• ETRI Journal
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• 제42권2호
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• pp.292-304
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• 2020

As side-channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side-channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning-based side-channel analysis. In particular, we outline how deep learning is applied to side-channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

• Smart Structures and Systems
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• 제24권2호
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• pp.233-242
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• 2019

In this paper, an inverse approach based on uniform load surface (ULS) is presented for structural damage localization and quantification. The ULS is excellent approximation for deformed configuration of a structure under distributed unit force applied on all degrees of freedom. The ULS make use of natural frequencies and mode shapes of structure and in mathematical point of view is a weighted average of mode shapes. An objective function presented to damage detection is discrepancy between the ULS of monitored structure and numerical model of structure. Solving this objective function to find minimum value yields damage's parameters detection. The teaching-learning based optimization algorithm has been employed to solve inverse problem. The efficiency of present damage detection method is demonstrated through three numerical examples. By comparison between proposed objective function and another objective function which make use of natural frequencies and mode shapes, it is revealed present objective function have faster convergence and is more sensitive to damage. The method has good robustness against measurement noise and could detect damage by using the first few mode shapes. The results indicate that the proposed method is reliable technique to damage detection in structures.

• 컴퓨터교육학회논문지
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• 제17권1호
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• pp.51-63
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• 2014

수업 목표는 수업 시간에 제시되는 목표로 수업 설계의 기본 방향과 틀을 제공하여 효과적인 교수 학습을 설계할 수 있게 해주고, 수업 평가의 지침이 된다. 다른 교과에서는 Bloom의 교육목표 분류법의 문제점을 수정한 Anderson의 분류법을 제시하고, 이를 통해 교과의 수업목표와 평가의 성취수준을 분석하는 연구가 최근 활발히 진행되고 있다. 이에 본 연구는 Anderson의 분류법이 정보 교과의 수업목표 진술에 적절한지 정보 교사의 설문을 통해 확인하고, Anderson의 분류법으로 중학교 정보 교과서 6종의 수업목표를 분석하였다. 설문 결과를 통해 Anderson의 분류법이 Bloom의 분류법보다 정보 교과의 목표 진술에 더 유용하다는 결과를 얻었고, 정보 교과서 6종의 분석 결과 개념적 지식과 절차적 지식, 이해하다와 적용하다의 목표들이 더 많이 제시되어 있음을 확인하였다. 본 연구 결과가 교육목표 분류법 연구와 교과서 개발에 좋은 사례 연구가 될 것으로 전망한다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제27권3호
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• pp.302-313
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• 2000

영한 기계번역에서 영어 문장의 동사구를 한국어로 정확하게 번역하기 위해서는 일반적으로 타동사와 목적어의 연어 관계를 이용한다. 본 논문에서는 k-최근점(k-nearest neighbor) 학습을 연어 관계에 적용하여 동사 번역을 선택하는 알고리즘을 제시하였는데 k-최근점 학습을 위해서 워드넷에서의 의미거리를 정의하여 사용하였다. 그리고 실시간 번역 시스템에 사용될 사전을 구성하기 위하여, 말뭉치로부터 타동사-목적어 쌍을 추출하여 학습예제를 구축하고, 이 예제의 크기를 번역률과 연관시켜 최적화시키는 알고리즘을 제시한다. 본 논문에서는 위의 알고리즘들을 사용하여 동사 'build'의 번역률을 약 90%로 유지하면서 사전의 크기를 최적화하였다.

• 대한예방한의학회지
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• 제16권1호
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• pp.31-42
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• 2012

Objective : To set up the concept and the category of the medical humanities in Korean medicine education through researching and analysing the curriculum of 12 colleges and school of Korean medicine (KM) in Korea. Methods : We collected self-evaluation reports from 12 KM institutions, and analyzed subjects regarding medical humanities and social medicine. The subjects' relevance with medical humanities was verified using the learning objectives of KOMEEI(Korea Oriental Medicine Education and Evaluation Institute). The number of relevant subjects, the credits and educational hours, and the time of opening, etc. were analysed. Results : 12 KM institutions provide 44 subjects as medical humanities and social medicine related subjects. Among them, 17 subjects were corresponded to the actual learning objective of medical humanities. These subjects account for an average of 7% in total curriculum. Most of the subjects are required courses for premedical students and the fourth year students of medical school. Conclusions : This paper suggests the public discussion on the learning objective and the categories of the medical humanities education in KM institutions. Further studies on developing the educational contents and evaluation tools are also needed to produce good doctors with ability and personality.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3277-3290
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• 2023

Nuclear power plant (NPP) operations with multiple objectives and devices are still performed manually by operators despite the potential for human error. These operations could be automated to reduce the burden on operators; however, classical approaches may not be suitable for these multi-objective tasks. An alternative approach is deep reinforcement learning (DRL), which has been successful in automating various complex tasks and has been applied in automation of certain operations in NPPs. But despite the recent progress, previous studies using DRL for NPP operations have limitations to handle complex multi-objective operations with multiple devices efficiently. This study proposes a novel DRL-based approach that addresses these limitations by employing a continuous action space and straightforward binary rewards supported by the adoption of a soft actor-critic and hindsight experience replay. The feasibility of the proposed approach was evaluated for controlling the pressure and volume of the reactor coolant while heating the coolant during NPP startup. The results show that the proposed approach can train the agent with a proper strategy for effectively achieving multiple objectives through the control of multiple devices. Moreover, hands-on testing results demonstrate that the trained agent is capable of handling untrained objectives, such as cooldown, with substantial success.

• 수산해양교육연구
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• 제24권2호
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• pp.272-285
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• 2012

The purpose of this study was to develop the Learning Strategy e-Learning Contents based on the storytelling in university students. The objective of the Learning Strategy e-Learning Contents based on the storytelling was to increase in learning skill which university students will use to keep major learning during their courses. The Learning Strategy e-Learning Contents was based on the results of pre-research on storytelling and learning skill. In order to verify the effectiveness of the Learning Strategy e-Learning Contents based on the storytelling, it was analyzed to validity of contents by five professionals. The results of the study were as follows. The Learning Strategy e-Learning Contents based on the storytelling for increasing in learning skill of university students consisted of 15 sessions which proceeding a per semester: the starting phase(1-2), the execution phase(3-13), and the ending phase(14-15). The subjects were 20 university students who had randomly assigned to an experimental group(10) and a control group(10). Subjects completed a learning skill scale. Data analyses were conducted using ANCOVA. The results of the analyses revealed that subjects of experimental group showed significantly higher scores on learning skill than one of control group. Based on the above results, it is concluded that the Learning Strategy e-Learning Contents based on the storytelling was effective in improving learning skill of university students.

• 대한간호학회지
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• 제35권1호
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• pp.65-76
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• 2005

Objective: The aim of this study is to explore the variation in perceptions about problem-based learning(PBL) according to the level of academic achievement and learning attitude in the nursing students of a junior college (3-year program). Method: Students (n=39) learned the respiratory and cardiac system with seven PBL packages and group-based learning for a semester in 2002. Students were asked to write reflective journals that focused on their learning perception after an experience with each learning package. A total of 208 journals were used for analysis. Result: Students positively perceived that PBL making them increase their sense of responsibility for learning and felt satisfaction with the learning process, and had a confidence in the use of clinical nursing interventions. On the other hand, they negatively perceived that PBL was a burden because it took more time than traditional learning tasks, and they experienced an anxiety about regular tests and felt conflicts and diffidences in the learning process. The negative perceptions were expressed more often from students with a low academic achievement and low learning attitude compared to others. Conclusion: Students perceived the PBL as effective in understanding the learning concepts in the clinical practice environment. PBL need to be supplemented by feedback-based lecture and facilitative strategies for academically low-achieved students.Objective: The aim of this study is to explore the variation in perceptions about problem-based learning(PBL) according to the level of academic achievement and learning attitude in the nursing students of a junior college (3-year program). Method: Students (n=39) learned the respiratory and cardiac system with seven PBL packages and group-based learning for a semester in 2002. Students were asked to write reflective journals that focused on their learning perception after an experience with each learning package. A total of 208 journals were used for analysis. Result: Students positively perceived that PBL making them increase their sense of responsibility for learning and felt satisfaction with the learning process, and had a confidence in the use of clinical nursing interventions. On the other hand, they negatively perceived that PBL was a burden because it took more time than traditional learning tasks, and they experienced an anxiety about regular tests and felt conflicts and diffidences in the learning process. The negative perceptions were expressed more often from students with a low academic achievement and low learning attitude compared to others. Conclusion: Students perceived the PBL as effective in understanding the learning concepts in the clinical practice environment. PBL need to be supplemented by feedback-based lecture and facilitative strategies for academically low-achieved students.

• Journal of the Korean Data and Information Science Society
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• 제27권3호
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• pp.827-833
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• 2016

In this paper, we propose a deep least squares support vector machine (LS-SVM) for regression problems, which consists of the input layer and the hidden layer. In the hidden layer, LS-SVMs are trained with the original input variables and the perturbed responses. For the final output, the main LS-SVM is trained with the outputs from LS-SVMs of the hidden layer as input variables and the original responses. In contrast to the multilayer neural network (MNN), LS-SVMs in the deep LS-SVM are trained to minimize the penalized objective function. Thus, the learning dynamics of the deep LS-SVM are entirely different from MNN in which all weights and biases are trained to minimize one final error function. When compared to MNN approaches, the deep LS-SVM does not make use of any combination weights, but trains all LS-SVMs in the architecture. Experimental results from real datasets illustrate that the deep LS-SVM significantly outperforms state of the art machine learning methods on regression problems.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 추계학술발표회논문집(1)
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• pp.73-78
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• 1996

In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller.