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A Comprehensive Approach for the clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles

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Engineering 2019年第4期5卷 795-802页

作者： Wei Li Siqi Chen Xiongbin Peng Mi Xiao Liang Gao Akhil Garg Nengsheng Bao State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Key Lab of Mechatronic Systems Intelligent Integration Technology Ministry of EducationShantou UniversityShantou 515063China

An energy-storage system comprised of lithium-ion battery modules is considered to be a core component of new energy vehicles,as it provides the main power source for the transmission system.However,manufacturing defects in battery modules lead to variations in performance among the cells used in series or parallel configuration.This variation results in incomplete charge and discharge of batteries and non-uniform temperature distribution,which further lead to reduction of cycle life and battery capacity over time.To solve this problem,this work uses experimental and numerical methods to conduct a comprehensive investigation on the clustering of battery cells with similar performance in order to produce a battery module with improved electrochemical performance.Experiments were first performed by dismantling battery modules for the measurement of performance parameters.The kmeans clustering and support vector clustering(SVC)algorithms were then employed to produce battery modules composed of 12 cells each.Experimental verification of the results obtained from the clustering analysis was performed by measuring the temperature rise in the cells over a certain period,while air cooling was provided.It was found that the SVC-clustered battery module in Category 3 exhibited the best performance,with a maximum observed temperature of 32℃.By contrast,the maximum observed temperatures of the other battery modules were higher,at 40℃for Category 1(manufacturer),36℃for Category 2(manufacturer),and 35℃for Category 4(k-means-clustered battery module).

关键词： clustering algorithm Battery module Equalization Electric vehicle

RSS-Based Selective clustering Technique Using Master Node for WSN

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Computers, Materials & Continua 2021年第12期69卷 3917-3930页

作者： Vikram Rajpoot Vivek Tiwari Akash Saxena Prashant Chaturvedi Dharmendra Singh Rajput Mohammed Alkahtani Mustufa Haider Abidi GLA University MathuraIndia DPSM IIIT Naya RaipurIndia Compucom Institute of Technology&Management JaipurIndia Lakshmi Narain College of Technology BhopalIndia Vellore Institute of Technology Vellore632014India Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia

Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmission.Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span.This paper introduces the RSS-based energy-efficient selective clustering technique using a master node(RESCM)to improve energy utilization using a master node.The master node positioned at the center of the network area and base station(BS)is placed outside the network area.During cluster head(CH)selection,the node with a high RSS value is more likely to become CH.The network is divided into segments according to the distance from the master node.All nodes near BS or master node transmit their data using direct transmission without the clustering process.The simulation results showed that the RESCM method improves the total network lifespan effectively.

关键词： Wireless sensor network received signal strength clustering base station master node

Evaluation of employee profiles using a hybrid clustering and optimization model Practical study

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International Journal of Intelligent Computing and Cybernetics 2016年第3期9卷 218-236页

作者： Mahsan Esmaeilzadeh Bijan Abdollahi Asadallah Ganjali Akbar Hasanpoor Department of Management Faculty of ManagementKharazmi UniversityTehranIran

Purpose-The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers.Employee profiles play a crucial role in the evaluation process to improve the training process performance.This paper focuses on the clustering of the employees based on their profiles into specific categories that represent the employees’characteristics.The employees are classified into following categories:necessary training,required training,and no training.The work may answer the question of how to spend the budget of training for the employees.This investigation presents the use of fuzzy optimization and clustering hybrid model(data mining approaches)as a fuzzy imperialistic competitive algorithm(FICA)and k-means to find the employees’categories and predict their training requirements.Design/methodology/approach-Prior research that served as an impetus for this paper is discussed.The approach is to apply evolutionary algorithms and clustering hybrid model to improve the training decision system directions.Findings-This paper focuses on how to find a good model for the evaluation of employee profiles.The paper introduces the use of artificial intelligence methods(fuzzy optimization(FICA)and clustering techniques(K-means))in management.The suggestion and the recommendations were constructed based on the clustering results that represent the employee profiles and reflect their requirements during the training courses.Finally,the paper proved the ability of fuzzy optimization technique and clustering hybrid model in predicting the employee’s training requirements.Originality/value-This paper evaluates employee profiles based on new directions and expands the implication of clustering view in solving organizational challenges(in TCT for the first time).

关键词： clustering Evolutionary algorithm Data mining Evolutionary computation Custer analysis Paper type Case study

Hierarchical clustering driven by cognitive features

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Science China(Information Sciences) 2014年第1期57卷 111-124页

作者： LI ChunZhong XU ZongBen QIAO Chen LUO Tao Institute of Statistics and Applied Mathematics Anhui University of Finance and Economics Institute for Information and System Science Xi'an Jiaotong University

For data sets of arbitrary shapes and densities,the existing clusterings have much space to be improved to obtain better results.In this paper,clustering is considered as a cognitive problem,and cognitive features are of vital importance to clustering.In combination with psychological experiment,we propose three cognitive features of clustering and model them as a flexible similarity measurement.Meanwhile a new clustering framework is put forward to integrate the cognitive features by employing the similarity measurement.The two attractive advantages are its low complexity and fitness for various types of data sets,such as data sets of diferent shapes and densities.Some synthetic and real data sets are employed to exhibit the superiority of the new clustering algorithm.

关键词： clustering direction consistent structural nearest neighborhood afecting field manifold

A Novel Hybrid clustering Based Transmission Protocol for Wireless Body Area Networks

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Computers, Materials & Continua 2021年第11期69卷 2459-2473页

作者： Neelam Sharma Harshita Chadha Karan Singh B.M.Singh Nitish Pathak Research Scholar Computer Science and EngineeringUttarakhand Technical University(UTU)DehradunIndia Computer Science and Engineering Maharaja Agrasen Institute of TechnologyNew DelhiIndia School of Computer&Systems Sciences Jawaharlal Nehru University(JNU)DelhiIndia College of Engineering Roorkee UTUDehradunIndia Department of Information Technology KIET Group of InstitutionsGhaziabadIndia

Wireless sensor networks are a collection of intelligent sensor devices that are connected to one another and have the capability to exchange information packets amongst themselves.In recent years,this field of research has become increasingly popular due to the host of useful applications it can potentially serve.A deep analysis of the concepts associated with this domain reveals that the two main problems that are to be tackled here are throughput enhancement and network security improvement.The present article takes on one of these two issues namely the throughput enhancement.For the purpose of improving network productivity,a hybrid clustering based packet propagation protocol has been proposed.The protocol makes use of not only clustering mechanisms of machine learning but also utilizes the traditional forwarding function approach to arrive at an optimum model.The result of the simulation is a novel transmission protocol which significantly enhances network productivity and increases throughput value.

关键词： Forwarding function wireless sensor networks WSNs WBANs energy characteristics clustering hybrid mechanism dissipated energy network stability

Node correlation clustering algorithm for wireless multimedia sensor networks based on overlapped FoVs

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The Journal of China Universities of Posts and Telecommunications 2013年第5期20卷 37-44页

作者： ZHANG Qian-yan WANG Ru-chuan SHA Chao HUANG Hai-ping Department of Computer Science and Technology Suqian College College of Computer Nanjing University of Posts and Telecommunications

In this paper, a clustering algorithm is proposed based on the high correlation among the overlapped field of views for the wireless multimedia sensor networks. Firstly, by calculating the area of the overlapped field of views （FoVs） based on the gird method, node correlations have been obtained. Then, the algorithm utilizes the node correlations to partition the network region in which there are high correlation multimedia sensor nodes. Meanwhile, in order to minimize the energy consumption for transmitting images, the strategy of the cluster heads election is proposed based on the cost estimation, which consists of signal strength and residual energy as well as the node correlation. Simulation results show that the proposed algorithm can balance the energy consumption and extend the network lifetime effectively.

关键词： wireless multimedia sensor networks overlapped FoVs node correlation clustering

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Enhancing clustering Stability in VANET: A Spectral clustering Based Approach

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China Communications 2020年第4期17卷 140-151页

作者： Gang Liu Nan Qi Jiaxin Chen Chao Dong Zanqi Huang College of electronic and information engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

Vehicles can establish a collaborative environment cognition through sharing the original or processed sensor data from the vehicular sensors and status map. clustering in the vehicular ad-hoc network(VANET) is crucial for enhancing the stability of the collaborative environment. In this paper, the problem for clustering is innovatively transformed into a cutting graph problem. A novel clustering algorithm based on the Spectral clustering algorithm and the improved force-directed algorithm is designed. It takes the average lifetime of all clusters as an optimization goal so that the stability of the entire system can be enhanced. A series of close-to-practical scenarios are generated by the Simulation of Urban Mobility(SUMO). The numerical results indicate that our approach has superior performance in maintaining whole cluster stability.

关键词： VANET spectral clustering force-directed algorithm whole cluster stability

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UAV clustering Scheme for FANETs using Elbow-Hybrid Metaheuristic Techniques

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Computer Systems Science & Engineering 2021年第9期38卷 321-337页

作者： Kundan Kumar Rajeev Arya Department of Electronics and Communication Engineering National Institute of Technology PatnaPatna800005India

Great strides have been made to realistically deploy multiple Unmanned Aerial Vehicles(UAVs)within the commercial domain,which demands a proper coordination and reliable communication among the UAVs.UAVs suffer from limited time of flight.Conventional techniques suffer from high delay,low throughput,and early node death due to aerial topology of UAV networks.To deal with these issues,this paper proposes a UAV parameter vector which considers node energy,channel state information and mobility of UAVs.By intelligently estimating the proposed parameter,the state of UAV can be predicted closely.Accordingly,efficient clustering may be achieved by using suitable metaheuristic techniques.In the current work,Elbow method has been used to determine optimal cluster count in the deployed FANET.The proposed UAV parameter vector is then integrated into two popular hybrid metaheuristic algorithms,namely,water cycle-moth flame optimization(WCMFO)and Grey Wolf-Particle Swarm optimization(GWPSO),thereby enhancing the lifespan of the system.A methodology based on the holistic approach of parameter and signal formulation,estimation model for intelligent clustering,and statistical parameters for performance analysis is carried out by the energy consumption of the network and the alive node analysis.Rigorous simulations are run to demonstrate node density variations to validate the theoretical developments for various proportions of network system sizes.The proposed method presents significant improvement over conventional stateof-the-art methods.

关键词： clustering elbow method hybrid UAVs FANETs energy consumption

A Novel clustering Technique for Efficient clustering of Big Data in Hadoop Ecosystem

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Big Data Mining and Analytics 2019年第4期2卷 240-247页

作者： Sunil Kumar Maninder Singh Directorate of Livestock Farms Guru Angad Dev Veterinary and Animal Sciences UniversityLudhiana 141001India Department of Computer Science Punjabi UniversityPunjab 147002India

Big data analytics and data mining are techniques used to analyze data and to extract hidden information.Traditional approaches to analysis and extraction do not work well for big data because this data is complex and of very high volume. A major data mining technique known as data clustering groups the data into clusters and makes it easy to extract information from these clusters. However, existing clustering algorithms, such as k-means and hierarchical, are not efficient as the quality of the clusters they produce is compromised. Therefore, there is a need to design an efficient and highly scalable clustering algorithm. In this paper, we put forward a new clustering algorithm called hybrid clustering in order to overcome the disadvantages of existing clustering algorithms. We compare the new hybrid algorithm with existing algorithms on the bases of precision, recall, F-measure, execution time, and accuracy of results. From the experimental results, it is clear that the proposed hybrid clustering algorithm is more accurate, and has better precision, recall, and F-measure values.

关键词： clustering Hadoop big data k-means hierarchical

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