• Journal of the Korean Society of Food Science and Nutrition
• /
• v.38 no.8
• /
• pp.1050-1061
• /
• 2009

The present study was performed to evaluate energy and nutrients intakes of stroke patients in Daegu Gyeongbuk region, and to analyze the relative risk of stroke related to the intake levels of energy and nutrients. The case subjects (n=100) were selected from newly diagnosed stroke patients at Kyungpook National University Hospital. The control subjects (n=150) were selected from community residents who did not have stroke history and were sex and age-matched with the case subjects. The survey was conducted by individual interviews by trained dietitians using semiquantitative food frequency questionnaires. The odds ratios were calculated by using unconditional logistic regression. In male subjects, patient group had significantly higher consumption than the control group in mean daily intakes of energy, all energy yielding nutrients, vitamin $B_1$, $B_6$, niacin, folic acid, vitamin E, phosphorus, potassium, zinc and dietary fiber, and also in the ratio of energy intake from protein and fat. In women subjects, the patient group consumed significantly lower intakes than the control group in fat, vitamin C, folic acid, vitamin E, iron, but vice versa in carbohydrate energy ratio. As for men, the increased intakes of energy, protein, carbohydrate vitamin $B_1$, E and niacin, zinc, total fatty acids, monoand poly-unsaturated, n-6 fatty acids significantly raised the relative risk of stroke. As for women, the increased intakes of fat, vitamin A, $B_2$, $B_6$, niacin, vitamin C and E, iron, sodium, potassium, selenium, mono-, poly-unsaturated, n-6 fatty acids, cholesterol, taurine and dietary fiber significantly lowered the relative risk of stroke. The results of the study demonstrated that the effect of several nutrient intake levels, such as niacin, vitamin E and fatty acids, on the relative risk of stroke was inconsistent between sex. The reason for this sex difference needs to be elucidated in a larger scale study.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.3
• /
• pp.265-270
• /
• 2014

Hearing-impaired people have difficulty in hearing, so it is also hard for them to learn letters that represent sound and text that conveys complex and abstract concepts. Therefore it has been natural choice for the hearing-impaired people to use sign language for communication, which employes facial expression, and hands and body motion. However, the major communication methods in daily life are text and speech, which are big obstacles for the hearing-impaired people to access information, to learn and make intellectual activities, and to get jobs. As delivering information via internet become common the hearing-impaired people are experiencing more difficulty in accessing information since internet represents information mostly in text forms. This intensifies unbalance of information accessibility. This paper reports web-based text-to-sign language translating system that helps web designer to use sign language in web page design. Since the system is web-based, if web designers are equipped with common computing environment for internet browsing, they can use the system. The web-based text-to-sign language system takes the format of bulletin board as user interface. When web designers write paragraphs and post them through the bulletin board to the translating server, the server translates the incoming text to sign language, animates with 3D avatar and records the animation in a MP4 file. The file addresses are fetched by the bulletin board and it enables web designers embed the translated sign language file into their web pages by using HTML5 or Javascript. Also we analyzed text used by web pages of public services, then figured out new words to the translating system, and added to improve translation. This addition is expected to encourage wide and easy acceptance of web pages for hearing-impaired people to public services.

• Journal of the Korean Institute of Gas
• /
• v.5 no.4 s.16
• /
• pp.1-7
• /
• 2001

This paper presents a 'consequence analysis' for vapor cloud explosions caused by heavy gas leakages from commercially used storage tanks at petrochemical plants. Particularly, this paper emphasizes on evaluating the results of various vapor cloud explosion accidents from Butane storage tanks. Also this paper analyses the impact of variables on the accidents in order to acquire the optimum conditions for variables. $SuperChems^{TM}$ Professional Edition was applied to analyse the impact (If atmospheric and other variables in the situation where vapor cloud continuously disperses from the ground level. Under the assumption that practical operating conditions are selected as a standard condition, and Butane leaks from the storage tank for 15 minutes, the results show that the maximum distance of LFL (Lower Flammable Limit) was 52 meters and overpressure by the vapor cloud explosion was 1 psi at 128.2 meters. It is observed that the impact of the variables on accidental Butane storage tank leakage mainly varied upon atmospheric stability, wind velocity, pipe line size, visible length, etc., and changes in the simulation result occurred as the variables varied. The maximum distance of the LFL (Lower Flammable Limit) increased as the visible length became shorter, the size of the leak became larger, the wind velocity was decreased, and the climatic conditions became more stable. Thus, by analysing the variables that influence the simulation results of explosions of Butane storage tanks containing heavy gases, I am presenting the most appropriate method for 'consequence analysis' and the selection of standards for suitable values of variables, to obtain the most optimal conditions for the best results.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.1-20
• /
• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.