• 한국정보전자통신기술학회논문지
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• 제8권2호
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• pp.163-168
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• 2015

지적 장애학생을 대상으로 하는 기존의 주입 및 일방적 학습 방법은 특수교육 성과에서 큰 한계를 노출하고 있다. 따라서 본 연구에서는 증강현실 기술과 다양한 사용자 인터랙션 기술을 활용하여 학습자 스스로가 콘텐츠를 조작하고 다양한 영상콘텐츠를 접하면서 학습에 몰입할 수 있는 디지털 마이크로 미러 시스템을 제안하였다. 본 논문에서 제안한 시스템을 상용화하여 특수교육 현장에서 수행한 전문가 검증 결과, 본 논문에서 제안한 시스템은 몰입감을 높여 학습효과를 증진시킬 수 있다는 점에서 특수교육에 매우 유용하다는 것을 확인하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1867-1870
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• 2002

In the present paper, we will focus on the characterization of the biological network behaviour, in terms of synchronization and desynchronization of the measured signals by Micro Electrode array. We evaluate a easy calculable estimator that implies de/synchronization property of the biological neural network.

• KDI Journal of Economic Policy
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• 제43권4호
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• pp.1-21
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• 2021

This paper aims to investigate whether there is empirical evidence supporting the learning-to-export hypothesis, which has received little attention in the literature. By taking full advantage of plant-product level data from Korea during 1990-1998, we find some evidence for the learning-to-export effect, especially for the innovated product varieties with delayed exporters: their productivity, together with research and development and investment activity, was superior to their matched sample. On the other hand, this learning-to-export effect was not significantly pronounced for industries protected by import tariffs. Thus, our empirical findings suggest that it would be desirable to implement certain policy tools to promote the learning-to-export effect, whereas tariff protection is not justifiable for that purpose.

• 실천공학교육논문지
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• 제15권1호
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• pp.169-181
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• 2023

본 연구는 직업능력개발훈련 교·강사의 보수교육과정을 살펴보고, 마이크로 크리덴셜을 적용하기 위한 방안을 살펴보기 위해 수행되었다. 이를 위해 K대학교 능력교육개발원의 직업능력개발훈련 교·강사의 보수교육과정 현황, 마이크로 크리덴셜의 특징, 보수교육과정과 마이크로 크리덴셜의 연계가능성을 살펴보고, 스타훈련교사의 인터뷰를 실시하여 분석한 결과는 다음과 같다. 첫째, 디지털 인증서에 대한 인식은 훈련과정 이수시 디지털 인증서인 디지털 크레딧, 디지털 뱃지 발급과 보수교육의 공인된 자격과정에 대한 인식은 대부분 긍정적으로 인식하고 있었다. 또한, 보수교육의 마이크로 크리덴셜 적용방안으로 다양한 사례로 보수교육보다 혜택을 주는 방안, 보수교육의 체계화, 자격과정의 체계화 및 등급화, 자격과정의 학점화 등을 제안하였다. 둘째, 마이크로 크리덴셜을 적용한 보수교육 활용성 제고를 위한 제도적 보완장치로 NCS기반 전공 보수교육 확대 필요성, 효율적인 학습콘텐츠와 학습방법 제공, 최소 이수시간 설정 등을 언급하였다. 그리고, 직업능력개발훈련 교·강사의 이해도 제고방안으로 가장 많이 응답한 내용은 마이크로 크리덴셜 홍보방안이었다. 셋째, 보수교육기관과 직업능력개발훈련 교·강사의 역할에서 보수교육기관은 교육품질 유지에 대해 가장 많이 언급하고 있으며, 직업능력개발훈련 교·강사의 역할로 가장 많이 응답한 내용은 적극적 참여였다. 본 연구로 직업능력개발훈련 교·강사의 보수교육과정과 마이크로 크리덴셜 간의 자격연계로 전문성 제고와 실무역량 이력 포트폴리오를 실질적으로 제공할 수 있는 직업훈련 환경구축을 기대한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.174-176
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• 2005

This treatise studies composing process that renew information mastered by interactive experience between environment and system via network among individuals. In the previous study map information regarding free space is learned by using of reinforced learning algorithm, which enable each individual to construct optimal action policy. Based on those action policy each individuals can obtain optimal path. Moreover decision process to distinguish best optimal path by comparing those in the network composed of each individuals is added. Also information about the finally chosen path is being updated. A self renewing method of each system information by sharing the each individual data via network is proposed Data enrichment by shilling the information of many maps not in the single map is tried Numerical simulation is conducted to confirm the propose concept. In order to prove its suitability experiment using micro-mouse by integrating and comparing the information between individuals is carried out in various types of map to reveal successful result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1258-1275
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• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

• 산업공학
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• 제20권4호
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• pp.448-457
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• 2007

The small & micro business has the characteristics of both consumer credit risk and business credit risk. In predicting the bankruptcy for small-micro businesses, the problem is that in most cases, the financial data for evaluating business credit risks of small & micro businesses are not available. To alleviate such problem, we propose a bankruptcy prediction mechanism using the credit card sales information available, because most small businesses are member store of some credit card issuers, which is the main purpose of this study. In order to perform this study, we derive some variables and analyze the relationship between good and bad signs. We employ the new statistical learning technique, support vector machines (SVM) as a classifier. We use grid search technique to find out better parameter for SVM. The experimental result shows that credit card sales information could be a good substitute for the financial data for evaluating business credit risk in predicting the bankruptcy for small-micro businesses. In addition, we also find out that SVM performs best, when compared with other classifiers such as neural networks, CART, C5.0 multivariate discriminant analysis (MDA), and logistic regression.

• Journal of East Asia Management
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• 제4권2호
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• pp.39-62
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• 2023

Online and offline mixed teaching mode has become an important way to promote the connotative development of higher education. Under the background that offline teaching has become mature, in order to further promote the development of online education, and promote the implementation of the mixed teaching mode, to mix and to provide basis for the construction of the mixed teaching mode, this study takes the online learning effect as the evaluation basis, adopts the online questionnaire survey to conduct statistical analysis of the online learning behavior of 2213 college students, and discusses the differentiation phenomenon of online learning groups from the micro, meso and macro perspectives. It is found that there are significant differences in the online learning effect of college students in terms of the type of learning platform, whether the school implements the online offline mixed teaching mode, education background, grade (bachelor's degree), and region. Colleges and universities should strengthen the promotion of online and offline mixed teaching mode; The online learning platform should improve the platform function and strengthen the functional differentiation design of learning resources for students. Education departments pay attention to the learning effect of online learners in different regions, and bridge the gap in regional education.

• 한국정보통신학회논문지
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• 제23권5호
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• pp.533-539
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• 2019

본 연구는 본 연구는 Microsoft Malware Classification Challenge 데이터 셋을 사용해 임의의 길이 입력 데이터에 대응할 수 있는 멀웨어 분류 모델을 제안한다. 우리는 기존 연구의 멜웨어 데이터를 이미지화 시키는 것을 기반으로 한다. 제안 모델은 멀웨어 데이터가 큰 경우는 많은 이미지를 생성하고, 작은 데이터는 적은 이미지를 생성한다. 생성된 이미지를 시계열 데이터로 Dynamic RNN으로 학습시킨다. RNN의 출력 값은 Attention 기법을 응용해 가장 가중치가 높은 출력만 사용하고, RNN 출력값을 다시 Residual CNN으로 학습시켜 최종적으로 멀웨어를 분류한다. 제안모델을 실험한 결과 검증 데이터 셋에서 Micro-average F1 score 92%를 기록하였다. 실험 결과 특별한 특징 추출 및 차원 축소 없이 임의 길이의 데이터를 학습 및 분류할 수 있는 모델의 성능을 검증할 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1309-1314
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• 2003

If an agent has a learning ability using previous knowledge, then it is expected that the agent can speed up learning by interacting with environment. In this paper, we present an improved reinforcement learning algorithm using domain knowledge which can be represented by problem-independent features and their classifiers. Here, neural networks are employed as knowledge classifiers. To show the validity of our proposed algorithm, computer simulations are illustrated, where navigation problem of a mobile robot and a micro aerial vehicle(MAV) are considered.