• Journal of the Korean Society of Radiology
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• v.12 no.1
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• pp.47-52
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• 2018

Brain perfusion CT scanning is often employed usefully in clinical conditions as it accurately and promptly provides information about the perfusion state of patients having acute ischemic stroke with a lot of time constraints and allows them to receive proper treatment. Despite those strengths of it, it also has a serious weakness that Lens may be exposed to a lot of dose of radiation in it. In this study, as a way to reduce the dose of radiation to Lens in brain perfusion CT scanning, this researcher conducted an experiment with Bismuth shielding and change of patients' position. TLD (TLD-100) was placed on both lens using the phantom (PBU-50), and then, in total 4 positions, parallel to IOML, parallel to IOML (Bismuth shielding), parallel to SOML, and parallel to SOML (Bismuth shielding), brain perfusion scanning was done 5 times for each position, and dose to Lens were measured. Also, to examine how the picture quality changed in different positions, 4 areas of interest were designated in 4 spots, and then, CT number and noise changes were measured and compared. According to the results of conducting one-way ANOVA on the doses measured, as the significance probability was found to be 0.000, so there was difference found in the doses of radiation to crystalline lenses. According to the results of Duncan's post-hoc test, with the scanning of being parallel to IOML as the reference, the reduction of 89.16% and 89.66% was observed in the scanning of being parallel to SOML and that of being parallel to SOML (Bismuth shielding) respectively, so the doses to Lens reduced significantly. Next, in the scanning of being parallel to IOML (Bismuth shielding), the reduction of 37.12% was found. According to the results, reduction in the doses of radiation was found the most significantly both in the scanning of being parallel to SOML and that of being parallel to SOML (Bismuth shielding). With the limit of the equivalent dose to Lens as the reference, this researcher conducted comparison with the dose to occupational exposure and dose to Public exposure in the scanning of being parallel to IOML and found 39.47% and 394.73% respectively; however in the scanning of being parallel to SOML (Bismuth shielding), considerable reduction was found as 4.08% and 40.8% respectively. According to the results of evaluation on picture quality, every image was found to meet the evaluative standards of phantom scanning in terms of the measurement of CT numbers and noise. In conclusion, it would be the most useful way to reduce the dose of radiation to Lens to use shields in brain perfusion CT scanning and adjust patients' position so that their lens will not be in the field of radiation.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.2
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• pp.71-78
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• 2000

Preferential flow has recently been the subject of increasing interest because these phenomena contribute to solute transport in soils. Commonly, preferential flow paths are associated with macropores or highly structured soils. We presented an analysis of the measured breakthrough curves (BTCs) of $Cl^-$ and $Cu^{2+}$ ions to test the occurrence of preferential flow in soils using miscible displacement technique under steady flow conditions. We also analyzed soil water retention curves and from this curves induced cumulative pore size distribution of undisturbed soils, which sampled from Ap1, B1, and C horizons of Songjeong series soils (the fine loamy, mesic family of Typic Hapludults). In this study, miscible displacement experiment on C horizon was excluded, because it is structureless sandy loam with saturated hydraulic conductivity of $5.2cmhr^{-1}$. The saturated hydraulic conductivity of Ap1 horizon was $2.0cmhr^{-1}$, which was about 7 times higher than that of B1 horizon ($0.27cm hr^{-1}$). Cumulative pore size distribution predicted that Ap1 horizon had more macropores (pore diameter larger than $49{\mu}m$, equivalent to -6 kpa of soil matric potential) than B1 horizon. The hydrodynamic dispersion coefficient from chloride BTCs was estimated as $1.3cm^2hr^{-1}$ for B1 and $34cm^2hr^{-1}$ for Ap1 horizon. However the retardation factors of B1 and Ap1 horizon were significantly different, i.e. 1 and 0.6, respectively, which means that there was distinct partition between mobile water and immobile phase in Ap1 horizon. The copper retardation effect of Ap1 horizon was less than that of B1 horizon, even though cation exchange capacity of Ap1 horizon was higher than that of B1 horizon. Thus, breakthrough curves of $Cl^-$ and $Cu^{2+}$ obviously showed the probability that preferential flow would occur in Ap1 horizon.

• Current Research on Agriculture and Life Sciences
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• v.32 no.4
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• pp.225-230
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• 2014

This study investigates the effect of organic certification of apples on consumer preference in China as a way to support the expanded export of Korean apples to China. A choice experiment was designed to analyze the apple consumption in China. A total of 298 Chinese consumers answered the survey, and multinomial logit models were used to analyze the results. Organic certification was identified as an important determinant of consumer preference for apples in China, affecting both the evaluation and choice of country of origin. The results also indicated that Korean organic certification significantly increased the probability of Chinese consumers choosing Korean apples. Thus, organic certification by the Korean government should be strengthened to promote apple exports to China, plus the results of this study may provide useful information to promote agricultural product exports and improve the organic certification system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.