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Lithium battery sorting method for high-rate operating conditions

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年第10-11期238卷 3372-3385页

作者： Hong, Hanchi Li, Hongze d'Apolito, Luigi Ye, Yangqi Xin, Qianfan Xiamen Univ Technol Sch Mech & Automot Engn 600 Ligong Rd Xiamen 361024 Fujian Province Peoples R China Tianjin Univ Sch Mech Engn Tianjin Peoples R China

Lithium batteries are increasingly used in electric vehicle applications. However, different manufacturing processes and technical constraints lead to battery inconsistency, even for batteries in the same production batch. High-rate discharging negatively affects battery consistency and results in service life reduction. A multi-parameter sorting method at high-rate operation was proposed in this study. The method was applied to sort batteries for cars. The sorted datasets were compared and analyzed by the fuzzy C-mean clustering method, the K-means clustering method, and the simulated annealing genetic algorithm. The comparisons proved that the genetic annealing algorithm was more suitable for battery classification. The clustered batteries were assembled into modules in series and parallel for experimental validation. The test results showed that the battery module cycle life was improved.

关键词： Lithium battery factor analysis clustering algorithm high-rate operating condition

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Study on online English learning resource push based on Bayesian inference

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2024年第1-2期74卷 90-98页

作者： Zhang, Wenfeng Jiujiang Univ Sch Foreign Languages Jiujiang Jiangxi Peoples R China

The current online English learning resource push methods have problems of poor customer satisfaction, low reliability of pushed resources and low recall rate of resource pushes. Therefore, this paper proposes an online English learning resource push method based on Bayesian inference. Firstly, obtain online English learning resource data and classify online learning resources. Then, by mining and analysing learner learning data, clustering algorithms are used to locate and infer the learner's learning style. Finally, based on Bayesian inference, a Naive Bayesian classifier is developed, and a network English online learning resource push model is developed to achieve effective network English online learning resource distribution. Through relevant experiments, it has been confirmed that the customer satisfaction of this method varies from 96.0% to 99.8%, the push reliability varies from 90.5% to 99.8% and the resource push recall rate is 99.9%, which has the characteristic of good push effect.

关键词： Bayesian inference online English learning resource push classify clustering algorithm Naive Bayesian classifier

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Bi-level economic dispatch strategy for virtual power plants based on electric vehicles aggregation

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期225卷

作者： Guo, Jinrui Dou, Chunxia Zhang, Zhijun Yue, Dong Nanjing Univ Posts & Telecommun Inst Adv Technol Carbon Neutral Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Automat & Artificial Intelligence Nanjing 210023 Jiangsu Peoples R China

The rapid growth of electric vehicles (EVs) imposes great challenges to the flexible management and economic dispatch of virtual power plants. To overcome these obstacles, this paper proposes a bi-level economic dispatch strategy based on EVs aggregation. First, an aggregation method based on improved artificial bee colony Kmeans clustering algorithm is presented to aggregate the EVs with different dynamic characteristics in the upper level. Aiming at the issue that multi-parameter weights and aggregation effects are difficult to determine in the clustering process, information entropy and Silhouette metric are proposed to enhance the accuracy of clustering. Furthermore, pre-dispatch is conducted based on aggregation information and electricity market prices. Next, in the lower level, the charging demands information is utilized to formulate specific dispatch strategies for EVs users, and the pre-dispatch deviation in the upper level is modified. Finally, the simulation results show that the proposed aggregation method improves the clustering effect by 22.4% and guarantees the quality of aggregation and the flexibility of system management. Besides, the proposed strategy can reduce the total system operation cost and the computation time by 5.83% and 55.95% respectively and ensure the economy of the system operation and the computational efficiency of the dispatch.

关键词： Virtual power plants Electric vehicles aggregation Bi-level dispatch clustering algorithm Information entropy Silhouette metric

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Pointwise norm-based clustering of data in arbitrary dimensional space

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Communications in Statistics Case Studies Data Analysis and Applications 2023年第2期9卷 121-134页

作者： Modak, Soumita Faculty Department of Statistics University of Calcutta Basanti Devi College Kolkata India

In this paper, a novel nonparametric norm-based clustering algorithm is proposed to classify real-valued continuous data sets given in arbitrary dimensional space. For data univariate, multivariate or high-dimensional, with the number of study variables close to or larger than the data size, our straightforward algorithm is implemented throughout under an univariate set-up, where we make use of the observation-wise (or pointwise) norms which quantify the distances of the observations from the origin zero or the null vector. The method begins with determination of the sample quantile using nonparamteric bootstrapping on the computed norms and always converges independently. By its design, the suggested algorithm is fast enough to detect the number of existing clusters itself and to form well-defined groups. Data study demonstrates its competitiveness in comparison to 2 popular clustering algorithms K-means and K-medoids. © 2023 Taylor & Francis Group, LLC.

关键词： arbitrary-dimensional data set bootstrapping clustering algorithm high-dimensional applicability nonparametric method pointwise norm

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Revolutionizing education: Harnessing data mining for enhanced resource repository in fundamental training programs

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 341-354页

作者： Wu, Xiaozhou Guizhou Univ Commerce Phys Educ Dept Twenty Sixth Ave Guiyang 550014 Peoples R China

As information technology and data mining techniques evolve at a breakneck pace, they bring transformative potential to the educational landscape. The burgeoning growth of online educational resources not only enriches learning but also introduces complexities in resource management and personalization. This study pioneers the construction of a Fundamental Training Educational Resource Repository (FTERR) by harnessing sophisticated data mining approaches. We initiate by intricately mapping students' learning needs through knowledge graph and semantic analysis, ensuring a deep alignment with individual educational journeys. Subsequently, we address the hurdles in resource management by introducing an innovative data storage model. This model, in synergy with advanced clustering algorithms, streamlines the retrieval process, rendering it both swift and intuitive. Our research transcends traditional data mining applications in education, steering towards a more informed and responsive educational ecosystem. This novel approach not only elevates the precision and efficiency of educational resource allocation but also significantly enriches the student learning experience, marking a leap forward in educational informatization. The main purpose of this study is to address the issue of insufficient personalized learning needs in online educational resources by introducing advanced data mining technologies, including knowledge graphs and semantic analysis. The study develops new data storage models and utilizes clustering algorithms to achieve rapid and visual retrieval of educational resources, thereby enhancing the utilization efficiency of educational resources and improving students' learning experiences. The methods and findings of this study can provide references for data mining applications in other fields and promote the application and development of data science in a broader range of fields.

关键词： data mining Fundamental Training Educational Resource Repository (FTERR) knowledge graph semantic analysis resource recommendation clustering algorithm educational informatization

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A clustering algorithm Applying to Transportation Market Segment

A Clustering Algorithm Applying to Transportation Market Seg...

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3rd International Conference on Civil Engineering and Transportation (ICCET 2013)

作者： Jing, Chongyi Sun, Hong Zhang, Peiwen Civil Aviat Flight Univ China Air Transportat Management Sch Guanghan 618307 Sichuan Peoples R China

ISBN: (纸本)9783038350064

An integration algorithm for clustering is presented, in which a maximization & minimum algorithm to determine the initial centers and BWP (Between-Within Proportion) index for input of optimal k. In theory, the bigger the BWP index, the better the clustering effectiveness. Then a numerical example of air transport market segment is presented to show the effectiveness and efficiency of the method presented in the document.

关键词： clustering algorithm Between-Within Proportion K-Means algorithm Transportation Market Segment

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Fuzzy clustering of structural members for efficient design optimization

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Asian Journal of Civil Engineering 2023年第3期24卷 711-725页

作者： Patil, Ishwaragouda S. Joshi, Sharad G. Patil, Shabarish V. Department of Civil Engineering KLE Institute of Technology Hubballi 580027 India KLE Institute of Technology Hubballi 580027 India

The basis for grouping structural elements is a tradeoff between optimization in design and construction. The grouped elements should have common optimal design specifications that are structurally safe and convenient for construction. Fuzzy clustering provides flexibility to regroup the elements using membership values indicating their affinity to a cluster for the considered parameter. The proposed Fuzzy C-Means algorithm demonstrates grouping efficacy based on a dominant design parameter and also provides leverage for regrouping of an element considering other design or construction aspects. The method is evaluated by applying it to footings of four buildings with varying complexity, taking load on footing as basis for grouping. Distinct and homogeneous clusters are obtained with small computational effort, with further scope for regrouping members based on other parameters using their membership values for the parameter considered. The paper illustrates various examples for grouping and optimizing structural design. The proposed methodology proved to be very efficient in grouping structural elements that optimizes the overall structural design. In case of truss example nearly 7% reduction in weight was found compared to previous research. Optimization methodology is also proposed for rebar design of Reinforced Cement Concrete structures with fixed topology and cross-sectional dimensions. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Automated grouping clustering algorithm Fuzzy C-means clustering Narrow design search Structural optimization

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A Geometric clustering algorithm and Its Applications to Structural Data

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18th Annual International Conference on Research in Computational Molecular Biology (RECOMB)

作者： Xu, Shutan Zou, Shuxue Wang, Lincong Jilin Univ Coll Comp Sci & Technol Changchun 130023 Jilin Peoples R China

ISBN: (纸本)9783319052694;9783319052687

An important feature of structural data especially those from structural determination and protein-ligand docking programs is that their distribution could be both uniform and non-uniform. Traditional clustering algorithms developed specifically for non-uniformly distributed data may not be adequate for their classification. Here we present a geometric partitional algorithm that could be applied to both uniformly and non-uniformly distributed data. The algorithm is a top-down approach that recursively selects the outliers as the seeds to form new clusters until all the structures within a cluster satisfy certain requirements. The applications of the algorithm to a diverse set of data from NMR structure determination, protein-ligand docking and simulation show that it is superior to the previous clustering algorithms for the identification of the correct but minor clusters. The algorithm should be useful for the identification of correct docking poses and for speeding up an iterative process widely used in NMR structure determination.

关键词： clustering algorithm structure determination protein-ligand docking structure classification

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A point symmetry clustering algorithm based on PSO

A point symmetry clustering algorithm based on PSO

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International Conference of Information Science and Management Engineering (ISME)

作者： Qu, Jianhua Shandong Normal Univ Sch Management Sci & Engn Jinan Peoples R China

ISBN: (纸本)9781845648299;9781845648282

This paper proposed an efficient PSO clustering algorithm with point symmetry distance based on cooperative evolution strategy. It not only determined the number of clusters, but also detected the proper partitions in data sets when the data sets possess the property of symmetry. In the algorithm, a new point symmetry distance is used to compute the similarity instead of the Euclid distance. Cooperative evolution strategy with multi-populations is introduced to prevent the PSO algorithm from trapping into the local optimal solution. The performance of the proposed algorithm is tested in two artificial data sets. The simulation results show that the performance of the algorithm is better than other algorithms mentioned in this paper.

关键词： clustering algorithm cooperative evolution point symmetry distance PSO

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Power backup Density based clustering algorithm for Maximizing Lifetime of Wireless Sensor Networks 4

Power backup Density based Clustering Algorithm for Maximizi...

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4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems (VITAE)

作者： Wagh, Sanjeev Prasad, Ramjee Univ Pune KJ Coll Engn & Mgmt Res Pune Maharashtra India Aalborg Univ Ctr TeleInfrastruktur CTIF Aalborg Denmark

ISBN: (纸本)9781479946235

WSNs consists several nodes spread over experimental fields for specific application temporarily. The spatially distributed sensor nodes sense and gather the information for intended parameters like temperature, sound, vibrations, etc for the particular application. In this paper, we evaluate the impact of different algorithms i.e. clustering for densely populated field application, energy backup by adding energy harvesting node in field, positioning energy harvesting node smartly in the field and also positioning the base station in sensor field to optimize the communication between cluster head and base station. The analysis and simulation results justifies that availability of power backup for cluster nodes using energy harvesting and positioning the energy harvesting node and also base station enhance the lifetime of sensor network fields. WSN with power backup density based clustering algorithm can be applied for many sensitive applications like military for hostile and remote areas or environmental monitoring where human intervention is not possible.

关键词： Wireless sensor networks clustering algorithm Network lifetime Energy harvesting nodes

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