• Korean Journal of Social Welfare
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• v.68 no.1
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• pp.23-52
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• 2016

This study aims to reveal the meaning and essence of male immigrant workers' life who underwent industrial accidents through specific experiences. This study is based on the Van Manen(1990)'s lived experience phenomenological method, which actively describes the experiences about the industrial accidents in the perspective of male immigrant worker. The in-depth interviews were carried out with the thirteen male immigrant workers participants who underwent the industrial accidents. Through the interview, 121 meaning units and 38 disclosed themes were constructed. The following is the summarized results as 9 essential themes: < the oppression of the Industrial accident compensation insurance's hospitals to the aliens >, < being treated like the surplus man who lost the labor force >, < the class rank below despite undergoing the industrial accidents >, < survival having resistance sentiments >, < living at the anonymous lands as the Homo sacer >, < the stratified strategies between the immigrant workers >, < the origins as the bodiless shadow >, < struggling to escape the present conditions >, < present circumstances tied by the past experiences without hope >, researcher sought the essential meaning structure about the four Life-world Existentials (Body, The other, Space and Time) of constructing human's live-world. Based on the study results, some suggestions were made to restore the male immigrant workers' damaged quality of their lives who experienced industrial accidents and to contribute the social integration in view of the social welfare.

• Health Policy and Management
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• v.34 no.2
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• pp.141-155
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• 2024

Background: This study aimed to identified the relationship between the risk of obstructive sleep apnea, subjective health, and health-related quality of life among the middle-aged and elderly population in Korea. Methods: Adults aged 40 or older were extracted from the total 22,559 respondents to the 2019-2020 Korea National Health and Nutrition Examination Survey VIII, and secondary analysis was conducted on a total of 6,659 middle-aged and elderly people with no missing values. Logistic regression analysis and multiple regression analysis were conducted to examine the relationship between obstructive sleep apnea risk factors and subjective health as well as quality of life. Results: The subjective health status decline in the high-risk group compared to the non-risk group for obstructive sleep apnea was statistically significantly higher, with an odds ratio of 1.84 (p<0.001). The health-related quality of life was also statistically significantly lower by 0.02 points (β, -0.02; p<0.001). As a result of subgroup analysis on specific variables, the association between the risk of obstructive sleep apnea and subjective health and health-related quality of life was statistically significant depending on gender, sleep time, presence of depression, household income, and number of household members. Based on the obstructive sleep apnea risk group, women had a higher correlation with low subjective health and lower health-related quality of life scores than men. Sleeping time of more than 8 hours or less than 6 hours was more associated with low subjective health and lower health-related quality of life score than sleeping time of 6-8 hours. Patients with depression were more likely to have low subjective health than those without depression. The lower the household income level and the smaller the number of household members, the higher the association with low subjective health and the lower the health-related quality of life score. Conclusion: It is essential to recognize that the risk of obstructive sleep apnea not only directly affects sleep disorders but also impacts individuals' subjective health and quality of life. Consequently, social support and education should be provided to raise awareness of this issue. Particularly, programs for preventing and managing obstructive sleep apnea should target vulnerable groups such as women, individuals in single-person households, low household income, and those with depression, aiming to improve their subjective health and quality of life.

• 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.

• Journal of Nutrition and Health
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• v.45 no.6
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• pp.562-576
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• 2012

Han-sik is a term in Korean that may indicate any Korean dish or food. At present, there is no general consensus on the definition of Han-sik among scholars or professionals in related fields. The aim of this study was to investigate perceptions of Han-sik by professionals in the fields of food, nutrition, and culinary arts using 512 dishes and foods commonly consumed by Koreans using the 4th Korean National Health and Nutrition Survey. A total of 117 professionals out of 185 initially contacted professionals participated in this online survey. We calculated the rate of respondents with a positive answer, that is "It is Han-sik', on each dish and food from the 512 items in 28 dish groups. Items were categorized into five groups according to their Han-sik perception rate: over 90%, 75-89%, 50-74%, 25-49%, and below 25%. Most items in the three dish groups 'Seasoned vegetables, cooked (Namul Suk-chae)', 'Kimchis', and 'Salt-fermented foods (Jeotgal)' showed high perception rates of Han-sik, with a higher than 90% positive response. Items in 'Soups', 'Stews', and 'Steamed foods' dish groups also showed high perception rates of Han-sik. However, no item showed a greater than 90% Han-sik perception rate in 'Fried foods (Twigim)', 'Meat, poultry and fishes', 'Legumes, nuts, and seeds', 'Milk and milk products', 'Sugars and confectioneries', and 'Soup'. Most items in the 'Milk and milk products', 'Sugars and confectioneries', and 'Soup' groups belonged to the lowest perception rate of below 25%. There was a significant difference in the proportion of items perceived as Han-sik by the length of living abroad to (p < 0.05). In summary, the perception rate of Han-sik seemed to be affected by the cooking method, ingredients, and length of time living abroad by the professionals. Further studies targeting subjects with different characteristics and socioeconomic status are warranted to define the concept of Han-sik.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.2
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• pp.318-323
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• 2013

This study was carried out to investigate the levels of awareness of fermented vegetable beverages according to consumers' gender. The data were obtained through questionnaires completed randomly by 441 respondents and analyzed using the SPSS, t-tests, ANOVA, X-square tests, multiple regression, and logistic regression analyses. Among 441 respondents, 32.9% of male and 30.2% of female ever consumed fermented vegetable beverages. This beverage were consumed as an alternative for water in males (6.3%) and in females (7.9%) which was the most common reason for consumption. As for the most common reason for non-consumption, males (5.9%) and females (6.6%) responded that they did not consume fermented vegetable beverages because people around them have never consumed fermented vegetable beverages. The awareness that fermented vegetable beverages help reduce hunger was higher in females (3.2%) than and in males (2.9%). These results revealed that the awareness of fermented vegetable beverages as hunger alleviators was more prevalent in females than in males which can be used as preliminary data for research on the development of fermented vegetable beverages.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.179-186
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• 2019

Based on the characteristics of the weapon system, a government-funded research institute or a defense industry company is selected as the R&D supervision institution. On the other hand, research for the selection of a R&D supervision institution has not been conducted actively. This paper proposes a methodology for selecting R&D supervision institutions, such as procedure and indices. First, candidates of the index were obtained using data investigation and consulting, and five indices were deduced using Delphi. The weight of the indices was set using AHP. The high element consisted of 'Technical elements' and 'Business element'. The low element of 'Technical elements' consisted of 'Possession and readiness of critical technology' and 'Experience of similar R&D'. The low element of 'Business element' consisted of 'Base circumstance of the project', 'Risk management', and 'Will for the project'. The total weights of the indices were 'Possession and readiness of critical technology' 0.405, 'Experience of similar R&D' 0.297, 'Base circumstance of the project' 0.124, 'Risk management' 0.127, and 'Will for the project' 0.047. The indices were applied to the 00 weapon system and the result was deduced.

• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.305-311
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• 2013

This study analyzed the oral status after recording the images by using QLF-D with targets of 38 youth people with hearing impairment and hearing language impairment. In order to investigate the state of oral hygiene, plaque index (O'Leary index) and contents of investigation of the state of the teeth included the number of sound teeth, the number of caries teeth, dental caries experience and the number of filling teeth. The following results were obtained. First, women lacked the management on plaque and had more caries teeth compared with men. In terms of impairment classification, subjects with both hearing and language impairment lacked the management on plaque and had more caries teeth. Second, subjects who did not get an oral exam for one year had more caries teeth. Oral hygiene score was the highest with the brushing time for 3-4 minutes. The number of sound teeth was increased as the brushing time was increased. In addition, the oral hygiene management time was the highest when cleaning the teeth, gums and tongue at the same time. Third, it was shown that the satisfaction of oral health education by using the new equipment was high. As a result of this study, in order to improve the oral health level of impaired students, they shall be trained to manage their teeth by themselves and educated to increase their motivation and practice. Thus, it is thought that various approaches which are differentiated from existing methods are required to be tried.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• Journal of Nutrition and Health
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• v.42 no.6
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• pp.577-595
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• 2009

This study aimed to reanalyze energy and nutrient intakes of 2007 Korean Nutrition and Health Examination Survey (KNHANES) using CAN-Pro 3.0, a commonly used nutrient analysis software in Korea. Food items and their codes were selected from 2007 KNHANES dietary intake file and converted to food codes of CAN-Pro 3.0 nutrient database (NDB). Of the 1,324 total food items, 1,155 items were converted by direct matching, 123 items were matched using other items in CAN-Pro 3.0 NDB and 42 items were matched using external sources. Consumption frequencies of items converted by direct matching contributed 94.5% of total consumption. Nutrient intakes of 4,091 participants of 2007 KNHANES, over 1 year old, were recalculated using CAN-Pro 3.0 NDB and compared with intakes in 2007 KNHANES dietary intake file. Intakes for energy and all nutrients except protein and Vitamin C calculated by two NDBs were significantly different by paired t-test (p < 0.001), but significantly correlated by Pearson' correlation coefficients (p < 0.001). Percent differences between the NDBs ranged from 0.3% to 15.1%, low for protein, energy, vitamin C, iron, vitamin B$_2$ (below 5%) but high for phosphorus, retinol, vitamin A, and $\beta$-carotene (over 10%). Age group, sex, and their interactions significantly influenced six nutrients (p < 0.05). Intake levels of zinc, vitamin B6, vitamin E, folate and cholesterol were not available in 2007 KNHANES but were calculated by CAN-Pro 3.0. Mean intake levels of zinc, vitamin B$_6$, vitamin E, and folate by age and sex groups revealed that some groups had mean levels below RI (Recommended Intake) or AI (Adequate Intake) levels. Intake level of cholesterol was higher than the recommended level (below 300 mg/day) in some groups, especially males. Results of the present study indicate the need for comparable and more comprehensive NDB to be used for dietary assessment of KNHANES and other researches. More rigorous evaluation of nutrients which have not been reported in KNHANES is needed.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.