• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.3
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• pp.132-136
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• 2014

We introduce a new architecture of TSK-based fuzzy inference system. The proposed model used fuzzy c-means clustering method(FCM) for efficient disposal of data. The premise part of fuzzy rules don't assume any membership function such as triangular, gaussian, ellipsoidal because we construct the premise part of fuzzy rules using FCM. As a result, we can reduce to architecture of model. In this paper, we are able to use four types of polynomials as consequence part of fuzzy rules such as simplified, linear, quadratic, modified quadratic. Weighed Least Square Estimator are used to estimates the coefficients of polynomial. The proposed model is evaluated with the use of Boston housing data called Machine Learning dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.6
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• pp.504-511
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• 2013

This paper proposes improved detection schemes for concealed micro-electronic devices using clustering and classification of radio frequency harmonics in order to protect intellectual property rights. In general, if a radio wave with a specific fundamental frequency is propagated from the transmitter of a classifier to a concealed object, the second and the third harmonics will be returned as the radio wave is reflected. Using this principle, we exploit the fuzzy c-means clustering and the ${\kappa}$-nearest neighbor classification for detecting diverse concealed objects. Simulation results indicate that the proposed scheme can detect electronic devices and metal devices in various learning environments by efficient classification. Thus, the proposed schemes can be utilized as an effective detection method for concealed micro-electronic device to protect intellectual property rights.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.47-57
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• 2010

The object detection is essential for identifying objects, location information, and user context-aware in the image. In this paper, we propose a robust object detection system. The System linearly transforms learning data obtained from the background images to Principal components. It organizes the Eigen-background with the selected Principal components which are able to discriminate between foreground and background. The Fuzzy-C-means (FCM) carries out clustering for images with inputs from the Eigen-background information and classifies them into objects and backgrounds. It used various patterns of backgrounds as learning data in order to implement a system applicable even to the changing environments, Our system was able to effectively detect partial movements of a human body, as well as to discriminate between objects and backgrounds removing noises and shadows without anyone frame image for fixed background.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.177-182
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• 2003

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.7-18
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• 2003

Due to the changes of shipping and logistic environment, Asian ports today face severe competition. To be a mega-hub port, Asian ports have achieved a big scale development. For these reasons, it has been widely recognized as an important study to analyze and evaluate characteristics of Asian ports, from the standpoint of Korea where Busan Port is located. Although some previous studies have been reported, most of them have been beyond the scope of Asian ports and analyzed the world's major ports; moreover, the studied ports have been about the ports which are well known from the previous research and reports. So, most studies is unlikely to be used as substantial indicators from the perspective of Busan Port. In addition. most of the existing studies have used hierarchical evaluation algorithm for port ranking, such as AHP (analytical hierarchy process) and clustering analysis. However, these two methods have fundamental weaknesses from the algorithm perspective. The aim of this study is to classify major Asian ports based on competitiveness level. Especially. in order to overcome serious problem of the existing studies, major Asian ports were analyzed by using objective indicators. and Fuzzy C-Means algorithm, which alleviates the weakness of the clustering method. It was found that 10 ports of 16 major Asian ports have their own phases and were classified into 4 port groups. This result implies that some ports have higher potential as ports to lead some zones in Asia. Based on those results. present status and future direction of Busan port were discussed as well.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.619-624
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• 1998

This paper presents a new clustering algorithm called FCM algorithm for the design of fuzzy controller. FCM is an extended version of FCM(Fuzzy c-Means) algorithm and can estimate the number of clusters automatically and give membership grades $u_{ik}$ suitable for making fuzzy control rules. This paper also shows an example of its application to the line pursuit control of a car.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1025-1028
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• 2000

In this paper, we apply to a genetic algorithm for fuzzy clustering. We propose initialization procedure and genetic operators such as selection, crossover and mutation, which are suitable for solving the problems. To illustrate the effectiveness of the proposed algorithm, we solve the manufacturing cell formation problem and present computational comparisons to generalized Fuzzy c-Means algorithm.

• Journal of IKEEE
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• v.15 no.3
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• pp.191-197
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• 2011

An image segmentation model using fuzzy c-means with intuition (FCM-I) model is proposed for the segmentation of magnetic resonance image in this paper. In FCM-I, a measurement called intuition level is adopted so that the intuition level helps to alleviate the effect of noises. A practical magnetic resonance image data set is used for image segmentation experiment and the performance is compared with those of some conventional algorithms. Results show that the segmentation method based on FCM-I compares favorably to several conventional clustering algorithms. Since FCM-I produces cluster prototypes less sensitive to noises and to the selection of involved parameters than the other algorithms, FCM-I is a good candidate for image segmentation problems.

• Transactions on Electrical and Electronic Materials
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• v.11 no.1
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• pp.42-47
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• 2010

Polymer insulating materials such as cross linked polyethylene (XLPE) are employed in electric cables used for extra high voltage. These materials can degrade due to chemical, mechanical and electric stress, possibly caused by voids, the presence of extrinsic materials and protrusions. Therefore, this study measured discharge patterns, discharge phase angle, quantity and occurrence frequency as well as changes in XLPE under different temperatures and applied voltages. To quantitatively analyze the irregular partial discharge patterns measured, the discharge patterns were examined using a statistical program. A three layer sample was fabricated, wherein the upper and lower layers were composed of non-void XLPE, while the middle layer was composed of an air void and copper particles. After heating to room temperature and $50^{\circ}C$ and $80^{\circ}C$ in silicone oil, partial discharge characteristics were studied by increasing the voltage from the inception voltage to the breakdown voltage. Partial discharge statistical analysis showed that when the K-means clustering was carried out at 9 kV to determine the void discharge characteristics, the amount discharged at low temperatures was small but when the temperature was increased to $80^{\circ}C$, the discharge amount increased to be 5.7 times more than that at room temperature because electric charge injection became easier. An analysis of the kurtosis and the skewness confirmed that positive and negative polarity had counterclockwise and clockwise clustering distribution, respectively. When 5 kV was applied to copper particles, the K-means was conducted as the temperature changed from $50^{\circ}C$ to $80^{\circ}C$. The amount of charge at a positive polarity increased 20.3% and the amount of charge at a negative polarity increased 54.9%. The clustering distribution of a positive polarity and negative polarity showed a straight line in the kurtosis and skewness analyses.