• Journal of Nutrition and Health
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• v.40 no.sup
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• pp.9-21
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• 2007

This study aimed to assess the total sugar intake for Korean and to identify major food sources contributing to those food components. Korean adults aged over 20 years old from the Korean National Health and Nutrition Examination Survey (KNHANES) 2001 and 2002 were selected. The data were analyzed to obtain nationally and seasonally representative information on the health and nutritional status of the Korean. Forty food groups were used in identifying food sources of total sugar and energy intake. Total sugar contents of foods in the KNHANES data sets were estimated by food code matching technique with Release 18 of the USDA National Nutrient Database for Standard Reference. Sample weighted means, standard errors, and population percentages were calculated using SAS and SUDAAN. The mean total sugar intake of the Korean was 60.3g in 2001, 40.9g in spring 2002, 45.7g in summer 2002, and 52.1g in fall 2002, which were 30-44% of intake of US people. Fresh fruit was identified as the most significant food source for total sugar intake in Korean population in all age groups and all seasons. The next major food sources following fresh fruits were candy/jelly/syrup/honey, coffee/coffee caream, vegetables, Kimchi, soft drinks, milk, fruit juice, cookie/cracker/cake, and vegetable juice/grain juice, which showed similar results through the seasons. While carbonated soft drink was the most significant food sources for total sugar or added sugar intakes for US people. The total sugar intakes were significantly higher in women, higher educational level, and residing in metropolitan area. As intake of total sugar increased, intakes of protein, fiber, calcium, phosphorus, iron, Vit A, B1, B2, C, niacin showed significantly increased, while high intakes of added sugars showed low intakes of some micronutrients in the US people. Percentages of people who consumed nutrients below EAR were less in higher total sugar intake group than in lower intake group. From these results, we can conclude that the food consumption habits including the total sugar intake of Korean people seems relatively good so far. More reliable database of total sugar and added sugar composition tables in public domain should be established in the future, and also more researches about total sugar and added sugar for Koreans should be continued.

• Journal of Nutrition and Health
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• v.31 no.9
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• pp.1481-1497
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• 1998

The purpose of this study was to determine the relation between serum lipid profiles, apolipoprotein E phenotype, and dietary patterns in a cross-section of healthy individuals from Cheju-Do. Age, gender, anthropometric measurements, blood pressure, dietary consumption, drinking / smoking habits and menopausal status were surveyed. Total cholesterol, LDL-cholesterol, HDL-cholesterol, triglyceride, fasting blood glucose, and insulin levels were measured from overnight fasting blood. The study involved a total of 286 individuals(147 men and 139 women) between the ages of 20 and 60 years old. All of the subjects were recruited from a population of healthy individuals living in Cheju-Do. The results of the study are as follows : 1) Among the males, those in their 20's had the maximum food intake, while those in their 40's had the minimum food intake. For the females, food intake was the highest for those in their 30's. Energy and nutrient intakes were directly proportional to the amount of food intake. Men in their 30's were heavier than other men and women in their 40's were heavier than other women. The activity index for men in their 20's and 30's appeared to be lower than that of men above 40. The activity index of women in their 20's appeared to be lowest among all aged groups, and the index appeared to increase from the age of 30 onwards. 2) In terms of changes In serum constituents with age, men in their 40's appeared to have the highest levels of serum constituents such as lipids, glucose, and insulin. Men in their 50's showed the highest levels of serum LDL-cholesterol and glucose. Men in their 30's showed peak levels of serum triglycerides. On the other hand, women in their 50's appeared to have peak levels of serum total cholesterol, LDL-cholesterol, and triglycerides. There was no ch:ange with age in HDL-cholesterol and insulin levels for men and women. The percentage of the subjects had the following apo E phenotypes : E3/3, 91.3% ; E3/2, 5.4% ; E4/3, 2.5% ; E4/2, 0.7%. Lee's reserch with Korean female college students showed that the percentage of ApoE3/3, E3/2, E 4/2, E4/3, and E4/4 were 84.8%, 6.7%, 6.7%, 0.9%, 0.9%, respectively. The number of samples with ApoE mutation was so small that there was no statistical significance in the relation between apolipoprotein E phenotype and se겨m lipids. 3) To investigate the relati onship between weight and serum constituents, the subjects of this study were divided into three groups by BMI underweight, normal weight, and overweight. The serum constituents of men and women below the age 40 in the overweight groups belonged to the normal domain. On the other hand, serum cholesterol levels of both men and women above the age 40 in the overweight groups remained in the borderline-high region(above 200mg/dl), and the mean value of LDL-cholesterol(above 130mg/dl) and triglycerides of men were above normal. Fasting blood glucose levels also remained in the borderline-high region. Total cholesterol levels of women above the age 40 in the overweight group was in the borderline-high region. (Korean J Nutrition 31(9) : 1481-1497, 1998)

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2783-2790
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• 2010

In this paper, we first review LDPC codes in general and a belief propagation algorithm that works in logarithm domain. LDPC codes, which is chosen 802.11n for wireless local access network(WLAN) standard, require a large number of computation due to large size of coded block and iteration. Therefore, we presented three kinds of low computational algorithms for LDPC codes. First, sequential decoding with partial group is proposed. It has the same H/W complexity, and fewer number of iterations are required with the same performance in comparison with conventional decoder algorithm. Secondly, we have apply early stop algorithm. This method reduces number of unnecessary iterations. Third, early detection method for reducing the computational complexity is proposed. Using a confidence criterion, some bit nodes and check node edges are detected early on during decoding. Through the simulation, we knew that the iteration number are reduced by half using subset algorithm and early stop algorithm is reduced more than one iteration and computational complexity of early detected method is about 30% offs in case of check node update, 94% offs in case of check node update compared to conventional scheme. The LDPC decoder have been implemented in Xilinx System Generator and targeted to a Xilinx Virtx5-xc5vlx155t FPGA. When three algorithms are used, amount of device is about 45% off and the decoding speed is about two times faster than convectional scheme.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.97-117
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• 2012

Nowadays, Internet users tend to do a variety of actions at the same time such as web browsing, social networking and multimedia consumption. While watching a video, once a user is interested in any product, the user has to do information searches to get to know more about the product. With a conventional approach, user has to search it separately with search engines like Bing or Google, which might be inconvenient and time-consuming. For this reason, a video annotation platform has been developed in order to provide users more convenient and more interactive ways with video content. In the future of smart TV environment, users can follow annotated information, for example, a link to a vendor to buy the product of interest. It is even better to enable users to search for information by directly discussing with friends. Users can effectively get useful and relevant information about the product from friends who share common interests or might have experienced it before, which is more reliable than the results from search engines. Social networking services provide an appropriate environment for people to share products so that they can show new things to their friends and to share their personal experiences on any specific product. Meanwhile, they can also absorb the most relevant information about the product that they are interested in by either comments or discussion amongst friends. However, within a very huge graph of friends, determining the most appropriate persons to ask for information about a specific product has still a limitation within the existing conventional approach. Once users want to share or discuss a product, they simply share it to all friends as new feeds. This means a newly posted article is blindly spread to all friends without considering their background interests or knowledge. In this way, the number of responses back will be huge. Users cannot easily absorb the relevant and useful responses from friends, since they are from various fields of interest and knowledge. In order to overcome this limitation, we propose a method to filter a user's friends for information search, which leverages semantic video annotation and social networking services. Our method filters and brings out who can give user useful information about a specific product. By examining the existing Facebook information regarding users and their social graph, we construct a user profile of product interest. With user's permission and authentication, user's particular activities are enriched with the domain-specific ontology such as GoodRelations and BestBuy Data sources. Besides, we assume that the object in the video is already annotated using Linked Data. Thus, the detail information of the product that user would like to ask for more information is retrieved via product URI. Our system calculates the similarities among them in order to identify the most suitable friends for seeking information about the mentioned product. The system filters a user's friends according to their score which tells the order of whom can highly likely give the user useful information about a specific product of interest. We have conducted an experiment with a group of respondents in order to verify and evaluate our system. First, the user profile accuracy evaluation is conducted to demonstrate how much our system constructed user profile of product interest represents user's interest correctly. Then, the evaluation on filtering method is made by inspecting the ranked results with human judgment. The results show that our method works effectively and efficiently in filtering. Our system fulfills user needs by supporting user to select appropriate friends for seeking useful information about a specific product that user is curious about. As a result, it helps to influence and convince user in purchase decisions.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.