• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.2028-2042
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• 1994

Properly defining signal and noise in a self-organizing system like ART(Adaptive Resonance Theory) neural network model raises a number of subtle issues. Pattern context must enter the definition so that input features, treated as irrelevant noise when they are embedded in a given input pattern, may be treated as informative signals when they are embedded in a different input pattern. The ATR automatically self-scales their computational units to embody context and learning dependent definitions of a signal and noise and there is no problem in categorizing input pattern that have features similar in nature. However, when we have imput patterns that have features that are different in size and nature, the use of only one vigilance parameter is not enough to differentiate a signal from noise for a good categorization. For example, if the value fo vigilance parameter is large, then noise may be processed as an informative signal and unnecessary categories are generated: and if the value of vigilance parameter is small, an informative signal may be ignored and treated as noise. Hence it is no easy to achieve a good pattern categorization. To overcome such problems, a Coupled-ART neural network capable of modularized categorization of patterns is proposed. The Coupled-ART has two layer of tightly coupled modules. the upper and the lower. The lower layer processes the global features of a pattern and the structural features, separately in parallel. The upper layer combines the categorized outputs from the lower layer and categorizes the combined output, Hence, due to the modularized categorization of patterns, the Coupled-ART classifies patterns more efficiently than the ART1 model.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1021-1028
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• 2005

The general solution for classification and regression problems can be found by matching and modifying matrices with the information in real world and then these matrices are teaming in neural networks. This paper treats primary space as a real world, and dual space that Primary space matches matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Further more the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-Curve, which are well known for getting regularization parameter, and kernel methods. Both GCV and L-Curve have excellent performance to get regularization parameters, and the performances are similar although they show little bit different results from the different condition of problems. However, these methods are two-step solution because both have to calculate the regularization parameters to solve given problems, and then those problems can be applied to other solving methods. Compared with UV and L-Curve, kernel methods are one-step solution which is simultaneously teaming a regularization parameter within the teaming process of pattern weights. This paper also suggests dynamic momentum which is leaning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV and L-Curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.291-300
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• 1999

인터넷을 기반으로 한 정보통신의 급속한 발전이라는 기업환경의 변화에 적응하기 위해서 기업은 점차 모든 경영시스템을 인터넷을 기반으로 하도록 변화시키고 있을 뿐만 아니라, 기업 조직 또한 전세계를 기반으로한 글로벌 기업 형태로 변화하고 있다. 이러한 급속한 경영환경의 변화로 인해서 기업 내에서는 종전과는 다른 형태의 부서간 상호의사결정조정 과정이 필요하게 되었다. 일반 기업들을 대상으로 한 상호의사결정의 지원과정에 대해서는 기존에 많은 연구들이 있었으나 글로벌기업과 같은 네트워크 형태의 새로운 형태의 기업에 있어서의 상호의사결정과정을 지원할 수 있는 의사결정지원시스템에 대해서는 단순한 그룹의사결정지원시스템 또는 분산의사결정지원시스템과 같은 연구들이 주를 이루고 있다. 따라서 본 연구에서는 인터넷 특히, 웹을 기반으로 한 기업의 글로벌경영 및 분산 경영에서 비롯되는 부서간 상호의사결정이라는 문제를 효율적으로 지원할 수 있는 기업의 글로벌경영 및 분산 경영에서 비롯되는 부서간 상호의사결정이라는 문제를 효율적으로 지원할 수 있는 메커니즘을 제시하고 이에 기반한 프로토타입 형태의 시스템을 구현하여 성능을 검증하고자 한다. 특히, 기업 내에서 가장 대표적으로 상호의사결정지원이 필요한 생산과 마케팅 부서를 대상으로 상호의사결정지원 메커니즘을 개발하고 실험을 진행하였다. 그 결과 글로벌 기업내의 생산과 마케팅 부서간 상호의사결정을 효율적으로 지원 할 수 있는 상호조정 메카니즘인 개선된 PROMISE(PROduction and Marketing Interface Support Environment)를 기반으로 한 웹 분산의사결정지원시스템 (Web-DSS : Web-Decision Support Systems)을 제안하는 바이다.자대상 벤처기업의 선정을 위한 전문가시스템을 구축중이다.의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.139-148
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• 1999

오늘날 기술집약적인 벤처기업들에 대한 관심이 집중되고 있다. 소수의 진취적인 벤처기업들이 기술개발 및 신상품 개발 등 두드러진 활약을 보이고 있기 때문이다. 그러나 실제 이 벤처기업의 성공 가능성은 그렇게 높지 않다. 특히 벤처기업 환경이 아직 미약한 국내의 경우 위험부담이 훨씬 더 크다. 이러한 벤처기업 환경에서 투자대상 벤처기업을 선정하는 것은 매우 전략적인 의사결정이다. 일반적으로 일반 벤처투자가들은 관심이 있는 산업에 해당하는 기업의 사업계획서와 기초적인 관련 정보를 토대로 투자여부를 결정한다. 그렇지만 실제로는 이와 같은 분석에 필수적으로 요구되는 정보가 불확실할 뿐만 아니라 기술분야에 대한 전문적 지식도 부족하기 때문에 투자 여부를 결정하는 것은 매우 복잡하고 어려운 문제이다. 그러므로 투자대상 벤처기업의 선정을 효과적으로 지원해주는 체계적인 접근이 필요하다. 특히 벤처 사업과 관련된 기술 동향 및 수준 등에 관련된 전문 지식과 경험이 체계적으로 제공되어야 하고 또한 벤처 투자가의 개인적 경험과 판단이 평가 프로세스에 직접적으로 반영될 수 있어야 한다. 이에 본 연구에서는 전문가의 지식과 경험을 체계화하고 투자가의 개인적 판단을 효과적으로 수용할 수 있는 전문가시스템의 접근방법을 제시하고자 한다. 투자대상 벤처기업의 선정을 위한 전문가시스템을 구축하기 위해 본 연구에서는 다양한 정보수집 과정을 거쳤다. 우선 벤처 투자와 관련된 기존 문헌을 심층 분석하였으며 아울러 벤처 투자 업계에서 활약중인 전문 벤처캐피탈리스트들과의 수차례 인터뷰를 통해 벤처기업 평가의 주요 요인과 의사결정 과정을 파악할 수 있었다. 이러한 과정을 통하여 본 연구에서는 벤처 투자의 90%를 차지하는 정보통신분야에 속한 기법 중에서 투자대상 벤처기업의 선정을 위한 전문가시스템을 구축중이다.의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and in

• Journal of the Korean earth science society
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• v.32 no.7
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• pp.860-870
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• 2011

This study is to explore the Pedagogical Content Knowledge of beginning earth science teachers about the porphyritic texture of igneous rocks, and to suggest the teaching device that can prevent a trial and error of students in earth science instruction. We developed an interview guideline concerned with basic perception on the porphyritic texture, formation condition and formation process of porphyritic rocks, teaching and learning on porphyritic rocks for it. And data was collected from 5 beginning earth science teachers (3 high schools, 2 middle schools) through a group discussion method. In result, despite the porphyritic texture can be found at hypabyssal rocks as well as volcano rocks and plutonic rocks, most beginning earth science teachers cognized that it could be found at hypabyssal rocks only by focusing the formation depth of hypabyssal rocks. Also, the formation of porphyritic texture should be considered the factors such as cooling rate, nucleation density, growth rate, growth time, etc. However they mainly reflected the formation temperature and growth rate as it's parameter. Participants have wrongly perceived that a phenocryst necessarily differs from a groundmass on chemical composition. And they are inclined to discriminate phenocryst from groundmass through their chemical differences, instead of grain size.

• The Journal of Engineering Geology
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• v.23 no.2
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• pp.137-147
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• 2013

For the effective management of groundwater resources, it is necessary to predict groundwater level fluctuations in response to rainfall events. In the present study, time series models using artificial neural networks (ANNs) and support vector machines (SVMs) have been developed and applied to groundwater level data from the Gasan, Shingwang, and Cheongseong stations of the National Groundwater Monitoring Network. We designed four types of model according to input structure and compared their performances. The results show that the rainfall input model is not effective, especially for the prediction of groundwater recession behavior; however, the rainfall-groundwater input model is effective for the entire prediction stage, yielding a high model accuracy. Recursive prediction models were also effective, yielding correlation coefficients of 0.75-0.95 with observed values. The prediction errors were highest for Shingwang station, where the cross-correlation coefficient is lowest among the stations. Overall, the model performance of SVM models was slightly higher than that of ANN models for all cases. Assessment of the model parameter uncertainty of the recursive prediction models, using the ratio of errors in the validation stage to that in the calibration stage, showed that the range of the ratio is much narrower for the SVM models than for the ANN models, which implies that the SVM models are more stable and effective for the present case studies.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.335-345
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• 1999

오늘날 날로 증가하는 물류비는 개별 기업은 물론 국가 전체의 수출 경쟁력을 약화시키는 주요 원인으로 지적되고 있다. 그러나 그동안 우리나라에서는 물류비 절감을 위한 종합적이고 체계적인 대책이 이루어지지 못하였다. 특히 본 논문의 연구대상인 육상물류의 경우 그 비중이 전체 화물 운송의 60% 이상을 차지함에도 불구하고 심각한 교통체증 및 물류기반 시설의 미비 등으로 인하여 물류비가 계속 증가하는 양상을 보여 왔다. 따라서 본 논문에서는 우리나라 육상물류시스템이 안고 있는 문제점의 해결을 위한 방안들 중의 하나로 정보기술의 활용에 관한 내용을 다루고 있다. 즉 영세한 기업들도 누구나 손쉽게 이용할 수 있도록 인터넷을 이용한 육상물류중개시스템의 개발에 관한 내용을 소개하고 있다. 육상물류중개시스템은 복합화물주선업체인 (주) 대형물류와 함께 개발한 시스템으로 인터넷을 통하여 화주의 화물 운송의뢰를 접수받아 이를 여러 운송업체에게 제공해 주는 역할을 수행하게 된다. 특히 육상물류중개시스템은 화물의 운송과 관련하여 발생하는 다양한 정보들을 데이터베이스에 저장하여 두었다가 세관을 비롯한 터미널에 대한 각종 신고업무에 이용할 수 있으며, 이밖에도 교통정보 및 화물 위치정보 등 다양한 서비스를 제공해 줄 수 있다. 따라서 운송업체의 공차율을 줄이고 화주에게는 자신의 화물에 대한 정보를 실 시간으로 전달해 줄 수 있다는 장점이 있다. 또한 이러한 육상물류중개시스템은 현재 개발중인 통합데이터베이스를 기반으로한 항만물류원스톱서비스 시스템과 연계되어 차후에는 물류원스톱시스템으로 발전할 수 있을 것이다.용되어져 왔다. 그러나 MCRDR 이론이 적용된 전문가시스템들의 경우 MCRDR이론을 기본으로한 개발 툴로서 개발된 시스템들이 아니고 해당분야에서 MCRDR이론을 적용한 엔진을 직접 설계 구현하여 온 것이 사실이다. KEE(Knowledge Engineer for Experts) 시스템은 최근 개발된 MCRDR기반 전문가시스템 개발 툴로서 본 논문에서는 이러한 분야별 전문가시스템 개발을 지양하고 MCRDR 이론을 기반으로 한 범용성 있는 전문가시스템 개발 툴의 개발에 관한 연구를 소개한다.-based Data Mining Architecture를 제시하였다. 본 연구의 의의로는 데이터 마이닝을 통한 귀납적 지식생성에 있어 귀납적 오류의 발생을 도메인 지식을 통해 설명가능 함을 보임으로 검증하고 아울러 이러한 설명을 통해 연역적으로 새로운 가설지식을 생성시켜 이를 가설검증방식으로 검증함으로써 귀납적 접근과 연역적 접근의 통합 데이터 마이닝 접근을 제시하였다는데 있다.osed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which