• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.89-93
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• 2000

구조 적응형 자기구성 지도는 일반적으로 자기구성 지도의 구조가 초기에 결정되어 학습이 끝날 때까지 변하지 않기 때문에 발생하는 문제를 해결하기 위해 지도의 구조를 학습 중에 적절하게 변경시킨다. 이때, 변화된 구조의 가중치를 어떻게 초기화시킬 것인가 하는 것이 중요한 문제이다. 이 논문에서는 기존의 비교사 학습방법에 LVQ 알고리즘을 이용한 교사 학습방법을 결합한 구조 적응형 자기구성 지도 모델에서 유전자 알고리즘을 이용하여 분화된 노드의 가중치를 결정하는 방법을 제안한다. 이 방법은 기존의 구조 적응형 자기구성 지도 알고리즘보다 빠르게 학습되었고, 인식률 면에서도 기존의 방법보다 높은 값을 나타내었으며, 자기구성 지도의 특성인 위상 보존도 잘 이루어졌다. 오프라인 필기 숫자 데이터로 실험한 결과, 제안한 방법이 유용함을 알 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.642-646
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• 2006

The fuzzy TAM(Topographical Attentive Mapping) network is a supervised method of pattern analysis which is composed of input layer, category layer, and output layer. But if we don't know the target value of the pattern, the network can not be trained. In this case, the target value can be replaced by a result induced by using an unsupervised neural network as the SOM (Self-organizing Map). In this paper, we apply the results of SOM to fuzzy TAM network and show its usefulness through the case study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.73-75
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• 2014

본 논문에서는 원본 초음파 영상에서 스케일을 측정한 후, 영상의 확대 비율을 분석하여 충수염 객체의 크기에 대한 범위를 설정한다. 제안된 방법은 초음파 영상에서 ROI 영역을 추출한 후, 사다리꼴 타입의 소속 함수를 이용한 Fuzzy 이진화와 8방향 윤곽선 추적 기법을 적용하여 잡음을 제거한 후에 근막을 추출한다. 추출된 복부 근육의 근막 하단 경계선을 Cubic Spline 보간법을 이용하여 근막의 하단 영역을 추출한다. 초음파 영상의 근막을 기준으로 근막 영역을 제거한 후, SOM(Self-Organizing Map) 알고리즘을 이용하여 충수염의 후보 영역을 추출한다. 추출된 충수염의 후보 영역에 8방향 윤곽선 추적기법을 적용하여 충수염을 추출한다. 제안된 방법을 초음파 영상에 적용하여 실험한 결과, 기존의 충수염 추출 방법보다 충수염 영역이 비교적 정확히 추출되고 충수염의 크기를 측정할 수 있는 것을 실험을 통하여 확인하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.790-794
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• 2012

우리나라는 여름철에 강우가 편중되어 있고 동고서저의 산악지형으로 수자원확보가 어려운 실정이며 이는 곧 하천의 유지유량확보의 어려움과도 직결된다. 이러한 수자원확보를 위해 최근 기존 저수지 둑을 높이는 사업이 전국적으로 활발히 진행되고 있으며 이는 저수지나 댐의 수체와 같은 수자원을 보다 적극적으로 활용하여 그 가치를 높임과 동시에 하천에 대한 활용도를 높이고자 하는 데 그 목적이 있다. 따라서 저수지나 댐의 저류량에 기여하는 강우량, 유입량과 같은 수문학적 자료의 심도 있는 분석이 필요하며 수문변수들이 나타내는 복잡한 패턴에 대한 연구가 이루어져야 할 것이다. 본 연구에서는 저수지나 댐의 저류량에 직접적으로 영향을 주는 수문변수들을 전체적으로 파악하기 위해 수집된 수문자료의 각각의 특성 및 자료들 사이의 복합적인 관계를 파악하였으며 이를 위하여 패턴분류 분야에서 그 적용타당성이 입증된 자기조직화 지도(Self-Organizing Map: SOM)를 이용하였다. 본 연구의 대상지점은 섬진강 유역내에 위치한 주암호를 대상지점으로 선정하였으며 패턴분석에 사용한 수문자료의 기간은 2007~2010년까지 5년간의 월평균 자료를 활용하였다. SOM의 적용 결과, 측정수문자료에 대한 전체적인 특성을 패턴분류를 통해 분류하였으며, 각 변수에 대한 패턴별 상대성을 고려한 클러스터별 특성 및 시간적 이질성을 파악할 수 있었다. 이는 측정 자료에 대한 분석 기법개발의 일환으로 향후 수자원 확보에 대한 개발 및 정책의 기초자료로 활용될 것으로 기대된다.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.363-373
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• 2023

Numerous studies have established a correlation between sociodemographic characteristics and water usage, identifying population as a primary independent variable in mid- to long-term demand forecasting. Recent dramatic sociodemographic changes, including urban concentration-rural depopulation, low birth rates-aging population, and the rise in single-person households, are expected to impact water demand and supply patterns. This underscores the necessity for operational and managerial changes in existing water supply systems. While sociodemographic characteristics are regularly surveyed, the conducted surveys use aggregate units that do not align with the actual system. Consequently, many water demand forecasts have been conducted at the administrative district level without adequately considering the water supply system. This study presents an upward water demand forecasting model that accurately reflects real water facilities and consumers. The model comprises three key steps. Firstly, Statistics Korea's SGIS (Statistical Geological Information System) data was reorganized at the DMA level. Secondly, DMAs were classified using the SOM (Self-Organizing Map) algorithm to consider differences in water facilities and consumer characteristics. Lastly, water demand forecasting employed the PCR (Principal Component Regression) method to address multicollinearity and overfitting issues. The performance evaluation of this model was conducted for DMAs classified as rural areas due to the insufficient number of DMAs. The estimation results indicate that the correlation coefficients exceeded 0.9, and the MAPE remained within approximately 10% for the test dataset. This method is expected to be useful for reorganization plans, such as the expansion and contraction of existing facilities.

• Korean Journal of Environment and Ecology
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• v.32 no.4
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• pp.425-439
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• 2018

W investigated the environmental factors and inhabiting biota such as macrophytes and zooplankton in 43 sites located on Jeju Island from May and June 2017 to evaluate the spread and ecosystem risk of dotted duckweed (landoltia punctata) which was recently found for the first time in Jeju Island. Dotted duckweeds were found in a total of 18 sites which tended to show low biomass of aquatic macrophyte species other than the dotted duckweed. We conducted a pattern analysis using SOM (Self-Organizing Map), which extracts information through competitive and adaptive properties, to analyze the effect of inhabiting biota on aquatic macrophytes such as the dotted duckweed and environmental factors. The SOM analysis showed that the inhabiting biota such as the zooplankton affected the biomass of aquatic macrophytes than they did the environmental factors. In particular, the biomass of dotted duckweed was positively related to plant-attached species (Alona, Chydorus, and Pleuroxus). Considering that low density of aquatic macrophytes covers the streams and wetlands on Jeju Island because of irregular water source and sharp change of water depth, the dotted duckweeds are likely to play an essential role as the vital habitat for micro-biota including zooplankton in wetlands and streams on Jeju Island. Furthermore, considering that organic matters are utilized as the primary food source in the areas occupied by dotted duckweed, dotted duckweeds have the role of being both habitat and food source. Although the dense growth of dotted duckweed adversely affects growth and development of some aquatic plants due to the shadow effect, it is due to the dominance of floating plants on the water surface should not be regarded as the risk of the dotted duckweed. In conclusion, the dotted duckweeds have spread and settled in most of the water systems on Jeju Island, their impact on inhabiting biota and the aquatic environment was minor. It is necessary to monitor the distribution and spread of dotted duckweeds in the inland areas outside of Jeju Island in the future.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1029-1038
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• 2010

In meteorology Korea has 2/3 of rain of annual total rainfall at the month of Jun through Sept and it has possibility to have serious flood damage because geographically it is composed of mountainous area with steep slope which account for 70% of its country. Also, the increase of impervious layer due to industrialization and urbanization causes direct runoff, which deteriorates contamination of rivers by moving the contaminated material on the surface at the beginning of rain. In particular, the area of leisure facilities needs the management of water quality absolutely because dense population requires space of park function and place to relax and increases moving capability of non-point pollution source. For disposition of rainfall & runoff, the standard of initial rainfall, which is to be used for the computation of disposition volume, is significant factors for the runoff study of non-point pollution source, Until now, a great deal of study has been done by many researchers. However, it is the current reality that the characteristics of runoff varies according to land protection comprising river basin and the standard of initial rainfall by each researcher is not clearly defined yet. Therefore, in this research, it is suggested that, with the introduction of SOM (Self-Organizing Map), the standard of initial rainfall be determined after analyzing each sectional data by executing pattern classification about runoff and water quality data measured at the test river basin for this research.

• Korean Journal of Environmental Biology
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• v.36 no.1
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• pp.21-32
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• 2018

This study was carried out to define the characteristics of the identified ground-dwelling invertebrate communities at Nari basin and Tonggumi area in Ulleungdo Island, designated as a nationally important agricultural heritage. The habitat types were divided into the following categories: crop land, forest, and ecotone, and the soil-dwelling invertebrates were collected according to habitat type. The ground-dwelling invertebrates were collected using a pitfall trap, and a self-organizing map (SOM) was applied to the invertebrates dataset to define the characteristics in invertebrates distribution. The SOM clearly classified the relevant information into four clusters, and extracted ecological information from the invertebrates dataset. The cluster II was composed of invertebrate communities which are collected in the Tonggumi area. The Tonggumi area is where mountainous areas were developed for agricultural purposes, which has geographical features commonly observed in Ulleungdo Island. It is noted that the cluster II has different characteristics as compared other clusters. The results of this study are expected to be used for the preservation of agricultural environment and maintenance of biodiversity by providing basic data, on the biotope of Ulleungdo Island designated as a nationally important agricultural heritage and information on the characteristics of the applicable ground-dwelling invertebrate communities.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.49-54
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• 2006

The recent fingerprint recognition system has unstable factors, such as copy of fingerprint patterns and hacking of fingerprint feature point, which mali cause significant system error. Thus, in this research, we used the fingerprint as the main recognition device and then implemented the multi-biometric recognition system in serial using the speech recognition which has been widely used recently. As a multi-biometric recognition system, once the speech is successfully recognized, the fingerprint recognition process is run. In addition, speaker-dependent DTW(Dynamic Time Warping) algorithm is used among existing speech recognition algorithms (VQ, DTW, HMM, NN) for effective real-time process while KSOM (Kohonen Self-Organizing feature Map) algorithm, which is the artificial intelligence method, is applied for the fingerprint recognition system because of its calculation amount. The experiment of multi-biometric recognition system implemented in this research showed 2 to $7\%$ lower FRR (False Rejection Ratio) than single recognition systems using each fingerprints or voice, but zero FAR (False Acceptance Ratio), which is the most important factor in the recognition system. Moreover, there is almost no difference in the recognition time(average 1.5 seconds) comparing with other existing single biometric recognition systems; therefore, it is proved that the multi-biometric recognition system implemented is more efficient security system than single recognition systems based on various experiments.