• Journal of Korean Tunnelling and Underground Space Association
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• v.13 no.4
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• pp.319-345
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• 2011

In case of tunnels in cold regions, a freeze of groundwater around tunnel may act as a barrier of tunnel drainage in winter, or may cause the inner extrusion of lining. In spite of that, a design of insulation for preventing the frost damage of tunnel lining has not been introduced in Korea, while foreign countries such as Norway and so on have a standard on insulation. In this study, a few freezing cases of road tunnels have been reviewed, and the results show that the freezing protection is necessary. In order to characterize the thermal distribution in the tunnel, following measurements have been performed at Hwa-ak tunnel; the temperature distribution by longitudinal lengths, the internal temperature of lining and the temperature distribution of the ground under pavement. From these measurements, the characteristics of the tunnel's internal temperature distribution due to temperature change in the air has been analyzed. Based on the measurement results on the temperature distribution at Hwa-ak tunnel, thermal flow tests on the rock specimen with and without insulation have been performed in the artificial climate chamber to investigate the performance of the insulation. Also, a number of 3D numerical analyses have been performed to propose appropriate insulation and insulation thicknesses for different conditions, which could prevent the frost damage of tunnel lining. As a result of the numerical analysis, air freezing index of 291$^{\circ}C{\cdot}$ Hr has been suggested as the threshold value for freezing criteria of groundwater behind the tunnel lining.

• 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.

• The Korean Journal of Community Living Science
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• v.27 no.3
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• pp.477-493
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• 2016

This research was conducted to investigate the performance of a weight control program in university students in Daejeon during 3 months from November 2014 to February 2015. This program measured body measurement and composition analysis, nutritional education, and counseling every month. The status of students' weight control was surveyed before and after the program. The participants were 17 males (24.5 years old) and 15 females (20.8 years old). Their weights before the program were 78.2 kg (male) and 57.2 kg (female), whereas after the program, weights were 77.6 kg (male) and 56.2 kg (female). Weight reduction in students was 53.1%, and weight increase was 40.6% by the weight control program. Body mass index (BMI) tended to decrease after the program. Body fat % and muscle masses were not significantly different by program practice. Body image decision of females showed mainly 'normal' status while that of male was 'over fatty constitution', Before and after the program, the most prevalent method of weight control was exercise for all students. The most selected exercise was 'walking and jogging' during the program. For the most effective weight control method, female acknowledged both 'reducing amount of meals' and 'increasing exercise' while males selected 'increasing exercise.' On the side effects of weight control, over 40% of all students chose 'no experience' and 'loss of volition'. The degree of student's goal achievement was largely 20 ~-20% compared with their goal weights. Accordingly, this program didn't show significant effects. For effective weight control, it is recommended to conduct nutritional education. Students can exercise regularly and control diet to sustain a healthy and satisfactory body status.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.3
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• pp.329-336
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• 2010

This study deals with the measurement of reflectivity as well as the distance accuracy with Terrestrial Laser Scanner(TLS) using time of flight methods and near infrared wave length, for a variety of user-made targets. Especially, point clouds' reflection to several targets was measured with Gretag Macbeth il spectrophotometer in the office. And the distance accuracy in comparison to reference distance for TLS performance evaluation, was tested after scanning the user-made targets and measuring the inter-pillars distances over the precise EDM calibration baseline. The results of test was shown that except white resin objects, with approx. 10m and 170m inter-pillar distances, other targets achieved the distance accuracy of several millimeters(mm) with respect to standard distances. Future work should be concentrate on a few parameters influencing on the distance accuracy such as atmospheric correction, instrument correction, the additive constant or zero/index correction, etc.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.249-261
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• 2014

Ever increasing "Big data" can only be effectively processed by parallel computing. Parallel computing refers to a high performance computational method that achieves effectiveness by dividing a big query into smaller subtasks and aggregating results from subtasks to provide an output. However, it is well-known that parallel computing does not achieve scalability which means that performance is improved linearly by adding more computers because it requires a very careful assignment of tasks to each node and collecting results in a timely manner. Hadoop is one of the most successful platforms to attain scalability. In this paper, we propose a measurement for Hadoop optimization by utilizing a Lorenz curve which is a proxy for the inequality of hardware resources. Our proposed index takes into account the intrinsic overhead of Hadoop systems such as CPU, disk I/O and network. Therefore, it also indicates that a given Hadoop can be improved explicitly and in what capacity. Our proposed method is illustrated with experimental data and substantiated by Monte Carlo simulations.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.226-231
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• 2019

A WTCI is an important criteria for evaluating an mount of patient's tongue coating in tongue diagnosis. However, Previous WTCI tongue coating evaluation methods is a most of quantitatively measuring ration of the extracted tongue coating region and tongue body region, which has a non-objective measurement problem occurring by exposure conditions of tongue image or the recognition performance of tongue coating. Therefore, a WTCI based on deep learning is proposed for classifying an amount of tonger coating in this paper. This is applying the AI deep learning method using big data. to WTCI for evaluating an amount of tonger coating. In order to verify the effectiveness performance of the deep learning in tongue coating evaluating method, we classify the 3 types class(no coating, some coating, intense coating) of an amount of tongue coating by using CNN model. As a results by testing a building the tongue coating sample images for learning and verification of CNN model, proposed method is showed 96.7% with respect to the accuracy of classifying an amount of tongue coating.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.7
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• pp.938-944
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• 2000

Recently, micro fin tube is widely used to heat exchanger for high performance. And, as the alternative refrigerants for R-22, hydrocarbons such as R-290, R-600 and R-600a are very promising because of their low GWP and ODP. Thus, R-290 was used as working fluid in this study. Most design of heat exchanger had been based on heat transfer characteristics of pure refrigerant although refrigerant oil exists in the refrigeration cycles. So, the influence of oil on heat transfer characteristics have to be considered for investigating exact evaporation heat transfer characteristics. But, this is an unresolved problem of refrigeration heat transfer. Therefore the influence of the refrigeration oil to the evaporation heat transfer characteristics of R-290 were conducted in a horizontal micro tin tube. The mineral oil was used as refrigeration oil. The experimental apparatus consisted of a basic refrigeration cycle and a system for oil concentration measurement. Test conditions are as the follows; evaporation temperature $5^{\circ}C$, mass velocity 100 $kg/m^2s$, heat flux 10 $kW/m^2$, oil concentration 0, 1.3, 3.3, 5.7 wt.%, and quality $0.07{\sim}1.0$. When refrigeration oil was entered, oil foaming was observed at the low quality region. And, very small bubbles were observed as quality was increased. Pressure drop and heat transfer coefficient increased as the concentration of refrigeration oil increased to 5 wt.%.. The performance index of heat exchanger was the highest near 3.3 wt.%.

• Journal of the Ergonomics Society of Korea
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• v.27 no.3
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• pp.1-6
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• 2008

Individual finger/total grip forces, and subjective preferences for various individual finger grip spans (i.e., four fingers had identical grip spans or different grip spans) were evaluated by using an "Adjustable Multi-Finger Force Measurement (MFFM) System". In this study, three grip spans were defined as follows: a 'favorite grip span' which is the span with the highest subjective preference; a 'maximum grip span' which is the span with the highest total grip force; a 'maximum finger grip span' which is a set of four grip spans that had maximum finger grip forces associated with the index, middle, ring, and little fingers, respectively. Ten males were recruited from university population for this study. In experiment I, each participant tested the maximum grip force with five grip spans (45 to 65mm) to investigate grip forces and subjective preferences for three types of grip spans. Results showed that subjective preferences for grip spans were not coincidence with the performance of total grip forces. It was noted that the 'favorite grip span' represented the lowest total grip force, whereas the 'maximum finger grip span' showed the lowest subjective preferences. The individual finger forces and the average percentage contribution to the total finger force were also investigated in this study. The findings of this study might be valuable information for designing ergonomics hand-tools to reduce finger/hand stress as well as to improve tool users' preferences and performance.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• Journal of Nutrition and Health
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• v.41 no.8
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• pp.786-796
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• 2008

This study examined the influence of the nutrient intake status, dietary habit, and academic stress of elementary school children on their academic achievement. Two-hundred and twenty-four fifth-graders in Bucheon-si, Gyeonggido were surveyed. The study included the 24 hr-recall, anthropometric measurement, assessment of stress level and academic achievement. The subjects were normal in height, weight and Rohrer index, but higher percentage of underweight was seen in girls and vice versa in boys. The overall nutrient intake and dietary habits were fairly good, but Ca and folate intake was less than 75% KDRIs and dietary habits of boys were inferior. Academic stress level of all the subjects was not high. In terms of academic performance and its relations with nutrient intake, the more the amount of nutrient intake, the higher the academic performance. Especially, it was true for the energy, protein, phosphorus, potassium, zinc, polyunsaturated fatty acids, and n-6 fatty acid intakes (p < 0.05). The overall academic performance was higher for those who eating-out less frequently. Children with higher comprehensive dietary habit scores have shown better academic performance (p < 0.05). Less stress implied to those shown higher academic achievement while those with relatively poor academic performance showed high stress level since their grade did not improve as much as they anticipated. In conclusion, the academic achievement was higher for those who have a better nutritional status, better dietary habits, and lower stress levels. Therefore, it is critical for nutritionists, parents, and teachers to improve the nutritional status and dietary habits as well as to help them manage their stress levels, which will eventually contribute to an enhanced academic performance.