• Clinical and Experimental Pediatrics
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• v.50 no.1
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• pp.40-46
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• 2007

Purpose : This study assessed the long term survival rate and long term complications of patients who had a modified Fontan operation for functionally univentricular cardiac anomaly. Methods : Between June 1986 and December 2000, 302 patients with a functional single ventricle underwent surgical interventions and were followed up until February 2006. The mean follow-up period was $8.3{\pm}5.3years$ (range 3.5-18 years). Their median age was 2.4 years at the Fontan operation. The survival rate, the incidence and the risk factor of late complications were evaluated retrospectively. Results : The verall survival rate was 91 percent at 5 years and 87 percent at 10 years. In multivariate analysis, early calendar year of operation and significant regurgitation were risk factors of death. The surviving patients showed NYHA functional class I in 82 percent, class II in 15 percent, and class III in 3 percent. Redo Fontan operations were necessary in 8.8 percent of patients at average $12.8{\pm}3.6years$ after initial Fontan operation. The most common cause of Fontan conversion was atrial arrhythmia. The incidence of thromboembolic events was 9.3% and these complications were associated with the occurrence of atrial tachyarrhythmia. Supraventricular tachycardia including atrial flutter or fibrillation were reported on the follow-up examination by 11.2 percent of survivors after $8.4{\pm}5.6years$. Atriopulmonary connection showed higher rates of late tachycardia than lateral tunnel operation. Conclusions : This study revealed that the recent survival rate of Fontan type operation was satisfactory, but the occurrence of late complications after a Fontan type operation increased with the longer survival. There is a need for strict follow up and early treatment of late complications in patients who had a Fontan operation.

• Journal of Korea Water Resources Association
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• v.46 no.9
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• pp.921-932
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• 2013

Runoff data availability is a substantial factor for precise flood control such as flood frequency or flood forecasting. However, runoff depths and/or peak discharges for small watersheds are rarely measured which are necessary components for hydrological analysis. To compensate for this discrepancy, a lumped concept such as a Storage Function Method (SFM) was applied for the partitioned Choongju Dam Watershed in Korea. This area was divided into 22 small watersheds for measuring the capability of spatial extension of runoff data. The chosen total number of flood events for searching parameters of SFM was 21 from 1991 to 2009. The parameters for 22 small watersheds consist of physical property based (storage coefficient: k, storage exponent: p, lag time: $T_l$) and flood event based parameters (primary runoff ratio: $f_1$, saturated rainfall: $R_{sa}$). Saturated rainfall and base flow from event based parameters were explored with respect to inflow at Choongju Dam while other parameters for each small watershed were fixed. When inflow of Choongju Dam was optimized, Youngchoon and Panwoon stations obtained average of Nash-Sutcliffe Efficiency (NSE) were 0.67 and 0.52, respectively, which are in the satisfaction condition (NSE > 0.5) for model evaluation. This result is showing the possibility of spatial data extension using a lumped concept model.

• Journal of the Korean Society for Railway
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• v.16 no.5
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• pp.386-393
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• 2013

In this study, the so-called repeated plate load bearing test (RPBT) used to get $E_{v2}$ values in order to check the degree of compaction of subgrade, and to get design parameters for determining the thickness of the trackbed foundation, is investigated. The test procedure of the RPBT method is scrutinized in detail. $E_{v2}$ values obtained from the field were verified in order to check the reliability of the test data. The $E_{v2}$ values obtained from high-speed rail construction sites were compared to converted modulus values obtained from resonant column (RC) test results. For these tests, medium-size samples composed of the same soils from the field were used after analyzing stress and strain levels existing in the soil below the repeated loading plates. Finite element analyses, using the PLAXIS and ABAQUS programs, were performed in order to investigate the impact of the strain influence coefficient. This was done by getting newly computed $I_z$ to get the precise strain level predicted on the subgrade surface in the full track structure; under wheel loading. It was verified that it is necessary to use precise loading steps to construct nonlinear load-settlement curves from RPBT in order to get correct $E_{v2}$ values at the proper strain levels.

• Journal of Nutrition and Health
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• v.41 no.8
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• pp.718-732
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• 2008

The purpose of this study was to investigate nutrients or food factors related to cognitive function of elderly having Alzheimer's disease. In this study 38 subjects who were over 65 years old have participated in dementia clinic at A medical center. After they were diagnosed to Alzheimer's Disease (AD) through blood analysis, neuropsychological test, brain image and interview by medical specialist, we examined for their general information, anthropometry, blood pressure and dietary intakes. Dietary intakes were investigated using the 24-hour recall record. Energy intake was adequate and the energy composition of carbohydrate, protein and fat was 60.8 : 16.2 : 23.0, but dietary intakes of calcium, vitamin A and folate were less than 75% of the recommended intake levels for Koreans. The multiple regression analysis adjusted with age, sex and educational level showed that cognitive function was positively related to intakes of zinc, fishes and shellfishes, beans & nuts, sugars and fats, and negatively related to intakes of plant calcium and eggs. These results indicate that intakes of specific nutrients or food groups are associated with the specific domains of cognitive function in elderly with AD.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.73-81
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• 2015

The objective of this study is in investigating the research for more accurately quantify the information on mountain forest by using the data on high density LiDAR. For the quantitative analysis of mountain forest resources, we investigated the method to acquire the data on high density LiDAR and extract mountain forest resources. Consequently, the height and girth of a tree each mountain forest resources could be extracted by using the data on high density LiDAR. When using the data on low density LiDAR of 2.5points/m² in average used to produce digital map, it was difficult to extract the exact height and girth of mountain forest resources. If using the data on high density LiDAR of 7points/m² by considering topography, the property of mountain forest resources, data capacity and process velocity, etc, it was found that multitudinous entities could be extracted. It was found that mountain topography and mixed topography were generally denser than plane topography and multitudinous mountain forest resources could be extracted. Furthermore, it was also found that the entity at the border could not be extracted, when each partition was individually processed and the area should be subdivided and extracted by considering the process time and property of target area rather than processing wide area at once. We expect to be studied more profoundly the absorption quantity of greenhouse gas later by using information on mountain forest resources in the future.

• The Journal of Engineering Geology
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• v.26 no.2
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• pp.235-250
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• 2016

In Korea, 70% of the annual rainfall falls in summer, and the number of days of extreme rainfall (over 200 mm) is increasing over time. Because rainfall is the most important trigger of landslides, it is necessary to decide a rainfall threshold for landslide warning and to develop a landslide warning model. This study selected 12 study areas that contained landslides with exactly known triggering times and locations, and also rainfall data. The feasibility of applying a Rainfall Triggering Index (RTI) to Korea is analyzed, and three RTI models that consider different time units for rainfall intensity are compared. The analyses show that the 60-minute RTI model failed to predict landslides in three of the study areas, while both the 30- and 10-minute RTI models gave successful predictions for all of the study areas. Each RTI model showed different mean response times to landslide warning: 4.04 hours in the 60-minute RTI model, 6.08 hours in the 30-minute RTI model, and 9.15 hours in the 10-minute RTI model. Longer response times to landslides were possible using models that considered rainfall intensity for shorter periods of time. Considering the large variations in rainfall intensity that may occur within short periods in Korea, it is possible to increase the accuracy of prediction, and thereby improve the early warning of landslides, using a RTI model that considers rainfall intensity for periods of less than 1 hour.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.15 no.3
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• pp.231-237
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• 2017

The present study researched and analyzed decontamination technology for decommissioning a nuclear power plant. The decision-making technique (EXPERT-CHOICE) was used to evaluate and select the optimal decontamination technology. In principle, this evaluation method is generally performed by a group of experts in the relevant field. The results of the weights were calculated by multiplying the weights with regard to each criterion and evaluation score. The evaluation scores were categorized into 3 ranges (high, medium, and low), and each range was weighted for differentiation. The level of the technology analysis was improved by additionally quantifying the weights with regard to each criterion and subdividing criteria into subcriteria. The basic assumption of the evaluation was that the weight values would decided on in an expert survey and assigned to each criterion. The evaluation criteria followed high weight for the 'High' range. Accordingly, H, M, and L were assigned weights of 10:5:1, respectively. This was based on the EXPERT-CHOICE optimal analysis. The minimum and maximum values were excluded, and the average value was used as the evaluation value for each scenario.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.6
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• pp.605-613
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• 2014

Epistemic uncertainty, the lack of knowledge, is often more important than aleatory uncertainty, variability, in estimating reliability of a system. While the probability theory is widely used for modeling aleatory uncertainty, there is no dominant approach to model epistemic uncertainty. Different approaches have been developed to handle epistemic uncertainties using various theories, such as probability theory, fuzzy sets, evidence theory and possibility theory. However, since these methods are developed from different statistics theories, it is difficult to interpret the result from one method to the other. The goal of this paper is to compare different methods in handling epistemic uncertainty in the view point of calculating the probability of failure. In particular, four different methods are compared; the probability method, the combined distribution method, interval analysis method, and the evidence theory. Characteristics of individual methods are compared in the view point of reliability analysis.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of Nutrition and Health
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• v.53 no.2
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• pp.141-154
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• 2020

Purpose: Atopic dermatitis (AD), a typical chronic disease in children, is an allergy disease that is highly associated with food. Thus, attention to food intake is needed to prevent and manage it. Therefore, we analyzed differences in food and nutrient intakes depending on AD status in under 12-year-old children. Methods: A total of 2,690 participants were enrolled in this study from the combined 2013-2015 Korea National Health and Nutrition Examination Survey. Subjects were divided into an AD group and normal group (non-AD group). General characteristic, food and nutrients intakes, and prevalence of insufficient and excessive nutrient intake were analyzed using χ² test and regression analyses. The AD odds ratio (OR) for insufficient and excessive nutrient intakes was analyzed using multiple logistic regression analyses. Results: Food and nutrient intakes were not significantly different between the AD and non-AD groups. However, the ratio of calcium intake to recommended nutrient intake was about 70% in both groups, which can be attributed to the overall lack of calcium intake among Korean children. There were no differences in energy or nutrient intakes between the groups, but compared with Korean Dietary Reference Intakes for Koreans, the appropriate intake ratios of fat and vitamin C in the AD group were higher than those in the non-AD group. The AD OR decreased when fat was consumed at above appropriate levels and vitamin C was consumed at lower or excess levels. Conclusion: In children, AD may be related to the nutrient intake ratio of fats and vitamin C, and we speculate that these results were affected by dietary restrictions for AD management.