• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1630-1643
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• 2023

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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• 제3권1호
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• pp.29-34
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• 2000

This paper proposes a long-term prediction of offshore structures in ocean waves. All short-term statistics is generated by the simulation for all the combinations of significant wave heights and spectral peak periods. The simulation has been tested first on linear system, whose analytic solution is known, to verify if the simulation works accurately. Then the scheme was applied to the nonlinear system. This paper demonstrated that the proposed scheme could be an efficient tool in estimating the response of offshore structures.

• 한국지반공학회지:지반
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• 제14권6호
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• pp.127-138
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• 1998

신선한 쓰레기 매립지에서는 쓰레기에 포함되어 있는 유기물의 분해로 인하여 장기간에 걸쳐 상당한 양의 침하가 유발되는 것으로 알려져 있다. 본 연구에서는 여러 신선한 쓰레기 매립지들의 침하자료에 대하여 기존에 제안된 몇몇 침하모델들을 적용하였으며. 얻어진 침하예측곡선과 장기침하량을 분석함으로써 분해로 의한 침하양상이 장기침하량 예측에 미치는 영향을 살펴보았다. 사용된 모델과는 상관없이 선정된 모델변수 값들이 분해효과를 포함하지 않는 한 장기침하를 적절히 평가할 수 없었다. 몇몇 예측방법 가운데 Gibson & Lo 모델과 쌍곡선 모델은 쓰레기 매립지의 장기침하 거동특성을 비교적 타당성 있게 예측한 반면에 power creep law는 상당히 과다예측하는 것으로 나타났다.

• Computers and Concrete
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• 제22권3호
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• pp.261-267
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• 2018

Long-term deformation of a steel-reinforced concrete (SRC) column is different from that of a reinforced concrete (RC) column due to the different moisture distribution. Wide-flange steel in an SRC column obstructs diffusion and makes long-term deformation slower. Previous studies analyzed the characteristics of long-term deformation of SRC columns. In this study, an additional experiment is conducted to more precisely investigate the effect of wide-flange steel on the long-term deformation of SRC columns. Long-term deformation, especially creep of SRC columns with various types of wide-flange steel, is tested. Wide-flange steel for the experiment is made of thin acrylic panels that can block diffusion but does not have strength, because the main purpose of this study is to exclusively demonstrate the long-term deformation of concrete caused by moisture diffusion, not by the reinforcement ratio. Experimental results show that the long-term deformation of a SRC column develops slower than that in a RC column, and it is slower as the wide-flange steel hinders diffusion more. These experimental results can be used for analytical prediction of long-term deformation of various SRC columns. An example of the analytical prediction is provided. According to the experimental and analytical results, it is clear that a new prediction model for long-term deformation of SRC columns should be developed in further studies.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2003년도 가을 학술발표회 논문집
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• pp.445-448
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• 2003

Polymer concrete using wastes PET recycled resin that is, in general, more excellent mechanical properties than portland cement concrete. A lot of works are carried out about short-term properties of polymer concrete, however, little work has done to define their long-term properties, that is, sustain load such as creep. In this study will show the data that can long-term behavior of polymer concrete by short term creep test of polymer concrete that was affect to the temperature and the time to predict to long-term creep behavior. Then prediction equation was similar tendency that was comparing to short-term creep test and long-term creep test.

• 품질경영학회지
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• 제47권3호
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• 무역학회지
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• 제46권6호
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• 한국컴퓨터정보학회논문지
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• 제28권2호
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• pp.201-207
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• 2023

본 논문에서는 Multi-Step Time Series의 세 가지 전략을 비교 분석하기 위해 LGBM, MLP, LSTM, GRU를 사용하여 농산물 중장기 가격 예측에 대한 최적의 모형을 제안한다. 제안 모형은 다각도로 전략을 선택하여 모델과 전략간 최적의 조합을 찾도록 설계되었다. 기존 농산물 가격 예측 연구에서는 전통 계량경제 모델인 ARIMA를 비롯하여 LSTM 계열 모델이 주로 사용된 반면 Multi-Step Time Series 관련 농산물 가격 예측 연구는 매우 제한적이다. 본 연구에서는 농산물 가격의 변동성 정도에 따라 두 개의 기간으로 나누어 실험을 진행하였으며, Direct, Hybrid, Multiple Outputs 등 세 전략의 중장기 가격 예측 결과 Hybrid 접근법이 상대적으로 우수한 성능을 보였다.본 연구 결과는 중장기 일별 가격 예측을 고도화할 수 있는 효과적인 대안을 제시한다는 측면에서 학술적, 실무적 의의를 갖는다.

• 한국태양에너지학회 논문집
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• 제39권5호
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• pp.41-52
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• 2019

The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.