• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.313-315
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• 2000

클러스터링에 있어서 K-means와 FCM(Fuzzy C-means)와 같은 기존의 알고리즘들은 지역적 최소 해에 수렴될 문제와 사전에 클러스터 개수를 결정해야 하는 문제점을 가지고 있다. 본 논문에서는 병렬 탐색을 통해 최적 해를 찾는 진화 알고리즘을 사용하여 지역적 최소 해에 수렴되는 문제점을 개선하였으며, 클러스터의 특성을 표준편차 벡터를 계산하여 중심으로부터 포함된 데이터가 얼마나 분포되어 있는지 알 수 있는 분산도와 임의의 데이터와 모든 중심들간의 거리의 비율로서 얻어지는 소속정도를 고려하여 클러스터간의 간격을 알 수 있는 분리도를 정의함으로써 자동으로 클러스터 개수를 결정할 수 있게 하였다. 실험데이터와 가우시안 분포에 의해 생성된 다차원 실험데이터를 사용하여 제안한 알고리즘이 이러한 문제점들을 해결하고 있음을 보인다.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.221-237
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• 2015

An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.911-917
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• 2009

This paper introduces a sequential clustering technique as a tool for an effective grouping of transmission systems. The interconnected network system retains information about the location of each line. With this information, this paper aims to carry out initial clustering through the transmission usage rate, compare the similarity measures of regional information with the similarity measures of location price, and introduce the techniques of the clustering method. This transmission usage rate uses power flow based on congestion costs and similarity measurements using the FCM(Fuzzy C-Mean) algorithm. This paper also aims to prove the propriety of the proposed clustering method by comparing it with existing clustering methods that use the similarity measurement system. The proposed algorithm is demonstrated through the IEEE 39-bus RTS and Korea power system.

• Journal of Navigation and Port Research
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• v.42 no.2
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• pp.117-126
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• 2018

Container port is one of the most vital link of the transportation chain that plays an important role in trading with other countries. Having a proper understanding of port operations could change the role of the port from a transportation node to an efficient point in a transportation chain. Development of transportation chains, logistics and progress of these networks have enhanced the sustainable condition and level of transportation. Therefore, evaluating the competitiveness of ports is obligatory for port users to make a decision in investment or exploitation. This paper introduces the use of Fuzzy C-means and TOPSIS for competitiveness comparison among a sample of container terminals in Korea and China and determine how to improve Korean port competitiveness and particularly in Busan port.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.620-622
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• 2004

Mobility of an indoor wheeled robot is affected by adhesion force that is related to various floor conditions. When the adhesion force between driving wheels and the floor decreases suddenly, the robot has a slip state. In order to overcome this slip problem, optimal slip velocity must be decided for stable movement of wheeled robot. First of all, this paper shows that conventional PI control can not be applied to a wheeled robot of the light weigh. Secondly, reposed fuzzy logic applied by the Takagi-Sugeno model for the configuration of fuzzy sets. For the design of Takaki-Sugeno model and fuzzy rule, proposed algorithm uses FCM(Fuzzy c-mean clustering method) algorithm. In additionally, this algorithm controls recovered driving torque for the restrain the re-slip. The proposed fuzzy logic controller(FLC) is pretty useful with prevention of the slip phenomena through that compare fuzzy with PI control for the controller performance in the re-adhesion control strategy. These procedures are implemented using a Pioneer 2-DXE wheeled robot parameter.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.82-89
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• 2012

This paper presents the intelligent tracking algorithm for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. Fuzzy c-mean clustering and predicted impact point are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by fuzzy c-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. The filtering process in a series of the algorithm which estimates the target value recognize the nonlinear maneuvering target as linear one because the filter recognize only remained noise by extracting acceleration from the positional error. After filtering process, we get the estimates target by compensating extracted acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. To maximize the effectiveness of the proposed system, we construct the multiple model structure. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.354-359
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• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.363-366
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• 2007

This paper demonstrates a novel application of Fuzzy C-Mean(FCM) to identify the causes of ground faults in Transmission system. The discrimination scheme which can automatically recognize the fault causes is proposed using artificial neural networks. By using the actual fault data, it is shown that the proposed method provides satisfactory results for identifying the fault causes.

• Journal of the Korean association of regional geographers
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• v.12 no.3
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• pp.382-391
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• 2006

The aim of this research is to classify the 21 container ports in European Union countries using components of competition and co-operation under the well-known methodology, FCM(Fuzzy C-Mean). Through this approach, those 21 ports were classified into six poet groups, and also membership degree of each port within the six port groups were suggested. As results, Rotterdam which positioned Group C, is turned out the most competitive independent port. The next competitive group is found out as Group B which consisted of port of Hamburg and Antwerp. In another point of view, Group A and B which have six and four ports respectively, were needed to search the co-operation strategies. Finally, the lowest competitive port groups in the targeted area were shown as Group D and F.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.435-442
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• 2007

To reduce damage from high concentration ozone in the air, we have researched how to predict high concentration ozone before it occurs. High concentration ozone is a rare event and its reaction mechanism has nonlinearities and complexities. In this paper, we have tried to apply and consider as many methods as we could. We clustered the data using the fuzzy c-mean method and took a rejection sampling to fill in the missing and abnormal data. Next, correlations of the input component and output ozone concentration were calculated to transform more correlated components by modified log transformation. Then, we made the prediction models using Dynamic Polynomial Neural Networks. To select the optimal model, we adopted a minimum bias criterion. Finally, to evaluate suggested models, we compared the two models. One model was trained and tested by the transformed data and the other was not. We concluded that the modified transformation effected good to ideal performance In some evaluations. In particular, the data were related to seasonal characteristics or its variation trends.