• 한국전산유체공학회:학술대회논문집
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• 2004.03a
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• pp.220-223
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• 2004

The initial conditions such as the film thickness and the void fraction at the onset of annular flow are required for the analytical dryout model. The Disturbance Wave Instability model(DWI model) is one of the model describing the Annular-to-Intermittent Flow regime Transition(AIFT). The experimental CHF conditions for the uniformly heated tube were compared with the predictions by the modified Levy model, for which the initial conditions at AIFT were estimated by the DWI model. For the flow through long tubes with small inlet subcooling, the effect of AIFT model on the dryout prediction was little. However, the use of DWI model gave better prediction of CHF in a short tube.

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.149-160
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• 2009

The purpose of this study is to comparatively analyse the behavior patterns of the first and the repeated offenders in DWI, and to develope the models of BAC(Blood Alcohol Concentration) by using multiple regression analysis method and a model of repeated DWI conviction by using logistic regression analysis method. The main results are as follows. First, the repeated offenders are more in criminal and traffic accidents records than that of the first offenders. The unlicenced drivers are in higher BAC than licenced drivers. Second, multiple regression model of BAC was developed, and the model revealed that criminal records and driving distance were important factors. Third, a model of repeated DWI conviction was developed, and the model revealed that traffic accidents records, whether or not having licence, and criminal records were most important factors.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1680-1689
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• 2021

Objective: To investigate the diagnostic yield of diffusion-weighted imaging (DWI) in patients with transient global amnesia (TGA) and identify significant parameters affecting diagnostic yield. Materials and Methods: A systematic literature search of the MEDLINE and EMBASE databases was conducted to identify studies that assessed the diagnostic yield of DWI in patients with TGA. The pooled diagnostic yield of DWI in patients with TGA was calculated using the DerSimonian-Laird random-effects model. Subgroup analyses were also performed of slice thickness, magnetic field strength, and interval between symptom onset and DWI. Results: Twenty-two original articles (1732 patients) were included. The pooled incidence of right, left, and bilateral hippocampal lesions was 37% (95% confidence interval [CI], 30-44%), 42% (95% CI, 39-46%), and 25% (95% CI, 20-30%) of all lesions, respectively. The pooled diagnostic yield of DWI in patients with TGA was 39% (95% CI, 27-52%). The Higgins I² statistic showed significant heterogeneity (I² = 95%). DWI with a slice thickness ≤ 3 mm showed a higher diagnostic yield than DWI with a slice thickness > 3 mm (pooled diagnostic yield: 63% [95% CI, 53-72%] vs. 26% [95% CI, 16-40%], p < 0.01). DWI performed at an interval between 24 and 96 hours after symptom onset showed a higher diagnostic yield (68% [95% CI, 57-78%], p < 0.01) than DWI performed within 24 hours (16% [95% CI, 7-34%]) or later than 96 hours (15% [95% CI, 8-26%]). There was no difference in the diagnostic yield between DWI performed using 3T vs. 1.5T (pooled diagnostic yield, 31% [95% CI, 25-38%] vs. 24% [95% CI, 14-37%], p = 0.31). Conclusion: The pooled diagnostic yield of DWI in TGA patients was 39%. DWI obtained with a slice thickness ≤ 3 mm or an interval between symptom onset and DWI of > 24 to 96 hours could increase the diagnostic yield.

• Investigative Magnetic Resonance Imaging
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• v.25 no.2
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• pp.118-128
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• 2021

Purpose: To investigate the value of MR textural analysis, including use of diffusion-weighted imaging (DWI) to differentiate malignant from benign soft-tissue tumors on 3T MRI. Materials and Methods: We enrolled 69 patients (25 men, 44 women, ages 18 to 84 years) with pathologically confirmed soft-tissue tumors (29 benign, 40 malignant) who underwent pre-treatment 3T-MRI. We calculated MR texture, including mean, standard deviation (SD), skewness, kurtosis, mean of positive pixels (MPP), and entropy, according to different spatial-scale factors (SSF, 0, 2, 4, 6) on axial T1- and T2-weighted images (T1WI, T2WI), contrast-enhanced T1WI (CE-T1WI), high b-value DWI (800 sec/mm²), and apparent diffusion coefficient (ADC) map. We used the Mann-Whitney U test, logistic regression, and area under the receiver operating characteristic curve (AUC) for statistical analysis. Results: Malignant soft-tissue tumors had significantly lower mean values of DWI, ADC, T2WI and CE-T1WI, MPP of ADC, and CE-T1WI, but significantly higher kurtosis of DWI, T1WI, and CE-T1WI, and entropy of DWI, ADC, and T2WI than did benign tumors (P < 0.050). In multivariate logistic regression, the mean ADC value (SSF, 6) and kurtosis of CE-T1WI (SSF, 4) were independently associated with malignancy (P ≤ 0.009). A multivariate model of MR features worked well for diagnosis of malignant soft-tissue tumors (AUC, 0.909). Conclusion: Accurate diagnosis could be obtained using MR textural analysis with DWI and CE-T1WI in differentiating benign from malignant soft-tissue tumors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.242-249
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• 2004

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for the practical purpose of forecasting forest fire danger. Forest fire in South Korea is highly influenced by humidity, wind speed, and temperature. To effectively forecast forest fire occurrence, we need to develop a forest fire danger rating model using weather factors associated with forest fire. Forest fore occurrence patterns were investigated statistically to develop a forest fire danger rating index using time series weather data sets collected from 8 meteorological observation centers. The data sets were for 5 years from 1997 through 2001. Development of the forest fire occurrence probability model used a logistic regression function with forest fire occurrence data and meteorological variables. An eight-province probability model by was developed. The meteorological variables that emerged as affective to forest fire occurrence are effective humidity, wind speed, and temperature. A forest fire occurrence danger rating index of through 10 was developed as a function of daily weather index (DWI).

• Investigative Magnetic Resonance Imaging
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• v.7 no.2
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• pp.116-123
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• 2003

Purpose : The present study was undertaken to evaluate the usefulness of cerebral diffusion (DWI) and perfusion MR imaging (PWI) in rabbit models with hyperacute cerebral ischemic infarction. Materials and Methods : Experimental cerebral infarction were induced by direct injection of mixture of Histoacryl glue, lipiodol, and tungsten powder into the internal cerebral artery of 6 New-Zealand white rabbits, and they underwent conventional T1 and T2 weighted MR imaging, DWI, and PWI within 1 hour after the occlusion of internal cerebral artery. The PWI scan for each rabbit was obtained at the level of lateral ventricle and 1cm cranial to the basal ganglia. By postprocessing using special imaging software, perfusion images including cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) maps were obtained. The detection of infarcted lesion were evaluated on both perfusion maps and DWI. MTT difference time were measured in the perfusion defect lesion and symmetric contralateral normal cerebral hemisphere. Results : In all rabbits, there was no abnormal signal intensity on T2WI. But on DWI, abnormal high signal intensity, suggesting cerebral infarction, were detected in all rabbits. PWI (rCBV, CBF and MTT map) also showed perfusion defect in all rabbits. In four rabbits, the calculated square of perfusion defect in MTT map is larger than that of CBF map and in two rabbits, the calculated size of perfusion defect in MTT map and CBF map is same. Any rabbits do not show larger perfusion defect on CBF map than MTT map. In comparison between CBF map and DWI, 3 rabbits show larger square of lesion on CBF map than on DWI. The others shows same square of lesion on both technique. The size of lesion shown in 6 MTT map were larger than DWI. In three cases, the size of lesion shown in CBF map is equal to DWI. But these were smaller than MTT map. The calculated square of lesion in CBF map, equal to that of DWI and smaller than MTT map was three. And in one case, the calculated square of perfusion defect in MTT map was largest, and that of DWI was smallest. Conclusion : DWI and PWI may be useful in diagnosing hyperacute cerebral ischemic infarction and in e-valuating the cerebral hemodynamics in the rabbits.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.348-356
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• 2018

Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behavior and its spread. Thus, meteorological factors as well as topographical and forest factors were considered in the fire danger rating systems. This study aims to develop an advanced national integrated daily weather index(DWI) using weather data in the spring and fall to support forest fire prevention strategy in South Korea. DWI represents the meteorological characteristics, such as humidity (relative and effective), temperature and wind speed, and we integrated nine logistic regression models of the past into one national model. One national integrated model of the spring and fall is respectively $[1+{\exp}\{-(2.706+(0.088^*T_{mean})-(0.055^*Rh)-(0.023^*Eh)-(0.014^*W_{mean}))\}^{-1}]^{-1}$, $[1+{\exp}\{-(1.099+(0.117^*T_{mean})-(0.069^*Rh)-(0.182^*W_{mean}))\}^{-1}]^{-1}$ and all weather variables significantly (p<0.01) affected the probability of forest fire occurrence in the overall regions. The accuracy of the model in the spring and fall is respectively 71.7% and 86.9%. One integrated national model showed 10% higher accuracy than nine logistic regression models when it is applied weather data with 66 random sampling in forest fire event days. These findings would be necessary for the policy makers in the Republic of Korea for the prevention of forest fires.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Journal of Veterinary Clinics
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• v.33 no.1
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• pp.10-15
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• 2016

The purpose of this study was to establish reproducible ischemic infarction model using allogenic blood clot in beagle dogs and identify induced ischemic lesion after middle cerebral artery occlusion using magnetic resonance imaging (MRI) and histopathologic findings. Twenty eight male beagle dogs with no evidence of neurologic disease were experimented. Allogenic embolus was made using a healthy beagle dog. After internal carotid artery (ICA) was exposure, 16G catheter was introduced through the ICA. The dog was administered 0.3 ml blood clot for 15 seconds followed by 3 ml of saline for 15 seconds. MRI scans were performed with 1.5T to evaluate ischemic lesion at 7 days after middle cerebral artery occlusion procedure. Evaluation parameters of MRI include location, distribution, infarction type, margin, shape, mass effect and intensity of T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid attenuated inversion recovery (FLAIR) sequence, diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC). On MRI, all dogs (28/28) showed focal or multifocal lesion including telencephalon and thalamus lesions, especially caudate nucleus (24/28). These lesions had well-defined margin from adjacent brain parenchyma, none or mild mass effect and various shape. Most of dogs appeared hyperintensity on T1WI, T2WI, FLAIR, and DWI/ADC, corresponding to chronic infarction. These lesions were histopathologically confirmed atrophic changes and unstained lesion. In conclusion, MRI is the useful method to provide information about ischemic infarction in dogs and the best reproducible ischemic infarction model was developed by using allogenic blood clot.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.45-52
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• 2021

Stock price fluctuations affect investor returns, particularly, in this pandemic situation that has triggered stock market shocks. As a result of this situation, investors prefer to move their money into a safer portfolio. Therefore, in this study, we approach an efficient portfolio model using smart beta and combining others to obtain a fast method to predict investment stock returns. Smart beta is a method to selects stocks that will enter a portfolio quickly and concisely by considering the level of return and risk that has been set according to the ability of investors. A smart beta portfolio is efficient because it tracks with an underlying index and is optimized using the same techniques that active portfolio managers utilize. Using the logistic regression method and the data of 100 low volatility stocks listed on the Indonesia stock exchange from 2009-2019, an efficient portfolio model was made. It can be concluded that an efficient portfolio is formed by a group of stocks that are aggressive and actively traded to produce optimal returns at a certain level of risk in the long-term period. And also, the portfolio selection model generated using the smart beta, beta, alpha, and stock variants is a simple and fast model in predicting the rate of return with an adjusted risk level so that investors can anticipate risks and minimize errors in stock selection.