• 제목/요약/키워드: Self-Organizing Map (SOM)

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SOM을 이용한 제품수명주기 기반 서비스 수요예측 (Product Life Cycle Based Service Demand Forecasting Using Self-Organizing Map)

  • 장남식
    • 지능정보연구
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    • 제15권4호
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    • pp.37-51
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    • 2009
  • 서비스 인적자원 운용의 효율성 제고와 부품 또는 시설 할당의 적정성 향상을 위해 서비스센터를 통해 접수되는 서비스 요청 건수를 보다 정확하게 예측하고자 하는 필요성이 제조업을 중심으로 증가하고 있다. 본 연구에서는 제품의 특성을 반영하여 제품수명주기 별로 제품들을 군집화하고 군집 별로 적절한 예측모형을 구축한 후 예측 값을 통합하는 개별예측방식을 LCD 모니터 제조사의 사례를 통해 제시한다. 또한 예측 결과를 총량방식 및 기존에 기업이 사용하고 있는 방식과 비교. 평가하여 우수성을 증명함으로써 제품이나 산업의 특성을 반영한 맞춤형 수요예측 기법 도입의 필요성을 부각하고, 그에 따른 이론적, 실무적 가이드라인을 제공한다.

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자기조직화 신경망을 이용한 고속도로 유지관리 서비스 등급 개선에 대한 연구 (A Study on Improvement of Level of Highway Maintenance Service Using Self-Organizing Map Neural Network)

  • 신덕순;박승범
    • 한국IT서비스학회지
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    • 제20권1호
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    • pp.81-92
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    • 2021
  • As the degree of economic development of society increases, the maintenance issues on the existing social overhead capital becomes essential. Accordingly, the adaptation of the concept of Level of service in highway maintenance is indispensable. It is also crucial to manage and perform the service level such as road assets to provide universal services to users. In this regards, the purpose of this study is to improve the maintenance service rating model and to focus on the assessment items and weights among the improvements. Particularly, in determining weights, an Analytic Hierarchy Process (AHP) is performed based on the survey response results. After then, this study conducts unsupervised neural network models such as Self-Organizing Map (SOM) and Davies-Bouldin (DB) Index to divide proper sub-groups and determine priorities. This paper identifies similar cases by grouping the results of the responses based on the similarity of the survey responses. This can effectively support decision making in general situations where many evaluation factors need to be considered at once, resulting in reasonable policy decisions. It is the process of using advanced technology to find optimized management methods for maintenance.

실시간 침입탐지를 위한 자기 조직화 지도(SOM)기반 트래픽 속성 상관관계 메커니즘 (Traffic Attributes Correlation Mechanism based on Self-Organizing Maps for Real-Time Intrusion Detection)

  • 황경애;오하영;임지영;채기준;나중찬
    • 정보처리학회논문지C
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    • 제12C권5호
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    • pp.649-658
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    • 2005
  • 네트워크 기반의 공격은 그 위험성과 피해의 규모가 크기 때문에 공격 초기에 빨리 탐지하는 것이 중요하다. 그러나 지도학습 데이터 마이닝을 이용한 네트워크상의 비정상 트래픽을 탐지하는 방법은 방대한 양의 데이터 전처리와 관리자의 분석이 요구되며 관리자의 분석이 정확하다는 보장이 없을 뿐만 아니라 각 네트워크의 실시간 특성을 고려하지 못하기 때문에 탐지의 어려움이 크다. 본 논문에서는 실시간 침입 탐지와 점진적 학습을 위해 비지도학습의 데이터마이닝 기법중 하나인 자기 조직화 지도를 기반으로 트래픽 속성 상관관계 메커니즘을 제안한다. 이는 세 단계로 이루어진다. 첫 번째 단계는 초기 학습이 이루어지는 단계로 비지도 학습을 통하여 성격이 비슷한 트래픽끼리 클러스터링 한 맵을 생성시킨다. 두 번째 단계는 맵의 각 클러스터가 정상과 비정상 트래픽의 클러스터로 구분되기 위해 각 공격별로 추출된 규칙(rule)을 적용하여 맵을 분석한다. 이 규칙은 지도 학습을 통한 규칙 기반의 방법으로, 각 데이터 항목마다 SOM을 이용한 속성별 맵의 상관관계(correlation) 분석을 통해 생성되었다. 마지막으로 분석된 맵을 이용하여 실시간 탐지와 함께 점진적 학습이 이루어지게 된다. 여러 실험을 통하여 비지도 학습과 지도 학습을 결합한 SOM 기반 트래픽 속성 상관관계 메커니즘이 지도 학습에 비해 실시간 탐지에 우수함을 증명하였다.

신속평가방법(Rapid Assessment Method)을 이용한 습지평가 및 평가항목의 개선 (Wetland Assessment and Improvement of Evaluation Index Using Rapid Assessment Method (RAM))

  • 최종윤;김성기;윤종학;주기재
    • 생태와환경
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    • 제50권3호
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    • pp.314-324
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    • 2017
  • 국내 습지의 가치와 등급을 평가하고 적용방안을 고찰하기 위해, 경상남도에 위치한 146개 습지를 대상으로 신속평가방법 (Rapid Assessment Method)을 이용한 습지평가를 수행하였다. 각 습지에서 평가된 8개 대항목과 주변 피복 비율 간 관계를 분석하기 위해 Self-Organizing Map(SOM) 알고리즘을 이용하여 패턴분석을 실시하였다. 총 8개의 항목 중, '식생다양성 야생동물 서식처'와 '미적 레크레이션' 항목 점수가 가장 높았으며, 대부분 2~3등급의 가치를 가지는 것으로 평가되었다. SOM 분석 결과, 식생다양성 야생동물 서식처 항목이 높은 습지에는 대부분 어류 양서 파충류 서식처 항목이 낮은 성향을 보였는데, 이는 어류 등은 식생다양성이 높은 지역을 선호하지 않기 때문인 것으로 사료된다. 습지 내 수생식물의 높은 풍부도는 미적인 부분을 충족시키기 때문에 미적 레크레이션 점수가 높은 습지는 대부분 식생다양성 야생동물 서식처가 높았다. 또한, 침식조절 기능의 경우 홍수 저장 조절 기능과 밀접하게 관련되며, 침식조절 기능이 높은 습지는 홍수 저장 조절 기능 또한 높은 경향을 가진다. 국내 습지에 신속평가방법을 적용한 결과, 일부 항목이 국내 습지 특성이나 범위에 맞지 않아 개선이 요구되며, 습지 보전 측면에서 '접근성'이나 '시각적 개방성' 등 항목들은 점수 체제의 전환이 필요한 것으로 나타났다. 따라서 신속평가방법을 국내 습지에 적용하기 위해서는 항목 내 평가기준의 조정 혹은 세분화, 현실화가 필요한 것으로 판단된다. 향후 신속평가방법을 이용한 등급화는 습지의 보전이나 관리 방안 마련에 중요한 지표로서 활용될 수 있으며, 잔존하는 습지를 보존하여 멸종위기종 등 생물상 유지에 크게 기여할 수 있을 것으로 사료된다.

부분방전 패턴인식기법으로서의 Neural Network 알고리즘 비교 분석 (A Comparative Study on Neural Network Algorithms for Partial Discharge Pattern Recognition)

  • 이호근;김정태
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2004년도 춘계학술대회 논문집 전기설비전문위원
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    • pp.109-112
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    • 2004
  • In this study, the applicability of SOM(Self Organizing Map) algorithm to partial discharge pattern recognition have been investigated. For the purpose, using acquired data from the artificial defects in GIS, SOM algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. As a result, basically BP algorithm was found out to be better than SOM algorithm. Therefore, it is needed to apply SOM algorithm in combination with BP algorithm in order to improve on-site applicability using the advantages of SOM. Also, for the pattern recognition by use of PRPDA(Phase Resolved Partial Discharge Analysis) it is required the normalization of the PRPDA graph. However, in case of the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

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비지도 학습 기법을 사용한 RF 위협의 분포 분석 (Analysis on the Distribution of RF Threats Using Unsupervised Learning Techniques)

  • 김철표;노상욱;박소령
    • 한국군사과학기술학회지
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    • 제19권3호
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    • pp.346-355
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    • 2016
  • In this paper, we propose a method to analyze the clusters of RF threats emitting electrical signals based on collected signal variables in integrated electronic warfare environments. We first analyze the signal variables collected by an electronic warfare receiver, and construct a model based on variables showing the properties of threats. To visualize the distribution of RF threats and reversely identify them, we use k-means clustering algorithm and self-organizing map (SOM) algorithm, which are belonging to unsupervised learning techniques. Through the resulting model compiled by k-means clustering and SOM algorithms, the RF threats can be classified into one of the distribution of RF threats. In an experiment, we measure the accuracy of classification results using the algorithms, and verify the resulting model that could be used to visually recognize the distribution of RF threats.

A New Approach for Hierarchical Dividing to Passenger Nodes in Passenger Dedicated Line

  • Zhao, Chanchan;Liu, Feng;Hai, Xiaowei
    • Journal of Information Processing Systems
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    • 제14권3호
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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.

Relationships between Fish Communities and Environmental Variables in Islands, South Korea

  • Kwon, Yong-Su;Shin, Man-Seok;Yoon, Hee-Nam
    • Proceedings of the National Institute of Ecology of the Republic of Korea
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    • 제3권2호
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    • pp.84-96
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    • 2022
  • Most of the islands of Korea are distributed in the South and West Sea, and it consists of independent small stream. As a result, the fish community that inhabits the island's stream is isolated from the mainland and other island. This study utilized a Self-Organizing Map (SOM) and a random forest model to analyze the relationship between environmental variables and fish communities inhabiting islands in South Korea. Through the SOM analysis, the fish communities were divided into three clusters, and there were differences in biotic and abiotic factors between these groups. Cluster I consisted of sites with relatively larger island areas and a higher number of species and population. It was found that 15 out of 16 indicator species were included. Meanwhile, the remaining clusters had fewer species and populations. Cluster II, especially, showed the lowest impact from physical variables such as water width and depth. As a result of predicting the species richness using the random forest model, physical variables in habitats, such as stream width and water depth, had a relatively higher importance on species richness. On the other hand, forest area was the most important variables for predicting Shannon diversity, followed by maximum water depth, and gravel. The results suggest that this study can be used as basic data for establishing a stream ecosystem management strategy in terms of conservation and protection of biological resources in streams of islands.

Application of data driven modeling and sensitivity analysis of constitutive equations for improving nuclear power plant safety analysis code

  • ChoHwan Oh;Doh Hyeon Kim;Jeong Ik Lee
    • Nuclear Engineering and Technology
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    • 제55권1호
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    • pp.131-143
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    • 2023
  • Constitutive equations in a nuclear reactor safety analysis code are mostly empirical correlations developed from experiments, which always accompany uncertainties. The accuracy of the code can be improved by modifying the constitutive equations fitting wider range of data with less uncertainty. Thus, the sensitivity of the code with respect to the constitutive equations is evaluated quantitatively in the paper to understand the room for improvement of the code. A new methodology is proposed which first starts by dividing the thermal hydraulic conditions into multiple sub-regimes using self-organizing map (SOM) clustering method. The sensitivity analysis is then conducted by multiplying an arbitrary set of coefficients to the constitutive equations for each sub-divided thermal-hydraulic regime with SOM to observe how the code accuracy varies. The randomly chosen multiplier coefficient represents the uncertainty of the constitutive equations. Furthermore, the set with the smallest error with the selected experimental data can be obtained and can provide insight which direction should the constitutive equations be modified to improve the code accuracy. The newly proposed method is applied to a steady-state experiment and a transient experiment to illustrate how the method can provide insight to the code developer.

Multi-Objective Design Exploration and its Applications

  • Obayashi, Shigeru;Jeong, Shin-Kyu;Shimoyama, Koji;Chiba, Kazuhisa;Morino, Hiroyuki
    • International Journal of Aeronautical and Space Sciences
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    • 제11권4호
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    • pp.247-265
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    • 2010
  • Multi-objective design exploration (MODE) and its applications are reviewed as an attempt to utilize numerical simulation in aerospace engineering design. MODE reveals the structure of the design space based on trade-off information. A self-organizing map (SOM) is incorporated into MODE as a visual data mining tool for the design space. SOM divides the design space into clusters with specific design features. This article reviews existing visual data mining techniques applied to engineering problems. Then, we discuss three applications of MODE: multidisciplinary design optimization for a regional-jet wing, silent supersonic technology demonstrator and centrifugal diffusers.