• 컴퓨터교육학회논문지
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• 제11권4호
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• pp.23-36
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• 2008

알고리즘은 효과적인 프로그래밍 기법을 익히는데 가장 핵심이 되는 과목이나 학생들이 개념 이해에 어려움을 느끼며 흥미도 낮은 것으로 나타났다. 특히, 알고리즘 관련 주제들 중 동적 프로그래밍 방법은 컴퓨터과학을 전공하는 학생들이 반드시 학습해야 하는 중요한 프로그래밍 기법임에도 불구하고 학부 과정에 있는 학생들이 어려워하는 부분으로 수업에서도 잘 다루어지지 않고 있다. 동적 프로그래밍은 추상적인 알고리즘 지식과 더불어 문제 해결 절차와 저장 공간에 대한 이해가 필요한 방법으로, 다른 개념 보다 이해하기 어려운 주제이다. 이에 본 논문에서는 동적 프로그래밍 기법에 대해 학습자가 이해하기 쉽고 흥미를 가지고 학습할 수 있도록 지원하는 시각화 시뮬레이터를 개발하였다. 본 학습 시뮬레이터는 학습자가 직접 동적 프로그래밍의 수행 단계를 진행하며 문제 해결 절차를 학습하고 저장 공간 및 프로그램의 진행 과정을 시각적으로 확인할 수 있도록 설계 및 구현되었다.

• 한국CDE학회논문집
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• 제5권2호
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• pp.127-135
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• 2000

Emergent computing paradigms such as genetic algorithms have found increased use in problems in engineering design. These computational tools have been shown to be applicable in the solution of generically difficult design optimization problems characterized by nonconvexities in the design space and the presence of discrete and integer design variables. Another aspect of these computational paradigms that have been lumped under the bread subject category of soft computing, is the domain of artificial intelligence, knowledge-based expert system, and machine learning. The paper explores a machine learning paradigm referred to as teaming classifier systems to construct the high-quality global function approximations between the design variables and a response function for subsequent use in design optimization. A classifier system is a machine teaming system which learns syntactically simple string rules, called classifiers for guiding the system's performance in an arbitrary environment. The capability of a learning classifier system facilitates the adaptive selection of the optimal number of training data according to the noise and multimodality in the design space of interest. The present study used the polynomial based response surface as global function approximation tools and showed its effectiveness in the improvement on the approximation performance.

• Nuclear Engineering and Technology
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• 제52권2호
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• pp.287-295
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• 2020

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

• 한국콘텐츠학회논문지
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• 제19권11호
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• pp.278-287
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• 2019

공간 정보 추출은 자연어 텍스트에 있는 정적 및 동적인 공간 정보를 공간 개체와 그들 사이의 관계로 명확히 표시하여 추출하는 것을 말한다. 이 논문은 2단계 양방향 LSTM-CRF 앙상블 모델을 사용하여 한국어 공간 정보를 추출할 수 있는 심층 학습 방법을 제안한다. 또한 공간 개체 추출과 공간 관계 속성 추출을 통합한 모델을 소개한다. 한국어 공간정보 말뭉치(Korean SpaceBank)를 사용하여 실험한 결과 제안한 심층학습 방법이 기존의 CRF 모델보다 우수함을 보였으며, 특히 제안한 앙상블 모델이 단일 모델보다 더 우수한 성능을 보였다.

• International Journal of Aeronautical and Space Sciences
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• 제11권4호
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• 한국항공우주학회지
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• 제35권3호
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• pp.238-247
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• 2007

유전자 알고리즘은 자연선택과 유전법칙을 적용하여 최적해를 탐색하는 방법으로, 본 연구에서 항공기용 가스터빈 엔진의 결함 진단을 위한 학습 알고리즘으로 사용되었다. 성능 저하를 고려한 구성요소는 압축기, 가스발생기 터빈, 동력 터빈이며, 설계점에서 엔진의 단일 구성요소에 대하여 각각 성능 저하 예측을 수행한 후, 이를 바탕으로 결함 진단을 수행하였다. 학습데이터 수의 증가가 유전자 알고리즘을 이용한 성능 저하 예측 및 결함 진단에 미치는 영향을 분석하였으며, 결과적으로 결함치에 대한 RMS 오차율이 모두 3% 이내로 예측됨을 확인하였다.

• 전기전자학회논문지
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• 제24권4호
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• pp.1148-1155
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• 2020

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.

• 한국정밀공학회지
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• 제12권6호
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• pp.120-127
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• 1995

A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

• Educational Technology International
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• 제9권1호
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• pp.79-96
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• 2008

The issue of connection between entities has a lengthy history in educational research, especially since it provides the necessary bridge between base and target in analogical transfer. Recently, the connection has been viewed through the application of technology to bridge between sequences in order to be cognitively useful. This study reports the effect of sequence type (AT vs. TA) and connection type (fading vs. popping) on the achievement and analogical transfer in a multimedia application. In the current research, 10th -grade and 11th -grade biology students in Korea were randomly assigned to five groups to test the effects of presentation sequence and entity connection type on analogical transfer. Consistent with previous studies, sequence type has a significant effect: analogical transfer performance was better when base representations were presented first followed by target representations rather than the reverse order. This is probably because presenting a familiar base first helps in understanding a less familiar target. However, no fully significant differences were found with the entity connection types (fading vs. popping) in analogical transfer. According to the Markman and Gentner's (2005) spatial model, analogy in a space is influenced only by the differences between concepts, not by distance in space. Thus connection types fail on the basis of this spatial model in analogical transfer test. The findings and their implications for sequence and connection research and practice are discussed. Leveraging on the analogical learning process, specific implications for scaffolding learning processes and the development of adaptive expertise are drawn.

• Current Optics and Photonics
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• 제7권5호
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• pp.545-556
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• 2023

This paper introduces volume-sharing multi-aperture imaging (VMAI), a potential approach proposed for volume reduction in space-borne imagers, with the aim of achieving high-resolution ground spatial imagery using deep learning methods, with reduced volume compared to conventional approaches. As an intermediate step in the VMAI payload development, we present a phase-1 design targeting a 1-meter ground sampling distance (GSD) at 500 km altitude. Although its optical imaging capability does not surpass conventional approaches, it remains attractive for specific applications on small satellite platforms, particularly surveillance missions. The design integrates one wide-field and three narrow-field cameras with volume sharing and no optical interference. Capturing independent images from the four cameras, the payload emulates a large circular aperture to address diffraction and synthesizes high-resolution images using deep learning. Computational simulations validated the VMAI approach, while addressing challenges like lower signal-to-noise (SNR) values resulting from aperture segmentation. Future work will focus on further reducing the volume and refining SNR management.