• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.285-292
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• 2023

Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.

• Radiation Oncology Journal
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• v.21 no.3
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• pp.238-244
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• 2003

Purpose: The Planning of High-Dose-Rate (HDR) brachytherapy treatments are becoming individualized and more dependent on the treatment planning system. Therefore, computer software has been developed to perform independent point dose calculations with the integration of an isodose distribution curve display into the patient anatomy images. Meterials and Methods: As primary input data, the program takes patients'planning data including the source dwell positions, dwell times and the doses at reference points, computed by an HDR treatment planning system (TPS). Dosimetric calculations were peformed in a $10\times12\times10\;Cm^3$ grid space using the Interstitial Collaborative Working Group (ICWG) formalism and an anisotropy table for the HDR Iridium-192 source. The computed doses at the reference points were automatically compared with the relevant results of the TPS. The MR and simulation film images were then imported and the isodose distributions on the axial, sagittal and coronal planes intersecting the point selected by a user were superimposed on the imported images and then displayed. The accuracy of the software was tested in three benchmark plans peformed by Gamma-Med 12i TPS (MDS Nordion, Germany). Nine patients'plans generated by Plato (Nucletron Corporation, The Netherlands) were verified by the developed software. Results: The absolute doses computed by the developed software agreed with the commercial TPS results within an accuracy of $2.8\%$ in the benchmark plans. The isodose distribution plots showed excellent agreements with the exception of the tip legion of the source's longitudinal axis where a slight deviation was observed. In clinical plans, the secondary dose calculations had, on average, about a $3.4\%$ deviation from the TPS plans. Conclusion: The accurate validation of complicate treatment plans is possible with the developed software and the qualify of the HDR treatment plan can be improved with the isodose display integrated into the patient anatomy information.

• Korean Journal of Food Science and Technology
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• v.30 no.2
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• pp.341-347
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• 1998

To develop simple and accurate analytical method for freezing time prediction of beef and tylose under various freezing conditions, freezing time (Y) was regressed against the reciprocal $(X_3)$ of difference of initial freezing point and freezing medium temperature, reciprocal $(X_4)$ of surface heat transfer coefficient, the initial temperature $(X_1)$ and thickness $(X_2)$ of samples which should cover most situations arising in frozen food industry. As results of the multiple regression analysis, equations were obtained as follows. $Y_{tylose}=3.45X_1+7642.84X_2+4642.67X_3+2946.89X_4-431.33\;(R^2=0.9568)$ and $Y_{beef}=0.68X_1+7568.98X_2+2430.78X_3+3293.26X_4-299.00\;(R^2=0.9897)$. These equations offered better results than Plank, Nagaoka and Pham's models, shown in satisfactory agreement with models of Cleland & Earle and Hung & Thompson when were compared to previous models, and the accuracy of its was very high as average absolute difference of about 10% in the difference between the fitted and experimental results. Also, thermal diffusivities of beef and tylose were measured as $4.43{\times}10^{-4}m^2/hr$ and $4.39{\times}10^{-4}m^2/hr$ at $6{\sim}7^{\circ}C$, $2.42{\times}10^{-3}m^2/hr$ and $3.32{\times}10^{-3}m^2/hr$ at $-10{\sim}-12^{\circ}C$. Initial freezing points of beef and tylose were $-1.2^{\circ}C\;and\;-0.6^{\circ}C$, respectively. Surface heat transfer coefficients were estimated $20.57\;W/m^2^{\circ}C$ with no-packing, $16.11\;W/m^2^{\circ}C$ with wrap packing and $13.07\;W/m^2^{\circ}C$ with Al-foil packing, and the cooling rate of immersion freezing method was about 10 times faster than that of air blast freezing method.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• 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.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.

• The Journal of Korean Society for Radiation Therapy
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• v.32
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• pp.93-109
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• 2020

Purpose: The radiochromic film (Gafchromic EBT3, Ashland Advanced Materials, USA) and 3-dimensional analysis system dosimetry checkTM (DC, MathResolutions, USA) were evaluated for patient-specific quality assurance (QA) of helical tomotherapy. Materials and Methods: Depending on the tumors' positions, three types of targets, which are the abdominal tumor (130.6㎤), retroperitoneal tumor (849.0㎤), and the whole abdominal metastasis tumor (3131.0㎤) applied to the humanoid phantom (Anderson Rando Phantom, USA). We established a total of 12 comparative treatment plans by the four geometric conditions of the beam irradiation, which are the different field widths (FW) of 2.5-cm, 5.0-cm, and pitches of 0.287, 0.43. Ionization measurements (1D) with EBT3 by inserting the cheese phantom (2D) were compared to DC measurements of the 3D dose reconstruction on CT images from beam fluence log information. For the clinical feasibility evaluation of the DC, dose reconstruction has been performed using the same cheese phantom with the EBT3 method. Recalculated dose distributions revealed the dose error information during the actual irradiation on the same CT images quantitatively compared to the treatment plan. The Thread effect, which might appear in the Helical Tomotherapy, was analyzed by ripple amplitude (%). We also performed gamma index analysis (DD: 3mm/ DTA: 3%, pass threshold limit: 95%) for pattern check of the dose distribution. Results: Ripple amplitude measurement resulted in the highest average of 23.1% in the peritoneum tumor. In the radiochromic film analysis, the absolute dose was on average 0.9±0.4%, and gamma index analysis was on average 96.4±2.2% (Passing rate: >95%), which could be limited to the large target sizes such as the whole abdominal metastasis tumor. In the DC analysis with the humanoid phantom for FW of 5.0-cm, the three regions' average was 91.8±6.4% in the 2D and 3D plan. The three planes (axial, coronal, and sagittal) and dose profile could be analyzed with the entire peritoneum tumor and the whole abdominal metastasis target, with planned dose distributions. The dose errors based on the dose-volume histogram in the DC evaluations increased depending on FW and pitch. Conclusion: The DC method could implement a dose error analysis on the 3D patient image data by the measured beam fluence log information only without any dosimetry tools for patient-specific quality assurance. Also, there may be no limit to apply for the tumor location and size; therefore, the DC could be useful in patient-specific QAl during the treatment of Helical Tomotherapy of large and irregular tumors.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.102-111
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• 1999

In this paper, we propose a fast full search block matching algorithm using the search region subsampling and the difference of adjacent pixels in current block. In the proposed algorithm, we calculate the lower bound of mean absolute difference (MAD) at each search point using the MAD value of neighbor search point and the difference of adjacent pixels in current block. After that, we perform block matching process only at the search points that need block matching process using the lower bound of MAD at each search point. To calculate the lower bound of MAD at each search point, we need the MAD value of neighbor search point. Therefore, the search points are subsampled at the factor of 4 and the MAD value at the subsampled search points are calculated by the block matching process. And then, the lower bound of MAD at the rest search points are calculated using the MAD value of the neighbor subsampled search point and the difference of adjacent pixels in current block. Finally, we discard the search points that have the lower bound of MAD value exceed the reference MAD which is the minimum MAD value of the MAD values at the subsampled search points and we perform the block matching process only at the search points that need block matching process. By doing so, we can reduce the computation complexity drastically while the motion compensated error performance is kept the same as that of full search block matching algorithm (FSBMA). The experimental results show that the proposed method has a much lower computational complexity than that of FSBMA while the motion compensated error performance of the proposed method is kept same as that of FSBMA.

• The Journal of Korean Academy of Prosthodontics
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• v.48 no.1
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• pp.1-7
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• 2010

Purpose: The purpose of this study was to evaluate the fit of zirconia core using MAD/MAM system comparing to that of conventional metal-ceramic and CAD/CAM system. Materials and methods: Duplicating the prepared resin tooth, 50 improved stone dies were fabricated. These dies are classified as a group of 5 to create the core. The groups were composed of metal-ceramic, $Cercon^{(R)}$, $Ceramill^{(R)}$, $Rainbow^{TM}$, and $Zirkonzhan^{(R)}$. Each core was cemented to stone die, and then, absolute marginal discrepancy was measured with microscope at a magnification of ${\times}50$. Statistical analysis was done with one-way ANOVA test and Tukey's HSD test. Results: The mean absolute marginal discrepancy for metal-ceramic was $51.97{\pm}23.38{\mu}m$, for $Cercon^{(R)}$ was $62.16{\pm}25.88{\mu}m$, for $Ceramill^{(R)}$ was $67.64{\pm}40.38{\mu}m$, for $Rainbow^{TM}$ was $125.07{\pm}42.19{\mu}m$, and for $Zirkonzhan^{(R)}$ was $105{\pm}44.61{\mu}m$. Conclusion: 1. Fit of margin was identified as in the order of metal-ceramic, $Cercon^{(R)}$, $Ceramill^{(R)}$, $Zirkonzhan^{(R)}$, and $Rainbow^{TM}$. 2. Absolute marginal discrepancy of the zirconia core that designed by MAD/MAM system had significant differences in order of $Ceramill^{(R)}$, $Zirkonzhan^{(R)}$, and $Rainbow^{TM}$. 3. The mean absolute marginal discrepancy between $Cercon^{(R)}$ and $Ceramill^{(R)}$ did not show significant differences.