• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1798-1804
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• 2017

This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm's performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.273-276
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• 1996

We propose a new method to extract valid lines from a visual image. Unsupervised clustering method is used to assign each line to any of the line groups according to its orientation. During the low-level image processing we use an adaptive threshold method to reduce human supervision and to automate the processing sequence. To reduce the misclassification rate and to suppress the superiors line support regions at the clustering stage, the adaptive threshold method is consistently applied. Performing principal component analysis on each line support region provides an efficient method of obtaining line equation. Finally we adopt the theory of robust statistics to guarantee the quality of each extracted line and to eliminate the lines of poor quality. We present the experimental results to verify our method. With the proposed method, one can extract the lines according to the internal orientation similarities and integrate the whole process into one adaptive procedure.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.59-69
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• 2005

Data mining technique is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. It has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on off-line or on-line and so on. We analyze Gyeongnam social indicator survey data by 2001 using twostep clustering technique for environment information. The twostep clustering is classified as a partitional clustering method. We can apply these twostep clustering outputs to environmental preservation and improvement.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.1-11
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• 2006

Data mining technique is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. It has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on off-line or on-line and so on. We analyze Gyeongnam social indicator survey data by 2001 using twostep clustering technique for environment information. The twostep clustering is classified as a partitional clustering method. We can apply these twostep clustering outputs to environmental preservation and improvement.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.10
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• pp.119-124
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• 2000

One of the main problems in 3D shape measuring systems that use the triangulation of line-shaped laser light is precise center line detection of line-shaped laser stripe. The intensity of a line-shaped laser light stripe on the CCD image varies following to the reflection angles, colors and shapes of objects. In this paper, a new center line detection algorithm to compensate the local intensity variation on a line-shaped laser light stripe is proposed. The 3-D surface shape measuring system using the proposed center line detection algorithm can measure 3-D surface shape with enhanced measurement resolution by using the dynamic shape reconstruction with adaptive pattern clustering of the line-shaped laser light. This proposed 3-D shape measuring system can be easily applied to practical situations of measuring 3-D surface by virtue of high speed measurement and compact hardware compositions.

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.655-670
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• 2014

In this paper, we propose an efficient and robust lane detection method for detecting immediate left and right lane boundaries of the lane in the roads. The proposed method are based on hyperbolic lane model and the reliable line segment clustering. The reliable line segment cluster is determined from the most probable cluster obtained from clustering line segments extracted by the efficient LSD algorithm. Experiments show that the proposed method works robustly against lanes with difficult environments such as ones with occlusions or with cast shadows in addition to ones with dashed lane marks, and that the proposed method performs better compared with other lane detection methods on an CMU/VASC lane dataset.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.97-108
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• 2003

Cluster analysis has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on on-line or off-line and so on. Clustering can identify dense and sparse regions among data attributes or object attributes. But it requires many hours to get clusters that we want, because of clustering is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new method of clustering using sample based on grid. It is more fast than any traditional clustering method and maintains its accuracy. It reduces running time by using grid-based sample. And other clustering applications can be more effective by using this methods with its original methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.9
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• pp.441-448
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• 2005

On-line dynamic security assessment is becoming more and more important for the stable operation of power systems as load level increases. The necessity is getting apparent under Electricity Market environments, as operation of power system is exposed to more various operating conditions. For on-line dynamic security assessment, fast transient stability analysis tool is required for contingency selection. The TEF(Transient Energy Function) method is a good candidate for this purpose. The clustering of critical generators is crucial for the precise and fast calculation of energy margin. In this paper, we propose a new method for fast decision of mode of instability by using stability indices. Case study shows very promising results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.95-101
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• 2022

The two-sided assembly line balancing problem is a kind of NP-hard problem. This problem primarily can be solved metaheuristic method. This paper suggests parallel clustering algorithm that each left and right-sided workstation assigned by operations with T_i = c^* ± α < c, c^* = ${\lceil}$W/m^*${\rceil}$ such that M^* = ${\lceil}$W/c${\rceil}$ for precedence diagram of two-sided assembly line with total complete time W and cycle time c. This clustering performs forward direction from left to right or reverse direction from right to left. For the 4 experimental data with 17 cycle times, the proposed algorithm can be obtain the minimum number of workstations m^* and can be reduce the cycle time to T_max < c then metaheuristic methods. Also, proposed clustering algorithm maximizes the line efficiency and minimizes the variance between workers operation times.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.150-158
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• 1994

In this paepr, Fuzzy Constraint Line Clustering (FCLC) method for optical flow estimation is proposed. FCLC represents the spatical and temporal gradients as fuzzy sets. Based on these sets, several constraint lines with different membership values are generated for the poxed whose velocity is to be estimated. We describe the process for obtaining the membership values of the spatial and temporal gradients and that of the corresponding constraint line. We also show the process for deciding the tightest cluster of point formalated by intersection between constraint lines. For the synthetic and real images, the results of FCLC are compared with of CLC.