• 한국컴퓨터정보학회논문지
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• 제25권4호
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• pp.197-205
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• 2020

본 연구에서는 다문화청소년이 지각한 사회적지지가 학업적응에 미치는 영향을 검증하고, 이 관계에서 자아존중감과 성취동기를 통해 발생하는 간접효과를 밝히고자 하였다. 이러한 관계를 구조방정식 모형분석을 통하여 분석하기 위해 다문화청소년 7차년도(2017년) 패널데이터를 활용하였다. 분석결과, 첫째, 다문화청소년이 지각한 사회적지지와 자아존중감 및 성취동기는 학업적응에 직접적으로 유의한 영향을 미치는 것으로 나타났다. 둘째, 다문화청소년의 자아존중감은 사회적지지에 유의한 영향을 미치는 것으로 나타났다. 셋째, 다문화청소년이 지각한 사회적지지가 학업적응에 미치는 영향은 직접효과 이외에도 자아존중감과 성취동기를 매개로 간접효과가 있는 것으로 확인되었다. 이러한 연구결과를 바탕으로 다문화청소년의 사회적지지 및 학업적응 향상을 위한 실천적 개입방안을 제시하였다.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제27권1호
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• pp.93-109
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• 2024

This study compared the perceptions of 548 Korean and Indonesian secondary school teachers in the COVID-19 era and examined factors affecting their intention to continue online learning. Based on survey data, the two countries were compared on seven factors (teaching readiness, challenges and issues, competencies that require training, sources of support, types of support, and endurance). Furthermore, we examined what factors influence the intention to continue online learning. As a result, Korean teachers perceived their teaching readiness for online learning to be less than that of Indonesian teachers. Indonesian teachers perceived that they did not receive sufficient support. Additionally, factors affecting the intention to continue online learning differed depending on the country. Based on these results, we suggested implications for integrating online learning into mathematics education.

• ETRI Journal
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• 제32권5호
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• pp.766-773
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• 2010

Support vector machine (SVM) active learning plays a key role in the interactive content-based image retrieval (CBIR) community. However, the regular SVM active learning is challenged by what we call "the small example problem" and "the asymmetric distribution problem." This paper attempts to integrate the merits of semi-supervised learning, ensemble learning, and active learning into the interactive CBIR. Concretely, unlabeled images are exploited to facilitate boosting by helping augment the diversity among base SVM classifiers, and then the learned ensemble model is used to identify the most informative images for active learning. In particular, a bias-weighting mechanism is developed to guide the ensemble model to pay more attention on positive images than negative images. Experiments on 5000 Corel images show that the proposed method yields better retrieval performance by an amount of 0.16 in mean average precision compared to regular SVM active learning, which is more effective than some existing improved variants of SVM active learning.

• 경영과정보연구
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• 제27권
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• pp.71-99
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• 2008

This study investigates the relationships between three BSC communication attributes(support of organizational culture, message valid, and knowledge sharing) and organizational learning and how that translates into relationship organizational performance in public organization. In this paper, first, past studies on BSC communication and organizational learning that identify the attributes of effective communication and organizational learning in organizational performance are reviewed. Second, a research model, key variables, and three hypotheses tested by PLS(partial least squares) are presented. The data was collected from BSC champions and managers of 53 public organizations in Korea. The results indicate, first, BSC communication (except for support of organizational culture) have not significant related to organizational performance. Therefore, H1 was not supported. Second, the structural path coefficient between support of organizational culture and message valid and organizational learning are statistically significant and in the hypothesized direction. But the knowledge sharing has not significant relationship with organizational learning. Therefore, H2 was partially supported. Third, organizational learning was significantly positively related to organizational performance. H3 was supported. Finally, organizational learning play a significantly positive role in mediating the relationship between BSC communication and organizational performance. The theoretical contributions, limitations, as well as future research directions are discussed at the end of the paper.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권8호
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• pp.578-584
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• 2004

The CSVM(Current Support Vector Machine) that is a digital architecture performing all phases of recognition process including kernel computing, learning, and recall of SVM(Support Vector Machine) on a chip is proposed. Concurrent operation by parallel architecture of elements generates high speed and throughput. The classification problems of bio data having high dimension are solved fast and easily using the CSVM. Quadratic programming in original SVM learning algorithm is not suitable for hardware implementation, due to its complexity and large memory consumption. Hardware-friendly SVM learning algorithms, kernel adatron and kernel perceptron, are embedded on a chip. Experiments on fixed-point algorithm having quantization error are performed and their results are compared with floating-point algorithm. CSVM implemented on FPGA chip generates fast and accurate results on high dimensional cancer data.

• 한국산업융합학회 논문집
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• 제25권6_2호
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• pp.1083-1090
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• 2022

Since renal cell carcinoma(RCC) has various examination and treatment methods according to clinical stage and histopathological characteristics, it is required to determine accurate and efficient treatment methods in the clinical field. However, the process of collecting and processing RCC medical data is difficult and complex, so there is currently no AI-based clinical decision support system for RCC treatments worldwide. In this study, we propose a clinical decision support system that helps clinicians decide on a precision treatment to each patient. RCC standard big database is built by collecting structured and unstructured data from the standard common data model and electronic medical information system. Based on this, various machine learning classification algorithms are applied to support a better clinical decision making.

• 한국항행학회논문지
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• 제13권3호
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• pp.426-431
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• 2009

최근 이러닝의 적용은 학습자에게 보다 편하고 효율적으로 학습을 진행할 수 있는 방법으로 변화되고 있다. 이를 위하여 PDA, 넷북, 타블렛 PC등의 이동형 기기를 통해 학습을 지원할 수 있는 U-러닝의 적용 연구가 활발히 진행되고 있다. 그러나 많은 U-러닝 프레임워크에서는 학습 콘텐츠 제작 및 처리 규격인 SCORM을 고려하지 않고 기존의 학습 콘텐츠를 이동형 기기에 맞도록 변환하는 처리만을 고려하고 있다. 본 연구에서는 SCORM을 고려한 U-러닝 학습콘텐츠 상호연결 프레임워크를 제시하였다. 이를 위하여 SCORM에서 제공하는 학습객체, 애셋 등을 통해 학습을 구성하고, 학습자의 이동형 기기 메타데이터를 통하여 해당 이동형 기기에 맞는 학습 콘텐츠로 재구성된 학습정보를 학습자에게 제공할 수 있도록 하였다.

• 문화기술의 융합
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• 제9권5호
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• pp.685-695
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• 2023

이 연구는 프랑스학 교육 분야에서 디지털 학습지원 매체가 실제적으로 어떻게 적용되고 있는지 활용 현황을 파악하고 시사점을 도출하여 향후 연구 방향을 모색하는 데 목적을 두었다. 이를 위해 프랑스학 교육 분야에서의 학습 과정별 디지털 매체가 어떻게 활용되었는지 검토하였으며, 최근 대두되고 있는 챗GPT의 활용은 외국어와 교육분야까지 확장하여 학습에 직접적 적용 현황을 확인하였다. 이상의 문헌 검토 결과, 첫째, 프랑스학 교육 분야에서는 디지털 학습지원 매체의 적용은 다소 제한적이었다. 주로 '온라인 수업 매체', '수업 전 학습', '효율적 학습 및 상호작용' 그리고 '자기주도적 학습'의 과정에 디지털 학습지원 매체를 선별적으로 활용하고 있는 것으로 나타났다. 특히 교수·학습 과정에서 '효율적 학습 및 상호작용'을 위하여 다양한 디지털 학습지원 매체가 활용되는 현황을 확인할 수 있었다. 둘째, 챗GPT의 경우, 프랑스학 교육 분야에서는 아직 연구가 진행되지 않은 것으로 나타났으며, 타 교육 분야에서 학습 과정의 실제적 적용에 관한 매우 소수의 연구가 진행되고 있었다. 챗GPT는 교수·학습자료의 개발과 학습 전 과정 및 평가, 그리고 학습자의 자기주도적 학습 측면 등 활용범위가 매우 넓고 학습자들의 긍정적 효과가 보고되었지만, 정보의 질, 출처 및 신뢰도 등의 측면에서는 윤리적 문제점도 함께 보고되고 있었다. 따라서 향후 국내 프랑스학 교육 분야에서는 이를 바탕으로 대학의 교수·학습 상황에 적합한 교육적 적용 및 그 효과 검증 그리고 디지털 학습지원 매체와 범 학문적 융합의 방향으로 이루어져야 할 필요가 있다.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제24권4호
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• pp.211-226
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• 2017

In this paper, machine learning models employed in various fields are discussed and applied to KOSPI200 stock index return forecasting. The results of hyperparameter analysis of the machine learning models are also reported and practical methods for each model are presented. As a result of the analysis, Support Vector Machine and Artificial Neural Network showed a better performance than k-Nearest Neighbor and Random Forest.

• 지식경영연구
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• 제12권1호
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• pp.17-37
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• 2011

This paper is aimed at investigating the influence of the learning organization's structural characteristics, learner's characteristics, and perceived organizational support (POS) on organizational knowledge creation. also the POS is tested as a moderator on the relationship between learner's characteristics including learning goal orientation and learning self-efficacy and organizational knowledge creation. the results are as follows. for main effect hypotheses, both connecting the organization to its environment and establishing systems to capture and share learning system representing learning organization's structural characteristics have significant positive impact on organizational knowledge creation. the POS also has a significant impact on organizational knowledge creation. However, learning goal orientation and learning self-efficacy have not significant impact on organizational knowledge creation. for moderating effect hypothesis, POS moderates the relationship between learning goal orientation and organizational knowledge creation, which means if the POS is high then learning goal orientation has more significant positive impact on it. Based on our findings, we conclude that structural characteristics of learning organization provide organizations with an opportunity of knowledge creation. in particular, interconnectedness of organization with environment and organizational knowledge sharing systems determine the ways of behaving that are related to learning within organizations. however, learner's characteristics did not have a significant effect on organizational knowledge creation, which could be interpreted due to the fact that employees are not motivated to create new knowledge if they are rarely required to involve challenging works, generate new knowledge, or share preexisted knowledge with others.