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Hyesook Kim,Won Jang,Ki-Nam Kim,Ji-Yun Hwang,Hae-Kyung Chung,Eun-Ju Yang,Hye-Young Kim,Jin-Hee Lee,Gui-Im Moon,Jin-Ha Lee,Tae-Seok Kang,Namsoo Chang 한국영양학회 2013 Nutrition Research and Practice Vol.7 No.3
This study was performed to compare the dietary food and nutrient intakes according to supplement use in pregnant and lactating women in Seoul. The subjects were composed of 201 pregnant and 104 lactating women, and their dietary food intake was assessed using the 24-h recall method. General information on demographic and socioeconomic factors, as well as health-related behaviors, including the use of dietary supplements, were collected. About 88% and 60% of the pregnant and lactating women took dietary supplements, respectively. The proportion of dietary supplements used was higher in pregnant women with a higher level of education. After adjusting for potential confounders, among the pregnant women, supplement users were found to consume 45% more vegetables, and those among the lactating women were found to consume 96% more beans and 58% more vegetables. The intakes of dietary fiber and β-carotene among supplement users were higher than those of non-users, by 23% and 39%, respectively. Among pregnant women, the proportion of women with an intake of vitamin C (from diet alone) below the estimated average requirements (EAR) was lower among supplement users [users (44%) vs. non-users (68%)], and the proportion of lactating women with intakes of iron (from diet alone) below the EAR was lower among supplement users [usesr (17%) vs. non-users (38%)]. These results suggest that among pregnant and lactating women, those who do not use dietary supplements tend to have a lower intake of healthy foods, such as beans and vegetables, as well as a lower intake of dietary fiber and β-carotene, which are abundant in these foods, and non-users are more likely than users to have inadequate intake of micro-nutrient such as vitamin C and iron.
Damaged cable detection with statistical analysis, clustering, and deep learning models
Yun Jang,Hyesook Son,Chanyoung Yoon,Yejin Kim,Linh Viet Tran,Seung-Eock Kim,Dong Joo Kim,Jongwoong Park 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.
Relationship between Work Hours and Smoking Behaviors in Korean Male Wage Workers
Sung-Mi Jang,Eun-hee Ha,Hyesook Park,Eunjeong Kim,Kyunghee Jung-Choi 대한직업환경의학회 2013 대한직업환경의학회지 Vol.25 No.-
Objectives: The purposes of this study are 1) to measure the prevalence of smoking according to weekly work hours by using data from the Korean Labor and Income Panel Study (KLIPS), and 2) to explain the cause of high smoking prevalence among those with short or long work hours by relative explanatory fraction. Methods: Data from a total of 2,044 male subjects who responded to the questionnaire in the 10th year (2007) and 11th year (2008) of the Korean Labor and Income Panel Study were used for analysis. Current smoking, smoking cessation, continuous smoking, start of smoking, weekly work hours, occupational characteristics, sociodemographic and work-related factors, and health behavior-related variables were analyzed. Log-binomial regression analysis was used to study the relationship between weekly work hours and smoking behaviors in terms of the prevalence ratio. Results: The 2008 age-adjusted smoking prevalence was 64.9% in the short work hours group, 54.7% in the reference work hours group, and 60.6% in the long work hours group. The smoking prevalence of the short work hours group was 1.39 times higher than that of the reference work hours group (95% confidence interval of 1.17-1.65), and this was explained by demographic variables and occupational characteristics. The smoking prevalence of the long work hours group was 1.11 times higher than that of the reference work hours group when the age was standardized (95% confidence interval of 1.03-1.19). This was explained by demographic variables. No independent effects of short or long work hours were found when the variables were adjusted. Conclusion: Any intervention program to decrease the smoking prevalence in the short work hours group must take into account employment type, job satisfaction, and work-related factors.
박혜숙(Park, Hyesook),장소영(Jang, So Young) 한국영어어문교육학회 2014 영어어문교육 Vol.20 No.1
This study conducts a comprehensive analysis with a reference to research methodology and topic by analyzing the 67 articles published in English Language & Literature Teaching. All journal articles related to English writing published between 1995 and 2013 were selected and analyzed in terms of eight research methods and seven content areas. Eight categories of research design were identified based on (1) case study, (2) ethnography, (3) survey, (4) quantitative descriptive study, (5) quantitative inference study, (6) experimental study, (7) mixed-method, and (8) theorybased literature review. The contents were also classified into 7 categories such as (1) text analysis, (2) writer & writing process, (3) feedback, (4) instructional design and teaching methods, (5) writing assessment, (6) materials analysis and (7) miscellaneous. The results revealed that most of English writing studies have carried out with diverse topics since 2000 and that writing studies employed more quantitative research method than qualitative method. Contents related to text/discourse analysis, feedback and teaching methods were more frequently selected than others as a research topic. On the basis of the analyses, some implications and suggestions were provided for the future English writing research and practices.
장새영(Saeyoung Jang),변하영(Hayoung Byun),임혜숙(Hyesook Lim) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8
Pending Interest Table (PIT) lookup is one of the main functions in Named Data Networking and it requires an efficient exact-matching algorithm. In this paper, we propose an efficient PIT lookup algorithm using functional Bloom filters (FBFs). For Interest packets with the same content name, the proposed algorithm stores multiple incoming faces of the content name by incrementally adding FBFs. For a Data packet, the algorithm lookups and deletes matching incoming faces at once.
Hwan-Hee Jang,Hwayoung Noh,Gichang Kim,Soo-Yeon Cho,Hyeon-Jung Kim,Jeong Seon Kim,Pekka Keski-Rahkonen,Heinz Freisling,Marc Gunter,Hyesook Kim,Oran Kwon 한국식품영양과학회 2021 한국식품영양과학회 학술대회발표집 Vol.2021 No.10
A holistic approach to personalized nutrition to maintain health and prevent disease is advancing with data mining technology. Our health could be affected by diet-gut microbiota interactions, together with metabolites derived by the interaction, resulting in personalized responses. This study will examine associations of diet-gut microbiota interactions with human metabolites and health parameters including metabolic health- related markers. In a cross-sectional study of 350 adults aged 19-60 years, we will assess diet using a food frequency questionnaire, gut microbiota in stool using 16s rRNA sequencing, and metabolites will be measured in fasting blood and 12h overnight urine using an untargeted metabolomic approach. We will develop machine-learning algorithms and predict individual risk by selecting core features related to metabolic health by integrating multiple data such as microbiome, metabolome, health parameters, and lifestyle factors. Healthy diets can be estimated by deriving foods that are relevant to personalized predictive scores. Further studies are needed to verify the health effects of food through personalized dietary interventions based on these predictions.
A novel method for vehicle load detection in cable-stayed bridge using graph neural network
Van-Thanh Pham,Hyesook Son,Cheol-Ho Kim,Yun Jang,Seung-Eock Kim 국제구조공학회 2023 Steel and Composite Structures, An International J Vol.46 No.6
Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.