• Proceedings of the KAIS Fall Conference
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• 2010.05b
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• pp.937-940
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• 2010

21세기 정보화 세계화 사회에서 창의적이고 자율적인 인간양성을 위한 교육개혁으로 창의적 인재양성, 자기주도적 학습능력 배양이 강조되어지고 있다. 그럼에도 불구하고 제주지역 농어촌의 경우 인구 집중이 도시로 이루어지면서 농어촌의 인구가 감소되어지고 그러한 결과로 소규모 학교는 통폐합이되면서 교육여건은 악화되고 있는 실정이다. 이에 제주지역 농어촌 교육문제를 해소하여 학생들의 이농현상을 줄일 수 있으며, 창의적이고 자기주도적인 학습 능력을 배양할 수 있는 프로그램 개발의 필요성을 느끼게 되었다. 본 연구에서는 도시와 농어촌의 서로 다른 교육여건에 기반하여 교육내용을 특성화함으로써 제주지역 농어촌 학생의 창의적 문제해결력 향상을 위한 통섭적 활동 모형과 프로그램을 개발하기 위한 이론적 고찰을 하였다. 도시와 농어촌의 학력격차를 해소하기 위해서는 농어촌 학생에게 적절한 교육내용과 활동으로 재편성하여 가르칠 필요가 있다. 특히 제주도는 농촌과 어촌, 도시형이 특별히 구분되어지는 것이 아니라 하나의 통합된 생활문화의 특징을 가지고 있다. 앞으로 본 연구의 이론적 고찰을 바탕으로 제주지역 농어촌 학생들의 일상적인 생활환경에서 접하게 되는 다양한 활동 속에서 창의적으로 문제해결을 할 수 있도록 통섭적 활동 모형과 프로그램을 개발하여 적용하게 되면 학생의 역량을 강화시키고 잠재해 있는 창의적 문제해결력을 개발하여 학력향상에 기여할 수 있을 것이다.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1155-1163
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• 2009

This paper proposes an automatic facial expression generation system of vector graphic character using gaussian process model. Proposed method extracts the main feature vectors from twenty-six facial data of character redefined based on Russell's internal emotion state. Also by using new gaussian process model, SGPLVM, we find low-dimensional feature data from extracted high-dimensional feature vectors, and learn probability distribution function (PDF). All parameters of PDF are estimated by maximization the likelihood of learned expression data, and these are used to select wanted facial expressions on two-dimensional space in real time. As a result of simulation, we confirm that proposed facial expression generation tool is working in the small facial expression datasets and can generate various facial expressions without prior knowledge about relation between facial expression and emotion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.595-598
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• 2009

최근 영재 및 영재교육에 관련된 연구가 다방면에서 진행되고 있으며, 초기에 수학 및 과학 분야 위주로 이루어졌던 영재교육은 정보, 발명, 인문, 예술 등의 기타 분야로 점차 확대되어 가고 있다. 사회적으로는 고도화된 정보화 사회로의 진행과 더불어 정보과학에서도 영재교육데 대한 관심과 중요성이 커지고 있다. 그러나 정보과학의 학문적 역사가 짧고 그 범위의 설정이 어려운 만큼 정보과학 분야의 영재교육에 있어서도 대상자의 선발과 교육이 어려운 것이 사실이다. 특히 영재교육 대상자의 선정과 교육에 필수적인 평가 방식에 대한 학문적 연구가 부족하여 교육 방식의 보완과 창의적인 대상자 선발에 있어 개선에 대한 목소리가 높다. 이에 본 연구에서는 여러 형태의 평가 방식 중 관찰평가가 평가도구로서 어떻게 작용하는지 다면 평가의 측면에서 지필평가와 보완적 작용을 하는지에 대해 연구하였다. 이를 위해 2년간의 학습자들의 지필평가 성적과 관찰평가 중 리커트 척도 방식의 체크리스트와 서술형 관찰 기록지 사이의 상관관계를 통계적으로 분석 하였다. 또한 항목간의 상관관계를 알아보기 위해 체크리스트와 서술형 관찰기록지의 하위 항목간의 상관관계를 분석하였다. 연구 결과 체크리스트의 하위항목 분석을 통해서는 태도와 문제해결 능력 간의 상관관계, 수학적인지영역과 문제해결 능력 간의 유의미한 상관 관계를 알 수 있었으며, 서술형 관찰 기록지 분석을 통해서는 투입 프로그램 적응 능력이라 할 수 있는 과정적 영역은 정의적 영역과 인지적 영역의 상관 관계가 중요함을 알 수 있었다. 또한 평가 방식간의 상관 관계는 지필 평가와 관찰 평가의 유의미한 연관성이 없다는 것이 밝혀졌다. 즉, 정보과학 분야 영재교육 학습자의 잠재 능력이나 사회성, 창의성, 문제해결력 등을 평가하기 위해서는 지필평가와 더불어 관찰평가가 반드시 필요하며 다면평가의 측면에서 상호 보완적인 역할을 한다는 것이다.

• (The) Korean Journal of Educational Psychology
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• v.31 no.2
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• pp.265-282
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• 2017

The purpose of this study was to applicate systematic RTI educational service by providing reading fluency program to identify high-risk students with dyslexia of a shadow zone based on their growth rate. Twenty-two students of 1st to 5th graders were selected as study subjects through "2016 Kyungi-Do Project of the Dyslexia Excellence Program". An individualized reading fluency program was provided through 8-10 sessions over a period of 3 months. The program was developed based on evidence-based reading strategies with the consideration of each student's educational needs. As results, three groups were identified with their learning growth rates: concerned, improving, and discrepancy groups. The study identified three students in a discrepancy group as students with dyslexia. Based on these results, implications and suggestions for further educational identification process along with effect programs were discussed.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.12-19
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• 2021

For strengthening the national defense, the function of tactical network is essential. tactics and strategies in wartime situations are based on numerous information. Therefore, various reconnaissance devices and resources are used to collect a huge amount of information, and they transmit the information through tactical networks. In tactical networks that which use contention based channel access scheme, high-speed nodes such as recon aircraft may have performance degradation problems due to unnecessary channel occupation. In this paper, we propose a learning-backoff method, which empirically learns the size of the contention window to determine channel access time. The proposed method shows that the network throughput can be increased up to 25% as the number of high-speed mobility nodes are increases.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.14-21
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• 2021

Green networking has become a issue to reduce energy wastes and CO2 emission by adding energy managing mechanism to wired data networks. Energy consumption of the overall wired data networks is driven by access networks, expect for end devices. However, on a global scale, it is more difficult to manage centrally energy, measure and model the real energy use and energy savings potential of the access networks. This paper presented the multiple linear regression model to predict energy consumption of wired access networks using supervised learning of machine learning with data collected by existing investigated materials, actual measured values and results of many models. In addition, this work optimized the performance of it by various experiments and predict energy consumption of wired access networks. The performance evaluation of the regression model was achieved by well-knowned evaluation metrics.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.351-360
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• 2022

Image blurring is a phenomenon that occurs due to factors such as movement of a subject and shaking of a camera. Recently, the research for image deblurring has been actively conducted based on convolution neural networks. In particular, the method of guiding the restoration process via the difference between blur and sharp images has shown the promising performance. This paper proposes a novel method for improving the deblurring performance based on the distortion information. To this end, the transformer-based neural network module is designed to guide the restoration process. The proposed method efficiently reflects the distorted region, which is predicted through the global inference during the deblurring process. We demonstrate the efficiency and robustness of the proposed module based on experimental results with various deblurring architectures and benchmark datasets.

• Journal of Creative Information Culture
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• v.6 no.1
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• pp.23-30
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• 2020

This study used the text mining method to analyze the research trend of the Journal of Creative Information Culture(JCIC) which is the journal of convergence. The existing research trend analysis method has a limitation in that the researcher's personality is reflected using the traditional content analysis method. In order to complement the limitations of existing research trend analysis, this study used topic modeling. The English abstract of the paper was analyzed from 2015 to 2019 of the JCIC. As a result, the word that appeared most in the JCIC was "education," and eight research topics were drawn. The derived subjects were analyzed by educational subject, educational evaluation, learner's competence, software education and maker culture, information education and computer education, future education, creativity, teaching and learning methods. This study is meaningful in that it analyzes the research trend of the JCIC using text mining.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.500-510
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• 2023

Passive sonar signals mainly contain both normal and abnormal signals. The abnormal signals mixed with normal signals are primarily detected using an AutoEncoder (AE) that learns only normal signals. However, existing AEs may perform inaccurate detection by reconstructing distorted normal signals from mixed signal. To address these limitations, we propose an abnormal signal detection model based on a Recurrent Neural Network (RNN) and vector quantization. The proposed model generates a codebook representing the learned latent vectors and detects abnormal signals more accurately through the proposed search process of code vectors. In experiments using publicly available underwater acoustic data, the AE and Variational AutoEncoder (VAE) using the proposed method showed at least a 2.4 % improvement in the detection performance and at least a 9.2 % improvement in the extraction performance for abnormal signals than the existing models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.281-288
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• 2024

The sixth generation(6G) wireless communication technology is advancing toward ultra-high speed, ultra-high bandwidth, and hyper-connectivity. With the development of communication technologies, the formation of a hyper-connected society is rapidly accelerating, expanding from the IoT(Internet of Things) to the IoE(Internet of Everything). However, at the same time, security threats targeting IoT devices have become widespread, and there are concerns about security incidents such as unauthorized access and information leakage. As a result, the need for security-enhancing solutions is increasing. In this paper, we implement an autoencoder-based anomaly detection model utilizing real-time collected network traffics in respond to IoT security threats. Considering the difficulty of capturing IoT device traffic data for each attack in real IoT environments, we use an unsupervised learning-based autoencoder and implement 6 different autoencoder models based on the use of noise in the training data and the dimensions of the latent space. By comparing the model performance through experiments, we provide a performance evaluation of the anomaly detection model for detecting abnormal network traffic.