• 한국실내디자인학회:학술대회논문집
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• 한국실내디자인학회 2005년도 춘계학술발표대회 논문집
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• pp.281-282
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• 2005

The present design, which is the result of English Zone Project for 'I' Girls' High School in Yeonsu dong, Incheon, purposed to produce atmosphere like a cafe so that students can attempt more comfortable and diverse learning methods, breaking away from the structure and atmosphere of traditional language labs while providing functions such as experiential learning, teaching learning and native speaker conversation. In addition, it applied colors close to primary colors so that students throw away their fixed idea of language lab as a special class and access it easily at any time. Moreover, it was designed for the maximum changeability using foldable and portable furniture so that various types of group study can be performed. Ultimately the design project is expected to suggest methods of experiential learning distinguished from existing knowledge delivering education as it provides teaching learning methods beyond simple interior design.

• 정보학연구
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• 제1권1호
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• pp.65-84
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• 1998

메모리 기반 추론 기법은 분류시 입력 패턴과 저장된 패턴들 사이의 거리를 이용하는 교사 학습 기법으로써, 거리 기반 학습 알고리즘이라고도 한다. 메모리 기반 추론은 k_NN 분류기에 기반한 것으로, 학습은 추가 처리 없이 단순히 학습 패턴들을 메모리에 저장함으로써 수행된다. 본 논문에서는 기존의 k-NN 분류기보다 효율적인 분류가 가능하고, 점진적 학습 기능을 갖는 새로운 알고리즘을 제안한다. 또한 제안된 기법은 노이즈에 민감하지 않으며, 효율적인 메모리 사용을 보장한다.

• 전기전자학회논문지
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• 제22권4호
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• pp.948-952
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• 2018

Haze removal is an object of scientific desire due to its various practical applications. Existing algorithms are founded upon histogram equalization, contrast maximization, or the growing trend of applying machine learning in image processing. Since machine learning-based algorithms solve problems based on the data, they usually perform better than those based on traditional image processing/computer vision techniques. However, to achieve such a high performance, one of the requisites is a large and reliable training database, which seems to be unattainable owing to the complexity of real hazy and haze-free images acquisition. As a result, researchers are currently using the synthetic database, obtained by introducing the synthetic haze drawn from the standard uniform distribution into the clear images. In this paper, we propose the enhanced equidistribution, improving upon our previous study on equidistribution, and use it to make a new database for training machine learning-based haze removal algorithms. A large number of experiments verify the effectiveness of our proposed methodology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2942-2960
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• 2022

The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels.

• KEPCO Journal on Electric Power and Energy
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• 제6권4호
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• pp.399-404
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• 2020

There are many attempts to apply AI technology to diagnose facilities or improve the work efficiency of the power industry. The emergence of new machine learning technologies, such as deep learning, is accelerating the digital transformation of the power sector. The problem is that traditional power systems face security risks when adopting state-of-the-art AI systems. This adoption has convergence characteristics and reveals new cybersecurity threats and vulnerabilities to the power system. This paper deals with the security measures and implementations of the power system using machine learning. Through building a commercial facility operations forecasting system using machine learning technology utilizing power big data, this paper identifies and addresses security vulnerabilities that must compensated to protect customer information and power system safety. Furthermore, it provides security guidelines by generalizing security measures to be considered when applying AI.

• 전자통신동향분석
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• 제38권5호
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• pp.51-60
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• 2023

Quantum machine learning (QML) is an area of quantum computing that leverages its principles to develop machine learning algorithms and techniques. QML is aimed at combining traditional machine learning with the capabilities of quantum computing to devise approaches for problem solving and (big) data processing. Nevertheless, QML is in its early stage of the research and development. Thus, more theoretical studies are needed to understand whether a significant quantum speedup can be achieved compared with classical machine learning. If this is the case, the underlying physical principles may be explained. First, fundamental concepts and elements of QML should be established. We describe the inception and development of QML, highlighting essential quantum computing algorithms that are integral to QML. The advent of the noisy intermediate-scale quantum era and Google's demonstration of quantum supremacy are then addressed. Finally, we briefly discuss research prospects for QML.

• 과학교육연구지
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• 제38권2호
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• pp.302-316
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• 2014

본 연구는 6학년 1학기 산과 염기 단원 중 5차시를 Small-Scale Chemistry를 적용한 실험 수업 프로그램으로 개발하였다. 개발한 프로그램 적용을 경남 창원시에 소재한 Y초등학교 6학년 3개반은 SSC를 활용한 과학수업(n=86)을, 3개 반은 전통적인 실험 수업(n=87)을 진행한 후 학생들의 과학 학업성취도와 과학 학업성취도에 미치는 영향을 알아보았다. 개발한 수업 프로그램을 학생들에게 적용하기에 앞서, 중간학력평가 과학 학업성취도 점수에 대한 t검증을 통해 실험집단과 비교집단 간의 동질성을 확인하였고 실험집단은 2인 1조 또는 개별로, 비교집단은 6명 1모둠으로 구성하여 5차시에 걸쳐 수업을 진행하였다. 그 결과 t-검증을 통한 과학 학업성취도에서 유의확률 0.034로 유의수준 0.05에서 실험집단과 비교집단 사이에 유의미한 차이가 있었다. 추가적으로 전통적인 실험을 한 1개 반과 2명 1조 SSC 적용 실험 수업을 한 1개 반, 개별 SSC 적용 실험 수업을 한 1개 반의 과학 학업성취도를 살펴보았다. 일원배치 분산분석을 통해 살펴 본 결과 F 통계값 3.759, 유의확률 0.027로 유의수준 0.05에서 유의미한 차이가 있었으며, 전통적인 실험반의 평균은 67.58, 2인 1조 SSC 적용 실험반은 75.86, 개별 SSC 적용 실험반은 80.89로 개별 SSC 적용 실험반에서 과학 학업성취도가 가장 높았다. 또한 SSC를 적용한 실험 수업 프로그램을 준비할 때 교사는 수업 준비 및 수업 시간에 대한 부담이 줄었으며, 수업시간 동안 학생활동을 적극적으로 도와 줄 수 있었을 뿐 아니라 학생들의 실험활동도 적극적으로 이루어졌다. 이러한 결과를 통해 SSC 적용 실험 수업 프로그램 개발은 의미가 있으며 기존의 전통적인 실험 방법보다 학생들의 과학 학업성취도를 향상시킬 수 있음을 제시하였다.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.325-333
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• 2016

The development of smart technology has brought the conversion of closed traditional e-learning contents into open flexible smart learning contents consisting of learner-centered modules, without the constraints of time and space by use of smart devices from the uniformed and passive classroom between teachers and learners. It has been demanded an open, personalized and customized teaching and learning contents of smart education and training systems according to wide supply of various smart devices. In this paper, we discuss about the status of the smart teaching and learning systems and analyze the characteristics and structure of the web contents for smart education and training systems by use of smart devices. And we propose a method how to block web contents, to extract them, and adapt personalized segments of web contents by adaptive algorithm into smart learning devices. We extract blocks from the web contents based on the smart device information and the preference information of the learners from existing web contents without the hassle of learners environment. After specifying a block priority from the extracted web contents by the adaptive segment algorithm, it can be displayed directly to the screen to fit the individual learning progress of the learners.

• 기본간호학회지
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• 제24권3호
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• pp.181-188
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• 2017

Purpose: Competency in physical assessment is an important component of nursing practice. However, some physical assessment skills are not being utilized within the current teacher-centered, content-heavy curriculum. This study was conducted to identify the effects of student-centered, self-directed learning in the physical assessment class. Methods: An experimental study with a post-test only control group design was used to compare an intervention group that was provided self-directed learning classes and a control group that was provided traditional lecture and practice classes. Competency in physical assessment, academic self-confidence, and learning satisfaction were evaluated. Collected data were analyzed using $x^2$-test (Fisher's exact test) and independent t-test. Results: Competency in physical assessment was significantly higher in the experimental group. However, academic self-confidence and learning satisfaction were not significantly different between the groups. Conclusion: The findings in this study indicate that self-directed learning can improve nursing students competency in physical assessment and that self-directed learning is a good education method to improve nursing students' competency in physical assessment during clinical practice and perform quality patient care by making active use of physical assessment skills.

• 정보교육학회논문지
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• 제20권3호
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• pp.313-322
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• 2016

초등학생을 대상으로 샌드박스형 게임인 마인크래프트 에듀를 활용한 게임기반학습을 적용하여 창의적 문제해결력과 학습몰입도에 미치는 영향을 분석하였다. 그 결과 기존의 전통적인 강의식 교수법보다 샌드박스형 게임을 활용한 게임기반학습을 적용했을 때 창의적 문제해결력과 학습몰입도에서 긍정적인 효과가 있는 것으로 나타났다. 창의적 문제해결력은 모든 하위요소에서 유의미한 차이가 나타났으며 학습몰입도는 통제감, 시간 감각의 왜곡을 제외한 하위요소에서 유의미한 차이가 나타났다.