• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.572-587
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• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1130-1132
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• 1997

This paper proposed an application of artificial neural networks to security assessment(SA) in power system. The SA is a important factor in power system operation, but conventional techniques have not achieved the desired speed and accuracy. Since the SA problem involves classification, pattern recognition, prediction, and fast solution, it is well suited for Kohonen neural network application. Self organizing feature map(SOFM) algorithm in this paper provides two dimensional multi maps. The evaluation of this map reveals the significant security features in power system. Multi maps of multi prototype states are proposed for enhancing the versatility of SOFM neural network to various operating state.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.577-579
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• 2003

본 논문에서는 생체인식 분야 중 얼굴인식의 검색 정확성 향상 및 검색 시간을 단축하기 위한 단계로 인종별 얼굴영상 데이터베이스에 대한 군집화 기법을 연구하였다. 우선, 일반적으로 얼굴 및 이미지 검색에 사용되는 다양한 특징을 추출하고, 추출한 다차원의 특징 데이터들로부터 다 인종 얼굴 데이터를 유사한 인종별로 정확하게 군집화 하기 위해 최적의 특징벡터를 자동으로 선택 할 수 있는 방법을 제안하였다. 군집결과 분석을 위해 자기 조직화 지도 모형을 이용하였는데, 이는 2차원 분석 및 가시화에 유용하며, 학습 후 코드북벡터를 사용하여 유사한 의미간의 거리부터 검색할 수 있는 특징을 가지고 있다. 특징추출에 관한 실험결과 인종별 구분을 위한 특징벡터로는 웨이블릿 주파수 성분(lowpass 성분)과 CbCr 특징벡터가 인종별 군집화에 가장 유용한 특징으로 선택되었으며. 추출된 특징을 바탕으로 semantic map을 구성하여 제안방법의 효율성을 제시하였다.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.10
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• pp.515-522
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• 2004

In this study, we tried to investigate that the advantages of SOM(Self Organizing Map) algorithm such as data accumulation ability and the degradation trend trace ability would be adaptable to the analysis of partial discharge pattern recognition. For the purpose, we analyzed partial discharge data obtained from the typical artificial defects in GIS and XLPE power cable system through SOM algorithm. As a result, partial discharge pattern recognition could be well carried out with an acceptable error by use of Kohonen map in SOM algorithm. Also, it was clarified that the additional data could be accumulated during the operation of the algorithm. Especially, we found out that the data accumulation ability of Kohonen map could make it possible to suggest new patterns, which is impossible through the conventional BP(Back Propagation) algorithm. In addition, it is confirmed that the degradation trend could be easily traced in accordance with the degradation process. Therefore, it is expected to improve on-site applicability and to trace real-time degradation trends using SOM algorithm in the partial discharge pattern recognition

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2061-2065
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• 2008

본 연구는 강우-유출 및 TOC의 패턴 분류를 위하여 광주 광산 강우관측소의 강우량자료와 나주지점의 유출량 그리고 기존의 BOD 및 COD 수질농도 측정값에 비하여 적은 오차요인과 빠른 시간에 결과 값을 얻을 수 있으며 유출량과 난분해성 물질에 대한 해석이 가능하고 재현성이 탁월한 TOC자료를 사용하였다. SOM을 적용하기 위해 먼저 Map의 크기는 Garcia가 제시한 $M=5{\sqrt{N}}$을 이용하여 결정한다. 이러한 비선형적인 다변량 자료를 분석하기 위해서 Map에 의해 구분된 자료 위치를 추출하여 원자료를 재구축하고 이를 통해 원자료를 패턴별로 분류 할 수 있었다. 이러한 패턴별 분류를 통해 유출량에 따른 TOC자료를 2차원의 Map 상에 시각적으로 가시화하여 비선형적인 경향이 강한자료의 분포적 양상을 이해하는데 큰 도움이 되며, 향후 이를 통해 예측을 위한 모형화 과정에도 크게 도움을 줄 것으로 기대된다. 또한, 강우자료 또는 유출량 자료만을 이용한 단일변량의 패턴분류를 위해 SOM의 적용이 가능할 것으로 판단되며, 이는 각 변량의 본질적인 특성을 파악할 수 있을 것으로 기대된다.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1039-1043
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• 2005

Despite the many significant advances made in robot architecture, the basic approaches are deliberative and reactive methods. They are quite different in recognizing outer environment and inner operating mechanism. For this reason, they have almost opposite characteristics. Later, researchers integrate these two approaches into hybrid architecture. In such architecture, Reactive module also called low-level motion control module have advantage in real-time reacting and sensing outer environment; Deliberative module also called high-level task planning module is good at planning task using world knowledge, reasoning and intelligent computing. This paper presents a framework of the integrated planning and control for mobile robot navigation. Unlike the existing hybrid architecture, it learns topological map from the world map by using MST (Minimum Spanning Tree)-based SOFM (Self-Organizing Feature Map) algorithm. High-level planning module plans simple tasks to low-level control module and low-level control module feedbacks the environment information to high-level planning module. This method allows for a tight integration between high-level and low-level modules, which provide real-time performance and strong adaptability and reactivity to outer environment and its unforeseen changes. This proposed framework is verified by simulation.

• Journal of Korea Port Economic Association
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• v.26 no.1
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• pp.43-60
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• 2010

The purpose of this paper is to show the clustering measurement way for Korean container terminals by using neural network based SOM(Self Organizing Map). Inputs[Number of Employee, Quay Length, Container Terminal Area, Number of Gantry Crane], and output[TEU] are used for 3 years(2002,2003, and 2004) for 8 Korean container terminals by applying both DEA and SOM models. Empirical main results are as follows: First, the result of DEA analysis shows the possibility for clustering among the terminals and reference terminals except Gamcheon and Gwangyang terminals because of the locational closeness. Second, the result of neural network based SOM clustering analysis shows the positive clustering in clustering positions 1, 2, 3, 4, and 5. Third, the results between SOM clustering and DEA clustering show the matching ratio about 67%. The main policy implication based on the findings of this study is that the port policy planner of Ministry of Land, Transport and Maritime Affairs in Korea should introduce the clustering measurement way for the Korean container terminals using neural network based SOM with DEA models for clustering Korean ports and terminals.

• Korean Journal of Environment and Ecology
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• v.32 no.3
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• pp.274-288
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• 2018

This study investigated the environmental factors and fish assemblage in 42 wetlands between spring and autumn of 2017 to evaluate the fish distribution and deduce the management strategy for improving biodiversity in created wetlands located at the Nakdong River basin. The investigation identified a total of 30 fish species and found that the most of wetlands were dominated by exotic fishes such as Micropterus salmoides and Lepomis macrochirus. Fish species such as Rhinogobius brunneus, Opsariichthys uncirostris amurensis, Zacco platypus were less abundant in the area with high density of Micropterus salmoides (static area) because they preferred the environment with active water flow. The pattern analysis of fish distribution in each wetland using the self-organizing map (SOM) showed a total of 24 variables (14 fish species and 10 environmental variables). The comparison of variables indicated that the distribution of fish species varied according to water depth and plant cover rate and was less affected by water temperature, pH, and dissolved oxygen. The plant cover rate was strongly associated with high fish density and species diversity. However, wetlands with low plant biomass had diversity and density of fish species. The results showed that the microhabitat structure, created by macrophytes, was an important factor in determining the diversity and abundance of fish communities because the different species compositions of macrophytes supported diverse fish species in these habitats. Based on the results of this study, we conclude that macrophytes are the key components of lentic freshwater ecosystem heterogeneity, and the inclusion of diverse plant species in wetland construction or restoration schemes will result in ecologically healthy food webs.

• Journal of the Korean Society of Safety
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• v.34 no.2
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• pp.48-55
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• 2019

For preventing the accidents generated from the chemical materials, thus far, MSDS (Material Safety Data Sheet) data have been made to notify how to use and manage the hazardous and chemical materials in safety. However, it is difficult for users who handle these materials to understand the MSDS data because they are only listed based on the alphabetical order, not based on the specific factors such as similarity of characteristics. It is limited in representing the types of chemical materials with respect to their characteristics. Thus, in this study, a lots of MSDS data are visualized based on relationships of the characteristics among the chemical materials for supporting safety managers. For this, we used the textmining algorithm which extracts text keywords contained in documents and the Self-Organizing Map (SOM) algorithm which visually addresses textual data information. In the case of Occupational Safety and Health Administration (OSHA) in the United States, the guide texts contained in MSDS documents, which include use information such as reactivity and potential risks of materials, are gathered as the target data. First, using the textmining algorithm, the information of chemicals is extracted from these guide texts. Next, the MSDS map is developed using SOM in terms of similarity of text information of chemical materials. The MSDS map is helpful for effectively classifying chemical materials by mapping prohibited and hazardous substances on the developed the SOM map. As a result, using the MSDS map, it is easy for safety managers to detect prohibited and hazardous substances with respect to the Industrial Safety and Health Act standards.