• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.117-140
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• 2019

As the aging phenomenon accelerates and various social problems related to the elderly of the vulnerable are raised, the need for effective elderly care solutions to protect the health and safety of the elderly generation is growing. Recently, more and more people are using Smart Toys equipped with ICT technology for care for elderly. In particular, log data collected through smart toys is highly valuable to be used as a quantitative and objective indicator in areas such as policy-making and service planning. However, research related to smart toys is limited, such as the development of smart toys and the validation of smart toy effectiveness. In other words, there is a dearth of research to derive insights based on log data collected through smart toys and to use them for decision making. This study will analyze log data collected from smart toy and derive effective insights to improve the quality of life for elderly users. Specifically, the user profiling-based analysis and elicitation of a change in quality of life mechanism based on behavior were performed. First, in the user profiling analysis, two important dimensions of classifying the type of elderly group from five factors of elderly user's living management were derived: 'Routine Activities' and 'Work-out Activities'. Based on the dimensions derived, a hierarchical cluster analysis and K-Means clustering were performed to classify the entire elderly user into three groups. Through a profiling analysis, the demographic characteristics of each group of elderlies and the behavior of using smart toy were identified. Second, stepwise regression was performed in eliciting the mechanism of change in quality of life. The effects of interaction, content usage, and indoor activity have been identified on the improvement of depression and lifestyle for the elderly. In addition, it identified the role of user performance evaluation and satisfaction with smart toy as a parameter that mediated the relationship between usage behavior and quality of life change. Specific mechanisms are as follows. First, the interaction between smart toy and elderly was found to have an effect of improving the depression by mediating attitudes to smart toy. The 'Satisfaction toward Smart Toy,' a variable that affects the improvement of the elderly's depression, changes how users evaluate smart toy performance. At this time, it has been identified that it is the interaction with smart toy that has a positive effect on smart toy These results can be interpreted as an elderly with a desire to meet emotional stability interact actively with smart toy, and a positive assessment of smart toy, greatly appreciating the effectiveness of smart toy. Second, the content usage has been confirmed to have a direct effect on improving lifestyle without going through other variables. Elderly who use a lot of the content provided by smart toy have improved their lifestyle. However, this effect has occurred regardless of the attitude the user has toward smart toy. Third, log data show that a high degree of indoor activity improves both the lifestyle and depression of the elderly. The more indoor activity, the better the lifestyle of the elderly, and these effects occur regardless of the user's attitude toward smart toy. In addition, elderly with a high degree of indoor activity are satisfied with smart toys, which cause improvement in the elderly's depression. However, it can be interpreted that elderly who prefer outdoor activities than indoor activities, or those who are less active due to health problems, are hard to satisfied with smart toys, and are not able to get the effects of improving depression. In summary, based on the activities of the elderly, three groups of elderly were identified and the important characteristics of each type were identified. In addition, this study sought to identify the mechanism by which the behavior of the elderly on smart toy affects the lives of the actual elderly, and to derive user needs and insights.

• The Journal of Korean Society for School & Community Health Education
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• v.8 no.1
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• pp.43-54
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• 2007

This study aimed at investigating the level of high school students' entrance exam stress and mental health first and also investigating if the entrance exam stress and mental health are related to gender, grade, character type, parenting style and economic status. This is expected to be used as a fundamental data for the development of health education program on high school students' stress and diagnosis of their mental health. To achieve those goals above, the questionnaire was used and the sample consisted of 600 students from general high schools in a large city, C and in a smaller city, A in Chungnam province through questionnaire and the conclusion, which was based on 582 proper questionnaires from the 600 questionnaires, through variable analysis, correlation analysis and multi-regression, is below. First, according to the information provided by respondents, the result showed the relationship between those background variables and the entrance exam stress and mental health level. As the students are more introverted and the parenting style is more authoritative, the entrance exam stress is higher and the mental health level is higher as the parenting style is more authoritative and the economic status is lower. In gender, the entrance exam stress level was high for male students in regard to parents pressure. For female students, it was due to the insufficient free time. In test performance, the good grade group showed high stress level when they don't have enough free time and the poor grade group showed high stress level when they have test tension and poor test performance. In character style, the introverted group showed high stress level in future uncertainty. In parenting style, the authoritative group showed significantly high level in all four sub-factors and there is no significant relationship with the stress level and economic statue. Female students reported higher mental health level than male students in somatization and depression. In academic achievement, the poor grade group showed high level in obsession, fear-anxiety and psychotism. In character style, the introverted group showed high level in sensitivity towards others and depression. And in parenting style, the authoritative group is higher in 9 sub-factors than the other two groups in the factor, economic status. The lower economic status group showed high mental health problem level in this order; in obsession, sensitivity towards others, depression, paranonia and psychotism. Second, the results revealed that there is a significant difference among the groups after comparing and analyzing the relationship between the mental health level according to the three groups, the first, second and third group divided by the degree of entrance exam stress. And the higher the entrance exam stress is, the higher the mental health problem level is. Verification showed that there was obvious difference among the groups. the entrance exam stress was positively correlated with the mental health level. The lack of free time, future uncertainty, test anxiety/poor test performance and parents pressure, these factors, in that order, were correlated with the mental health level. when the prediction variables which influenced on mental health are analyzed, test-anxiety/poor test performance was found to be related to mental health most. And after the factor, test-anxiety, future uncertainty and the lack of free time were listed(ranked), however results did not show any correlation with parents' pressure.

• Journal of Yeungnam Medical Science
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• v.20 no.1
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• pp.62-70
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• 2003

Background: Most of all studies about the relation between the health risk and obesity are based on the European and American data. The purpose of this study is to examine the relation between adiposity and risk factors for cardiovacular disease (CVD) in normal weight individuals. Materials and Methods: Normal weight subjects with a body mass index (BMI) between 18.5 and $23kg/m^2$ (76 subjects) and overweight subjects with a BMI between 23 and $25kg/m^2$ (53 subjects) were retained for this study. Normal weight subjects were divided into three group of each adiposity variable, then three group and the overweight group were evaluated for the presence of CVD risk factors and analyze the correlation coefficients between adiposity variables and risk factors controlled for age in normal weight, overweight groups. Using logistic regression analysis, the odds ratio (OR) for the prevalence of risk factors for each group of adiposity variables and the overweight group was estimated relative to the first group in normal weight subjects. Results: Systolic BP, diastolic BP, LDL cholestrol, HDL cholesterol, triglycerides in normal weight subjects were significantly correlated with all adiposity variables (P<0.01). Third group (3.7 for %fat and 4.7 for fat mass)of adiposity variables in the normal weight group and the overweight group (6.6 for %fat and 11.5 for fat mass) tended to have higher ORs compared to first group for risk factor variables. Conclusion: Normal weight subjects with elevated adiposity had higher prevalence of risk factors than normal weights subjects with less adiposity. Measuring of adiposity added additional information of cardiovascular disease risk factors in normal weight subjects.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• Korean Journal of Agricultural Science
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• v.13 no.1
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• pp.17-32
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• 1986

To obtain basic information needed for developing better pearl barley varieties, a total of 148 lines of pearl barley were collected from nationwide survey except for Kangwon and Chejoo provinces and classified by principal component analysis. The results are summarized as follows : 1. Variabilities of characters for all lines except for leaf width and 100 K. Wt.(Unpolished) were high enough to indicate variation of lines. 2. Correlation coefficients among 18 characters were high enough and they showed the shape of normal distribution, more or less, inclined toward positive values. 3. The lines could be classified into four groups by correlation coefficient for 18 characters : Group I was characterized as the lines composed of grain and plant type, Group II maturity, Group III the number of tillers, and Group IV the nature of germination, respectively. 4. About 60% of the total variation could be appreciated by the first four principal components and about 89% of the total variation by the first ten principal components. 5. Contribution of characters to principal components was variable and was high at upper principal components and low at lower principal components. 6. The value of eigen vector corresponding to those which had high significant correlation coefficient between characters was almost of the same value. 7. The lines were classified into four groups by principal component analysis. 8. The lines were also classified into four groups by taxonomic distance. Group I included 79 lines, Group II 40 lines, Group III 22 lines, and Group IV 7 lines, respectively. 9. Four groups classified by taxonomic distance could be characterized as follow : Group I : medium height plant, small kernels, medium maturity, and narrow and short leaf, Group II : short height plant, small kernels, early maturity, and narrow and short leaf. Group III : tall height plant, large kernels, late maturity, and broad and long leaf. Group IV : short height plant, large kernels, medium maturity, and narrow and short leaf.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.699-708
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• 2017

The present study was to develop an approach for predicting soybean yield using a crop growth simulation model at the regional level where the detailed and site-specific information on cultivation management practices is not easily accessible for model input. CROPGRO-Soybean model included in Decision Support System for Agrotechnology Transfer (DSSAT) was employed for this study, and Illinois which is a major soybean production region of USA was selected as a study region. As a first step to predict soybean yield of Illinois using CROPGRO-Soybean model, genetic coefficients representative for each soybean maturity group (MG I~VI) were estimated through sowing date experiments using domestic and foreign cultivars with diverse maturity in Seoul National University Farm ($37.27^{\circ}N$, $126.99^{\circ}E$) for two years. The model using the representative genetic coefficients simulated the developmental stages of cultivars within each maturity group fairly well. Soybean yields for the grids of $10km{\times}10km$ in Illinois state were simulated from 2,000 to 2,011 with weather data under 18 simulation conditions including the combinations of three maturity groups, three seeding dates and two irrigation regimes. Planting dates and maturity groups were assigned differently to the three sub-regions divided longitudinally. The yearly state yields that were estimated by averaging all the grid yields simulated under non-irrigated and fully-Irrigated conditions showed a big difference from the statistical yields and did not explain the annual trend of yield increase due to the improved cultivation technologies. Using the grain yield data of 9 agricultural districts in Illinois observed and estimated from the simulated grid yield under 18 simulation conditions, a multiple regression model was constructed to estimate soybean yield at agricultural district level. In this model a year variable was also added to reflect the yearly yield trend. This model explained the yearly and district yield variation fairly well with a determination coefficients of $R^2=0.61$ (n = 108). Yearly state yields which were calculated by weighting the model-estimated yearly average agricultural district yield by the cultivation area of each agricultural district showed very close correspondence ($R^2=0.80$) to the yearly statistical state yields. Furthermore, the model predicted state yield fairly well in 2012 in which data were not used for the model construction and severe yield reduction was recorded due to drought.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.6
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• pp.327-340
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• 2017

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2016. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of flow, heat and mass transfer, the reduction of pollutant exhaust gas, cooling and heating, the renewable energy system and the flow around buildings. CFD schemes were used more for all research areas. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results of the long-term performance variation of the plate-type enthalpy exchange element made of paper, design optimization of an extruded-type cooling structure for reducing the weight of LED street lights, and hot plate welding of thermoplastic elastomer packing. In the area of pool boiling and condensing, the heat transfer characteristics of a finned-tube heat exchanger in a PCM (phase change material) thermal energy storage system, influence of flow boiling heat transfer on fouling phenomenon in nanofluids, and PCM at the simultaneous charging and discharging condition were studied. In the area of industrial heat exchangers, one-dimensional flow network model and porous-media model, and R245fa in a plate-shell heat exchanger were studied. (3) Various studies were published in the categories of refrigeration cycle, alternative refrigeration/energy system, system control. In the refrigeration cycle category, subjects include mobile cold storage heat exchanger, compressor reliability, indirect refrigeration system with $CO_2$ as secondary fluid, heat pump for fuel-cell vehicle, heat recovery from hybrid drier and heat exchangers with two-port and flat tubes. In the alternative refrigeration/energy system category, subjects include membrane module for dehumidification refrigeration, desiccant-assisted low-temperature drying, regenerative evaporative cooler and ejector-assisted multi-stage evaporation. In the system control category, subjects include multi-refrigeration system control, emergency cooling of data center and variable-speed compressor control. (4) In building mechanical system research fields, fifteenth studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, renewable energies, etc. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which could be help for improving the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the analyses of indoor thermal environments controlled by portable cooler, the effects of outdoor wind pressure in airflow at high-rise buildings, window air tightness related to the filling piece shapes, stack effect in core type's office building and the development of a movable drawer-type light shelf with adjustable depth of the reflector. The subjects of building energy were worked on the energy consumption analysis in office building, the prediction of exit air temperature of horizontal geothermal heat exchanger, LS-SVM based modeling of hot water supply load for district heating system, the energy saving effect of ERV system using night purge control method and the effect of strengthened insulation level to the building heating and cooling load.

• Korean Journal of Community Nutrition
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• v.1 no.2
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• pp.239-249
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• 1996

Despite the importance of prenatal nutrition, many studies find inadequate calcium intake among pregnant women. The purpose of this study was to investigate the value of the Theory of Planned Behavior in explaining the intentions and the actual consumption of milk among pregnant women participating in or eligible for WIC. A cross-sectional survey was conducted to collect information regarding attitudes, subjective norms, perceived control, milk allocation within the family, intentions and consumption of milk. The survey questionnaire was developed using open-ended questions and interviews with 112 pregnant women. One-hundred-eighty women recruited from prenatal clinics completed the survey questionnaire. Multiple regression was used separately to investigate the association of factors to intentions and to the consu-mption of milk, as proposed in the theory. Milk allocation within the family was used as an exploratory variable to explain milk consumption. Study findings revealed that all three factors, attitudes, subjective norms and perceived control contributed to the model in explaining intentions (explained variance : 36.2%), with perceived control being most important. For milk consumption, intentions and perceived control were related significantly to actual consumption, while milk allocation within the family was not (explained variance : 44.6%). These findings suggest that perceived control is important in understanding both intentions and milk consumption, providing empirical evidence for the Theory of Planned Behavior. With respect to the role of perceived control, more strong evidence was provided in explaining intentions. Findings suggest that educational interventions to increase milk consumption among pregnant women should incorporate strategies to enhance the perception of control, and to strengthen positive attitudes and to elicit social support from significant other. (Korean J Community Nutrition 1(2) 239-249, 1996)