• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2066-2070
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• 2008

본 연구는 인공신경망(Artificial Neural Networks: ANNs)기법의 일종인 자기조직화(Self Organizing Map: SOM) 이론을 이용한다. 자기조직화 특성을 이용하여 스스로 학습이 가능하고, 구조상 수행이 빨라 학습 단계에 소요되는 시간을 줄 일 수 있는 장점을 가진 자기조직화 이론을 도입하고, 수질자료 중 전체 유기물의 양을 나타내며 난분해성 물질에 대한 해석이 가능하고 재현성이 탁월한 TOC 와 강우-유출량 자료의 분포적 양상과 특징을 분석하여 예측을 위한 모형화 과정에 기여하고자 한다. 최적의 Map Size와 Map Array 결정을 위해 수집된 강우와 유출량자료 및 TOC 자료에 대해 Garcia의 경험식을 이용하여 Map을 구성하는 단위구조의 총 수(M)를 산정하여 M값에 따른 종방향 및 횡방향 크기를 결정하는 다수의 Map 크기를 검토하고, 또한 Map 배열은 2차원 배열의 사각형배열(Rectangular array)과 육각형배열(Hexagonal array)에 대해서도 복합적으로 검토하여 최적의 특성조건을 결정하여 강우-유출 및 TOC 관계의 분할특성을 분석한다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.75-83
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• 2007

IEC code based decision nile have been widely applied to detect incipient faults in power transformers. However, this method has a drawback to achieve the diagnosis with accuracy without experienced experts. In order to resolve this problem, we propose an artificial diagnosis algorithm to detect faults of power transformers using Self-Organizing Feature Map(SOM). The proposed method has two stages such as model construction and diagnostic procedure. First, faulty model is constructed by feature maps obtained by unsupervised learning for training data. And then, diagnosis is performed by compare feature map with it obtained for test data. Also the proposed method usぉms the possibility and degree of aging as well as the fault occurred in transformer by clustering and distance measure schemes. To demonstrate the validity of proposed method, various experiments are unformed and their results are presented.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.649-658
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• 2005

Since the Network based attack Is extensive in the real state of damage, It is very important to detect intrusion quickly at the beginning. But the intrusion detection using supervised learning needs either the preprocessing enormous data or the manager's analysis. Also it has two difficulties to detect abnormal traffic that the manager's analysis might be incorrect and would miss the real time detection. In this paper, we propose a traffic attributes correlation analysis mechanism based on self-organizing maps(SOM) for the real-time intrusion detection. The proposed mechanism has three steps. First, with unsupervised learning build a map cluster composed of similar traffic. Second, label each map cluster to divide the map into normal traffic and abnormal traffic. In this step there is a rule which is created through the correlation analysis with SOM. At last, the mechanism would the process real-time detecting and updating gradually. During a lot of experiments the proposed mechanism has good performance in real-time intrusion to combine of unsupervised learning and supervised learning than that of supervised learning.

• Spatial Information Research
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• v.20 no.6
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• pp.23-32
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• 2012

This study applied an exploratory analysis based on Self Organizing Map and GIS to cause specific age-standardized regional death rates data related to ten types of male cancers to find meaning patterns in the data. Then the patterns revealed from the exploratory analysis was evaluated to investigate possible relationship between these patterns and regional socio-economic status represented by regional educational attainment levels of head of household. The results from this analysis show that SI-GUN-GUs in Korea can be clustered to eighteen unique clusters in the stand point of male cancer death rates and these clusters are also spatially clustered. Also, the results reveal that regions with higher socio-economic status show lower level of the death rates compared with the regions with lower socio-economic status. However, for some cancer types, the regions with higher socio-economic status show relatively higher death rates. These patterns imply that the prevention, detection, and treatment of male cancers might be strongly affected by regional factors such as socio-economic status, environmental factors, and cultures and norms in Korea. Especially, one of the eighteen clusters, which includes Gangnam-Gu and Seocho-Gu, shows lower death rates in many of male cancer types. This implies that socio-economic status may be one of the most influential factors for regional cancer control.

• Korean Journal of Ecology and Environment
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• v.50 no.3
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• pp.314-324
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• 2017

In order to consider application and evaluation of value and class of domestic wetland, we investigated 146 wetlands located Gyeongsangnam-do using Rapid Assessment Method (RAM). We utilized Self-Organizing-Map (SOM) to analysis relationship between evaluation index and land coverage ratio surrounding wetland. Among total 8 evaluation index, 'Fish and herptile habitat' and 'Aesthetic value' were higher, most of the wetlands evaluated as 2, 3 grade. Result of SOM analysis, 'vegetation diversity and wild animals habitat' is negatively related to the 'Fish and herptile habitat', because fishs were not prefer habitat excessively occupied by plant. However, high vegetation diversity can be support high score of 'Aesthetic' in wetland. Also, 'Erosion control' and 'Flood storage and control' were closely related, wetlands with high score of 'Erosion control' have high score of 'Flood storage and control'. When applied RAM in domestic wetland, six out of 6 evaluation index induced biased results, the index of RAM need a little change as some new or modify evaluation index. Therefore, we consider to need adjustable, subdivide, and actualization of some evaluation index for application of RAM in domestic wetlands. Consequently, wetland assessment and class using RAM can be utilized as important indicate for conservation and management of wetland, and contributed greatly to maintain biodiversity include to endangered species by preserving remaining wetland.

• Korean Journal of Environment and Ecology
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• v.29 no.3
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• pp.410-420
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• 2015

In order to estimate the influence of environmental factors on zooplankton communities in lentic freshwater ecosystems, 20 reservoirs and wetlands were monitored by season in 2013. A total of 109 species of zooplankton were identified during the study period. Zooplankton assemblage showed a different distribution in its density and diversity in accordance with the seasons. In particular, the density of zooplankton (98 species and 603ind. L-1) was the most in autumn when compared to the other seasons. In order to effectively analyze zooplankton distribution that are affected by various environmental factors, a Self-Organizing Map (SOM) was used, which extracts information through competitive and adaptive properties. A total of 11 variables (8 environment factors and 3 groups of zooplankton) were patterned on to the SOM. Based on a U-matrix, four clusters were identified from the model. Among zooplankton communities, rotifer displayed a positive relationship with water temperature, and cladocerans and copepod were positively related to conductivity, chlorophyll a, and nutrient factor (i. e. TN and TP). In contrast, high dissolved oxygen appeared to have a negative effect on zooplankton distribution. Consequently, the SOM results depicted a clear pattern of zooplankton density clusters partitioned by environmental factors, which play a key role in determining the seasonal distribution of zooplankton groups in lentic freshwater ecosystem.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.245-253
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• 2011

Due to the imprudent spending of the fossil fuels, the environment was contaminated seriously and the exhaustion problems of the fossil fuels loomed large. Therefore people become taking a great interest in alternative energy resources which can solve problems of fossil fuels. The wind power energy is one of the most interested energy in the new and renewable energy. However, the plants of wind power energy and the traditional power plants should be balanced between the power generation and the power consumption. Therefore, we need analysis and prediction to generate power efficiently using wind energy. In this paper, we have performed a research to predict power generation patterns using the wind power data. Prediction approaches of datamining area can be used for building a prediction model. The research steps are as follows: 1) we performed preprocessing to handle the missing values and anomalous data. And we extracted the characteristic vector data. 2) The representative patterns were found by the MIA(Mean Index Adequacy) measure and the SOM(Self-Organizing Feature Map) clustering approach using the normalized dataset. We assigned the class labels to each data. 3) We built a new predicting model about the wind power generation with classification approach. In this experiment, we built a forecasting model to predict wind power generation patterns using the decision tree.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.49-59
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• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• 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.