• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.11
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• pp.901-907
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• 2021

The present study introduces an artificial neural network (ANN) that can predict the missile aerodynamic coefficients for various missile nose shapes and flow conditions such as Mach number and angle of attack. A semi-empirical missile aerodynamics code is utilized to generate a dataset comprised of the geometric description of the nose section of the missiles, flow conditions, and aerodynamic coefficients. Data normalization is performed during the data preprocessing step to improve the performance of the ANN. Dropout is used during the training phase to prevent overfitting. For the missile nose shape and flow conditions not included in the training dataset, the aerodynamic coefficients are predicted through ANN to verify the performance of the ANN. The result shows that not only the ANN predictions are very similar to the aerodynamic coefficients produced by the semi-empirical missile aerodynamics code, but also ANN can predict missile aerodynamic coefficients for the untrained nose section of the missile and flow conditions.

• Journal of Wetlands Research
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• v.25 no.3
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• pp.167-175
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• 2023

Precipitation data is one of the essential input datasets used in various fields such as wetland management, hydrological simulation, and water resource management. In order to efficiently manage water resources using precipitation data, it is essential to secure as much data as possible by minimizing the missing rate of data. In addition, more efficient hydrological simulation is possible if precipitation data for ungauged areas are secured. However, missing precipitation data have been estimated mainly by statistical equations. The purpose of this study is to propose a new method to restore missing precipitation data using machine learning algorithms that can predict new data based on correlations between data. Moreover, compared to existing statistical methods, the applicability of machine learning techniques for restoring missing precipitation data is evaluated. Representative machine learning algorithms, Artificial Neural Network (ANN) and Random Forest (RF), were applied. For the performance of classifying the occurrence of precipitation, the RF algorithm has higher accuracy in classifying the occurrence of precipitation than the ANN algorithm. The F1-score and Accuracy values, which are evaluation indicators of the classification model, were calculated as 0.80 and 0.77, while the ANN was calculated as 0.76 and 0.71. In addition, the performance of estimating precipitation also showed higher accuracy in RF than in ANN algorithm. The RMSE of the RF and ANN algorithms was 2.8 mm/day and 2.9 mm/day, and the values were calculated as 0.68 and 0.73.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.801-804
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• 2008

Fiber reinforced plastics (FRP) are light in weight, non-corrosive and exhibits high tensile strength. FRPs having superior material properties to corrosive steels have been widely replacing steel bars or tendons used in concrete structures as flexural reinforcements. Although current design guidelines for estimating shear strength of FRP concrete beam follow the format of conventional reinforced concrete design method, there are noticeable differences among the existing formulas in calculating the contributions of concrete to shear resistance. In this paper, the artificial neural network (ANN) technique is employed as an analytical alternative to existing methods for predicting shear capacity of FRP concrete beams. Influential factors on shear strength were identified through literature review and input in ANN and the ANN was trained for the target ultimate shear obtained from database. The results from ANN were compared with existing formulas for its accuracy. It was found that the developed ANN were more closely predicting the test data than those of the currently available predictive equations.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.3
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• pp.282-289
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• 2005

This paper proposes a fuzzy neural network controller based on the vector control for interior permanent magnet synchronous motor(IPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability This paper does not oかy presents speed control of IPMSM using neuro-fuzzy control(NFC) but also speed estimation using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Thus, it is presented the theoretical analysis as well as the analysis results to verify the effectiveness of the proposed method in this paper.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.110-110
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• 2018

상수관로의 노후화는 단수유발, 수압부족 및 수질악화, 싱크홀 발생 피해와 누수로 인한 경제적 손실 등을 초래한다. 최근 상수관로의 노후화에 의한 피해가 심각해짐에 따라, 환경부에서는 전국적으로 노후관로를 개량 및 교체하는 작업을 시행하고 있다. 다만, 모든 노후관로를 일시에 보수 및 교체하는 것은 불가능하므로, 사용 중인 관로의 노후도를 정량적으로 판단하여 개량우선순위를 결정해야한다. 현재 국내에서는 '상수도 기술진단' 매뉴얼에 따른 관망성능평가 결과를 이용하여 상수관로의 노후화 정도를 평가하고 있다. 이는 평가항목 별로 기준을 나누어 조건값과 가중치를 부여하고, 총 점수를 합산하여 해당 관로의 평가 점수에 따라 등급을 판정하게 되는 점수평가법이다. 본 연구에서는 기존의 점수평가법과의 비교를 통하여, ANN(Artificial Neural Network)-Clustering 기법이 상수관로의 노후도 평가를 위한 새로운 평가방법이 될 수 있음을 제시하였다. 본 연구는 강원도 Y지역의 상수관로를 대상으로 진행하였으며, 기존의 관망성능평가 항목을 이용하여 전체 관로를 세 가지 등급으로 분류하여 노후도를 평가하였다. 또한 ANN-Clustering방법의 적용 가능성을 판단하기 위하여 기존의 점수평가법 결과와 비교분석을 실시하였으며, 전체 대상관로의 노후도 정도를 직관적으로 파악할 수 있도록 계산된 노후도 등급을 관망도에 도시하였다. ANN-Clustering방법은 관로의 다양한 특성값을 손쉽게 변경하여 적용할 수 있으며, 기존의 점수평가법과 더불어 상수관로의 유지관리를 위한 보다 객관적이고 합리적인 관망성능평가법이 될 수 있을 것으로 기대한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.39-39
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• 2020

Low-flow 하천에서의 최저수위를 나타내는 지표이다. 일반적으로 유황곡선의 갈수량(Q355)를 대표적으로 사용한다. Low-flow는 물 공급 관리 및 계획, 관개용수, 생태계등 다양한 분야에 영향을 미친다. 이러한 Low-flow를 산정하기 위해서는 충분한 기간의 유량자료가 필요하다. 하지만 국토의 70%가 산지지형으로 구성되어 있는 우리나라의 경우 국가하천과 1급하천을 제외한 산지유역은 수위관측소가 부재하거나 결측으로 인해 자료가 충분하지 않아 Low-flow분석에 한계가 있다. 이에 과거에는 미계측지역의 갈수량을 예측하기 위해서 다중회귀분석, ARIMA 모형 등 다양한 기법을 사용하였지만, 최근들어 머신러닝 모형의 수요가 증가하고 있다. 이에 본 연구에서는 새로운 패러다임에 맞는 머신러닝 기법인 DNN기법을 사용하고자 한다. DNN기법은 ANN기법의 단점인 학습과정에서 최적 매개변수값을 찾기 어렵고, 학습시간이 느린 단점을 보완한 방법이다. 따라서 본연구에서는 머신러닝 기법인 DNN기법을 통해 미계측지역에 적용 가능한 지역화 Low-flow indices를 산정하고자 한다. 먼저, Low-flow에 영향을 미치는 인자들을 수집하고 인자들간의 상관분석, 다중공선성 분석을 통해 통계적으로 유의한 변수를 선정하여, 머신러닝 모형에 입력자료를 구축하였다. 또한 기존의 갈수량 예측기법인 다중회귀분석 결과와 비교하여 머신러닝 기법의 효용성을 검토하였다.

• Journal of Wetlands Research
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• v.15 no.1
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• pp.115-125
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• 2013

This study established a methodology for the application of downscaling technique in a mountainous area having large spatial variations of rainfall and tried to estimate the change of rainfall characteristics in the future under climate change using the established method. The Namhan river basin, which is in the mountainous area of the Korean peninsula, has been chosen as the study area. Artificial Neural Network - Simple Kriging with varying local means (ANN-SKlm) has been built by combining artificial neural network, which is one of the general downscaling techniques, and SKlm technique, which can reflect the geomorphologic characteristics like elevation of the study area. The evaluation of SKlm technique was done by using the monthly rainfalls at six weather stations which KMA(Korea Meteorological Administration) is managing in the basin. The ANN-SKlm technique was compared with the Thiessen technique and ordinary kriging(OK) technique. According to the evaluation result of each technique the SKlm technique showed the best result.

• Membrane Journal
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• v.33 no.1
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• pp.34-45
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• 2023

Two computational intelligence techniques namely artificial neural networks (ANN) and support vector machine (SVM) are employed to model the permeate flux based on seven input variables including time, transmembrane pressure, rotating velocity, the pore diameter of the membrane, dynamic viscosity, concentration and density of the feed fluid. The best-fit model was selected through the trial-error method and the two statistical parameters including the coefficient of determination (R²) and the average absolute relative deviation (AARD) between the experimental and predicted data. The obtained results reveal that the optimized ANN model can predict the permeate flux with R² = 0.999 and AARD% = 2.245 versus the SVM model with R² = 0.996 and AARD% = 4.09. Thus, the ANN model is found to predict the permeate flux with high accuracy in comparison to the SVM approach.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.329-331
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• 2006

Load models are important for improving the accuracy of stability analysis. Load characteristics are changed for voltage and frequency condition. In this research, ANN with LMBP learning rule is used to construct the load model. Characteristics of some residential loads are tested under various voltage and frequency conditions. Acquired data are used to construct load models by ANN. Constructed ANN load model are applied to transient stability analysis.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.395-398
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• 2004

It is effective and desirable for a proper customer relational management(CRM) to send an email of product sales' advertisement bills for the prospective customers rather than to send spam mails for non specific customers. This study identifies the prospective customers with high probability to buy the specific products using Artificial Neural Network(ANN) and Induction Rule(IR) technique. We suggest an integrated model, IRANN of ANN and IR of decision tree program C5.0 and, also compare and analyze the accuracy of ANN, IR, and IRANN each other.