• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.3-4
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• 2015

A self-organizing map (SOM) is a good tool to visualize applied data in the form of a feature map. With the help of such functions, a disaster risk diagnosis based on the residential house structure is tried in this study. According to some computer simulations with actual residential data, it is found that overall tendencies in the developed feature map are acceptable. Then, it is concluded that the proposed method is an effective means to estimate disaster risk appropriately.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.408-415
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• 2020

In this study, the AE-SOM method, which combines auto-encoder and self-organizing map, is used to detect and diagnose faults in EVA production process. Then, the fault propagation pathways are identified using Granger causality test. One year and seven months of operation data were obtained to detect faults of the process, and the process variables of the autoclave reactor are mainly analyzed. In the data pretreatment process, the data are standardized and 200 samples of each grade are randomly chosen to obtain a fault detection model. After that, the best matching unit (BMU) of each grade is confirmed by applying AE-SOM. The faults are determined based on each BMU. When a fault is found, the most causative variable of the fault is identified by using a contribution plot, and the fault propagation pathway is identified by Granger causality test. The prognostic of the two shutdowns is detected, and the fault propagation pathway caused by the faulty variable was analyzed.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.440-448
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• 2006

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantization, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.94-103
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• 2019

Self Organizing Map (SOM) is a neural network that is effective in classifying patterns that form the feature map by extracting characteristics of the input data. In this study, we propose an algorithm to determine the cell formation and the machine layout within the cell for the cell formation problem with operation sequence using the SOM. In the proposed algorithm, the output layer of the SOM is a one-dimensional structure, and the SOM is applied to the parts and the machine in two steps. The initial cell is formed when the formed clusters is grouped largely by the utilization of the machine within the cell. At this stage, machine cell are formed. The next step is to create a flow matrix of the all machine that calculates the frequency of consecutive forward movement for the machine. The machine layout order in each machine cell is determined based on this flow matrix so that the machine operation sequence is most reflected. The final step is to optimize the overall machine and parts to increase machine layout efficiency. As a result, the final cell is formed and the machine layout within the cell is determined. The proposed algorithm was tested on well-known cell formation problems with operation sequence shown in previous papers. The proposed algorithm has better performance than the other algorithms.

• Journal of IKEEE
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• v.12 no.4
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• pp.217-224
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• 2008

This paper provides a framework for semantic correspondence of heterogeneous databases using self- organizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to clustering using edit distance algorithm, principal component analysis (PCA), and normalization function to identify the features necessary for clustering.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.115.4-115
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• 2001

This paper deals with recognizing ten korean voiced vowels using Self Organizing Map. SOM is a good classifier. The output layer is composed of two dimensions. The input vector is the frequency values having the characteristic of voiced vowels. The short time frequency transform is used getting input vector. The final neural networks is attached SOM output layer.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.9-18
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• 2005

In order to systematically and visually understand well-known but qualitative and rotatively complicated relationships between synoptic fields in the BAIU season and heavy rainfall events in Japan, these synoptic fields were classified using the Self-Organizing Map (SOM) algorithm. This algorithm can convert complex nonlinear features into simple two-dimensional relationships, and was followed by the application of the clustering techniques of the U-matrix and the K-means. It was assumed that the meteorological field patterns be simply expressed by the spatial distribution of wind components at the 850 hPa level and Precipitable Water (PW) in the southwestern area including Kyushu in Japan. Consequently, the synoptic fields could be divided into eight kinds of patterns (clusters). One of the clusters has the notable spatial feature represented by high PW accompanied by strong wind components known as Low-Level Jet (LLJ). The features of this cluster indicate a typical meteorological field pattern that frequently causes disastrous heavy rainfall in Kyushu in the rainy season. From these results, the SOM technique may be an effective tool for the classification of complicated non-linear synoptic fields.

• Korean Journal of Environmental Biology
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• v.26 no.3
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• pp.203-213
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• 2008

Benthic macro invertebrate communities were collected at six different sampling sites in the Musucheon stream in Korea from July 2006 to July 2007, and ecological exergy values were calculated based on five different functional feeding groups (collector-gatherer, collector-filterer, predator, scrapper, and shredder) of benthic macro invertebrates. Each sampling site was categorized to three stream types (perennial, intermittent and drought) based on the water flow condition. Exergy values were low at all study sites right after a heavy rain and relatively higher in the perennial stream type than in the intermittent or the drought stream type. Self-Organizing Map (SOM), unsupervised artificial neural network, was implemented to pattern spatial and temporal dynamics of ecological exergy of the study sites. SOM classified samples into four clusters. The classification reflected the effects of floods and droughts on benthic macroinvertebrate communities, and was mainly related with the stream types of the sampling sites. Exergy values of each functional feeding group also responded differently according to the different stream types. Finally, the results showed that exergy is an effective ecological indicator, and patterning changes of exergy using SOM is an effective way to evaluate target ecosystems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.251-254
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• 2001

본 논문에서는 Self Organizing Map을 이용하여 fMRI data를 분석해 보았다. fMRl (functional Magnetic Resonance Imaging)는 인간의 뇌에 대한 비 침투적 연구 방법 중 최근에 각광받고 있는 것이다. Motor task를 수행하고 있는 피험자로부터 image data를 얻어내어 SOM을 적용하여 clustering한 결과 motor cortex 영역이 뚜렷하게 clustering 되었음을 알 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.325-327
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• 2012

스마트 홈은 단순한 가정 내 네트워크 연결이 아닌 주택(건물)내의 정보 기술 요소를 구현하는 토털 홈 정보 제어 시스템 서비스, 솔루션을 총칭한다. 현재는 언제, 어디서, 어떤 기기로건 인터넷에 접속할 수 있는 유비쿼터스(Ubiquitous) 시대이자, 개별 사물들이 인터넷에 연결되어 스스로 필요한 정보를 주고받게 될 시대가 도래함에 따라 사람들의 주요 생활공간에서도 활용도가 점차 커지는 것이다. 수시로 변화하는 상황에 적응하며 정확도가 높은 스마트 서비스의 제공을 위해서는 사용자의 의도에 부합하는 Semantic-Context 정보생성을 위한 SOM(Self-Organizing Map)추론 방식의 알고리즘과 정보의 의미화로 다양한 서비스를 지원할 수 있는 인프라 대비 최대 서비스가 요구된다. 이에 따라 본 논문에서는 스마트 홈에서 이종 가전기기들의 상황정보를 센서 데이터로부터 추출하여 사용자 맞춤형 서비스를 제공하기 위한 SOM 추론 기반의 스마트 홈을 설계한다.