• Korean Journal of Ecology and Environment
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• v.55 no.4
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• pp.285-293
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• 2022

Sampling gears for collecting fish are diverse, and the community of fish varies according to the selection and characteristics of the sampling gears. The present study compared the characteristics of fish communities in Yedang reservoir using four sampling gears (kick net, cast net, gill net, and fyke net). The kick net and cast net were inefficient in collecting the number of individuals. However, they increased the species diversity of fish inhabiting the waterfront. Although not many individuals were collected, the gill net mainly collected large fish. The largest number of individuals was collected in the fyke net, and the dominance was high due to the high species selectivity. Through Self-Organizing Map (SOM) analysis, large fish were collected in the gill net, whereas small fish were collected in the fyke net. The characteristics and efficiency of the fish differed depending on the sampling gears. It is expected that researchers will need to use it appropriately according to the characteristics of the sampling gears when investigating the fish community.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.358-366
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Korean Journal of Ecology and Environment
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• v.43 no.4
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• pp.477-491
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• 2010

Benthic macroinvertebrates were collected two times from 116 sites at the $1^{st}{\sim}4^{th}$ order streams in forest areas in Gyungsang province in late spring and late summer. The sample sites belonged to slightly and moderately polluted states. When communities were classified by the Self-Organizing Map (SOM), the gradient was observed according to degree of pollution. Within clusters of slightly polluted sites, however, seasonality was further observed. Scrapers, gatherer-collectors, and filterer-collectors were abundantly observed in late spring while shredders appeared more in late summer. The number of predator species increased in late summer. Behavior types were mostly clingers in two seasons. Community compositions at the moderately polluted sites were not much differentiated in different seasons. Gatherer-collectors and burrowers were dominantly collected in both seasons.

• Proceedings of the Microbiological Society of Korea Conference
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• 2005.05a
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• pp.195.3-195
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• 2005

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.573-575
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• 2004

This study presents a new customer segmentation method based on features that can predict the customer's buying behavior. In this method, we consider all variables that can affect the customer's buying behavior including demographics, psychographics, technographics, transaction pattern-related variables, etc. We define several features which are the combination of variables with the interaction effect by using C5.0, use SOM (Self-Organizing Map) neural networks in odor to extract the feature's patterns and classify, and then make features' rules using C5.0 far the prediction of customer buying behavior

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.148-151
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• 2004

This study presents a new intelligent yield management methodology which can forecast the yield level of a production unit based on features' behaviors. In this proposed methodology, we identify the existing features using C5.0 that are combination of nodes (i.e., variables) in the decision tree generated by C5.0, use SOM(Self-Organizing Map) neural networks in oder to extract the feature's patterns and classify, and then make features' control rules using C5.0.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.137-140
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• 2011

시설의 안전성 평가에 대한 연구는 안전성에 영향을 주는 데이터를 정량화하여 획일적인 자동 수행하는 안전관리가 주를 이루고 있다. 이와 달리 자율수행은 수집 된 상황 정보나 상태 데이터를 이용하여 안전성을 예측하고 사고 위험성을 경보하여 사고를 예방 할 수 있다. 본 연구에서는 다양한 시설물 중에서 가스배관의 부식에 대한 판단을 위해서 신경망의 대표적 비지도학습인 자기조직화지도를 적용한다. SOM의 적용에서는 주변효과를 보완하기 위해서 관계적관점지도로 맵을 구성한다. 학습 할 데이터는 가스배관의 방식전위이다. 배관의 부식상태를 확인하기 위하여 수집 된 데이터인 방식전위에는 부식에 대한 위험요인이 내재되어 있다. 학습 후 새로운 데이터가 입력되면 각 상태 군집의 중심뉴런과 맵핑된 뉴런의 유사도를 측정하여 배관의 부식상태를 결정한다. 제안 된 방법으로 판단 된 결과를 기존에 사람이 판단한 결과와 비교하여 검증한다. 이를 통해 배관의 부식상태를 자율적이고 신속하게 판단하여 지능화 된 가스배관 관리로 활용한다.

• Science of Emotion and Sensibility
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• v.14 no.2
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• pp.197-206
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• 2011

The purpose of the study was to suggest automatic recognition of the subject's level of arousal into high arousal and low arousal with neural network SOM learning. The automatic recognition in the level of arousal is composed of three stages. First, it is a stage of ECG measurement and analysis. It measures the subject playing a shooting game with ECG and extracts characteristics for SOM learning. Second, it is a stage of SOM learning. It learns input vectors extracting characteristics. Finally, it is a stage of arousal recognition which recognize the subject's level of arousal when new vectors are input after SOM learning is completed. The study expresses recognition results in the level of arousal and the level of arousal in numerical value and graph when SOM learning results in the level of arousal and new vectors are input. Finally, SOM evaluation was analyzed average 86% by comparing emotion evaluation results of the existing research with automatic recognition results of SOM in order. The study could experience automatic recognition with other levels of arousal by each subject with SOM.

• Journal of Climate Change Research
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• v.9 no.3
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• pp.209-221
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• 2018

The characteristics of heat wave events in Seoul are analyzed using weather station data from Korea Meteorological Administration (KMA) and European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data from 1979 to 2016. Heat waves are defined as events in the upper 10th percentile of the daily maximum temperatures. The associated synoptic weather patterns are then classified into six clusters through Self-Organizing Map (SOM) analysis for sea-level pressure anomalies in East Asia. Cluster 1 shows an anti-cyclonic circulation and weak troughs in southeast and west of Korea, respectively. This synoptic pattern leads to southeasterly winds that advect warm and moist air to the Korean Peninsula. Both clusters 2 and 3 are associated with southerly winds formed by an anti-cyclonic circulation over the east of Korea and cyclonic circulation over the west of Korea. Cluster 4 shows a stagnant weather pattern with weak winds and strong insolation. Clusters 5 and 6 are associated with F?hn wind resulting from an anti-cyclonic circulation in the north of the Korean Peninsula. In terms of long-term variations, event frequencies of clusters 4 and 5 show increasing and decreasing trends, respectively. However, other clusters do not show any long-term trends, indicating that the mechanisms that drive heat wave events in Seoul have remained constant over the last four decades.