• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.38-45
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• 2020

This study presents a systematic procedure for developing a short-term prediction deep learning model of rebar price using bidirectional LSTM, Random Search, data combination, Dropout. In general, users intuitively determine these values, making it time-consuming and repetitive attempts to explore results with good predictive performance, and the results found by these attempts cannot be guaranteed to be excellent. With the proposed approach presented in this study, the average accuracy of short-term price forecasts is approximately 98.32%. In addition, this approach could be used as basic data to produce good predictive results in a study that predicts prices with time series data based on statistics, including building materials other than rebars.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.229-235
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• 2012

Forex trading participants, due to the intensified economic internationalization exchange risk avoidance measures are needed. In this research, Model suitable for estimation of time-series data, such as stock prices and exchange rates, through the concealment of HMM and estimate the short-term exchange rate forecasting model is applied to the prediction of the future. Estimated by applying the optimal model if the real exchange rate data for a certain period of the future will be able to predict the movement aspect of it. Alleged concealment of HMM. For the estimation of the model to accurately estimate the number of states of the model via Bayesian Information Criterion was confirmed as a model predictive aspect of physical exercise aspect and predict the movement of the two curves were similar.

• Journal of IKEEE
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• v.23 no.1
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• pp.134-142
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• 2019

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.187-194
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• 2022

LNG is known as the transitional energy source for the future eco-friendly, attracting enormous market attention due to global eco-friendly regulations, Covid-19 Pandemic, Russia-Ukraine War. In addition, since new LNG suppliers such as the U.S. and Australia are also diversifying, the LNG spot market is expected to grow. On the other hand, research on the LNG transportation market has been marginalized. Therefore, this study attempted to predict short-term LNG 160K spot rates and compared the prediction performance between artificial neural networks and the ARIMA model. As a result of this paper, while it was difficult to determine the superiority and superiority of ARIMA and artificial neural networks, considering the relative free of ANN's contraints, we confirmed the feasibility of ANN in LNG 160K spot rate prediction. This study has academic significance as the first attempt to apply an artificial neural network to forecasting LNG 160K spot rates and are expected to contribute significantly in practice in that they can improve the quality of short-term investment decisions by market participants by increasing the accuracy of short-term prediction.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.21-23
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup rates when control rod and boron were adjusted in load following operations. Data of UCN 3 were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and seems to be utilized as a handy tool for the use of a plant simulation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.9
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• pp.567-569
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by the concentration of xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup states when control rods and boron were adjusted in load following operations. Data of the Korea Next Generation PWR were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and the developed model seems to be utilized as a handy tool for the use of a plant simulation.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.155-169
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• 2016

In this study, we developed a multi-sensor blending short-term rainfall forecasting technique using radar and satellite data during extreme rainfall occurrences in Busan and Gyeongnam region in August 2014. The Tropical Z-R relationship ($Z=32R^{1.65}$) has applied as a optimal radar Z-R relation, which is confirmed that the accuracy is improved during 20mm/h heavy rainfall. In addition, the multi-sensor blending technique has applied using radar and COMS (Communication, Ocean and Meteorological Satellite) data for quantitative precipitation estimation. The very-short-term rainfall forecasting performance was improved in 60 mm/h or more of the strong heavy rainfall events by multi-sensor blending. AWS (Automatic Weather System) and MAPLE data were used for verification of rainfall prediction accuracy. The results have ensured about 50% or more in accuracy of heavy rainfall prediction for 1-hour before rainfall prediction, which are correlations of 10-minute lead time have 0.80 to 0.53, and root mean square errors have 3.99 mm/h to 6.43 mm/h. Through this study, utilizing of multi-sensor blending techniques using radar and satellite data are possible to provide that would be more reliable very-short-term rainfall forecasting data. Further we need ongoing case studies and prediction and estimation of quantitative precipitation by multi-sensor blending is required as well as improving the satellite rainfall estimation algorithm.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1077-1093
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• 2020

Sea Surface Temperature (SST) is an important environmental indicator that affects climate coupling systems around the world. In particular, coastal regions suffer from abnormal SST resulting in huge socio-economic damage. This study used Long Short Term Memory (LSTM) and Convolutional Long Short Term Memory (ConvLSTM) to predict SST up to 7 days in the south sea region in South Korea. The results showed that the ConvLSTM model outperformed the LSTM model, resulting in a root mean square error (RMSE) of 0.33℃ and a mean difference of -0.0098℃. Seasonal comparison also showed the superiority of ConvLSTM to LSTM for all seasons. However, in summer, the prediction accuracy for both models with all lead times dramatically decreased, resulting in RMSEs of 0.48℃ and 0.27℃ for LSTM and ConvLSTM, respectively. This study also examined the prediction of abnormally high SST based on three ocean heatwave categories (i.e., warning, caution, and attention) with the lead time from one to seven days for an ocean heatwave case in summer 2017. ConvLSTM was able to successfully predict ocean heatwave five days in advance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1119-1124
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• 2009

This paper deals with the variability analysis of short term creep rupture test data based on the previous creep rupture tests and the possibility of the creep life prediction. From creep tests performed by constant uniaxial stresses at 600, 650 and $700^{\circ}C$ elevated temperature, in order to investigate the variability of short-term creep rupture data, the creep curves were analyzed for normalized creep strain divided by initial strain. There are some variability in the creep rupture data. And, the difference between general creep curves and normalized creep curves were obtained. The effects of the creep rupture time (RT) and steady state creep rate (SSCR) on the Weibull distribution parameters were investigated. There were good relation between normal Weibull parameters and normalized Weibull parameters. Finally, the predicted creep life were compared with the Monkman-Grant model.

• Atmosphere
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• v.31 no.1
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• pp.73-83
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• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.