• 예술인문사회 융합 멀티미디어 논문지
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• 제7권6호
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• pp.115-123
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• 2017

본 연구는 한국 내 중국 유학생의 학습태도 유형을 분석하고, 각 유형별 특징을 파악하는데 목적이 있다. 이를 위해 질적 연구방법과 양적 연구방법의 장점을 갖춘 방법론으로 개인의 생각이나 태도와 같은 주관적 행위를 객관적으로 측정할 수 있는 Q방법론을 적용하였다. 연구의 결과 중국 유학생의 학습태도 유형은 네 가지로 분류되었다. 제1유형은 자기 자신에 대하여 만족감을 느끼지만, 학습 환경 및 자원에 대하여 불만이 있는 '학습 환경 불만형', 제2유형은 대학생활에 잘 적응하면서 즐겁게 공부하는 '적극융합형', 제3유형은 학위취득의 목표를 두지만 학습을 위한 의지가 부족한 '학습동력 부족형', 제4유형은 자신의 생각과 행동이 다르게 나타나는 '갈등·혼란형'이다. 논의 결과 중국 유학생이 고향에 떠나 외국에 유학하는 과정에서 성공적인 학습을 위하여 학습에 대한 동기를 명확하게 가지고, 한국어 능력을 더 높여야 하며, 학습방법의 정확한 이해와 활용이 필요하였다. 향후 중국인 유학생이 더욱 늘어나 것에 대비해 학습태도 조절과 학업의 적응을 위한 노력이 대학과 국가적 차원에서 지원할 수 있는 여건을 보다 적극적으로 행할 필요가 있다.

• 산업경영시스템학회지
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• 제34권3호
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• pp.49-56
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• 2011

Estimating optimum hardware capacity of an e-learning system is very important process to grasp reasonable size of designing technique architecture and budget during step of ISP(information strategic planning) and development. It hugely influences cost and quality of the whole project. While investment on information system hardware has been continuously increased, there was no certified hardware capacity estimating method in e-learning system development. A guideline for hardware sizing of information systems was established by Telecommunication Technology Association in 2008. However, the guideline is not appropriate for estimating optimum hardware capacity of an e-learning system because it was designed to provide general standards for estimating hardware capacity of various types of projects. The purpose of this paper is to provide a methodology for estimating optimum hardware capacity in e-learning system development. To develop the methodology, this study, first of all, analyzes two e-learning development projects, in which the guideline was applied to estimate optimum hardware capacity. Then, this study finds out several key factors influencing on hardware capacity. Finally, this study suggests a methodology for estimating optimum hardware capacity of an e-learning system, in which weights for the factors are determined through AHP analysis.

• Nuclear Engineering and Technology
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• 제53권4호
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• pp.1199-1209
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• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• 동의신경정신과학회지
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• 제25권4호
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• pp.333-350
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• 2014

Objectives: The purpose of this study was to research learning based on brain science and the learning methodology of Korean Medicine according to disparity of age. Through this, the study aimed to provide a guideline to related Korean Medicine treatments as well as the common nurturing/educational institutions. Methods: All journals and dissertations on brain science based learning methods studied in Korea to date that could be found in the National Assembly Library and the RISS were implemented in the analysis. The terminology used for search was as follows: 1st search, 'Brain'; 2nd search, 'Learning', 'Education'; 3rd search, 'Baby, 'Infant', 'Child'. For the learning methodology of Korean Medicine according to disparity of age, the related contents were extracted from Donguibogam and Liuyi, Sasang constitutional medicine. Results: A total of 30 studies, were collected as data. In the baby stage, the development and myelination of brain neurons are accelerated by experience and learning, highly influenced by social, cognitive and emotional movement. In infancy, the frontal lobe actively develops, so education for development of the prefrontal cortex is suggested. The brain of the infant at this stage can be developed by arts and physical education. In the child stage, the parietal and temporal lobe develop actively. Thus, programs to stimulate brain activity including brain respiration would be helpful in enhancing learning ability, concentration, etc. As evidence for learning and nurturing methodology according to disparity of age from Korean Medicine prospective, the following are listed: Location and time for sexual intercourse before pregnancy, stabilization during pregnancy, baby nurturing methods for nurturing from Donguibogam. Also Liuyi and Sasanag constitutional medicine can be the learning methodology according to disparity of age. And there are acupuncture points on each head section according to age in Donguibogam. Conclusions: Studies on 'brain-science based learning' are continuously being conducted. Based on these studies, diverse new brain-science based learning will be developed in the future. There is also a need to develop the learning methodology of Korean Medicine according to disparity of age in a more systematic and diverse way.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1995년도 추계학술발표회논문집(1)
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• pp.293-299
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• 1995

A methodology is applied to identify tile learning trend related to the safety and availability of U.S. commercial nuclear power plants. The application is intended to aid in reducing likelihood of human errors. To assure that tile methodology ran be easily adapted to various types of classification schemes of operation data, a data bank classified by the Transient Analysis Classification and Evaluation(TRACE) scheme is selected for the methodology. The significance criteria for human-initiated events affecting tile systems and for events caused by human deficiencies were used. Clustering analysis was used to identify the learning trend in multi-dimensional histograms. A computer rode is developed based on tile K-Means algorithm and applied to find the learning period in which error rates are monotonously decreasing with plant age.

• 공학교육연구
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• 제15권4호
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• pp.94-100
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• 2012

The needs for enhancing creativity in engineering design education continue to increase. Recent studies about a learning environment and learning support tools provide some new possibilities. The education of creative thinking however must begin from the change of attitude of students to creativity. The experimental results and some lessons for modification of systematic engineering design methodology to creative were reported from the course 'engineering design'.

• Asia pacific journal of information systems
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• 제6권2호
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• pp.77-105
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• 1996

The accuracy of knowledge is a major concern for expert system developers and users. Machine learning approaches have recently been found to be useful in knowledge acquisition for expert systems. However, the accuracy of concept acquired from machine learning could not be analyzed in most cases. In this paper we develop a comprehensive knowledge error analysis methodology for practical use of expert systems. Decision tree induction is an important type of machine learning method for business expert systems. Here we start to analyze with knowledge acquired from decision tree induction method, and extend the results to develop error analysis methodology for general machine learning methods. We give several examples and illustrations for these results. We also discuss the applicability of these results to multistrategy learning approaches.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4060-4066
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• 2021

Flow-accelerated corrosion (FAC) of carbon steel piping is a significant problem in nuclear power plants. The basic process of FAC is currently understood relatively well; however, the accuracy of prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a convolutional neural network, which is confined to a straight pipe without geometric changes. Furthermore, a methodology to generate training data is proposed to efficiently train the neural network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation.

• 산업경영시스템학회지
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• 제45권4호
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• pp.86-98
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• 2022

Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manufacturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection methodology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제29권4호
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• pp.723-743
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• 2015

본 연구는 수학학습 상담 시 활용할 사전 진단 검사지를 개발하기 위한 연구이다. 이를 위하여 학생들을 진단하기 위한 발문들을 도출해 내어 검사지를 구성하고, 이에 대한 표준화 작업을 실시하여 초등 5~6학년용과 중등 1~2학년용의 진단 검사지를 제작하였다. 검사지는 총 3부로 나누어, 1부 수학학습심리, 2부 수학학습 방법, 3부 수학학습 개인 성향으로 구성하였다. 수학학습심리는 '수학학습능력 자신감', '수학불안', '수학학습 태도' 요인으로, 수학학습 방법은 '수학학습 자기관리'와 '수학학습 전략' 요인으로, 수학학습 개인 성향은 '수학학습 습관'과 '수학학습 관리 방법', '동기', '성향'을 묻는 문항으로 구성하였다. 이러한 진단 검사지는 학생들의 수학학습 상담을 위한 기초자료로 활용할 수 있다.