• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2102-2107
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• 2008

This paper demonstrates a enhanced FCM to identify the causes of ground faults in power distribution systems. The discrimination scheme which can automatically recognize the fault causes is proposed using Fuzzy RBF networks. By using the actual fault data, it is shown that the proposed method provides satisfactory results for identifying the fault causes.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.118-121
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• 1996

This paper describes ECG signal labeling based on Fuzzy clustering, which is necessary at automated ECG diagnosis. The NPPA(Non parametric partitioning algorithm) compares the correlations of wave forms, which tends to recognize the same wave forms as different when the wave forms have a little morphological variation. We propose to apply Fuzzy clustering to ECG QRS Complex labeling, which prevents the errors to mistake by using If-then comparision. The process is divided into two parts. The first part is a parameters extraction process from ECG signal, which is composed of filtering, QRS detection by mapping to a phase space by time delay coordinates and generation of characteristic vectors. The second is fuzzy clustering by FCM(Fuzzy c-means), which is composed of a clustering, an assessment of cluster validity and labeling.

• Journal of Mechanical Science and Technology
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• v.18 no.12
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• pp.2137-2147
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• 2004

The recycling cell formation problem means that disposal products are classified into recycling part families using group technology in their end-of-life phase. Disposal products have the uncertainties of product status by usage influences during product use phase, and recycling cells are formed design, process and usage attributes. In order to deal with the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem for disposal products. Fuzzy C-mean algorithm and a heuristic approach based on fuzzy ART neural network is suggested. Especially, the modified Fuzzy ART neural network is shown that it has a good clustering results and gives an extension for systematically generating alternative solutions in the recycling cell formation problem. Disposal refrigerators are shown as examples.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.998-1007
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• 2018

The advent of Wireless Sensor Networks (WSN) has led to their use in numerous applications. Sensors are autonomous in nature and are constrained by limited resources. Designing an autonomous topology with criteria for economic and energy conservation is considered a major goal in WSN. The proposed honey-hive clustering consumes minimum energy and resources with minimal transmission delay compared to the existing approaches. The honey-hive approach consists of two phases. The first phase is an Intra-Cluster Min-Max Discrepancy (ICMMD) analysis, which is based on the local honey-hive data gathering technique and the second phase is Inter-Cluster Frequency Matching (ICFM), which is based on the global optimal data aggregation. The proposed data aggregation mechanism increases the optimal connectivity range of the sensor node to a considerable degree for inter-cluster and intra-cluster coverage with an improved optimal energy conservation.

• Smart Structures and Systems
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• v.21 no.2
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• pp.225-234
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• 2018

E-Health allows you to supersede the central patient wireless healthcare system. Wireless Body Sensor Network (WBSN) is the first phase of the e-Health system. In this paper, we aim to understand e-Health architecture and configuration, and attempt to minimize energy consumption and latency in transmission routing protocols during restrictive latency in data delivery of WBSN phase. The goal is to concentrate on polling protocol to improve and optimize the routing time interval and schedule communication to reduce energy utilization. In this research, two types of network models routing protocols are proposed - elemental and clustering. The elemental model improves efficiency by using a polling protocol, and the clustering model is the extension of the elemental model that Destruct Supervised Decision Tree (DSDT) algorithm has been proposed to solve the time interval conflict transmission. The simulation study verifies that the proposed models deliver better performance than the existing BSN protocol for WBSN.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.87-96
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• 2019

In this paper, we propose a MapReduce-supported clustering technique for collecting and classifying distributed workflow enactment event logs as a preprocessing tool. Especially, we would call the distributed workflow enactment event logs as Workflow BIG-Logs, because they are satisfied with as well as well-fitted to the 5V properties of BIG-Data like Volume, Velocity, Variety, Veracity and Value. The clustering technique we develop in this paper is intentionally devised for the preprocessing phase of a specific workflow process mining and analysis algorithm based upon the workflow BIG-Logs. In other words, It uses the Map-Reduce framework as a Workflow BIG-Logs processing platform, it supports the IEEE XES standard data format, and it is eventually dedicated for the preprocessing phase of the ${\rho}$-Algorithm that is a typical workflow process mining algorithm based on the structured information control nets. More precisely, The Workflow BIG-Logs can be classified into two types: of activity-based clustering patterns and performer-based clustering patterns, and we try to implement an activity-based clustering pattern algorithm based upon the Map-Reduce framework. Finally, we try to verify the proposed clustering technique by carrying out an experimental study on the workflow enactment event log dataset released by the BPI Challenges.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.263-272
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• 2016

Modular product design has contributed to flexible product modification and development, production lead time reduction, and increasing product diversity. Modular product design aims to develop a product architecture that is composed of detachable modules. These modules are constructed by maximizing the similarity of components based on physical and functional interaction analysis among components. Accordingly, a systematic procedure for clustering the components, which is a main activity in modular product design, is proposed in this paper. The first phase in this procedure is to build a component-to-component correlation matrix by analyzing physical and functional interaction relations among the components. In the second phase, network flow modeling is applied to find clusters of components, maximizing their correlations. In the last phase, a network flow model formulated with linear programming is solved to find the clusters and to make them modular. Finally, the proposed procedure in this research and its application are illustrated with an example of modularization for a vacuum cleaner.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.17-23
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• 2015

In this paper, we propose a mobile app clustering method using word clusters. Considering the quick change of mobile app trends, the proposed method divides the mobile apps into some semantically similar mobile apps by applying a clustering algorithm to the mobile app set, rather than the predefined category system. In order to alleviate the data sparseness problem in the short mobile app description texts, the proposed method additionally utilizes the unigram, the bigram, the trigram, the cluster of each word. For the purpose of accurately clustering mobile apps, the proposed method manages to avoid exceedingly small or large mobile app clusters by using the word clusters. Experimental results show that the proposed method improves 22.18% from 57.48% to 79.66% on overall accuracy by using the word clusters.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1893-1894
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• 2008

Advances in hardware and wireless network technologies have placed us at the doorstep of a new era where small wireless devices will provide access to information anytime, anywhere as well as actively participate in creating smart environments. In this paper, we propose location-aware clustering method in wireless sensor networks. Previous clustering algorithm assumes that all nodes know its own location by GPS. But, it is unrealistic because of GPS module cost and large energy consumption. So, we operate localization ahead of cluster set-up phase. And Considering node density and geographic information, Cluster Heads are elected uniformly. Moreover, communication between CHs is prolonged network lifetime.