• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Nutrition and Health
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• v.49 no.3
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• pp.165-178
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• 2016

Purpose: This study was performed to examine associations of intakes of milk and dairy products, pulses, and soy foods with bone health in Koreans aged 50 yr and older. Methods: A total of 3,201 men and 3,581 women aged 50 yr and older who participated in the 2008~2011 Korea National Health and Nutrition Examination Survey were grouped by sex and age groups of 50~64 yr and 65 yr and older. Subjects within each sex and age group were divided into three bone health groups: normal, osteopenia, and osteoporosis groups based on bone mineral density. Intakes of nutrients and foods derived from 24-hour recall data were compared among three bone health groups. Associations between intake frequencies of foods, including milk, yogurt, tofu, or soy milk, and osteoporosis risk were evaluated based on confounding risk factor-adjusted logistic regression. Results: Calcium intake was in the order of normal, osteopenia, and osteoporosis in men (p < 0.01) and women (p < 0.05) aged 50~64 yr as well as in men aged 65 yr and older (p < 0.001). In women aged 50~64 yr, intake of milk and dairy products was lower in the osteoporosis group (p < 0.01) as compared with the osteopenia group. Intake of pulses or tofu was not significantly different among bone health groups. Odds ratio (OR) for milk intake frequency (${\geq}2$ times/week) compared to intake frequency less than 1 time/month was 0.45 (95% CI 0.24~0.85, p for trend = 0.022) in men aged 65 yr and older. The OR for yogurt intake frequency (1 time/month~1 time/week) was 0.47 (95% CI 0.30~0.73, p for trend = 0.019) in women aged 50~64 yr. Intake frequency of tofu or soy milk was not associated with reduced risk of osteoporosis in all groups. Conclusion: Dairy food intake was significantly associated with bone health, and its effect was sex- and age group-specific, whereas soy food intake was not. Dietary intervention to prevent osteoporosis would be effective for women aged 50~64 yr old and for men aged 65 yr and older.

• The Korean Journal of Nuclear Medicine
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• v.32 no.5
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• pp.414-424
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• 1998

Purpose: Cross-modality coregistration of positron emission tomography (PET) and magnetic resonance imaging (MR) could enhance the clinical information. In this study we propose a refined technique to improve the robustness of registration, and to implement more realistic visualization of the coregistered images. Materials and Methods: Using the sinogram of PET emission scan, we extracted the robust head boundary and used boundary-enhanced PET to coregister PET with MR. The pixels having 10% of maximum pixel value were considered as the boundary of sinogram. Boundary pixel values were exchanged with maximum value of sinogram. One hundred eighty boundary points were extracted at intervals of about 2 degree using simple threshold method from each slice of MR images. Best affined transformation between the two point sets was performed using least square fitting which should minimize the sum of Euclidean distance between the point sets. We reduced calculation time using pre-defined distance map. Finally we developed an automatic coregistration program using this boundary detection and surface matching technique. We designed a new weighted normalization technique to display the coregistered PET and MR images simultaneously. Results: Using our newly developed method, robust extraction of head boundary was possible and spatial registration was successfully performed. Mean displacement error was less than 2.0 mm. In visualization of coregistered images using weighted normalization method, structures shown in MR image could be realistically represented. Conclusion: Our refined technique could practically enhance the performance of automated three dimensional coregistration.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Journal of Nutrition and Health
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• v.42 no.1
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• pp.38-47
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• 2009

The purpose of the study was to develop an eating habit checklist for screening elementary school children at risk of inadequate micronutrient intake. Eating habits, food intake, and anthropometric data were collected from 142 children (80 boys and 62 girls) in the $4^{th}$ to $6^{th}$ grades of elementary schools. Percentage of Recommended Intakes (RI) and Mean Adequacy Ratio (MAR) of six micronutrients; vitamin A, riboflavin, vitamin C, calcium, iron, zinc, and the number of nutrients the children consumed below EAR among the six nutrients were used as indices to detect the risk of inadequate micronutrient intake. Pearson correlation coefficients were calculated between eating habit scores and inadequate micronutrient intake indices in order to select questions included in the checklist. Meal frequency, enough time for breakfast, regularity of dinner, appetite, eating frequencies of Kimchi, milk, fruits and beans showed significant correlations with indices of inadequate micronutrient intake. Stepwise regression analysis was performed to give each item a different weight by prediction strength. To determine the cut-off point of the test score, sensitivity, specificity, and positive predictive values were calculated. The 8-item checklist with test results from 0 to 12 points was developed, and those with equal or higher than 6 points were diagnosed as high-risk group of inadequate micronutrient intake, and those with 4 or 5 points were diagnosed as moderate-risk group. Among our subjects 14.1% was diagnosed as high-risk group, and 30.3% as moderate-risk group. The proportions of the subjects who consumed below EAR of all micronutrients but vitamin C were highest in the high-risk group, and there were significant differences in the proportions of the subjects with intake below EAR of all micronutrients except vitamin B6 among the three groups. This checklist will provide a useful screening tool to identify children at risk of inadequate micronutrient intake.

• Journal of the Korean association of regional geographers
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• v.3 no.1
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• pp.63-80
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• 1997

This study attempts to establish the criteria of site selection for establishing solid waste disposal facility, to determine optimal solid waste disposal sites with the criteria, and to examine the suitability of the selected sites. The Multi-Criteria Evaluation(MCE) module in Idrisi is used to determine optimal sites for solid waste disposal. The MCE combines the information from several criteria in interval and/or ratio scale to form a single index of evaluation without leveling down the data scale into ordinal scale. The summary of this study is as follows: First, the considerable criteria are selected through reviewing the literature and the availability of data: namely, percent of slope, fault lines, bedrock characteristics, major residential areas, reservoirs of water supply, rivers, inundated area, roads, and tourist resorts. Second, the criteria maps of nine factors have been developed. Each factor map is standardized and multiplies by its weight, and then the results are summed. After all of the factors have been incorporated, the resulting suitability map is multiplied by each of the constraint in turn to "zero out" unsuitable area. The unsuitable areas are discovered in urban district and its adjacencies, and mountain region as well as river, roads, resort area and their adjacency districts. Third, the potential sites for establishing waste disposal facilities are twenty five districts in Youngyang-gun. Five districts are located in Subi-myun Sinam-ri, nine districts in Chunggi-myun Haehwa-ri and Moojin-ri, and eleven districts in Sukbo-myun Posan-ri. The first highest score of suitability for waste disposal sites is shown at number eleven district in Chunggi-myun Moojin-ri and the second highest one is discovered at number twenty one district in Sukbo-myun Posan-ri that is followed by number nine district in Chunggi-myun Haehwa-ri, number seventeen and twenty three in Sukbo-myun Posan-ri, and number two in Subi-myun Sinam-ri. The first lowest score is found in number six district in Chunggi-myun Haehwa-ri, and the second lowest one is number five district in Subi-myun Sinam-ri. Finally, the Geographic Information System (GIS) helps to select optimal sites with more objectively and to minimize conflict in the determination of waste disposal sites. It is important to present several potential sites with objective criteria for establishing waste disposal facilities and to discover characteristics of each potential site as a result of that final sites of waste disposal are determined through considering thought of residents. This study has a limitation of criteria as a result of the restriction of availability of data such as underground water, soil texture and mineralogy, and thought of residents. To improve selection of optimal sites for a waste disposal facility, more wide rage of spatial and non-spatial data base should be constructed.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.36-49
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• 2019

The objectives of this study were to extract "Field Survey Based Infection Tree of Pine Wilt Disease(FSB_ITPWD)" and "Object Classification Based Infection Tree of Pine Wilt Disease(OCB_ITPWD)" from the Research Forest at Kangwon National University, and evaluate the spatial distribution characteristics and occurrence intensity of wood infested by pine wood nematode. It was found that the OCB optimum weights (OCB) were 11 for Scale, 0.1 for Shape, 0.9 for Color, 0.9 for Compactness, and 0.1 for Smoothness. The overall classification accuracy was approximately 94%, and the Kappa coefficient was 0.85, which was very high. OCB_ITPWD area is approximately 2.4ha, which is approximately 0.05% of the total area. When the stand structure, distribution characteristics, and topographic and geographic factors of OCB_ITPWD and those of FSB_ITPWD were compared, age class IV was the most abundant age class in FSB_ITPWD (approximately 55%) and OCB_ITPWD (approximately 44%) - the latter was 11% lower than the former. The diameter at breast heigh (DBH at 1.2m from the ground) results showed that (below 14cm) and (below 28cm) DBH trees were the majority (approximately 93%) in OCB_ITPWD, while medium and (more then 30cm) DBH trees were the majority (approximately 87%) in FSB_ITPWD, indicating different DBH distribution. On the other hand, the elevation distribution rate of OCB_ITPWD was mostly between 401 and 500m (approximately 30%), while that of FSB_ITPWD was mostly between 301 and 400m (approximately 45%). Additionally, the accessibility from the forest road was the highest at "100m or less" for both OCB_ITPWD (24%) and FSB_ITPWD (31%), indicating that more trees were infected when a stand was closer to a forest road with higher accessibility. OCB_ITPWD hotspots were 31 and 32 compartments, and it was highly distributed in areas with a higher age class and a higher DBH class.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.31-42
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• 2021

The importance of startups and ventures in the Korean economy is growing. This study measured whether the start-up and venture ecosystem is growing, including the growth of startups and ventures. The startup and venture ecosystem consists of startups and ventures, investors, and government, which are the main actors of the 'ecosystem', and their movements were measured with 25 quantitative indicators. Based on the original data of the time series from 2010 to 2020, the startup and venture ecosystem index was calculated by applying weights through the comprehensive stock index method and AHP. In 2020, the startup and venture ecosystem grew 2.9 times compared to 2010, and the increase in the government index had a significant impact on growth. Also, the individual indicators that make up each index in 2020, the corporate index had the greatest impact on the growth of the number of 100-billion ventures, while the investment index had a recovery amount and the government index had a significant impact. Based on the original data, the startup and venture ecosystem index was analyzed by dividing it into ecosystems (startup ecosystem and venture ecosystem), industry by industry (all industries and manufacturing industry), and region (Korea and Busan). As a result, the growth of the startup ecosystem over the past decade has been slightly larger than that of the venture ecosystem. The manufacturing was lower than that of all industries, and Busan was lower than that of the nation. This study was intended to use it for the establishment and implementation of support policies by developing, measuring, and monitoring the startup and venture ecosystem index. This index has the advantage of being able to research the interrelationships between major actors, and anyone can calculate the index using the results of official statistical surveys. In the future, it is necessary to continuously update this content to understand how economic and social events or policy support have affected the startup and venture ecosystem.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.1-25
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• 2023

ESG management is becoming a necessity of the times, but there are about 600 ESG evaluation indicators worldwide, causing confusion in the market as different ESG ratings were assigned to individual companies according to evaluation agencies. In addition, since the method of applying ESG was not disclosed, there were not many ways for companies that wanted to introduce ESG management to get help. Accordingly, the Ministry of Trade, Industry and Energy announced the K-ESG guideline jointly with the ministries. In previous studies, there were few studies on the comparison of evaluation grades by ESG evaluation company or the application of evaluation diagnostic items. Therefore, in this study, the ease of application and improvement of the K-ESG guideline was attempted by applying the K-ESG guideline to companies that already have ESG ratings. The position of the K-ESG guideline is also confirmed by comparing the scores calculated through the K-ESG guideline for companies that have ESG ratings from global ESG evaluation agencies and domestic ESG evaluation agencies. As a result of the analysis, first, the K-ESG guideline provide clear and detailed standards for individual companies to set their own ESG goals and set the direction of ESG practice. Second, the K-ESG guideline is suitable for domestic and global ESG evaluation standards as it has 61 diagnostic items and 12 additional diagnostic items covering the evaluation indicators of global representative ESG evaluation agencies and KCGS in Korea. Third, the ESG rating of the K-ESG guideline was higher than that of a global ESG rating company and lower than or similar to that of a domestic ESG rating company. Fourth, the ease of application of the K-ESG guideline is judged to be high. Fifth, the point to be improved in the K-ESG guideline is that the government needs to compile industry average statistics on diagnostic items in the K-ESG environment area and publish them on the government's ESG-only site. In addition, the applied weights of E, S, and G by industry should be determined and disclosed. This study will help ESG evaluation agencies, corporate management, and ESG managers interested in ESG management in establishing ESG management strategies and contributing to providing improvements to be referenced when revising the K-ESG guideline in the future.

• Journal of Nutrition and Health
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• v.48 no.1
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• pp.46-57
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• 2015

Purpose: The aim of this study was to determine an appropriate energy level of breakfast with less risk of chronic disease for Korean adults. Methods: Using data from the 2007~2009 Korean National Health & Nutrition Examination Survey, from a total of 12,238 adults aged 19~64, the final 7,769 subjects were analyzed except subjects who were undergoing treatment for cancer or metabolic disorder. According to the percent of breakfast energy intake versus their estimated energy requirement (EER), the subjects were divided into four groups: < 10% (very low, VL), 10~20% (low, L), 20~30% (moderate, M), ${\geq}30%$ (sufficient, S). All data were analyzed on the metabolic risk and nutritional state after application of weighted value and adjustment of sex, age, residential area, income, education, job or jobless, and energy intake using a general linear model or logistic regression. Results: The subjects of group S were 16.9% of total subjects, group M 39.2%, group L 37.6%, and group VL 6.3%. The VL group included more male subjects, younger-aged (19 to 40 years), urban residents, higher income, higher education, and fewer breakfasts eaters together with family members. Among the 4 groups, the VL group showed the highest waist circumference, while the S group showed the lowest waist circumference, body mass index, and serum total cholesterol. The groups of VL and L with lower intake of breakfast energy showed high percent of energy from protein and fat, and low percent of energy from carbohydrate. With the increase of breakfast energy level, intake of energy, most nutrients and food groups increased, and the percentage of subjects consuming nutrients below EAR decreased. The VL group showed relatively higher intake of snacks, sugar, meat and eggs, oil, and seasonings, and the lowest intake of vegetable. Risk of obesity by waist circumference was highest in the VL group by 1.90 times of the S group and the same trend was shown in obesity by BMI. Risk of dyslipidemia by serum total cholesterol was 1.84 times higher in the VL group compared to the S group. Risk of diabetes by Glu-FBS (fasting blood sugar) was 1.57 times higher in the VL group compared to the S group. Conclusion: The results indicate that higher breakfast energy level is positively related to lower metabolic risk and more desirable nutritional state in Korean adults. Therefore, breakfast energy intake more than 30% of their own EER would be highly recommended for Korean adults.