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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Bommy, M. Bhargavi, V. Sesha Makeshkumar, M. Isaac, Samson Banu, A. Hajira Reddy, R. Vijaya Kumar Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Madanapalle India Department of Information Technology G Narayanamma Institute of Technology and Science Hyderabad Shaikpet India Department of Mechanical Engineering Kongu Engineering College Perundurai638060 India Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Tamilnadu Dindigul624622 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andra Pradesh Vaddeswaram India

ISBN: (纸本)9798350305418

Rural surplus labor migration is a common concern in the global industrialization process. For India, which is both a big agricultural and highly populated country, this phenomenon not only has a direct influence on the country's socioeconomic growth but also has important practical significance in the quest of overall prosperity. Since India's reforms and opening up, the rural labor force has been a critical component in the country's road toward economic prosperity. The migration and redistribution of this labor force is a critical mechanism for improving labor resource allocation in response to regional, status, and structural alterations. It provides a fundamental incentive for economic and social advancement. The fuzzy clustering approach is used in this work to examine the transfer of rural surplus labor force. Each data point's degree of membership is defined by its closeness to a given cluster. The core premise entails applying statistical methods to calculate distances in a multi-attribute space, guaranteeing that each data point is closest to the center of its corresponding category, and giving a degree of membership accordingly. The study illustrates the algorithm's superiority over older approaches and emphasizes its broad application in actual circumstances. © 2023 IEEE.

关键词： Agriculture fuzzy clustering algorithm Surplus labor force transfer trend

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A fuzzy clustering-algorithm WITH THE CRITERION OF MODIFIED OBJECTIVE FUNCTION FOR APPLICATION OF AUTOMATIC TARGET RECOGNITION SYSTEMS

A FUZZY CLUSTERING-ALGORITHM WITH THE CRITERION OF MODIFIED ...

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1993 International Joint Conference on Neural Networks (IJCNN 93-Nagoya)

作者： WANG, SL ZHENG, XS TONGJI UNIV INST INFORMAT & CONTROLSHANGHAI 200092PEOPLES R CHINA

ISBN: (纸本)0780314212

Structure organization of the physical geometry in real data is significantly indicated by characteristics of embodied pattern classifiers. The requirement that this paper considers for resolving this issue is a suitable measure of clusters with the criterion of objective functionals. It involves the optimizing of partition coefficient and entropy and separation coefficients through implementation of an MFCM algorithm to evaluate the clustering. In this way a clustering experiment for recognition of an ambiguous photograph of a MIG-29 fighter taken by spy satellite has been successfully developed by computer calculation.

关键词： image classification geometry fuzzy set theory entropy fuzzy clustering algorithm modified objective function automatic target recognition systems physical geometry partition coefficient separation coefficient entropy coefficient MFCM algorithm MIG-29 fighter spy satellite

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A Novel Rough fuzzy clustering algorithm with A New Similarity Measurement

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JOURNAL OF INTERNET TECHNOLOGY 2019年第4期20卷 1145-1156页

作者： Li, Yang Fan, Jian-cong Pan, Jeng-Shyang Mao, Gui-han Wu, Geng-kun Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Shandong Peoples R China Shandong Univ Sci & Technol Prov Key Lab Informat Technol Wisdom Min Shandong Qingdao Shandong Peoples R China Shandong Univ Sci & Technol Prov Expt Teaching Demonstrat Ctr Comp Qingdao Shandong Peoples R China

With the emergence of exponential growth of datasets in various fields, fuzzy theory-based approaches are widely used to improve or optimize the data clustering algorithms. These improved algorithms can achieve better results than the original counterparts in practical applications. However, the fuzzy clustering algorithms including the traditional improved algorithms normally ignore the clustering boundary uncertainty, inter-class compactness and complex data problems, thereby result in the unsatisfactory clustering results. To address this issue, in this paper, a novel rough fuzzy clustering algorithm based on a new similarity measure is proposed by utilizing the upper approximation and lower approximation of rough set. We also develop the method of transforming fuzzy clustering model into rough set model. Our experiment results show that the improved algorithm can get better clustering effect.

关键词： Similarity measure fuzzy clustering algorithm Rough set clustering

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Effect of Chinese Sports Nutrition Supplements on Physical Recovery and Improvement of Football Player Based on fuzzy clustering algorithm

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Advance Journal of Food Science and Technology 2016年第3期11卷 185-189页

作者： Liao Hong Jingdezhen College Jingdezhen City Jiangxi Province 333000 China

Chinese sports nutrition supplement is a auxiliary driving means of movement training and match and the application of it on physical recovery and improvement of football player is studied in depth. Firstly, the development situation of Chinese sports nutrition supplements is summarized. Secondly, fuzzy clustering algorithm based on Monte Carlo T inspection is analyzed and main indexes are introduced, respectively. Thirdly, the experimental objective and method are set. Finally, the experimental results show that the Chinese sports nutrition supplement can be applied in the physical recovery and improvement of football player effectively.

关键词： Chinese sports nutrition supplement football player fuzzy clustering algorithm Monte Carlo T inspection physical recovery and improvement

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Design of Music Melody Contour Extraction algorithm Based on fuzzy clustering algorithm

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Procedia Computer Science 2022年 208卷 427-434页

作者： Sanjun Yao

Now is the era of big data, and a large amount of data will be generated in any industry. How to mine useful information from this huge amount of data is a huge challenge we face. Efficient clustering algorithm is an important means to mine useful information, and it is also a hot topic of current research. Among many drama algorithms, fuzzy and other methods that use fuzzy mathematics to describe and classify things, In recent years, it has attracted the attention of scholars at home and abroad. With the development of Internet technology, clustering plays an important role in various fields. It can actively group data points, so that data points belonging to the same cluster have high similarity, and data points belonging to different clusters have great differences. The algorithm extracts the song pitch sequence from the humming segment, and converts it into the melody contour sequence after regularization, merging and segmentation, and then converts this sequence into the melody contour feature using the standard tone difference table generated by the standard tone. The results show that the system has good robustness to environmental noise; In the search space containing 405 songs, the success rate of retrieving the top five is more than 90%.The improved algorithm is simulated on rechset music data set, and the results show that the pitch estimation accuracy of 62.04% and the total accuracy of 55.08% are achieved.

关键词： Big data fuzzy clustering algorithm music melody contour robustness

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Construction And Performance Evaluation of Big Data Prediction Model Based on fuzzy clustering algorithm in Cloud Computing Environment

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JOURNAL OF ELECTRICAL SYSTEMS 2023年第4期19卷 1-13页

作者： Hu, Yanhua Zhang, Chunyu Cui, Yanan Wei, Ling Ni, Zhiping Liuzhou Inst Technol Coll Informat Sci & Engn Liuzhou 545616 Guangxi Peoples R China Xizang Minzu Univ Coll Informat Engn Xianyang 712082 Shaanxi Peoples R China

In the evolving landscape of Big Data Prediction, where multimodal approaches are increasingly crucial for reliable user authentication, this research presents a comprehensive study. The primary focus is on the construction and performance evaluation of a robust big data prediction model within a cloud computing environment. The advent of big data and cloud computing has revolutionized the field of Big Data Prediction, offering immense potential for advanced data analysis and prediction. This research presents the development and evaluation of a robust prediction model for multimodal Data in cloud computing environment applications. The proposed model incorporation of Reliable Discrete Variable Topology (RDVT) into the prediction model. RDVT introduces a topological data structure that enhances data reliability and ensures the integrity of multimodal biometric information. The construction and training of the prediction model are meticulously detailed, encompassing data preprocessing, feature extraction, clustering, classification, and model evaluation. Additionally, the integration of a fuzzy clustering algorithm enhances the model's ability to handle uncertainty and imprecision in Data. The advancement of multimodal biometrics in the cloud computing environment field by introducing the Reliable Discrete Variable Topology (RDVT) and a big data prediction model based on a fuzzy clustering algorithm in a cloud computing environment. The model's performance is rigorously assessed through extensive experimentation, including accuracy, precision, recall, and F1-score measurements.

关键词： Multimodal biometrics big data prediction model fuzzy clustering algorithm Big Data Prediction Biometric modalities User authentication.

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Modelling and analysis of system robustness for mechanical product based on axiomatic design and fuzzy clustering algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2015年第8期7卷 1687814015598694-1687814015598694页

作者： Cheng, Xianfu Zhang, Shengcai Wang, Tao East China Jiaotong Univ Sch Mech & Elect Engn Nanchang 330013 Jiangxi Peoples R China

Robust performance is the most important concern in the design of any product, especially in system design stage that precedes parameter design, because it actually determines the attainable level of product robustness in the parameter design phase. In this article, a framework of modelling and analysis of system robustness is proposed, which includes system modelling, cluster analysis and design of experiments. In the process of system modelling, the metamodel of general design theory was utilized to describe the function-structure model of product design, and the customer needs are transformed into functional requirements. Based on the independent axiom and zigzag mapping mode of axiomatic design, the functional requirements are mapping to design parameters, and the design matrix is created, which is then converted into design structure matrix by identifying the relationship between functional requirements and the sensitivity of functional requirements to design parameters. The fuzzy clustering algorithm is utilized to cluster the design parameters and to group the system components into modules in design structure matrix, and the interface among modules can be identified and system robustness incidence matrix is developed. Then the incidence parameters are considered as controllable factors, and experimental design techniques are utilized to analyse the influence of incidence parameters on the design objectives, if any, that may result in a robust system. The proposed framework is illustrated with the trolley design of overhead travelling crane.

关键词： Robustness analysis design of experiments axiomatic design fuzzy clustering algorithm trolley

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Electric Vehicle Braking Energy Recovery Model Based on Dynamic Matching Model and fuzzy clustering algorithm

Electric Vehicle Braking Energy Recovery Model Based on Dyna...

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作者： Xianhuan Wu School of Mechanical and Electrical Engineering and Automotive Services Wuhan Business University

With the rapid development of the automotive industry and the continuous improvement of the quality of life of people,the number of car ownership continues to *** the same time,it will inevitably bring about a series of world problems such as traffic congestion,road congestion,air pollution,and energy shortage and noise *** order to protect the living environment of human beings,maintain the sustainable development of energy,and protect the health of human beings,all governments have invested huge manpower and material resources to find the best way to solve energy and environmental *** this context,electric vehicles came into being and became the research hotspot of the current automotive *** of the advantages of an electric car is its ability to recover braking *** the vehicle is slowing down or braking,the drive motor can be operated in a power generation state,thereby converting the kinetic energy of the vehicle into electrical energy,and storing it in the power battery for *** the same time,with the participation of the motor brake,the brake wear can be reduced.

关键词： Dynamic matching model fuzzy clustering algorithm Data analysis Electric vehicle Braking effect,Energy recovery model

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fuzzy Supervised clustering algorithm with the Particle Swarm Optimization

Fuzzy Supervised Clustering Algorithm with the Particle Swar...

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2018 International Conference on Computer,Communication and Network Technology(CCNT 2018)

作者： Yuan-horng LIN Jeng-ming YIH Shin-hua WU Taichung University of Education Min-Hwei Junior College of Health Care Management Hui-Wei Elementary School

In GK-algorithm, fuzzy clustering algorithm with preserved volume was used. However, the added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. A fuzzy C-Means algorithm based on Mahalanobis distance(FCM-HM) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. The singular problem and the selecting initial values problem are improved. We pointed out that the initial memberships of fuzzy c-mean algorithm which was based on Mahalanobis distance algorithm and the traditional fuzzy c-means algorithm(FCM) algorithm can't be all equal. The other important issue is how to approach the global minimum value that can improve the cluster accuracy. The methods to detect the local extreme value were developed by this paper. Focusing attention to these two faults, an improved new algorithm, "fuzzy C-Means based on Particle Swarm Optimization with Mahalanobis distance(PSO-FCM-HM)", is proposed. We have two aims and goals of our research summary. One is to compare the classification accuracies of fuzzy clustering algorithms based on Mahalanobis distances and Euclidean distances. The other is to choose the initial membership to promote the classification accuracies.

关键词： Particle swarm optimization fuzzy clustering algorithm Mahalanobis distance fuzzy c-means algorithm

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A novel heat load prediction algorithm based on fuzzy C-mean clustering and mixed positional encoding informer

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APPLIED ENERGY 2025年 388卷

作者： Song, Jiancai Wang, Kangning Bian, Tianxiang Li, Wen Dong, Qianxing Chen, Lei Xue, Guixiang Wu, Xiangdong Tianjin Univ Commerce Sch Informat & Engn Tianjin 300134 Peoples R China Hebei Univ Technol Sch Civil & Transportat Engn Tianjin 300401 Peoples R China Hebei GongDa Keya Energy Technol Co Ltd Shijiazhuang 051130 Peoples R China

With the rapid urbanization in China, sustainable energy development and environmental challenges arising from the massive amount of energy consumed by district heating systems (DHS) have attracted widespread attention. An accurate heat load prediction algorithm contributes to the optimized regulation of DHS. However, heat load has significant nonlinear and thermally inertia properties, and traditional prediction models suffer from weak generalization ability and low sensitivity to dynamic changes. In this paper, a hybrid heat load prediction model based on fuzzy C-mean clustering and mixed positional encoding Informer (FCM-MPE-Informer) is innovatively proposed by integrating the advantages of supervised and unsupervised learning. Two different mixed positional encoding strategies were first designed to improve the Informer and investigated in detail to explore the enhancement of their sensitivity to changes in long distance series. The model sufficiently incorporates the strengths of the FCM algorithm in dealing with uncertainty and complex relationships while incorporating the mixed positional encoding Informer algorithm to efficiently handle the interdependencies between the long time series of heat load and influencing factors. The comprehensive prediction performance comparison and ablation experimental results sufficiently validate the superiority of the proposed FCM-MPEInformer prediction algorithm. The mean absolute percentage error (MAPE) indicators for the four heat exchange stations were 2.575 %, 2.510 %, 2.584 %, and 2.640 %, respectively.

关键词： Heat load prediction fuzzy clustering algorithm Informer District heating systems

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