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Lateral Body Shapes of Males in Their 20s for the Development of Educational Dress Forms (Part 2)
( Hyun Yoo ),( Boo Ja Shim ) 한국패션비즈니스학회 2005 패션 비즈니스 Vol.9 No.3
The study with the subjects of 200 adult males in Busan in their 20s had a purpose of analyzing lateral body shapes to develop men`s educational dress forms. The following are the conclusions: 1. Comparison of the Body Dimensions of Busan Male Adults in Their 20s and the 5th Survey of Korean Body Measurement In the comparison of the Busan and national body dimensions by way of Mollison`s relative deviation, all compared items were under the deviation of 0.7. Therefore, the sample of Busan male grown-ups is understood to represent the body shapes of the average Korean male adults in their 20s. 2. Results of Lateral Body Shape Classification From factor analysis, seven factors were produced to explain 75.45% of all variables. Those 7 factors to compose lateral body shapes were hip prominence, back-neck sides, upper body`s front-back depth, lateral upper body depth, hip-waist depth, front chest-waist depth, and hip and waist height. Cluster analysis revealed four characteristic lateral body shapes. Type 1 with the appearance rate of 11.70%, named D, had the greatest upper chest angle and tanterior neck lower angle. The front side was more developed. Type 2 with 33.51%, named I, was generally long and slender. Type 3 with 24.47%, named d, had the biggest depth differences in hip-chest as well as more prominent back hip. Type 4 with 30.32%, named q, had the biggest dorsal upper angle and the tiniest chest upper angle as the back area was a little bent.
Deep Learning-based Evolutionary Recommendation Model for Heterogeneous Big Data Integration
( Hyun Yoo ),( Kyungyong Chung ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.9
This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual’s importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.
IoT-Based Health Big-Data Process Technologies: A Survey
( Hyun Yoo ),( Roy C. Park ),( Kyungyong Chung ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.3
Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.
Solubility of TiO2 in NaF‑CaF2‑BaF2 Melts
Jeong‑Hyun Yoo,Sung‑Wook Cho 대한금속·재료학회 2018 METALS AND MATERIALS International Vol.24 No.6
The solubility of TiO2in NaF-CaF2-BaF2 ternary eutectic melts was investigated at the temperature range of 1025–1150 °C. The least-squares equation was obtained from the relationship between the reciprocal temperature and the natural logarithm ofthe titanium concentration in the melts saturated with TiO2. The corresponding partial molar enthalpy of dissolution of TiO2was found to be 188 kJ/mol. The titanium saturation concentration was 3.73 wt% at 1100 °C. From the titanium concentrationchange with the added amount of TiO2at different holding time after a final stirring, it was found that not only completedissolution of TiO2but also enough sedimentation of excessive TiO2should be guaranteed to obtain more reliable solubilitydata. The holding time of 10 h was found to be enough for the excessive TiO2particles to settle down in our experimentalconditions. It is noteworthy that in case of adding TiO2in excess of its solubility, the Ba1.12(Ti8O16) phase was observed atthe lower and bottom of the solidified salt ingots.