• The Journal of Korean Obstetrics and Gynecology
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• v.33 no.1
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• pp.116-127
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• 2020

Objectives: The purpose of this study is to analyze csatisfaction with clinica1 training in the department of Korean Medicine Obstetrics and Gynecology and the related factors. Methods: All the students on the graduating class of school of Korean Medicine, Dong-Shin University must be trained for 1 year in the department of Korean Medicine Obstetrics and Gynecology according to clinical training guideline including Objective Structured Clinical Examination (OSCE). After completing clinical training, we distributed questionaries to them about a reflex of the object of study, usefulness, difficulty, satisfaction and preference. And then, we analyzed the related factors including descriptive statistics, frequency analysis and correlation analysis by SPSS 12.0. Results: In general, a reflex of the aim of study (7.31±1.59), satisfaction with contents (7.44±1.58), and difficulty (6.13±1.34) were evaluated restectively. There is no statistically significant difference with sex. There is a statistically significant positive correlation between a reflex of the aim of study and satisfaction with contents. The primary preferences of clinical practice were Clinic Training, Ward Training, PBL (Problem Based Learning), OSCE. In OSCE, a reflex of the aim of study (7.04±1.83) and usefulness (7.02±1.97) were highly evaluated. There is no statistically significant difference with sex. The primary preferences for clinical OSCE practice were breast examination, genital examination, ultrasound scan, and pap smear. Facilities and equipment (6.84±2.01), contents of clinical training (7.29±1.83), the handout (7.20±1.98) and time scheduling (7.29±1.90) were evaluated respectively in general composition of clinical training. Conclusions: Clinical training program in the department of Korean Medicine Obstetrics and Gynecology can be highly estimated from the viewpoint of satisfaction with clinical training and the related factors.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.83-83
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• 2017

수자원공학에서 일강수량 모의기법은 다양한 목적으로 활용되고 있지만, 일반적으로 홍수와 가뭄의 영향을 고려할 수 있는 수공구조물의 위험도 및 신뢰성 평가 및 수자원 계획을 수립하기 위한 입력 자료생성을 목적으로 활용된다. 유역 단위의 분석시 단일 지점에 대한 강수 모의 기법을 적용할 경우 각각의 지점에서 관측된 강수 자료의 시계열 및 통계치 특성이 효과적으로 재현되지만 공간적으로 발생하는 즉, 지점 간의 종속관계를 재현하지 못하는 문제가 발생한다. 이러한 이유로 공간적인 전이 특성이 있는 가뭄 분석 및 유역내 유출량의 공간적 변동 특성 분석에 단일지점별 모의 결과를 이용할 경우 관측 자료와 상반된 공간적 변동성으로 인하여 잘못된 가뭄 및 유출 분석 결과가 도출되는 문제점이 있다. 따라서, 실제적으로 발생하는 강수 특성을 반영한 유역 단위의 홍수 및 가뭄 등의 수문 분석을 위해서는 지점간의 종속성을 반영할 수 있는 다지점 강수 모의 모형의 적용이 필수적이다. 본 연구에서는 다지점 모의에 있어서, Wilks 모형의 지점별 시변동 특성과 공간상관성 재현 능력, HMM 모형이 갖는 강수 사상별로 분포된 양적 분포 패턴 재현 능력을 복합적으로 나타낼 수 있는 새로운 다지점 일강수량 모의 모형인 기계학습 기반 범주화 기법을 이용한 다지점 일강수량 모의 모형(ML-MRS)을 개발하였다. 또한, 지점별 강수량에 적용되는 확률분포모형은 Gamma 분포로 구성된 혼합모형을 적용하여 단일 확률 분포 모형의 자료 적합 문제를 개선하였다. 모의를 통한 일강수량 시계열 자료는 일 강수자료의 통계량을 효과적으로 모의하였으며, 다지점 모형의 모의 결과를 적용한 가뭄 모의 결과 관측 자료에서 나타나는 공간적 패턴이 재현되었다. 본 모형은 시 공간적 사상을 효과적으로 재현함으로서 지역의 변동특성을 반영한 가뭄, 홍수, 기상 현상 분석 등 활용도가 매우 높을 것으로 판단된다.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.12
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• pp.85-94
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• 2018

The purpose of this paper was to propose a model that recognizes potential users' emotional response toward design by classifying Electroencephalography(EEG). Studies in neuroscience and psychology have made an effort to recognize subjects' emotional response by analyzing EEG data. And this approach has been adopted in design since it is critical to monitor users' subjective response in the preface of design. Moreover, the building design process cannot be reversed after construction, recognizing clients' affection toward design alternatives plays important role. An experiment was conducted to record subjects' EEG data while they view their most/least liked images of small-house designs selected by them among the eight given images. After the recording, a subjective questionnaire, PANAS, was distributed to the subjects in order to describe their own affection score in quantitative way. Google TensorFlow was used to build and train the model. Dataset for model training and testing consist of feature columns for recorded EEG data and labels for the questionnaire results. After training and testing, the measured accuracy of the model was 0.975 which was higher than the other machine learning based classification methods. The proposed model may suggest one quantitative way of evaluating design alternatives. In addition, this method may support designer while designing the facilities for people like disabled or children who are not able to express their own feelings toward alternatives.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.922-934
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• 2020

The talent trading platform is a platform that brokers transactions such as program coding, media content production (video, music, presentation materials, etc.), design, learning, and repair. Existing talent trading platforms provide a server-client model-based service, which incurs server operating costs and arbitration labor costs for transactions, which has a disadvantage that users bear high service fees. This paper proposes a method to reduce server and database operation costs by uploading transaction information to blocks through the system as a distributed app (dApp) based on the Ethereum platform. In addition, it proposes a method to lower transaction fees by reducing the labor cost of transaction arbitrators through smart contracts. Compare and analyze the cost processing procedure and transaction fee size of the blockchain-based talent trading platform and the existing talent trading platform.

• Korean Journal of Community Nutrition
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• v.25 no.6
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• pp.451-463
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• 2020

Objectives: The purpose of this study was to develop and evaluate booklets and video clips to prevent children from picky eating. Methods: Based on a survey conducted on food preferences of preschool children aged 2 to 5 years, 14 kinds of less preferred vegetables were selected. Accordingly, educational videos, activity books, and teaching-learning guides were produced for preschool children using the 'food bridge' theory, and the educational materials were named "Friendly vegetables". Educational materials were distributed to childcare institutions, and their effectiveness was investigated for preschool children who were instructed on these materials once every 30 days from March to November, 2019. The children were examined for changes in their knowledge of names, colors, taste/texture, methods of cultivation, and preferences for vegetables before and after the instructional course. Results: The awareness of vegetables increased significantly in younger children and the picky eating group. When the assessment was carried out in terms of vegetable knowledge, it was observed that the younger the age or the pickier the in eating food, the more effective the education is compared to the counter part. The preference for vegetables also increased after the instruction compared to the pre-instruction period, but significant changes were seen only in the 2~3 year age group for boys and girls. Also, only the picky group of girls showed changes in preference. The children's average interest in the education materials was 3.85 points out of 5 points. Conclusions: Through this study, we have developed educational materials for standalone use in childcare facilities and confirmed that they have a significant effect on improving awareness and preferences related to vegetables. In summary, the younger the age or the pickier the child in eating food, the more effective the education. It is believed that additional education on mealtime guidance is needed which can alter the eating behavior of preschool children and improve their diet. It is proposed to widen the scope of use of the materials by collecting diverse opinions from child care teachers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.368-376
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• 2021

With the recent development of deep learning technology, autonomous things technology is attracting attention, and DNNs are widely used in embedded systems such as drones and autonomous vehicles. Embedded systems that can perform large-scale operations and process multiple DNNs for high recognition accuracy without relying on the cloud are being released. DNNs with various levels of priority exist within these systems. DNNs related to the safety-critical applications of autonomous vehicles have the highest priority, and they must be handled first. In this paper, we propose a priority-based scheduling framework for DNNs when multiple DNNs are executed simultaneously. Even if a low-priority DNN is being executed first, a high-priority DNN can preempt it, guaranteeing the fast response characteristics of safety-critical applications of autonomous vehicles. As a result of checking through extensive experiments, the performance improved by up to 76.6% in the actual commercial board.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.1-9
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• 2022

UML class diagrams are used to visualize the static aspects of a software system and are involved from analysis and design to documentation and testing. Software modeling using class diagrams is essential for software development, but it may be not an easy activity for inexperienced modelers. The modeling productivity could be improved with a dataset of class diagrams which are classified by domain categories. To this end, this paper provides a classification method for a dataset of class diagram images. First, real class diagrams are selected from collected images. Then, class names are extracted from the real class diagram images and the class diagram images are classified according to domain categories. The proposed classification model has achieved 100.00%, 95.59%, 97.74%, and 97.77% in precision, recall, F1-score, and accuracy, respectively. The accuracy scores for the domain categorization are distributed between 81.1% and 95.2%. Although the number of class diagram images in the experiment is not large enough, the experimental results indicate that it is worth considering the proposed approach to class diagram image classification.

• Composites Research
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• v.34 no.6
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• pp.394-399
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• 2021

Piecewise Integrated Composite (PIC) is a new concept to design composite structures of multiple stacking angles both for in-plane direction and through the thickness direction in order to improve stiffness and strength. In the present study, PIC beam was suggested based on 3D training data instead of 2D data, which did offer a limited behavior of beam characteristics, with enhancing the stiffness accompanied by reduced tip deformation. Generally training data were observed from the designated reference finite elements, and preliminary FE analysis was conducted with respect to regularly distributed reference elements. Also triaxiality values for each element were obtained in order to categorize the loading state, i.e. tensile, compressive or shear. The main FE analysis was conducted to predict the mechanical characteristics of the PIC beam.

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.19-36
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• 2022

Purpose: The purpose of this study is to analyze the effects of COVID 19. The effects of COVID 19 are grouped into 5; economic stress, mental stress, health stress, task concern, self-confidence. We introduce the concept of personal ambidexterity that is necessary power for cabin crews to provide appropriate and efficient service to passengers. Ambidexterity consists of exploiting existing resources to sustain and exploring the new including method of performing task, customer, market etc. The former is necessary to maintain present condition while the latter is necessary to prepare for the future. Also motive is considered as a stimulating factor for task. Previous studies show that motive affects ambidexterity and we try to analyze whether COVID 19 effects influence this relationship. Research design, data, and methodology: Considering the relationship between the variables, we designed to measure the influence of the effects of COVID 19 by analyzing the moderating effects of them. For empirical analysis we distributed survey questionnaire and collected. Total of 361 samples are used fo the analysis. For analysis program, SPSS version 23 was used. Regression analysis and moderating effect analysis were conducted. Results: Study results show that first, the variables of economic stress, mental stress, health stress, task concern, self confidence affects personal ambidexterity and service provision. Also ambidexterity affects service provision significantly. Among COVID 19 effects, economic stress, task concern, and self confidence has moderating effects. On the other hand, new work environment does not have moderating effect. Conclusions: In conclusion, the effects of COVID 19 are wide and various. Among them the most serious effect is that COVID 19 is depriving workers of self confidence and passion toward the work. To remedy stresses and restore self confidence and passion, each worker should make his/her own efforts, such as, learning more to become more competitive, also firms should do make efforts to protect employees and to rebuild trust between firm and employees in every respect. Especially firms should realize that economic stress can be treated by economic compensation as the situation turns to normal but trust as well as self confidence and passion is not easy to restore.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.19-27
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• 2022

Graduate well-being is foundational to academic success, and they are becoming more and more vulnerable. This is as they suffer from mental health challenges like anxiety and depression at rates six times higher than the general population. When the nature of their educational experience changes, such as when they had to stay in their homes during the COVID-19 pandemic, the stress on their mental health increases. The number of cases of emotional wellness among university students is considered a public health problem, but these young people often do not seek appropriate treatment. This study, therefore, aims to identify the influence of health behavior factors on graduate emotional wellness. This study used a questionnaire with a cross-sectional survey design. Questionnaires were distributed online to graduates from selected Private and Public Higher Education Institutions in Malaysia. The Partial Least Square Equation Model (PLS-SEM) was used to analyze the results of the study. Overall findings indicate that the health behavior factors have a significant influence on graduate emotional wellness. The findings from this study will benefit the management, academics, counselors, and other entities, including the Students' Representative Council, in identifying ways to improve services and upgrade the necessary facilities to enhance the graduate's emotional wellness.