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11th International Conference on Management of Digital EcoSystems (MEDES)

作者： Mancellari, Enea Bedalli, Erind Rada, Rexhep Epoka Univ Comp Engn Dept Tirana Albania Univ Elbasan Dept Informat Elbasan Albania

ISBN: (纸本)9781450362382

Data compression is the process of reducing the amount of necessary memory for the representation of a given piece of information. This process is of great utility especially in digital storage and transmission of the multimedia information and it typically involves various encoding/decoding schemes. In this work we will be primarily focused on some compression schemes which employ specific forms of clustering known as fuzzy clustering. In the data mining context, fuzzy clustering is a versatile tool which analyzes heterogeneous collections of data providing insights on the underlying structures involving the concept of partial membership. Several models employing the fuzzy clustering techniques in data compression systems are demonstrated and image compression based on fuzzy transforms for compression and decompression of color videos is described in details.

关键词： fuzzy clustering algorithms data compression compression rate image compression image clustering

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Enhancing Pedestrian Traffic Analysis with fuzzy clustering and WEKA Data Mining Techniques 2

Enhancing Pedestrian Traffic Analysis with Fuzzy Clustering ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Nachappa, M.N. Sharma, Anu University Karnataka Bangalore India College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350369960

In this paper, we address the burgeoning field of Computer Vision and AI with an innovative approach to automated human behaviour detection, a critical component in the development of intelligent systems. Our research focuses on distinguishing human actions in dynamic settings, a task that has gained importance due to the extensive visual data captured by Closed Circuit Television (CCTV) systems. We introduce a sophisticated model for recognizing human activities by analyzing trajectory data extracted from video sequences within the CAVIAR dataset. The core of our proposed model is a threetiered process encompassing feature extraction, behaviour learning, and trajectory segmentation. Initially, the model employs fuzzy logic to partition the trajectory, aligning with the dynamic motion characteristics present in the dataset. Subsequently, it extracts temporal and spatial features critical for understanding human movements. To evaluate our model's performance, we simulate four distinct pedestrian behaviors and utilize the RepTree algorithm - a decision tree method known for its efficiency and accuracy in handling complex data. The model's efficacy is demonstrated through its ability to capture instantaneous features and to discern variations across different regions, resulting in high accuracy in activity detection. This work not only contributes to advancements in the domains of ML and data mining by refining feature extraction methods but also enhances the interpretation capabilities of AI systems for realworld surveillance and security applications. Our findings offer promising insights into the development of more autonomous and context-aware surveillance systems, bridging the gap between static observational data and dynamic behavioural analysis. © 2023 IEEE.

关键词： fuzzy clustering algorithms Pedestrian Path Analysis Spatial Data Partitioning Techniques trajectory WEKA Data Mining Applications

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Multi-fuzzy clustering validity index ensemble: A Dempster-Shafer theory-based parallel and series fusion

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EGYPTIAN INFORMATICS JOURNAL 2023年第4期24卷

作者： Wang, Hong-Yu Wang, Jie-Sheng Wang, Guan Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

clustering validity evaluation is a key part in clustering process. To adapt the complex data structure, the traditional fuzzy clustering validity index (FCVI) is designed more complex. The weighted combined validity evaluation method (WCVEM) is simple in structure but difficult in weight selection. Therefore, this paper proposed an ensemble method based on multi-fuzzy clustering algorithms and multi-FCVI. Firstly, multi-FCVI are calculated by using the multiple sets of cluster centers and membership degrees that obtained by multi-fuzzy clustering algorithms. This can improve the robustness of the multi-FCVI. Secondly, multi-FCVI are ensembled by Dempster-Shafer (DS) theory. The validity index basic probability assignment function can be obtained by calculating the credibility of each validity index with different clusters number. Finally, the decision module is used to output the optimal clusters number. This paper ensembles multi-fuzzy clustering algorithms, multi-FCVI, and the DS theory by using series and parallel structure to verify performance of the proposed model and the degree of information retention of the FCVI. The proposed method is simple in structure and does not need to be select weighted. 6 artificial datasets and 12 UCI datasets were selected to simulate and verify the method. When facing different data, the simulation results show that the parallel structure has the highest accuracy, and the series structure is even worse than the weighted method in some datasets. In addition, the paper changes the value of fuzzy weighted, and experimental results show that the ensemble method has better stability than other methods in the face of different fuzzy weighted strategy.

关键词： fuzzy clustering validity index Information ensemble fuzzy clustering algorithms D-S evidence theory Parallel and serial ensemble

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Improved FIFO Scheduling Algorithm Based on fuzzy clustering in Cloud Computing

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INFORMATION 2017年第1期8卷 25页

作者： Li, Jian Ma, Tinghuai Tang, Meili Shen, Wenhai Jin, Yuanfeng Nanjing Univ Informat Sci & Technol Sch Comp Software Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Engn Ctr Network Monitoring CICAEET Nanjing 210044 Jiangsu Peoples R China Natl Meteorol Informat Ctr Beijing 100080 Peoples R China YanBian Univ Dept Math Yanji 133002 Peoples R China

In cloud computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO) scheduling algorithm. To reduce tasks' waiting time, we propose a task scheduling algorithm based on fuzzy clustering algorithms. We construct a task model, resource model, and analyze tasks' preference, then classify resources with fuzzy clustering algorithms. Based on the parameters of cloud tasks, the algorithm will calculate resource expectation and assign tasks to different resource clusters, so the complexity of resource selection will be decreased. As a result, the algorithm will reduce tasks' waiting time and improve the resource utilization. The experiment results show that the proposed algorithm shortens the execution time of tasks and increases the resource utilization.

关键词： cloud computing fuzzy clustering algorithms FIFO scheduling algorithm resource clustering

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Cluster-based fuzzy models for groundwater flow in the unsaturated zone

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ADVANCES IN WATER RESOURCES 2007年第4期30卷 701-714页

作者： Vernieuwe, H. Verhoest, N. E. C. De Baets, B. Hoeben, R. De Troch, F. P. Univ Ghent Lab Hydrol & Water Management B-9000 Ghent Belgium Univ Ghent Dept Appl Math Biometr & Proc Control B-9000 Ghent Belgium

In this paper fuzzy models are used as an alternative to describe groundwater flow in the unsaturated zone. The core of these models consists of a fuzzy rule-based model of the Takagi-Sugeno type. Various fuzzy clustering algorithms are compared in the data-driven identification of these Takagi-Sugeno models. The performance of the resulting fuzzy models is evaluated on the training surface on which they were identified, and on time series measurements of water content values obtained through an experiment carried out by the non-vegetated terrain (NVT) workgroup of the European Microwave Signature Laboratory (EMSL) (see [Mancini M, Hoeben R, Troch PA. Multifrequency radar observations of bare surface soil moisture content: a laboratory experiment. Water Resour Res 1999;35(6):1827-38] and [Hoeben R, Troch PA. Assimilation of active microwave observation data for soil moisture profile estimation. Water Resour Res 2000;36(10):2805-19]). Despite higher errors at the borders of high water content values in the training surface, good results are obtained on the simulation of the time series. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： fuzzy clustering algorithms fuzzy rule-based models Takagi-Sugeno models unsaturated groundwater flow

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Novel protection scheme of stator single-phase-to-ground fault for powerformers

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期53卷 321-328页

作者： Wang, Yuanyuan Zeng, Xiangjun Dong, Zhaoyang Huang, Yue Changsha Univ Sci & Technol Sch Elect & Informat Engn Hunan Prov Key Lab Smart Grids Operat & Control Changsha 410114 Hunan Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Chongqing Univ Coll Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China

This paper presents a novel stator single-phase-to-ground fault protection of Powerformer in parallel based on the fuzzy clustering algorithms. First, the direction and magnitude of zero-sequence current and leakage current are analyzed, and four fault characters are selected as historical data. Then the historical data are divided into two groups by fuzzy clustering algorithms, and cluster center of each group is calculated. The space relative distance among detected pattern and two cluster centers is finally calculated to discriminate the faulty Powerformer. Simulation results have shown that, under different fault conditions, the new scheme can distinguish reliably internal faults from external faults, and can detect stator single-phase-to-ground fault occurred in which Powerformer with resistance 5 k Omega. A 100% of the winding can be fully protected. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Powerformer Stator single-phase-to-ground fault fuzzy clustering algorithms

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EFFECTIVE algorithms FOR THE NEAREST-NEIGHBOR METHOD IN THE clustering PROBLEM

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PATTERN RECOGNITION 1993年第5期26卷 741-746页

作者： HATTORI, K TORII, Y Department of Electrical Engineering and Electronics Toyohashi University of Technology Tempaku-cho Toyohashi 441 JapanAuthor to whom correspondence should be addressed.

Two effective algorithms are presented for the nearest neighbor method in the hierarchical agglomerative clustering procedures. One is effective, when the number of clusters into which a data set should be classified is already known. The other is effective to search for several probable clustering solutions, when the number of clusters to be obtained is not known in advance. The computation times of the algorithms are shown to be O(N2) for clustering of N objects. Therefore, the algorithms are very powerful for the nearest neighbor method to classify a large data set.

关键词： AGGLOMERATIVE clustering NEAREST NEIGHBOR METHOD SIMILARITY MATRIX fuzzy clustering algorithms COMPUTATION TIME

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A fuzzy k-partitions model for categorical data and its comparison to the GoM model

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fuzzy SETS AND SYSTEMS 2008年第4期159卷 390-405页

作者： Yang, Miin-Shen Chiang, Yu-Hsuan Chen, Chiu-Chi Lai, Chien-Yo Chung Yuan Christian Univ Dept Appl Math Chungli 32023 Taiwan

The grade of membership (GoM) model uses fuzzy sets as memberships of each individual to extreme profiles (or classes) on the likelihood function of multivariate multinomial distributions. The GoM clustering algorithm derived from the GoM model is used in cluster analysis for categorical data, but it is iterated with complicated calculations. In this paper we create another approach, termed a fuzzy k-partitions (FkP) model, which is also based on the likelihood function of multivariate multinomial distributions. However, the calculations of the FkP algorithm for clustering categorical data derived from the proposed FkP model are simpler. The proposed FkP clustering algorithm is not only easier in calculation than the GoM, but also has more accuracy and computation efficiency. To verify it, we employ real empirical data and also some simulation data. We find that FkP has superior results to GoM. We then apply these two algorithms to classification of pathology. The results show the superiority of the FkP clustering algorithm. Moreover, the proposed FkP algorithm can be used as a fuzzy clustering algorithm for categorical data. Some comparisons between FkP and two popular algorithms, fuzzy k-modes and fuzzy centroids, are made. These results show that the FkP clustering algorithm can be another useful toot in analyzing categorical data. (c) 2007 Elsevier B.V. All rights reserved.

关键词： categorical data grade of membership (GoM) model fuzzy k-partitions (FkP) model fuzzy clustering algorithms fuzzy k-modes fuzzy centroids

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Novel Protection Scheme of Single-Phase Earth Fault for Radial Distribution Systems With Distributed Generators

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IEEE TRANSACTIONS ON POWER DELIVERY 2018年第2期33卷 541-548页

作者： Wang, Yuanyuan Wei, Gen Yang, Hongming Chen, Haowei Ouyang, Zi Changsha Univ Sci & Technol Hunan Prov Engn Res Ctr Elect Transportat & Smart Hunan Prov Key Lab Smart Grids Operat & Control Coll Elect & Informat Engn Changsha 410114 Hunan Peoples R China Univ New South Wales Sch Photovolta & Renewable Energy Engn Sydney NSW 2052 Australia

This paper presents a new method based on the relay agent for single-phase earth fault protection in radial distribution systems with distributed generators. In order to get the most informative fault features to discriminate fault for this system, traditional protection schemes based on different fault features are analyzed. The selection of fault features is discussed. Moreover, the cluster center of historical fault data is calculated to analyze the space distribution of fault data for each feeder by applying a fuzzy clustering algorithm. The space relative distance between the online sample data and the cluster center and the minimum value of the space relative distance among all of the relays at the relay agent are obtained. Finally, the coordination strategy of the relay agent is proposed to discriminate the faulty feeder. The proposed protection scheme is evaluated in a radial distribution network with distributed generations using PSCAD simulation.

关键词： Radial distribution systems distributed generations fuzzy clustering algorithms single-phase earth fault protection

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Accelerating compute-intensive image segmentation algorithms using GPUs

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JOURNAL OF SUPERCOMPUTING 2017年第5期73卷 1929-1951页

作者： Shehab, Mohammed Al-Ayyoub, Mahmoud Jararweh, Yaser Jarrah, Moath Jordan Univ Sci & Technol Dept Comp Sci Irbid Jordan Jordan Univ Sci & Technol Dept Comp Engn Irbid Jordan

Image segmentation is an important process that facilitates image analysis such as in object detection. Because of its importance, many different algorithms were proposed in the last decade to enhance image segmentation techniques. clustering algorithms are among the most popular in image segmentation. The proposed algorithms differ in their accuracy and computational efficiency. This paper studies the most famous and new clustering algorithms and provides an analysis on their feasibility for parallel implementation. We have studied four algorithms which are: fuzzy C-mean, type-2 fuzzy C-mean, interval type-2 fuzzy C-mean, and modified interval type-2 fuzzy C-mean. We have implemented them in a sequential (CPU only) and a parallel hybrid CPU-GPU version. Speedup gains of 6x to 20x were achieved in the parallel implementation over the sequential implementation. We detail in this paper our discoveries on the portions of the algorithms that are highly parallel so as to help the image processing community, especially if these algorithms are to be used in real-time processing where efficient computation is critical.

关键词： Image segmentation GPUs Performance evaluation fuzzy clustering algorithms

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