• Journal of Information Processing Systems
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• v.14 no.3
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• pp.694-708
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• 2018

China possesses a passenger dedicated line system of large scale, passenger flow intensity with uneven distribution, and passenger nodes with complicated relations. Consequently, the significance of passenger nodes shall be considered and the dissimilarity of passenger nodes shall be analyzed in compiling passenger train operation and conducting transportation allocation. For this purpose, the passenger nodes need to be hierarchically divided. Targeting at problems such as hierarchical dividing process vulnerable to subjective factors and local optimum in the current research, we propose a clustering approach based on self-organizing map (SOM) and k-means, and then, harnessing the new approach, hierarchical dividing of passenger dedicated line passenger nodes is effectuated. Specifically, objective passenger nodes parameters are selected and SOM is used to give a preliminary passenger nodes clustering firstly; secondly, Davies-Bouldin index is used to determine the number of clusters of the passenger nodes; and thirdly, k-means is used to conduct accurate clustering, thus getting the hierarchical dividing of passenger nodes. Through example analysis, the feasibility and rationality of the algorithm was proved.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1293-1302
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• 1999

In this paper, an on-line process scheme is presented for implementation of a intelligent on-line modeling of nonlinear complex system. The proposed on-line process scheme is composed of FNN-based model algorithm and PLC-based simulator, Here, an adaptive fuzzy-neural networks and HCM(Hard C-Means) clustering method are used as an intelligent identification algorithm for on-line modeling. The adaptive fuzzy-neural networks consists of two distinct modifiable sturctures such as the premise and the consequence part. The parameters of two structures are adapted by a combined hybrid learning algorithm of gradient decent method and least square method. Also we design an interface S/W between PLC(Proguammable Logic Controller) and main PC computer, and construct a monitoring and control simulator for real process system. Accordingly the on-line identification algorithm and interface S/W are used to obtain the on-line FNN model structure and to accomplish the on-line modeling. And using some I/O data gathered partly in the field(plant), computer simulation is carried out to evaluate the performance of FNN model structure generated by the on-line identification algorithm. This simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.252-254
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• 2007

This paper introduces the method of sequential clustering as a tool for the effective clustering of mass unit electrical 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 results of similarity measures for regional information with similarity measures for regional price, and introduce the technicalities of the clustering method. This transmission usage rate used power flow based on congestion costs and modified similarity measurements using the FCM 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.

• 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.

• Korean Journal of Poultry Science
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• v.49 no.3
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• pp.157-165
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• 2022

Chickens are a species of vertebrate with varying colors. Various colors of chickens must be classified to find color-related genes. In the past, color scoring was performed based on human visual observation. Therefore, chicken colors have not been measured with precise standards. In order to solve this problem, a computer vision approach was used in this study. Image quantization based on k-means clustering for all pixels of RGB values can objectively distinguish inherited colors that are expressed in various ways. This study was also conducted to determine whether plumage color differences exist in the reciprocal cross lines between two breeds: black Yeonsan Ogye (YO) and White Leghorn (WL). Line B is a crossbred line between YO males and WL females while Line L is a reciprocal crossbred line between WL males and YO females. One male and ten females were selected for each F₁ line, and full-sib mating was conducted to generate 883 F₂ birds. The results indicate that the distribution of light and dark colors of k-means clustering converged to 7:3. Additionally, the color of Line B was lighter than that of Line L (P<0.01). This study suggests that the genes underlying plumage colors can be identified using quantification values from the computer vision approach described in this study.

• Journal of KIISE:Software and Applications
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• v.27 no.6
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• pp.660-667
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• 2000

In this paper, a word segmentation method for handwritten Korean text line images is proposed. The method uses gap information to segment words in line images, where the gap is defined as a white run obtained after vertical projection of line images. Each gap is assigned to one of inter-word gap and inter-character gap based on gap distance. We take up three distance measures which have been proposed for the word segmentation of handwritten English text line images. Then we test three clustering techniques to detect the best combination of gap metrics and classification techniques for Korean text line images. The experiment has been done with 305 text line images extracted manually from live mail pieces. The experimental result demonstrates the superiority of BB(Bounding Box) distance measure and sequential clustering approach, in which the cumulative word segmentation accuracy up to the third hypothesis is 88.52%. Given a line image, the processing time is about 0.05 second.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.2
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• pp.108-113
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• 2003

Research results have demonstrated that conventional client-server databases have scalability problem in the presence of many concurrent clients. The multi-tier architecture that exploits similarities in clients' object access behavior partitions clients into logical clusters according to their object request pattern. As a result, object requests that are served inside the clusters, server load and request response time can be optimized. Management of clustering by utilizing clients' access pattern-based is an important component for the system's goal. Off-line methods optimizes the quality of the global clustering, the necessary cost and clustering schedule needs to be considered and planned carefully in respect of stable system's performance. In this paper, we propose methods that detect changes in access behavior and optimize system configuration in real time. Finally this paper demonstrates the effectiveness of on-line change detection and results of experimental investigation concerning reconfiguration.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.191-196
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• 1997

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-Means(FCM) algorithm is most frequently used for fuzzy clustering. But some fixed point of FCM algorithm, know as Tucker's counter example, is not a reasonable solution. Moreover, FCM algorithm is impossible to perform the on-line learning since it is basically a batch learning scheme. This paper presents unsupervised learning networks as an attempt to improve shortcomings of the conventional clustering algorithm. This model integrates optimization function of FCM algorithm into unsupervised learning networks. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of a fuzzy objective function. Using the result of formal derivation, two algorithms of fuzzy cluster analysis, the batch learning version and on-line learning version, are devised. They are tested on several data sets and compared with FCM. The experimental results show that the proposed algorithms find out the reasonable solution on Tucker's counter example.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.