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Application of Image Segmenting Technology Based on Fuzzy c-means algorithm in competition Video Referee

IEEE AccESS 2024年 12卷 34378-34389页

作者： Yan, Xu North China Elect Power Univ Dept Phys Educ Baoding 071003 Peoples R China

As sports developing and popularizing, people have put forward increasing demands for fairness and transparency in competitions. However, traditional competition referee systems rely too heavily on human subjective judgment, which often leads to significant subjectivity and in-accuracy issues. To overcome these difficulties, some improvements have been made on the basis of the fuzzy c-means algorithm to preserve more information, while also suppressing membership to improve the convergence speed of the algorithm. These results confirm that the accuracy of the model converged approximately in the 600th round, with an accuracy of 96.1%. compared with the standard fuzzy c-means algorithm, the convergence speed has been improved by 33% and the accuracy has been improved by 2.1%. This experiment verifies the effectiveness and practicality of the video referee in this competition. This provides new ideas for the development of image segmenting technology and has certain application potential in sports competition refereeing, judicial notarization refereeing, and other fields.

关键词： Image segmentation Fuzzy sets clustering algorithms Mathematical models Object segmentation Fuzzy logic Biomedical imaging Visual analytics Fuzzy theory c-means algorithm image segmenting technology video image recognition video referee

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Rough Hypercuboid Based Generalized and Robust IT2 Fuzzy c-means algorithm

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IEEE TRANSAcTIONS ON cYBERNETIcS 2021年第7期51卷 3641-3652页

作者： Maji, Pradipta Garai, Partha Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India Kalyani Govt Engn Coll Dept Comp Applicat Kalyani 741235 W Bengal India

One of the important issues in pattern recognition and machine learning is how to find natural groups present in a dataset. In this regard, this paper presents a novel clustering algorithm, called rough hypercuboid-based interval type-2 fuzzy c-means (RIT2FcM). It judiciously integrates the merits of the rough hypercuboid approach, c-means algorithm, and interval type-2 fuzzy set, to address the uncertainty associated with real-life datasets. Using the concept of hypercuboid equivalence partition matrix (HEM) of rough hypercuboid approach, the lower approximation and boundary region of each cluster are implicitly defined, without using any prespecified threshold parameter. The interval-valued fuzzifier is applied to address the uncertainty coupled with different parameters of rough-fuzzy clustering algorithms, where the determination of the appropriate value of fuzzifier is a difficult task. An analytical formulation on the convergence analysis of the proposed RIT2FcM algorithm, along with a theoretical bound of its fuzzifier, is also introduced. The efficacy of the proposed RIT2FcM method is extensively compared with that of several existing clustering algorithms, using some cluster validity and classification rate indices on various real-life datasets. The proposed algorithm performs better than the state-of-the-art c-means algorithms in 92.59% cases, with respect to different cluster validity indices, in lesser computation time.

关键词： c-means algorithm clustering interval type-2 fuzzy set pattern recognition rough sets

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A New Method for Bad Data Identification of Oilfield System Based on Enhanced Gravitational Search-Fuzzy c-means algorithm

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IEEE TRANSAcTIONS ON INDUSTRIAL INFORMATIcS 2019年第11期15卷 5963-5970页

作者： Zhao, Yang Xu, Jianjun Wu, Jingchun Northeast Petr Univ Key Lab Enhanced Oil Recovery Minist Educ Daqing 163318 Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligen Daqing 163318 Peoples R China

Aiming at the shortcomings of current methods of bad data detection and identification in a power system, this article advances a new method to identify bad data of the power system based on the enhanced gravitation search-fuzzy c-mean algorithm (EGSA-FcM). By using the enhanced gravity search algorithm (EGSA), a better initial solution to search the measurement data uploaded by ScADA system is obtained. Then, the FcM algorithm was used to obtain the classification of benign data and bad data. Finally, through the cOS clustering validity judgment index, the optimal clustering number is determined and the optimal clustering results and bad data were obtained. This method has already been applied to the IEEE 14-node power system and a regional power grid in Daqing, china. The results indicated that the proposed method is more accurate than the traditional ones and effectively avoid the occurrence of false detection and missed detection.

关键词： Bad data identification c-means algorithm enhanced gravity search algorithm (EGSA) fuzzy clustering

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cAST-WSN: The Presentation of New clustering algorithm Based on Steiner Tree and c-means algorithm Improvement in Wireless Sensor Networks

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WIRELESS PERSONAL cOMMUNIcATIONS 2017年第1期97卷 1323-1344页

作者： Baradaran, Amir Abbas Navi, Keivan Shahid Beheshti Univ Dept Elect & Comp Engn Tehran Iran Shahid Beheshti Univ Fac Elect & Comp Engn Tehran Iran

In recent years, both energy consumption and network lifetime have been among the most important challenges in wireless sensor networks. Due to the limited energy of sensor nodes and the inability sensors recharge, many clustering-based methods have been offered to transmit the received information to the base station by sensors. In this paper, a new method called cAST-WSN has been presented, which is based on three criteria: the node distance from the gravity center of the cluster, the node distance from the gravity center of cluster nodes and the node distance from the energy center of nodes in each cluster. Based on these distances and Steiner tree structure, there exist two types of clustering. In this research, we present a function to evaluate the quality of cluster by means of which the quality of clusters in the two presented states can be examined and the best type of clustering can be selected. cAST-WSN method has been successful in bringing significant improvements to c-means clustering algorithm. The results from simulation indicate that the clustering based on cAST-WSN method, as compared with the previous similar methods such as LEAcH, LEAcH-c, LEAcH-Mobile, ALEAcH, LEAcH-SWDN, cBL, PEcRP, HUcL, O-LEAcH and DcH, has improved the network lifetime. Furthermore, cAST-WSN method has improved a main criterion called cluster density. cAST-WSN simulation results show that the clustering process in this method is better than c-means algorithm.

关键词： Wireless sensor network Steiner tree c-means algorithm clustering

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Agent Based Segmentation of the MRI Brain Using a Robust c-means algorithm

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Journal of computer and communications 2016年第10期4卷 13-21页

作者： Hanane Barrah Abdeljabbar cherkaoui Driss Sarsri Laboratory of Innovative Technologies National School of Applied Sciences Abdelmalek Essadi University Tangier Morocco

In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFcM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints.

关键词： Agents and MAS MR Images Fuzzy clustering c-means algorithm Image Segmentation

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Grouping and long term prediction of sunspot cycle characteristics-A fuzzy clustering approach

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ASTRONOMY AND cOMPUTING 2024年 48卷

作者： Anilkumar, B. T. Sabarinath, A. Coll Engn Thiruvananthapuram Kerala India Vikram Sarabhai Space Ctr Thiruvananthapuram Kerala India

Based on the pattern recognition algorithm called fuzzy c-means clustering, grouping of sunspot cycles has been carried out. It is found that, optimally the sunspot cycles can be divided in to two groups;we name it as Large Group and Small Group. Based on the fuzzy membership values the groups are derived. According to our analysis, cycles 1,5,6,7,12,13,14,15,16 and 24 belongs to the Small class, where as cycles 2,3,4,8,9,10,11,17,18,19,20,21,22, and 23 belongs to the Large class. Based on the features of each group and its fuzzy cluster center, prediction of cycle 25 is also been made. Also on the periodicity of the occurrence of the groups, a new cyclic behaviour has been found for the occurrences of the identical sunspot cycles. According to our study cycle 25 belongs to small class and further we predict that the future cycle up to cycle 32 may fall in small group.

关键词： Sunspot numbers Fuzzy clustering c-means algorithm

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Metaheuristic-based possibilistic multivariate fuzzy weighted c-means algorithms for market segmentation

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APPLIED SOFT cOMPUTING 2020年 96卷 106639-106639页

作者： Kuo, R. J. Amornnikun, Patipharn Thi Phuong Quyen Nguyen Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei 106 Taiwan Univ Sci & Technol Univ Danang Fac Project Management 54 Nguyen Luong Bang Danang Vietnam

This study proposed the metaheuristic-based possibilistic multivariate fuzzy weighted c-means (PMFWcM) algorithm for clustering mixed data PMFWcM algorithm itself is normally used for numerical data. To implement in the real application of market segmentation, where the data usually contains both numerical and categorical attributes model improvement is a need. First, the distance between two mixed-attribute objects is calculated by using the object-cluster similarity measure. Then, three metaheuristics, i.e., genetic algorithm (GA), particle swarm optimization algorithm (PSO), and sine cosine algorithm (ScA), are employed to integrate with the PMFWcM algorithm for cluster analysis. This combination aims to improve the clustering performance of the PMFWcM algorithm and to make the clustering results more stable. To cluster a real-world dataset certainly, the experiment with benchmark datasets from UcI machine learning repository is conducted to verify the performance of the proposed algorithms. The experiment results show that the clustering performance of the ScA-PMFWcM, GA-PMFWcM and PSO-PMFWcM algorithms are better than that of the PMFWcM algorithm. Moreover, from case study results, the ScA-PMFWcM algorithm gives the smallest sum of squared error and computational time compared with the GA-PMFWcM, PSO-PMFWcM, and PMFWcM algorithms. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Possibilistic multivariate fuzzy weighted c-means algorithm Mixed data Market segmentation Metaheuristics Sine cosine algorithm

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Shadowed set-based rough-fuzzy clustering using Random Feature Mapping

Shadowed set-based rough-fuzzy Clustering using Random Featu...

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International conference on Security, Pattern Analysis, and cybernetics (IcSPAc)

作者： Kong, Lingning chen, Long Univ Macau Dept Comp & Informat Sci Taipa Macau Peoples R China

ISBN: (纸本)9781538630167

The shadowed set-based rough fuzzy clustering (SRFcM) methods have shown great performance on the data with outliers. But for the data with non-spherical clusters, the SRFc approaches cannot produce good results. The reason is the SRFcM, just like classical fuzzy c-means algorithms, works on the original data space and assures the linear separability of different clusters. The kernel methods can be combined with fuzzy clustering to deal with the non-spherical problem, but the size of kernel matrix is the square of the number of the input data, which makes the kernel fuzzy clustering is not suitable for very large data. But if we approximate the kernel space by using Fourier random feature mappings, the SRFc can be directly applied over the random features generated by data. This approach combines the advantages of SRFcM in handling outliers and the random features in processing non-spherical clusters. The experimental results show good performance of the SRFcM in the random feature space.

关键词： c-means algorithm Shadowed sets Rough sets Fuzzy sets Random Fourier Features

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A Method for the Reduction of Linear Higher Order Discrete-Time SISO System

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IETE Journal of Education 2017年第1期58卷 7-10页

ABSTRAcTABSTRAcTThis paper presents a method for the reduction of higher order linear discrete time system into a desired reduced order model. The proposed algorithm uses the concept of pattern recognition which is adopted here for clustering the poles of the system. The centres of the clusters are treated as poles of the reduced order model and the overall transfer function of the reduced order model is obtained by both clustering algorithm and matching the ratio of coefficients of original and reduced order models. The original algorithm for c-means clustering is modified in order to avoid some limitations in model order reduction procedure. This new method makes the procedure simple, in addition to retaining the stability of the original higher order system in their reduced order models.

关键词： c-means algorithm Discrete time systems Large scale systems Order reduction Pattern recognition Pole clustering

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Agent Based Fuzzy clustering algorithm

Agent Based Fuzzy Clustering Algorithm

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Mediterranean conference on Information and communication Technologies (MedcT)

作者： Barrah, Hanane cherkaoui, Abdeljabbar Natl Sch Appl Sci Lab Innovat Technol Tangier Morocco

ISBN: (纸本)9783319302980;9783319302966

Recently, the agents and multi-agent systems (MAS) field has known a significant breakthrough and covered a wide range of application domains. In order to keep up with this technological evolution, this paper concentrates on developing a MAS based on a fuzzy clustering algorithm, which is a widely useful clustering method in several fields, in the aim of making it easy to be extended and incorporated into other MASs. In the demonstration section, we show the effectiveness of our algorithm against the standard fuzzy c-means algorithm, and demonstrate theoretically how this system can reduce the running time of the applications based on it.

关键词： Agents Multi-agent system Fuzzy clustering c-means algorithm Mahalanobis distance

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