• The Korean Journal of Nuclear Medicine
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• v.39 no.4
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• pp.257-262
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• 2005

Purpose: Gastric emptying scan (GES) is usually acquired up to 2 hours. Our study investigated whether a fraction of meal-retention in the stomach at 120 minutes (FR120) was predicted from the data measured for 90 minutes by using non-linear curve fitting. We aimed at saving the delayed imaging by utilizing mathematical models. Materials and Methods: Ninety-six patients underwent GES immediately after taking a boiled egg with 74 MBq (2 mCi) Tc-99m DTPA. The patients were divided into Group I ($T_{1/2}\;{\leq}90\;min$) and Group II ($90\;min). Group I (n=51) had 21 men and 30 women, and Group II (n=45) 15 men and 30 women. There was no significant difference in age and sex between the two groups. Simple exponential, power exponential, and modified power exponential curves were acquired from the measured fraction of meal-retention at each time (0, 15, 30, 45, 60, 75, and 90 min) by non-linear curve fitting ($MATLAB^{\circledR}$ 5.3) and another simple exponential fitting was performed on the fractions at late times (60, 75, and 90 min). A predicted FR120 was calculated from the acquired functional formulas. A correlation coefficient between the measured FR120 and the predicted FR120 was computed ($MedCalc^{\circledR}$ 6.0). Results: Correlation coefficients(r) between the measured FR120 and the predicted FR120 of each mathematical functions were as follows: simple exponential function (Group I: 0.8558, Group II: 0.5982, p<0.0001), power exponential function (Group I: 0.8755, Group II: 0.6008, p<0.0001), modified power exponential function (Group I: 0.8892, Group II: 0.5882, p<0.0001), and simple exponential function at the late times(Group I: 0.9085, Group II: 0.6832, p<0.0001). In all the fitting models, the predicted FR120 were significantly correlated with the measured FR120 in Group I but not in Group II. There was no statistically significant difference in correlation among the 4 mathematical models. Conclusion: In the cases with $T_{1/2}\;{\leq}90\;min$, the predicted FR120 is significantly correlated with the measured FR120. Therefore, FR120 can be predicted from the data measured for 90 minutes by using non-linear curve fitting, saving the delayed imaging after 90 minutes when $T_{1/2}\;{\leq}90\;min$ is ascertained.

• Tuberculosis and Respiratory Diseases
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• v.46 no.6
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• pp.786-794
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• 1999

Back ground : Arm span measurements provide a practical substitute for standing height to predict normal spirometric values in subjects unable to stand or those with a skeletal deformity such as kyphoscoliosis. The relationship between arm span and height has previously been reported as either a fixed ratio unaffected by age or as a regression equation in which the ratio varies as a function of age. The fixed ratio or regression equation is known to be specific for sex and race. Methods : We studied the relationship between standing height, arm span, and age in 381 Korean adult female subjects (ages 20 to 69 yrs) sampled in a general population. Results : The mean ratio for arm span to height is 1.004. Multiple linear analysis found arm span and age to be predictive of standing height (p=0.0001, $r^2$=0.76). We performed the analysis of the difference between the predicted height using either fixed ratio or regression equation and actual height. At the extremes of arm span and age, the ratio method either underestimated(at smaller arm span or younger age) or overestimated(at larger arm span or older age) as compared with actual height (p=0.0001). Conclusion : This results indicate that the estimated height using the fixed ratio method provides a less acceptable method of estimating height for the prediction of lung volumes in the Korean adult women when compared with the regression equations, especially at the extremes of stature or age.

• Korean journal of applied entomology
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• v.55 no.4
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• pp.445-452
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• 2016

Exorista japonica is one of the major natural enemies of noctuid larvae, Mythimna separata and Spodoptera litura. The examined parasitoid was obtained from host species M. separata, collected at Gimje city and identified by DNA sequences (partial cytochrome oxidase I, 16S, 18S, and 28S). For purposed of this study, laboratory reared S. litura served as the host species for the development of the E. japonica. The developmental period of E. japonica immature stages were investigated at seven constant temperatures (16, 19, 22, 25, 28, 31, $34{\pm}1^{\circ}C$, RH 20~30%). Temperature-dependent developmental rates and development completion models were developed. E. japonica was successfully developed from egg to adult in $16{\sim}31^{\circ}C$ temperature regimes. Developmental duration was the shortest at $34^{\circ}C$ (8.3 days) and the longest at $16^{\circ}C$ (23.4 days) from egg to pupa development. Pupal development duration was the shortest at $28^{\circ}C$ (7.3 days). Total immature-stage development duration decreased with increasing temperature, and was the shortest at $31^{\circ}C$ (16.3 days) and the longest at $16^{\circ}C$ (45.4 days). The lower developmental threshold was $7.8^{\circ}C$ and thermal constant required to complete total immature-stage development was 370.4 degree days. Among four non-linear temperature-dependent developmental rate models, Briere 1 model had the highest adjusted R-squared (0.96). The distribution model of development completion for total immature stage development of E. japonica was well described by all model ($r^2_{adj}=0.90$) based on the standardized development duration. These results of study would be necessary not only to develop population dynamics model but also to understand fundamental biology of E. japonica.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.281-293
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• 2020

Recently, large and small fires have been happening more often in Korea. Fire is one of the most frequent disasters along with traffic accidents in korean cities, and this frequency is closely related to the land use and the type of facilities. Therefore, in this study, the significance of fires was analyzed by considering land use, facility types, human and social factors and using 10 years of fire data in Jinju city. Based on this, OLS (Ordinary Least Square) regression analysis, SLM (Spatial Lag Model) and SEM (Spatial Error Model) using space weights, were compared and analyzed considering the location of the fire and each factor, then a statistical model with high suitability was presented. As a result, LISA analysis of spatial distribution patterns of fires in Jinju city was conducted, and it was proved that the frequency of fires was high in the order as follow, central commercial area, industrial area and residential area. Multiple regression analysis was performed by integrating demographic, social, and physical variables. Therefore, the three models were compared and analyzed by applying spatial weighting to the derived factors. As a result of the significance test, the spatial error model was analyzed to be the most significant. The facilities that have the highest correlation with fire occurrence were second type neighborhood facilities, followed by detached house, first type neighborhood facilities, number of households, and sales facilities. The results of this study are expected to be used as significant data to identify factors and manage fire safety in urban areas. Also, through the analysis of the standard deviation ellipsoid, the distribution characteristics of each facility in the residential area, industrial area, and central commercial area among the use areas were analyzed. In, the second type neighborhood facility with the highest fire risk was concentrated in the center. The results of these studies are expected to be used as useful data for identifying factors and managing fire safety in urban areas.

• Journal of the Mineralogical Society of Korea
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• v.22 no.4
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• pp.313-324
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• 2009

We explore the effect of particle shape and size on 3-dimensional (3D) network and pore structure of porous earth materials composed of glass beads and silica gel using NMR micro-imaging in order to gain better insights into relationship between structure and the corresponding hydrologic and seismological properties. The 3D micro-imaging data for the model porous networks show that the specific surface area, porosity, and permeability range from 2.5 to $9.6\;mm^2/mm^3$, from 0.21 to 0.38, and from 11.6 to 892.3 D (Darcy), respectively, which are typical values for unconsolidated sands. The relationships among specific surface area, porosity, and permeability of the porous media are relatively well explained with the Kozeny equation. Cube counting fractal dimension analysis shows that fractal dimension increases from ~2.5－2.6 to 3.0 with increasing specific surface area from 2.5 to $9.6\;mm^2/mm^3$, with the data also suggesting the effect of porosity. Specific surface area, porosity, permeability, and cube counting fractal dimension for the natural mongolian sandstone are $0.33\;mm^2/mm^3$, 0.017, 30.9 mD, and 1.59, respectively. The current results highlight that NMR micro-imaging, together with detailed statistical analyses can be useful to characterize 3D pore structures of various porous earth materials and be potentially effective in accounting for transport properties and seismic wave velocity and attenuation of diverse porous media in earth crust and interiors.

• Radiation Oncology Journal
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• v.16 no.4
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• pp.517-529
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• 1998

Purpose : Although many studies have investigated the dosimetric aspects of stereotactic radiosurgery in terms of target volume, the absorbed doses at extracranial sites: especially the lens or thyroid - which are sensitive to radiation for deterministic or stochastic effect -have infrequently been reported. The aim of this study is to evaluate what effects the parameters of radiosurgery have on the absorbed doses of the lens and thyroid in patients treated by stereotactic radiosurgery, using a systematic plan in a humanoid phantom. Materials and Methods : Six isocenters were selected and radiosurgery was planned using the stereotactic radiosurgery system which the Department of Therapeutic Radiology at Seoul National University College of Medicine developed. The experimental radiosurgery plan consisted of 6 arc planes per one isocenter, 100 degrees for each arc range and an accessory collimator diameter size of 2 cm. After 250 cGy of irradiation from each arc, the doses absorbed at the lens and thyroid were measured by thermoluminescence dosimetry. Results : The lens dose was 0.23$\pm$0.08$\%$ of the maximum dose for each isocenter when the exit beam did not pass through the lens and was 0.76$\pm$0.12$\%$ of the maximum dose for each isocenter when the exit beam passed through the lens. The thyroid dose was 0.18$\pm$0.05$\%$ of the maximum dose for each isocenter when the exit beam did not pass through the thyroid and was 0.41$\pm$0.04$\%$ of the maximum dose for each isocenter when the exit beam Passed through the thyroid. The passing of the exit beam is the most significant factor of organ dose and the absorbed dose by an arc crossing organ decides 80$\%$ of the total dose. The absorbed doses of the lens and thyroid were larger as the isocenter sites and arc planes were closer to each organ. There were no differences in the doses at the surface and 5 mm depth from the surface in the eyelid and thyroid areas. Conclusion : As the isocenter and arc plane were placed closer to the lens and thyroid, the doses increased. Whether the exit beams passed through the lens or thyroid greatly influenced the lens and thyroid dose. The surface dose of the lens and thyroid consistently represent the tissue dose. Even when the exit beam passes through the lens and thyroid, the doses are less than 1$\%$ of the maximum dose and therefore, are too low to evoke late complications, but nevertheless, we should try to minimize the thyroid dose in children, whenever possible.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Radiation Protection and Research
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• v.41 no.1
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• pp.49-55
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• 2016

Background: For the purpose of baseline data collection and enhancement of environmental monitoring the distribution studies of $^{137}Cs$ and $^{90}Sr$ in the soil of Uljin province was performed and the relation between surface soil activities and soil properties (pH, TOC and median of the surface soil) was analyzed. Materials and Methods: For 14 spots within 10 km from the NPP surface soil samples were collected and soils for depth profile were sampled for 3 spots in April 2011. Using ${\gamma}$-ray spectrometry with HPGe detector, the concentrations of $^{137}Cs$ were determined and the concentrations of $^{90}Sr$ were measured by counting ${\beta}$-activity of $^{90}Y$ (in equilibrium with $^{90}Sr$) in a gas flow proportional counter. Results and Discussion: The concentration ranges of $^{137}Cs$ and $^{90}Sr$ were $<0.479-39.6Bq{\cdot}(kg-dry)^{-1}$ (avg. $7.51Bq{\cdot}(kg-dry)^{-1}$) and $0.209-1.85Bq{\cdot}(kg-dry)^{-1}$ (avg. $0.74Bq{\cdot}(kg-dry)^{-1}$) which were similar to the reported values from other regions in Korea. The activity ratio of $^{137}Cs$ to $^{90}Sr$ in surface soils was around 9.67, which is much bigger than the initial value of 1.75 for worldwide fallouts because of faster downward movement of $^{90}Sr$ after fallout than that of $^{137}Cs$. For depth profile studies soils were collected down to 40 cm depth for the locations of Deokgu, Hujeong and Maehwa. The $^{137}Cs$ concentration distribution of the first two showed maximum values at top soils and decreased rapidly in exponential manner, while $^{90}Sr$ showed two local maximum values for soils near top and about 30 cm depth. Through linear fittings between the $^{137}Cs$ and $^{90}Sr$ concentrations of surface soil and pH, TOC and median of the surface soil, the only probable relationship obtained was between $^{137}Cs$ and TOC (determination coefficient $R^2=0.6$). Conclusion: The concentration ranges of $^{137}Cs$ and $^{90}Sr$ in Uljin were similar to the reported values from other regions in Korea. The only probable relationship obtained between activities and soil properties was between $^{137}Cs$ and TOC.