• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.3
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• pp.323-333
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• 2014

In this paper, the wakes behind a square cylinder were simulated using two kinds of different turbulence models for the eddy viscosity concept such as the zero- and the one-equation model in which the former is the mixing length model and the latter is the k-equation model. For comparison between numerical and analytical solutions, we employed three skill assessments: the correlation coefficient(r) for the similarity of the wake shape, the error of maximum velocity difference(EMVD) for the accuracy of wake velocity and the ratio of drag coefficient(RDC) for the pressure distribution around the structure. On the basis of the numerical results, the feasibility of each model for wake simulation was discussed and a suitable value for the empirical constant was suggested in these turbulence models. The zero-equation model, known as the simplest turbulence model, overestimated the EMVD and its absolute mean error(AME) for r, EMVD and RDC was ranging from 20.3 % to 56.3 % for all test. But the AME by the one-equation model was ranging from 3.4 % to 19.9 %. The predicted values of the one-equation model substantially agreed with the analytical solutions at the empirical mixing length scale $L=0.6b_{1/2}$ with the AME of 3.4 %. Therefore it was concluded that the one-equation model was suitable for the wake simulation behind a square cylinder when the empirical constant for eddy viscosity would be properly chosen.

• 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.

• YAKHAK HOEJI
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• v.49 no.2
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• pp.115-121
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• 2005

In this study, the performance of a portable NIR system and a FT NIR spectrometer were compared to determine water content of hairless mouse skin. The stratum corneum parts wer e separated from the epidermal tissues by trypsin solution. NIR diffuse reflectance spectra of hairless mouse skin were acquired using a fiber optic probe. In the near infrared, water molecules show two clear absorption bands at 1450 nm from first overtone of O-H stretching and 1940 nm from the combination involving O-H stretching and O-H deformation. It was found that the variations of O-H absorption band according to water content. Partial least squares regression (PLSR) was applied to develop a calibration model. The PLS model showed a good correlation between NIR predicted value and the absolute water content of separated hairless mouse skin, in vitro. For both the portable and the FT NIR spectrometer, These studies showed the possibility of a rapid and nondestructive skin moisture measurement using NIR spectroscopy. The portable NIR spectrometer with a photodiode arrays-microsensor could be more rapidly applied for the determination of water content with comparable accuracy with the performance of a FT spectrometer .

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.2
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• pp.101-110
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• 2000

This study focuses on examination of the availability and effectiveness about application of the differential GPS methods for navigation and acquisition of the geo-spatial information. For this, the algorithms related to a navigation solution and differential GPS were implemented in MATLAB code, a number of software simulations on test model were carried out to assess its performance, comparing the results with those obtained from the commercial software. Expecially, the results coming from tracking test on test model of the OO's WADGPS which is the commercial real-time satellite-based augmentation system via geostationary satellite (GEOs), which has been investigated with those from the above GPS methods. And also, the accuracy of absolute positioning by Navigation solution and WADGPS before and after SA-off has been compared. The above results show that DGPS methods are very reliable and efficient methods for acquisition of the geo-spatial information.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1326-1336
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• 2018

This paper proposes a numerical approach to predict the critical flashover voltages of air gaps under lightning impulses. For an air gap, the impulse voltage waveform features and electric field features are defined to characterize its energy storage status before the initiation of breakdown. These features are taken as the input parameters of the predictive model established by support vector machine (SVM). Given an applied voltage range, the golden section search method is used to compute the prediction results efficiently. This method was applied to predict the critical flashover voltages of rod-rod, rod-plane and sphere-plane gaps over a wide range of gap lengths and impulse voltage waveshapes. The predicted results coincide well with the experimental data, with the same trends and acceptable errors. The mean absolute percentage errors of 6 groups of test samples are within 4.6%, which demonstrates the validity and accuracy of the predictive model. This method provides an effectual way to obtain the critical flashover voltage and might be helpful to estimate the safe clearances of air gaps for insulation design.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1769-1780
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• 2022

A new method of dose calculation algorithm, called GPU-accelerated Monte Carlo and collapsed cone Convolution (GMCC) was developed to improve the calculation speed of BNCT treatment planning system. The GPU-accelerated Monte Carlo routine in GMCC is used to simulate the neutron transport over whole energy range and the Collapsed Cone Convolution method is to calculate the gamma dose. Other dose components due to alpha particles and protons, are calculated using the calculated neutron flux and reaction data. The mathematical principle and the algorithm architecture are introduced. The accuracy and performance of the GMCC were verified by comparing with the FLUKA results. A water phantom and a head CT voxel model were simulated. The neutron flux and the absorbed dose obtained by the GMCC were consistent well with the FLUKA results. In the case of head CT voxel model, the mean absolute percentage error for the neutron flux and the absorbed dose were 3.98% and 3.91%, respectively. The calculation speed of the absorbed dose by the GMCC was 56 times faster than the FLUKA code. It was verified that the GMCC could be a good candidate tool instead of the Monte Carlo method in the BNCT dose calculations.

• Journal of Drive and Control
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• v.20 no.4
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• pp.100-106
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• 2023

We developed a crawler-based driving platform that can perform harvesting, transportation, pest control, and rotary operation by equipping it with various implements, and analyzed its performance. This single platform was developed to perform as pepper harvester, peanut harvester, and transporter with a 46-kW engine. A simulation model was developed to study the specifications of the platform, and the accuracy was also analyzed. The absolute percentage error ranged from 0.2 to 5.9%, which made it possible to predict the platform performance using simulation model. In T-test, both torque and speed on field and asphalt showed a significant difference (1%). Driving torque required differed depending on the nature of the field, and the speeds also changed based on soil load. The developed platform has the advantage of being equipped with a variety of working tools, expected to be used to harvest root crops in the future.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.236-244
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• 2024

A representative problem in domestic chestnut industry is the high loss of flesh due to excessive knife peeling in order to increase the peeling rate, resulting in a decrease in production efficiency. In this study, a prediction model for weight loss rate of chestnut by stage of knife peeling process was developed as undergarment study to optimize conditions of the machine. 51 control conditions of the two-stage blade peeler used in the experiment were derived and repeated three times to obtain a total of 153 data. Machine learning(ML) models including artificial neural network (ANN) and random forest (RF) were implemented to predict the weight loss rate by chestnut peel stage (after 1st peeling, 2nd peeling, and after final discharge). The performance of the models were evaluated by calculating the values of coefficient of determination (R), normalized root mean square error (nRMSE), and mean absolute error (MAE). After all peeling stages, RF model have better prediction accuracy with higher R values and low prediction error with lower nRMSE and MAE values, compared to ANN model. The final selected RF prediction model showed excellent performance with insignificant error between the experimental and predicted values. As a result, the proposed model can be useful to set optimum condition of knife peeling for the purpose of minimizing the weight loss of domestic chestnut flesh with maximizing peeling rate.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.3
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• pp.237-247
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• 2011

As of 2011, 72 Permanent GPS Stations are installed to control DGPS reference points by the National Geographic Information Institute in South Korea. As the center of the Earth's mass continues to move, the coordinates of the permanent GPS stations become inconsistent over time. Thus, a reference frame using a set of coordinates and their velocities of a global network of stations at a specific period has been used to solve the inconsistency. However, the relative movement of the permanent GPS stations can lower the accuracy of GPS relative positioning. In this research, we first analyzed the data collected daily during the past 30 months at the 40 permanent GPS stations within South Korea and the 5 IGS permanent GPS stations around the Korean Peninsula using a global network adjustment. We then calculated the absolute and relative amount of movement of the GPS permanent stations. We also identified the optimum renewal period of the permanent GPS stations considering the accuracy of relative GPS surveying. Finally, we developed a Korean a Korean crustal movement model that can be used to improvement of accuracy.