• 산업공학
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• 제21권1호
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• pp.85-95
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• 2008

Reliability prediction serves many purposes during the life of a system, so several methods have been developed to predict the parts and systems reliability. MIL-HDBK-217F, among the those methods, has been widely used as a requisite tool for the reliability prediction which is applied to nuclear power plants and their safety regulations. This paper presents the reliability prediction for the DSP(Digital Signal Processor) module composed of three assemblies. One of the assemblies has a monitoring and self test function which is used to enhance the module reliability. The reliability of each assembly is predicted by MIL-HDBK-217F. Based on these predicted values, Markov modelling is finally used to predict the module reliability. Relax 7.7 software of Relax software corporation is used because it has many part libraries and easily handles Markov processes modelling.

• Journal of Electrical Engineering and Technology
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• 제8권5호
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• pp.1040-1048
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• 2013

In this paper, a sensorless pitch angle control method for a wind generation system is suggested. One-step-ahead prediction control law is adopted to control the pitch angle of a wind turbine in order for electric output power to track target values. And it is shown that this control scheme using the inverse dynamics of the controlled system enables us to predict current wind speed without an anemometer, to a considerable precision. The inverse input-output of the controlled system is realized by use of an artificial neural network. The proposed control and wind speed prediction method is applied to a Double-Feed Induction Generation system connected to a simple power system through computer simulation to show its effectiveness. The simulation results demonstrate that the suggested method shows better control performances with less control efforts than a conventional Proportional-Integral controller.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1997년도 춘계학술대회논문집; 경주코오롱호텔; 22-23 May 1997
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• pp.452-459
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• 1997

For computer aided design and construction of low noisy power plants, indoor and outdoor noise prediction program has been developed. The program utilizes the predefined data of noise sources and building materials and has the faculty to estimate the source level using the empirical formula in case of the measured data not being available. In the noise prediction, the mutual noise propagation between indoor and outdoor sites are considered. The outdoor noise source in the calculation of geometric divergence effects is modelled as the omni-directional finite line or planar source according to the source geometry and the receiving points. Outdoor noise prediction is carried out to consider the diffraction effect due to plant structures as well as the attenuation effect due to atmospheric absorption and soft ground. The results of indoor and outdoor noise prediction for a recently constructed diesel engine power plant show good agreement with the measured.

• 통합자연과학논문집
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• 제11권4호
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권7호
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• pp.307-314
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• 2022

최근 전력수요를 예측하기 위해 통계기반 시계열 분석 기법을 대체하기 위해 딥러닝 기법을 활용한 연구가 활발히 진행되고 있다. 딥러닝 기반 전력수요 예측 연구 결과를 분석한 결과, LSTM 기반 예측 모델의 성능이 우수한 것으로 규명되었으나 장기간의 지역 범위 전력수요 예측에 대해 LSTM 기반 모델의 성능이 충분하지 않음을 확인할 수 있다. 본 연구에서는 기온 데이터를 반영하여 24시간 이전에 전력수요를 예측하는 WaveNet 기반 딥러닝 모델을 개발하여, 실제 사용하고 있는 통계적 시계열 예측 기법의 정확도(MAPE 값 2%)보다 우수한 예측 성능을 달성하는 모델을 개발하고자 한다. 먼저 WaveNet의 핵심 구조인 팽창인과 1차원 합성곱 신경망 구조를 소개하고, 전력수요와 기온 데이터를 입력값으로 모델에 주입하기 위한 데이터 전처리 과정을 제시한다. 다음으로, 개선된 WaveNet 모델을 학습하고 검증하는 방법을 제시한다. 성능 비교 결과, WaveNet 기반 모델에 기온 데이터를 반영한 방법은 전체 검증데이터에 대해 MAPE 값 1.33%를 달성하였고, 동일한 구조의 모델에서 기온 데이터를 반영하지 않는 것(MAPE 값 2.31%)보다 우수한 전력수요 예측 결과를 나타내고 있음을 확인할 수 있다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• 한국산업정보학회논문지
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• 제18권3호
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• pp.35-46
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• 2013

국내 전력 수요량 예측을 위한 정확한 분석 모델을 개발하기 위하여 고차원 데이터 군집 분석에 적합한 차원 축소 개념의 부분공간 군집 기법과 SMO 분류 기법을 결합한 전력 수요 패턴 예측 방법을 제안하였다. 전력 수요 패턴 예측은 무선부하감시 데이터 뿐 아니라 소지역 단위의 센서스 정보를 통합하여 시간대별 전력 부하 패턴 분석과 인구통계학 및 지리학적 특성 분석이 가능하다. 서울지역 대상의 센서스 정보 및 전력 부하를 이용한 소지역 전력 수요 패턴 예측 결과 총 18개의 특성 군집을 구성하였으며, 전력 수요 패턴 예측 정확도는 약 85%를 보였다.

• 디지털콘텐츠학회 논문지
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• 제17권3호
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• pp.157-164
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• 2016

태양광 발전량은 날씨에 큰 영향을 받는다. 기상 예보를 사용할 수 있는 환경이라면, 기상 예보 정보를 사용하여 미래의 태양광 발전량을 단기예측 할 수 있다. 하지만, 섬이나 산과 같이 네트워크의 단절에 의해 기상예보 정보를 사용할 수 없는 제한된 환경에서는 기상예보를 사용한 태양광 발전량 예측 모델을 사용할 수 없다. 따라서 본 논문에서는 시스템 자체적으로 수집할 수 있는 정보만을 이용하여 태양광 발전량을 단기 예측할 수 있는 시스템을 제안하였다. 예측의 정확도를 높이기 위하여 이전 온도정보와 발전량 정보를 이용하여 단기 예측모델을 생성하였다. 실험을 통하여 실데이터에 제안한 예측 모델을 적용하여 유용한 결과를 보였다.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1987년도 학술발표회 논문집
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• pp.24-30
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• 1987

SEA method have been developed for prediction sound radiation power from vibration of machinery. In this study, sound radiation power was predicted from coupled structures by transmission of vibration, which composed of two plates welded at right angle. The predicted sound radiation power is agreement within 2 or 3 dB on octave band comparing with values obtained from direct measurements. Also, in order to prove the validity of this method in changes of sound radiation power associated with modifications to structures, rubber pad stuck on a plate. This result is agreement approximately within 3 or 5 dB.

• Environmental Engineering Research
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• 제22권2호
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• pp.175-185
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• 2017

As the energy and environmental problems are increasingly severe, researches about carbon dioxide emissions has aroused widespread concern. The accurate prediction of carbon dioxide emissions is essential for carbon emissions controlling. In this paper, we analyze the relationship between carbon dioxide emissions and influencing factors in a comprehensive way through correlation analysis and regression analysis, achieving the effective screening of key factors from 16 preliminary selected factors including GDP, total population, total energy consumption, power generation, steel production coal consumption, private owned automobile quantity, etc. Then fruit fly algorithm is used to optimize the parameters of least squares support vector machine. And the optimized model is used for prediction, overcoming the blindness of parameter selection in least squares support vector machine and maximizing the training speed and global searching ability accordingly. The results show that the prediction accuracy of carbon dioxide emissions is improved effectively. Besides, we conclude economic and environmental policy implications on the basis of analysis and calculation.