• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.265-282
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• 2022

Sea surface temperature (SST) is a factor that greatly influences ocean circulation and ecosystems in the Earth system. As global warming causes changes in the SST near the Korean Peninsula, abnormal water temperature phenomena (high water temperature, low water temperature) occurs, causing continuous damage to the marine ecosystem and the fishery industry. Therefore, this study proposes a methodology to predict the SST near the Korean Peninsula and prevent damage by predicting abnormal water temperature phenomena. The study area was set near the Korean Peninsula, and ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF) was used to utilize SST data at the same time period. As a research method, Long Short-Term Memory (LSTM) algorithm specialized for time series data prediction among deep learning models was used in consideration of the time series characteristics of SST data. The prediction model predicts the SST near the Korean Peninsula after 1- to 7-days and predicts the high water temperature or low water temperature phenomenon. To evaluate the accuracy of SST prediction, Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) indicators were used. The summer (JAS) 1-day prediction result of the prediction model, R²=0.996, RMSE=0.119℃, MAPE=0.352% and the winter (JFM) 1-day prediction result is R²=0.999, RMSE=0.063℃, MAPE=0.646%. Using the predicted SST, the accuracy of abnormal sea surface temperature prediction was evaluated with an F1 Score (F1 Score=0.98 for high water temperature prediction in summer (2021/08/05), F1 Score=1.0 for low water temperature prediction in winter (2021/02/19)). As the prediction period increased, the prediction model showed a tendency to underestimate the SST, which also reduced the accuracy of the abnormal water temperature prediction. Therefore, it is judged that it is necessary to analyze the cause of underestimation of the predictive model in the future and study to improve the prediction accuracy.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.368-377
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• 2004

A mathematical model was developed for the prediction of the average temperature and RDT(RM Delivery temperature) in a roughing mill. The model consisted of three parts as follows (1) The intermediate numerical model calculated the deformation and heat transfer phenomena in the rolling: region by steady state FEM and the heat transfer phenomena in the interpass region by unsteady state FEM (2) The Off-line prediction model was derived from non-linear regression analysis based on the results of intermediate numerical model considering the various rolling conditions, (3) Using the heat flux in rolling region, temperature profile along thickness direction was calculated. For validation of the presented model, the rolling force per pass and RDT measued in on-line process was compared with those of model and the results showed close agreement with the existing data. In order to demonstrate the effectiveness of the proposed model, the various rolling conditions was tested.

• Smart Structures and Systems
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• v.32 no.2
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.299-309
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• 2022

Recently, The sea water temperature around Korean Peninsula is steadily increasing. Water temperature changes not only affect the fishing ecosystem, but also are closely related to military operations in the sea. The purpose of this study is to suggest which model is more suitable for the field of water temperature prediction by attempting short-term water temperature prediction through various prediction models based on deep learning technology. The data used for prediction are water temperature data from the East Sea (Goseong, Yangyang, Gangneung, and Yeongdeok) from 2016 to 2020, which were observed through marine observation by the National Fisheries Research Institute. In addition, we use Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) techniques that show excellent performance in predicting time series data as models for prediction. While the previous study used only LSTM, in this study, the prediction accuracy of each technique and the performance time were compared by applying various techniques in addition to LSTM. As a result of the study, it was confirmed that Bidirectional LSTM and GRU techniques had the least error between actual and predicted values at all observation points based on 1 hour prediction, and GRU was the fastest in learning time. Through this, it was confirmed that a method using Bidirectional LSTM was required for water temperature prediction to improve accuracy while reducing prediction errors. In areas that require real-time prediction in addition to accuracy, such as anti-submarine operations, it is judged that the method of using the GRU technique will be more appropriate.

• Transactions of Materials Processing
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• v.25 no.6
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• pp.396-401
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• 2016

Recently, Multi-strength hot stamping process has been widely used to achieve lightweight and crashworthiness in automotive industry. In concept of multi-strength hot stamping process, process design of tailor rolled blank(TRB) in partial heating is difficult because of thickness and temperature variation of blank. In this study, springback prediction of TRB in partial heating process was performed considering its thickness and temperature variation. In partial heating process, TRB was heated up to $900^{\circ}C$ for thicker side and below $Ac_3$ transformation temperature for thinner side, respectively. Johnson-Mehl-Avrami-Kolmogorov(JMAK) equation was applied to calculate austenite fraction according to heating temperature. Calculated austenite fraction was applied to FE-simulation for the prediction of springback. Experiment for partial heating process of TRB was also performed to verify prediction accuracy of FE-simulation coupled with JMAK equation.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2610-2615
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• 2021

The prediction of irradiation-induced transition temperature shift for RPV steels is an important method for long term operation of nuclear power plant. Based on the irradiation embrittlement data, an irradiation-induced transition temperature shift prediction model is developed with machine learning method XGBoost. Then the residual, standard deviation and predicted value vs. measured value analysis are conducted to analyze the accuracy of this model. At last, Cu content threshold and saturation values analysis, temperature dependence, Ni/Cu dependence and flux effect are given to verify the reliability. Those results show that the prediction model developed with XGBoost has high accuracy for predicting the irradiation embrittlement trend of RPV steel. The prediction results are consistent with the current understanding of RPV embrittlement mechanism.

• Journal of the Korean Society of Visualization
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• v.19 no.1
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• pp.62-67
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• 2021

Selective catalytic reduction(SCR) is an exhaust gas reduction device to remove nitro oxides (NOx). SCR operation of ship can be controlled through valves for minimizing economic loss from SCR. Valve in SCR-high pressure (HP) system is directly connected to engine exhaust and operates in high temperature and high pressure. Long-term thermal deformation induced by engine heat weakens the sealing of the valve, which can lead to unexpected failures during ship sailing. In order to prevent the unexpected failures due to long-term valve thermal deformation, a failure prediction system using autoregressive integrated moving average (ARIMA) was proposed. Based on the heating experiment, virtual data mimicking temperature range around the SCR-HP valve were produced. By detecting abnormal temperature rise and fall based on the short-term ARIMA prediction, an algorithm determines whether present temperature data is required for failure prediction. The signal processed by the data collection algorithm was interpolated for the failure prediction. By comparing mean average error (MAE) and root mean square error (RMSE), ARIMA model and suitable prediction instant were determined.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.2
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• pp.55-62
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• 2009

The power consumption of a high-end microprocessor increases very rapidly. High power consumption will lead to a rapid increase in the chip temperature as well. If the temperature reaches beyond a certain level, chip operation becomes either slow or unreliable. Therefore various approaches for Dynamic Thermal Management (DTM) have been proposed. In this paper, we propose a learning based temperature prediction scheme for a multi-core system. In this approach, from repeatedly executing an application, we learn the thermal patterns of the chip, and we control the temperature in advance through DTM. When the predicted temperature may go beyond a threshold value, we reduce the temperature by decreasing the operation frequencies of the corresponding core. We implement our temperature prediction on an Intel's Quad-Core system which has integrated digital thermal sensors. A Dynamic Frequency System (DFS) technique is implemented to have four frequency steps on a Linux kernel. We carried out experiments using Phoronix Test Suite benchmarks for Linux. The peak temperature has been reduced by on average $5^{\circ}C{\sim}7^{\circ}C$. The overall average temperature reduced from $72^{\circ}C$ to $65^{\circ}C$.

• Journal of the Korean Society of Safety
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• v.32 no.2
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• pp.14-20
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• 2017

A numerical study was conducted to identify the predictive performance for the bare-bead thermocouple (TC) using FDS (Fire Dynamics Simulator) in simulated thermal environments of fire. A relative prediction bias of TC temperature calculated from reverse-radiation correction by FDS was evaluated with the comparison of previous experimental data. As a result, it was identified that the TC temperatures predicted by FDS were lower than the temperatures measured by bare-bead TC for the ranges of heat flux and gas temperature considered. The relative prediction bias of TC temperature by FDS was gradually increased with the increase in radiative heat flux and also significantly increased with the decrease in the gas temperature. Quantitatively, at the gas temperature of $20^{\circ}C$, the TC temperature predicted by FDS had the relative bias of approximately -20% with the radiative heat flux of $20kW/m^2$ corresponding to thermal radiation level of the flashover. It is predicted from the present study that more accurate validation of fire modeling will be possible with the quantitative prediction bias occurred in the process of reverse-radiation correction of temperature predicted by FDS.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.599-605
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• 2012

It is important to decide traffic opening time for construction plan of epoxy asphalt pavement. For this purpose, strength prediction model of epoxy asphalt concrete is required. In this study, Marshall stability was measured according to temperature and time for making strength properties equation. Strength prediction model was developed using chemical kinetics considering temperature variation. The traffic opening time of epoxy asphalt pavement on bridge deck has been predicted using the developed model. The prediction and actual traffic opening times were different by 17-days, because weathers of year 2009-2011 used in prediction model were different from weather of year 2012. When the prediction model used the actually measured temperatures of pavement, the difference between real opening time and prediction opening time was two days. The correlation analysis result between measured strength and prediction strength revealed that the $R^2$ using accurate temperature of pavement was 0.95. An improved precise prediction result is to be obtained if the prediction model uses accurate temperature data of pavement.