• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.1
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• pp.103-110
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• 1998

The modeling of load characteristics is a difficult problem because of uncertainty of load. This research uses artificial neural networks which can approximate nonlinear problem to represent load characteristics. After the selection of typical load, active and reactive power for the variation of voltage and frequency is obtained from experiments. We constructed and learned ANN based on these data for component load identification. The learned ANN identified load characteristics for other voltage and/or frequency variation. In addition, the results of component load identification are presented to demonstrate the potentiality of the proposed method.method.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.945-945
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• 2012

본 연구에서는 낙동강 수계의 안동댐 유역을 대상지역으로 선정하여 미래 기후변화 시나리오에 따른 댐 유역의 수환경 영향을 예측해 보고자 하였다. 특히 미래기후에 대한 수환경 평가는 기후자료를 입력 값으로 요구하는 강우-유출모형을 이용하거나 유량 이외에 유사, 영양물질과 같은 수질인자를 동시에 모의할 수 있는 유역모형을 이용하여 평가하는 것이 일반적이다. 이를 위해 선행연구로 IPCC(Intergovernmental Panel on Climate Change)에서 제공하는 AR4 시나리오의 RCM 자료를 ANN(Artificial Neural Network)기법을 이용하여 안동댐 유역의 총 4개 기상관측소에 대한 과거 20년(1991~2010) 실측자료를 바탕으로 미래 강수 및 습도 그리고 온도에 대해 상세화 하여 미래 기후 시나리오를 생산하였다. 또한 안동댐 유역 단위의 수질을 예측하기 위해 토양과 토지이용 및 토지관리 상태에 따른 수문-수질 모의가 가능한 유역모형인 SWAT(Soil and Water Assessment Tool)을 이용하였다. 과거의 기상자료와 수질자료를 이용하여 유역모델의 검 보정을 실시하였으며 모형의 보정 및 검증결과에 따른 적합성과 상관성을 판단하기 위해 결정계수($R^2$)와 평균제곱근오차(Root Mean Square Error, RMSE)를 사용하였으며, 모형의 효율성 검증으로는 Nash and Sutcliffe(1970)가 제안한 모형효율성계수(NSE)를 사용하였다. 최종적으로 기후 시나리오에 대해서 전망된 지역상세기후를 유역모형의 입력자료로 이용하여 안동댐 유역의 미래수문 및 수질을 예측하고자 하였다.

• Journal of the Korean Society for Heat Treatment
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• v.31 no.4
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• pp.180-186
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• 2018

High temperature flow behaviors of Fe-Cr-Mo-V-W-C tool steel were investigated using isothermal compression tests on a Gleeble simulator. The compressive test temperature was varied from 850 to $1,150^{\circ}C$ with the strain rate ranges of 0.05 and $10s^{-1}$. The maximum height reduction was 45%. The dynamic softening related to the dynamic recrystallization was observed during hot deformation. The constitutive model based on Arrhenius-typed equation with the Zener-Hollomon parameter was proposed to simulate the hot deformation behavior of Fe-Cr-Mo-V-W-C steel. An artificial neural network (ANN) model was also developed to compare with the constitutive model. It was concluded that the ANN model showed more accurate prediction compared with the constitutive model for describing the hot compressive behavior of Fe-Cr-Mo-V-W-C steel.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.417-420
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• 2014

Importance of security system to prevent recently increased financial security accident is increasing. Biometric system between the security systems is focused. Fingerprint recognition has many useful aspects such as security, reliability and portability. In this treatise, fingerprint recognition technique is realized by using artificial neural network. Artificial Neural Network(ANN) is a mathematics learning model that makes specific patterns that a program can recognize to show a nerve network's characteristic on a computer. Input fingerprint images have a preprocessing process such as equalization, binarization and thinning. We extract minutiae feature in the images and program can recognize a fingerprint through ANN.

• Transactions of Materials Processing
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• v.33 no.2
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• pp.96-102
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• 2024

This study addresses evaluation of performance of hardening model for a titanium alloy (Ti6Al4V) based on the artificial neural network (ANN) regarding the strain rate and the temperature. Uniaxial compression tests were carried out at different strain rates from 0.001 /s to 10 /s and temperatures from 575 ℃ To 975 ℃. Using the experimental data, ANN models were trained and tested with different hyperparameters, such as size of hidden layer and optimizer. The input features were determined with the equivalent plastic strain, strain rate, and temperature while the output value was set to the equivalent stress. When the number of data is sufficient with a smooth tendency, both the Bayesian regulation (BR) and the Levenberg-Marquardt (LM) show good performance to predict the flow behavior. However, only BR algorithm shows a predictability when the number of data is insufficient. Furthermore, a proper size of the hidden layer must be confirmed to describe the behavior with the limited number of the data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.11
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• pp.1397-1405
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• 2013

The optimal rib pattern design of B-pillar trim considering occupant head protection can be determined by two methods. One is the conventional approximate optimization method that uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by trial-and-error. The quality of results strongly depends on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, called the sequential design of experiments (SDOE), to reduce the trial-and-error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

• Korean Journal of Construction Engineering and Management
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• v.12 no.6
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• pp.110-119
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• 2011

Global warming caused by excessive greenhouse gas emission is causing climate change all over the world. In Korea, greenhouse gas emission from residential buildings accounts for about 10% of gross domestic emission. Also, the number of deteriorated multi-family housing complexes is increasing. Therefore, the goal of this research is to establish the bases to manage energy consumption continuously and methodically during MR&R period of multi-family housings. The research process and methodologies are as follows. First, research team collected the data on project characteristics and energy consumption of multi-family housing complexes in Seoul. Second, an ontology-based breakdown structure was established with some primary characteristics affecting the energy consumption, which were selected by statistical analysis. Finally, a predictive model of energy consumption was developed based on the ontology-based breakdown structure, with application of CBR, ANN, MRA and GA. In this research, PASW (Predictive Analytics SoftWare) Statistics 18, Microsoft EXCEL, Protege 4.1 were utilized for data analysis and prediction. In future research, the model will be more continuous and methodical by developing the web-base system. And it has facility manager of government or local government, or multi-family housing complex make a decision with definite references regarding moderate energy consumption.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

• Annual Conference on Human and Language Technology
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• 1989.10a
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• pp.154-158
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• 1989

본 연구는 통제적 모델에 의한 연속 숫자음의 인식에 관한 것으로 4 연속 숫자음을 인식 대상으로하여 실험한다. 시스템은 크게 음향 음성 처리부 및 어휘 해석부 두 부분으로 나뉜다. 음향 음성 처리부에서는 입력 음성으로부터 특정 벡터인 12차의 LPC cepstrum 계수를 구하여, 프레임 레이블링과 소음소 레이블링 (phone labelling)을 한다. 프레임 레이블링인 베이스 분류법을 이용하였으며, 소음소 레이블링은 프레임 레이블과 사후확률 (posteriori probability)로 부터 이루어 졌다. 어휘 해석부분에서는 소음소 단위를 입력으로 받아 음운규칙을 통해 작성된 소음소 망을 거쳐 연속 숫자음 출력을 얻도록 했다. 본실험은 화자 3 명이 발음한 35 개의 4 연속 숫자음을 인식 대상으로 하였으며, 4 연속 숫자음을 평가단위로 80%의 인식율을 얻었고, 각 숫자음의 음절을 단위로 95%의 인식율을 얻어 제시한 알고리즘의 유효성을 입증하였다.