMMG optimizes the sub-basin division method of urban stormwater models, avoiding the tedious and complex parameter calibration process, and improving modeling efficiency to some extent. 2) The two-dimensional visualization of inundation provided by GIS spatial analysis techniques better meets the production requirements of current urban flood simulation. 3) k-SWMMG outperforms SWMM in terms of simulation performance, with improvements in absolute error (AE), relative error (RE), Nash-Sutcliffe efficiency coefficient (NSE), and coefficient of determination (R2) by 0.019m, 5.36%, 0.068, and 0.042, respectively. The findings can provide scientific decision-making references for urban flood forecasting and early warning.

关键词： Urban flood forecast k-SWMMG Uncertainty parameters City functional area k-means clustering algorithm GIS

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Cluster Dynamic Partitioning Strategy Based on Distributed Photovoltaic Output Prediction and Improved clustering algorithm

Cluster Dynamic Partitioning Strategy Based on Distributed P...

引用

IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Chen, Shengjuan Tan, Xiaolin Hu, Ming Chen, Jian Zhong, Jiayong State Grid Chongqing Fengdu Elect Power Supply Br Power Dispatch Control Ctr Chongqing Peoples R China State Grid Chongqing Elect Power Co Elect Power Res Inst Chongqing Peoples R China

ISBN: (纸本)9798350339345

Aiming at the problems of distributed photovoltaic power stations, such as wide distribution and difficult scheduling, a cluster dynamic partitioning strategy based on distributed photovoltaic output prediction and improved clustering algorithm is proposed. Firstly, the output data of photovoltaic power station is analyzed for correlation, and new sample data is constructed and sent to the deep recurrent neural network for prediction, so as to obtain reliable output prediction results. Then, the grey wolf optimization algorithm is used to improve the k-means clustering algorithm, which is used to analyze the data set containing the output value and environmental parameters of photovoltaic power plants, so as to obtain the distributed photovoltaic power plant cluster with the best dynamic supply and demand balance. Finally, based on the IEEE 33 node system, the proposed strategy is tested and analyzed. The experimental results show that the modularity and dynamic supply and demand balance values of its cluster division are 0.783 and 0.819 respectively, and the photovoltaic output prediction effect is ideal.

关键词： Distributed photovoltaic power station output prediction deep recurrent neural network k-means clustering algorithm grey wolf optimization algorithm cluster division dynamic supply and demand balance of cluster

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Predicting stability of integrated circuit test equipment using upper side boundary values of normal distribution

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The Journal of China Universities of Posts and Telecommunications 2024年第2期31卷 85-93页

作者： Zhan Wenfa Hu Xinyi Zheng Jiangyun Yu Chuxian Cai Xueyuan Zhang Lihua School of Electronic Engineering and Intelligent Manufacturing Anqing Normal UniversityAnqing 246133China School of Computer and Information Technology Anqing Normal UniversityAnqing 246133China

In response to the growing complexity and performance of integrated circuit(IC),there is an urgent need to enhance the testing and stability of IC test equipment.A method was proposed to predict equipment stability using the upper side boundary value of normal ***,the k-means clustering algorithm classifies and analyzes sample *** accuracy of this boundary value is compared under two common confidence levels to select the optimal threshold.A range is then defined to categorize unqualified test *** experimental verification,the method achieves the purpose of measuring the stability of qualitative IC equipment through a deterministic threshold value and judging the stability of the equipment by comparing the number of unqualified data with the threshold value,which realizes the goal of long-term operation monitoring and stability analysis of IC test equipment.

关键词： k-means clustering algorithm the upper side boundary of normal distribution threshold integrated circuit(IC)test equipment stability analysis

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