• Journal of Korean Society on Water Environment
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• v.30 no.5
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• pp.491-502
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• 2014

As meteorology is the driving force for lake thermodynamics and mixing processes, the effects of climate change on the physical limnology and associated ecosystem are emerging issues. The potential impacts of climate change on the physical features of a reservoir include the heat budget and thermodynamic balance across the air-water interface, formation and stability of the thermal stratification, and the timing of turn over. In addition, the changed physical processes may result in alteration of materials and energy flow because the biogeochemical processes of a stratified waterbody is strongly associated with the thermal stability. In this study, a novel modeling framework that consists of an artificial neural network (ANN), a watershed model (SWAT), a reservoir operation model(HEC-ResSim) and a hydrodynamic and water quality model (CE-QUAL-W2) is developed for projecting the effects of climate change on the reservoir water temperature and thermal stability. The results showed that increasing air temperature will cause higher epilimnion temperatures, earlier and more persistent thermal stratification, and increased thermal stability in the future. The Schmidt stability index used to evaluate the stratification strength showed tendency to increase, implying that the climate change may have considerable impacts on the water quality and ecosystem through changing the vertical mixing characteristics of the reservoir.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2001.04a
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• pp.168-168
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• 2001

제지공정의 지절 현상은 많은 공정 변수들이 복합적으로 작용하여 발생하는 가장 큰 공정 트러블 중의 하나이다. 지절은 생산량 감소 뿐만 아니라 발생 후 공정의 복구 와 정리, 생산재가동 및 공정의 재안정화를 위해 많은 시간과 비용, 그리고 노력이 투 입되어야 하므로 공정의 효율과 생산성을 크게 저하시키는 요인이다. 그러나 지절 현상 의 복잡성 때문에 이에 대해 쉽게 접근하거나 해결하지 못하고 있는 것이 현실이지만 그 필요성은 더욱 더 증대되고 있다. 본 연구에서는 최근 들어 각종 산업분야에서 복잡 한 공정상의 결점 발견 및 진단에 효과적이라고 인정받고 있는 예측 분석기법인 인공 신경망(artificial neural network) 시율레이션과 일반적인 통계기법 중의 하나인 주성분 분석을 이용하여 제지 공정의 지절 현상의 검토 가능성을 타진하였다. 인공신경망이란 인간두뇌에서 일어나는 자극-반응-학습과정을 모사하여 현실세계에 존재하는 다양한 현상들의 업력벡터와 출력상태 간의 비선형 mapping올 컴퓨터 시율 레이션을 통하여 분석하고자 하는 기법으로, 여러 가지 현상들을 학습을 통해서 인식하 는 신경망 내의 신경단위들이 병렬처리에 의해 많은 양의 자료에 대한 추론이나 판단 을 신속하고 정확하게 해주는 특징이 있으며 실시간 패턴인식이나 분류 응용분야에도 매우 매력적으로 이용되고 있는 방법이다. 이러한 인공 신경망 기법 중에서도 본 연구 에서는 퍼셉트론의 한계점을 극복하기 위하여 입력총과 출력층에 한 개 이상의 은닉층 ( (hidden layer)을 사용하여 다층 네트워으로 구성하고, 모든 입력패턴에 대하여 발생하 는 오차함수를 최소화하는 방향으로 연결강도를 조정하는 back propagation 학습 알고 리즘을 사용하였다. 지절의 원인으로 추정 가능한 공정인자들을 변수로 하여 최적의 인 공신경망을 구축하기 위해 학습률과 모멘트 상수의 변화 및 은닉층의 수와 출력층의 뉴런 수를 조절하는 동의 작업을 거쳐 네트워크의 정확도가 높은 인공신경망을 설계하 였다. 또한 이러한 인공신경망과의 비교분석을 위해 동일한 공정 데이터들올 이용하여 보편적으로 사용하는 통계기법 중의 하나인 주성분회귀분석을 실시하였다. 주성분 분석은 여러 개의 반응변수에 대하여 얻어진 다변량 자료의 다차원적인 변 수들을 축소, 요약하는 차원의 단순화와 더불어 서로 상관되어있는 반응변수들 상호간 의 복잡한 구조를 분석하는 기법이다. 본 발표에서는 공정 자료를 활용하여 인공신경망 과 주성분분석을 통해 공정 트러블의 발생에 영향 하는 인자들을 보다 현실적으로 추 정하고, 그 대책을 모색함으로써 이를 최소화할 수 있는 방안을 소개하고자 한다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1323-1328
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• 2011

In the current design process for the lower arm used in automobile parts, an optimal solution of its various design variables should be found through exploration of the design space approximated using the response surface model formulated with a first- or second-order polynomial equation. In this study, a multi-level computational DOE (design of experiment) was carried out to explore the design space showing nonlinear behavior, in terms of factors such as the total weight and applied stress of the lower arm, where a fractional-factorial orthogonal array based on the artificial neural network model was introduced. In addition, the tolerance robustness of the optimal solution was estimated using a tolerance optimization with six sigma constraints, taking into account the tolerances occurring in the design variables.

• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.74-82
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• 2015

A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river. Direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Using the time series models the effect of river stage on groundwater level data was filtered out by setting a constant value for river stage inputs. The filtered data were applied to the hybrid water table fluctuation method in order to estimate the groundwater recharge. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.3
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• pp.28-38
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• 2008

The purpose of this study are to compare existing presence-absence predictive models and to predict suitable habitat for Goral (Nemorhaedus caudatus raddeanus) that is an endangered and protected species in Seoraksan national park using the best model among existing predictive models. The methods of this study are as follows. First, 375 location data and 9 environmental data layers were implemented to build a model. Secondly, 4 existing presence-absence models : Generalized Linear Model (GLM), Generalized Addictive Model (GAM), Classification and Regression Tree (CART), and Artificial Neural Network (ANN) were tested to predict the Goal habitat. Thirdly, ROC (Receiver Operating Characteristic) and Kappa statistics were used to calculate a model performance. Lastly, we verified models and created habitat suitability maps. The ROC AUC (Area Under the Curve) and Kappa values were 0.697/0.266 (GLM), 0.729/0.313 (GAM), 0.776/0.453 (CART), and 0.858/0.559 (ANN). Therefore, ANN was selected as the best model among 4 models. The models showed that elevation, slope, and distance to stream were the significant factors for Goal habitat. The ratio of predicted area of ANN using a threshold was 31.29%, but the area decreased when human effect was considered. We need to investigate the difference of various models to build a suitable wildlife habitat model under a given condition.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.65-71
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• 2012

Recently the high-resolution satellite images are helpfully using the land cover, status data for the natural resources or environment management. The effective satellite analysis process for these satellite images that require high investment can be increase the effectiveness has become increasingly important. In this Study, the statistical value of the training data is calculated and analyzed during the preprocessing. Also, that is explained about the maximum likelihood classification of traditional classification method, artificial neural network (ANN) classification method and Support Vector Machines(SVM) classification method and then the IKONOS high-resolution satellite imagery was produced the land cover map using each classification method. Each result data had to analyze the accuracy through the error matrix. The results of this study prove that SVM classification method can be good alternative of the total accuracy of about 86% than other classification method.

• Journal of Korean Society for Quality Management
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• v.48 no.1
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• pp.171-186
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• 2020

Purpose: The purpose of this study is conducting of predictive models that considered multicollinearity of independent variables in order to carry out more efficient and reliable predictions about differential pressure in seawater reverse osmosis. Methods: The main variables of each RO system are extracted through factor analysis. Common variables are derived through comparison of RO system # 1 and RO system # 2. In order to carry out the prediction modeling about the differential pressure, which is the target variable, we constructed the prediction model reflecting the regression analysis, the artificial neural network, and the support vector machine in R package, and figured out the superiority of the model by comparing RMSE. Results: The number of factors extracted from factor analysis of RO system #1 and RO system #2 is same. And the value of variability(% Var) increased as step proceeds according to the analysis procedure. As a result of deriving the average RMSE of the models, the overall prediction of the SVM was superior to the other models. Conclusion: This study is meaningful in that it has been conducting a demonstration study of considering the multicollinearity of independent variables. Before establishing a predictive model for a target variable, it would be more accurate predictive model if the relevant variables are derived and reflected.

• Journal of Digital Contents Society
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• v.7 no.1
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• pp.47-52
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• 2006

This paper proposes an algorithm for human sensibility evaluation using 4-channel EEG signals of the prefrontal and the parietal lobes. The algorithm uses an artificial neural network and the multiple templates. The linear prediction coefficients are used as the feature parameters of human sensibility. Comfortableness and temperature/humidity are evaluated. Many conventional researches have emphasized that a wave of left prefrontal lobe is activated in case of positive sensibility and that of right prefrontal lobe is activated in case of negative sensibility. So the power ratio of n wave is obtained from for computation and the results are compared. The results of the comfortableness evaluation for temperature and humidity showed that the outputs of the proposed algorithm coincided with corresponding sensibilities depending on the task of the temperature and the humidity. The conventional method using a wave is hardly related with comfortableness. And it is also observed that the uncomfortable state due to the high temperature and humidity can be easily changed to the comfortable state by small drop of the temperature and the humidity.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1171-1186
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• 2011

Identifying Emerging Technology Trends is crucial for decision makers of nations and organizations in order to use limited resources, such as time, money, etc., efficiently. Many researchers have proposed emerging trend detection systems based on a popularity analysis of the document, but this still needs to be improved. In this paper, an emerging trend detection classifier is proposed which uses both academic and industrial data, SCOPUS and PATSTAT. Unlike most pre-vious research, our emerging technology trend classifi-er utilizes supervised, semi-automatic, machine learning techniques to improve the precision of the results. In addition, the citation information from among the SCOPUS data is analyzed to identify the early signals of emerging technology trends.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.1
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• pp.1-6
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• 2015

The purpose of this study is to suggest the effective operation method by developing prediction model for the gas production rate, an indicator of the effectiveness of anaerobic digestion tank, using data mining. At the result, gas production estimate model is developed by using ANN within 10% error. It is expected to help operation of anaerobic digestion by suggesting selected parameter. Meanwhile case based reasoning is applied to develop dewatering cake management technology. Case based reasoning uses the most similar examples of past when a new problem occurs, therefore in this study, management measures are developed that proposes dewatering cake minimization with the minimum change by applying the case based reasoning to sludge disposal process.