• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.986-988
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• 1995

The ART(Adaptive Resonance Theory) model is self- organized with nonstationary input patterns in real time. But there is a multirun problem caused by fault clustering, or pertubated clustering and confines the advantage of the stationary real-time processing in ART model. In this paper, we propose the incremental vigilance threshold approach to decrease the number of multiruns. The incremental vigilance threshold approach is to learn with incremental vigilance threshold and competition with clusters.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.3
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• pp.139-145
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• 1993

Partitioning is an important step in the hierarchical design of very large scale integrated circuits. In this research, a new effective partitioning algorithm based on 2-level hierarchy is presented. At the beginning, clusters are formed to reduce the problem size. To overcome the weakness of the iterative improvement techniques that the partitioning result is dependent on the initial partitioning and to consistently produce good results, the cluster-level partitioning is performed several times using several sets of parameters. Then the best result of cluster-partitioning is used as the initial solution for lower level partitioning. For each partitioning, the gradual constraint enforcing partitioning method has been used. The clustering-based partitioning algorithm has been applied to several benchmark examples and produced promising results which show that this algorithm is efficient and effective.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1396-1403
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• 2011

It is not easy to analyze the strong nonlinear time series and effectively design a good prediction system especially due to the difficulties in handling the potential uncertainty included in data and prediction method. To solve this problem, a new design method for fuzzy prediction system is suggested in this paper. The proposed method contains the followings as major parts ; the first-order difference detection to extract the stable information from the nonlinear characteristics of time series, the fuzzy rule generation based on the hierarchically classifying clustering technique to reduce incorrectness of the system parameter identification, and the IT2TSK fuzzy logic system to reasonably handle the potential uncertainty of the series. In addition, the design of the multiple predictors is considered to reflect sufficiently the diverse characteristics concealed in the series. Finally, computer simulations are performed to verify the performance and the effectiveness of the proposed prediction system.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.439-446
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• 2015

Recently, the importance of one-class classification problem is more increasing. However, most of existing algorithms have the limitation on providing the information that effects on the prediction of the target value. Motivated by this remark, in this paper, we suggest an efficient one-class classifier using hyper-rectangles (H-RTGLs) that can be produced from intervals including observations. Specifically, we generate intervals for each feature and integrate them. For generating intervals, we consider two approaches : (i) interval merging and (ii) clustering. We evaluate the performance of the suggested methods by computing classification accuracy using area under the roc curve and compare them with other one-class classification algorithms using four datasets from UCI repository. Since H-RTGLs constructed for a given data set enable classification factors to be visible, we can discern which features effect on the classification result and extract patterns that a data set originally has.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.894-896
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• 2010

In mobile wireless sensor network, Whole nodes can move. In mobile wireless sensor network based on clustering, there can be frequent re-configuration of cluster according to frequent changes of location. Frequent reconfiguration of the cluster cause a lot of power consumption and data loss. To solve this problem, we suggest relay method for sending reliable data and decreases a number of re-configuration of cluster using relay node.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1046-1051
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• 1994

It is known that SOFM has the property of effectively creating topographically the organized map of various features on input signals, SOFM can effectively be applied to the recognition of Korean phonemes. However, is isn't guaranteed that the network is sufficiently learned in SOFM algorithm. In order to solve this problem, we propose the learning algorithm combined with the conventional K-means clustering algorithm in fine-tuning stage. To evaluate the proposed algorithm, we performed speaker dependent recognition experiment using six phoneme classes. Comparing the performances of the Kohonen's algorithm with a proposed algorithm, we prove that the proposed algorithm is better than the conventional SOFM algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4624-4644
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• 2019

With the development of films, games and animation industry, analysis and reuse of human motion capture data become more and more important. Human motion segmentation, which divides a long motion sequence into different types of fragments, is a key part of mocap-based techniques. However, most of the segmentation methods only take into account low-level physical information (motion characteristics) or high-level data information (statistical characteristics) of motion data. They cannot use the data information fully. In this paper, we propose an unsupervised framework using both low-level physical information and high-level data information of human motion data to solve the human segmentation problem. First, we introduce the algorithm of CFSFDP and optimize it to carry out initial segmentation and obtain a good result quickly. Second, we use the ACA method to perform optimized segmentation for improving the result of segmentation. The experiments demonstrate that our framework has an excellent performance.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.106-116
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• 2021

To increase the quality of the system in the subjective-type question grading and document classification, we need the paragraph detection. But it is not easy because it is accompanied by semantic analysis. Many researches on the paragraph detection solve the detection problem using the word based clustering method. However, the word based method can not use the order and dependency relation between words. This paper suggests the paragraph detection system using syntactic-semantic relation between words with the Korean syntactic-semantic analysis. This system is the hybrid system of word based, concept based, and syntactic-semantic tree based detection. The experiment result of the system shows it has the better result than the word based system. This system will be utilized in Korean subjective question grading and document classification.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.476-487
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• 2009

The virtual memory system in 4.4 BSD operating systems exploits a clustering scheme to reduce disk I/Os in paging out (or flushing) modified pages that are intended to be replaced in order to make free rooms in memory. Upon the page out of a victim page, the scheme stores a cluster (or group) of modified pages contiguous with the victim in the virtual address space to swap disk at a single disk write. However, it fails to find large clusters of contiguous pages if applications change pages not adjacent with each other in the virtual address space. To address the problem, we propose a new clustering scheme called Multiple-Object Clustering (MOC), which together stores multiple clusters in the virtual address space at a single disk write instead of paging out the clusters to swap space at separate disk I/Os. This multiple-cluster transfer allows the virtual memory system to significantly decrease disk writes, thus improving the page-out performance. Our experiments in the FreeBSD 6.2 show that MOC improves the execution times of realistic benchmarks such as NS2, Scimark2 SOR, and nbench LU over the traditional clustering scheme ranging from 9 to 45%.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.87-97
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• 2024

This study analyzed the water shortage hotspot areas in South Korea using spatial clustering analysis for water shortage estimates in 2030 of the Master Plans for National Water Management. To identify the water shortage cluster areas, we used water shortage data from the past maximum drought (about 50-year return period) and performed spatial clustering analysis using Local Moran's I and Getis-Ord Gi*. The areas subject to spatial clusters of water shortage were selected using the cluster map, and the spatial characteristics of water shortage areas were verified based on the p-value and the Moran scatter plot. The results indicated that one cluster (lower Imjin River (#1023) and neighbor) in the Han River basin and two clusters (Daejeongcheon (#2403) and neighbor, Gahwacheon (#2501) and neighbor) in the Nakdong River basin were found to be the hotspot for water shortage, whereas one cluster (lower Namhan River (#1007) and neighbor) in the Han River Basin and one cluster (Byeongseongcheon (#2006) and neighbor) in the Nakdong River basin were found to be the HL area, which means the specific area have high water shortage and neighbor have low water shortage. When analyzing spatial clustering by standard watershed unit, the entire spatial clustering area satisfied 100% of the statistical criteria leading to statistically significant results. The overall results indicated that spatial clustering analysis performed using standard watersheds can resolve the variable spatial unit problem to some extent, which results in the relatively increased accuracy of spatial analysis.