• 한국멀티미디어학회논문지
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• 제6권4호
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• pp.630-637
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• 2003

This study is designed to use the stepwise learning system for solving learner-oriented problem on the Web, which can help learners probe studying targets and contents of mathematics as well as search for a study-related materials. The study provides a Web-based Courseware design model based on the general multimedia systematic professor design model. It develops a program for remote lecture that can solve problems through interaction among a professor and the other learners. It also implements a remote teaming system for real-time environment of mathematical problems oriented by the learner. The system designed either as a Web-based mathematical Courseware or as a text mode has the purpose of providing a Web-based stepwise learning system for solving mathematical problems oriented by the learner.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제57차 동계학술대회논문집 26권1호
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• pp.93-94
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• 2018

Software Defined Networking(SDN)은 데이터 부와 컨트롤 부를 나눠 관리하는 혁신적인 방식이다. SDN 환경에서가 아닌 기존의 IP 네트워크에서의 트래픽 분류는 많은 연구가 진행되어 왔다. 트래픽 분류 방법에는 Port 번호를 이용한 트래픽 분류 방법, Payload를 이용한 트래픽 분류 방법, Machine Learning을 이용한 트래픽 분류 방법 등이 있다. 본 논문에서는 Port 번호, Payload, Machine Learning을 이용한 트래픽 분류 방법들을 소개 및 장단점을 설명하고 SDN 환경에서 Machine Learning을 이용한 좀 더 정확한 트래픽 분류 방법을 제안한다.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.10-19
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• 2022

AI-based Network Intrusion Detection Systems (AI-NIDS) detect network attacks using machine learning and deep learning models. Recently, unsupervised AI-NIDS methods are getting more attention since there is no need for labeling, which is crucial for building practical NIDS systems. This paper aims to test the impact of designing autoencoder models that can be applied to unsupervised an AI-NIDS in real network systems. We collected security events of legacy network security system and carried out an experiment. We report the results and discuss the findings.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.125-136
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• 2021

The development of systems that can generate automatically instructional material is a challenging goal for the e-learning community. These systems pave the way towards large scale e-learning deployment as they produce instruction on-demand for users requesting to learn about any topic, anywhere and anytime. However, realizing such systems is possible with the availability of vast repositories of web information in different formats that can be searched, reused and integrated into information-rich environments for interactive learning. This paradigm of learning relieves instructors from the tedious authoring task, making them focusing more on the design and quality of instruction. This paper presents a mobile learning system (Mole) that supports the generation of instructional material in M-Learning (Mobile Learning) contexts, by reusing and integrating heterogeneous hypermedia web resources. Mole uses open hypermedia repositories to build a Learning Web and to generate learning objects including various hypermedia resources that are adapted to the user context. Learning is delivered through a nice graphical user interface allowing the user to navigate conveniently while building their own learning path. A test case scenario illustrating Mole is presented along with a system evaluation which shows that in 90% of the cases Mole was able to generate learning objects that are related to the user query.

• 한국소프트웨어감정평가학회 논문지
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• 제14권1호
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• pp.9-17
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• 2018

기업과 개인 여러 방면에서 오픈소스 SW의 사용과 중요성은 날이 갈수록 증가하고 있다. 그러나 사용자를 위한 소프트웨어 서비스인 소프트웨어 평가, 추천, 필터링들의 기반 연구인 소프트웨어 분류에 대해서 오픈소스 SW의 특성에 맞게 유연하게 대처하지 못하고 고정된 분류 체계를 사용하고 있다. 본 연구에서는 오픈소스 SW를 대상으로 분류에 대한 조사와 새로운 오픈소스 SW 범주에 대해서 유연하게 대처할 수 있는 머신러닝 기반의 오픈소스 카테고리 분류 모델에 대해 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4028-4042
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• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1300-1304
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• 2003

The following paper deals with a control problem of a mobile robot in home network environment. The home network causes the mobile robot to communicate with sensors to get the sensor measurements and to be adapted to the environment changes. To get the improved performance of control of a mobile robot in spite of the change in home network environment, we use the fuzzy inference system with multiple reward reinforcement learning. The multiple reward reinforcement learning enables the mobile robot to consider the multiple control objectives and adapt itself to the change in home network environment. Multiple reward fuzzy Q-learning method is proposed for the multiple reward reinforcement learning. Multiple Q-values are considered and max-min optimization is applied to get the improved fuzzy rule. To show the effectiveness of the proposed method, some simulation results are given, which are performed in home network environment, i.e., LAN, wireless LAN, etc.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1015-1033
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• 2020

With promising results and enormous capability, deep learning technology has attracted more and more attention to both theoretical research and applications for a variety of image processing and computer vision tasks. In this paper, we investigate 32 research contributions that apply deep learning techniques to the agriculture domain. Different types of deep neural network architectures in agriculture are surveyed and the current state-of-the-art methods are summarized. This paper ends with a discussion of the advantages and disadvantages of deep learning and future research topics. The survey shows that deep learning-based research has superior performance in terms of accuracy, which is beyond the standard machine learning techniques nowadays.

• 한국정보시스템학회지:정보시스템연구
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• 제32권4호
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• pp.189-210
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• 2023

Purpose This study aims to investigate factors that positively influence the continuous Artificial Intelligence(AI) Learning Continuity of business major students. Design/methodology/approach To evaluate the impact of AI education, a survey was conducted among 119 business-related majors who completed a software/AI course. Frequency analysis was employed to examine the general characteristics of the sample. Furthermore, factor analysis using Varimax rotation was conducted to validate the derived variables from the survey items, and Cronbach's α coefficient was used to measure the reliability of the variables. Findings Positive correlations were observed between business major students' AI Learning Continuity and their AI Interest, AI Awareness, and Data Analysis Capability related to their majors. Additionally, the study identified that AI Project Awareness and AI Literacy Capability play pivotal roles as mediators in fostering AI Learning Continuity. Students who acquired problem-solving skills and related technologies through AI Projects Awareness showed increased motivation for AI Learning Continuity. Lastly, AI Self-Efficacy significantly influences students' AI Learning Continuity.

• 정보교육학회논문지
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• 제19권4호
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• pp.499-508
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• 2015

정보 사회로 발전하면서 세계의 주요 나라에서는 정보 교과의 교육을 강화해 가고 있으며 최근에는 소프트웨어 교육을 확대한 정보교과 교육과정이 제시되고 있다. 따라서 한국정보교육학회에서는 소프트웨어 교육을 위한 표준 모델에 대하여 연구해서 2014년에 발표된 표준 모델을 보완하여 발표하였다. 여기에서는 소프트웨어 교육 내용을 '소프트웨어', '컴퓨터 시스템' 및 '융합 활동'의 세 영역으로 구분하는 소프트웨어 교육 표준 모델을 제시하였다. 이에 따라 본 논문에서는 한국정보교육학회에서 제시한 소프트웨어 교육 표준모델의 '소프트웨어' 대영역의 '정보' 영역에 대한 초등학교의 성취 기준을 단계별로 마련하였다. 그리고 이 성취 기준을 도달하기 위한 교수-학습 방법과 평가 방안을 단계별로 세분화하여 제시하였다. 이와 같이 본 연구에서 제시한 '정보' 영역에 대한 성취 기준, 교수 학습 방법, 평가 방법은 향후 초등학교 소프트웨어 교육 과정 개발에 기여할 것으로 기대된다.