• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.40.3-40.3
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• 2018

We present results from our two experiments of using machine learning algorithms in processing and analyzing the KMTNet imaging data. First, density estimation and clustering methods find meaningful structures in the metric space of imaging quality measurements described by photometric quantities. Second, we also develop a method to separate out light curves of reliable microlensing event candidates from spurious events, estimating reliability scores of the candidates.

• Korean Institute of Interior Design Journal
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• v.24 no.3
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• pp.48-58
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• 2015

This study is to conclude a direction for Information Commons, which supports the university library in a new role. The study explains perspectives on the changing role of the university library by examining the approaches, histories, and theories practiced by various researchers on Information Commons. The study aims to discover ways of improving the library space that are dedicated to technology using Information Commons, it also examines ways of creating a unified "library space" that will support learning and access to knowledge and information. The features of Cultural Commons include making improvements to technology-centered space, and providing support to research, freedom of speech, creative approach, public freedom and collaboration, and interaction. The functions of Cultural Commons within the university library are listed: First, it supports programs that will transform the library into a social hub within the university. The space specifically blurs the boundary between the library building and its surroundings, and unifies these spaces to enhance its catalytic role in aiding social interactions and human-centered approach. Second, it supports active participation through cultural programs and provides a fluid and interactive space with virtual resources. Third, it enhances user experience to supports behaviors and activities that involve fixtures and equipment in the space to promote learning. The study notes that, with the emergence of these characteristics, the university library is changing by implementing Cultural Commons for on-campus social space and new learning. Accordingly, this implementation is expected to enhance active acceptance of the library space in the future.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.409-420
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• 2012

The decision-making by agents in games is commonly based on reinforcement learning. To improve the quality of agents, it is necessary to solve the problems of the time and state space that are required for learning. Such problems can be solved by Macro-Actions, which are defined and executed by a sequence of primitive actions. In this line of research, the learning time is reduced by cutting down the number of policy decisions by agents. Macro-Actions were originally defined as combinations of the same primitive actions. Based on studies that showed the generation of Macro-Actions by learning, Macro-Actions are now thought to consist of diverse kinds of primitive actions. However an enormous amount of learning time and state space are required to generate Macro-Actions. To resolve these issues, we can apply insights from studies on the learning of tasks through Programming by Demonstration (PbD) to generate Macro-Actions that reduce the learning time and state space. In this paper, we propose a method to define and execute Macro-Actions. Macro-Actions are learned from a human subject via PbD and a policy is learned by reinforcement learning. In an experiment, the proposed method was applied to a car simulation to verify the scalability of the proposed method. Data was collected from the driving control of a human subject, and then the Macro-Actions that are required for running a car were generated. Furthermore, the policy that is necessary for driving on a track was learned. The acquisition of Macro-Actions by PbD reduced the driving time by about 16% compared to the case in which Macro-Actions were directly defined by a human subject. In addition, the learning time was also reduced by a faster convergence of the optimum policies.

• Journal of the Korean Society of Earth Science Education
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• v.15 no.1
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• pp.27-46
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• 2022

The purpose of this study was to examine the perceptions of novelty space among pre-service elementary and secondary earth science teachers. We conducted a survey to explore the perceptions of 38 pre-service elementary school teachers at the National University of Education and 31 pre-service secondary earth science teachers at the Department of Earth Science Education at B University. Semi-structured interviews were conducted with 12 participants, including three pre-service elementary teachers and nine pre-service secondary science teachers. In addition to the elements of novelty space, prior knowledge (cognition), prior outdoor learning experience (psychology), familiarity (geography) with outdoor field learning, and social and technical elements were added. When classified based on elementary and secondary levels, there were statistically significant differences in cognitive, psychological, geographic, and social areas for the elements of novelty space. Statistical differences indicated that the experience or capital related to outdoor learning may have resulted from more pre-service secondary earth science teachers than pre-service elementary teachers. In additional interviews, both elementary and secondary pre-service teachers reported that competencies in the technical domain would be emphasized in the future owing to the necessity and the technical development of virtual-reality-based outdoor field learning programs. This study emphasizes the academic significance of novelty space that should be considered to conduct geological field learning for elementary and secondary earth science pre-service teachers while considering the current post-pandemic educational context.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.5
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• pp.407-415
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• 2016

Recent wireless communication trends have emphasized the importance of energy-efficient transmission. In this paper, link adaptation with machine learning mechanism for maximum energy efficiency in multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) wireless system is considered. For reflecting frequency-selective MIMO-OFDM channels, two-dimensional capacity(2D-CAP) feature space is proposed. In addition, machine-learning-based bit and power adaptation(ML-BPA) algorithm that performs classification-based link adaptation is presented. Simulation results show that 2D-CAP feature space can represent channel conditions accurately and bring noticeable improvement in link adaptation performance. Compared with other feature spaces, including ordered postprocessing signal-to-noise ratio(ordSNR) feature space, 2D-CAP has distinguished advantages in either efficiency performance or computational complexity.

• Journal of Space Technology and Applications
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• v.1 no.1
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• pp.49-63
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• 2021

The exploration of the solar system is carried out through various payloads, and accordingly, many research results are emerging. We tried to apply deep-learning as a method of studying the bodies of solar system. Unlike Earth observation satellite data, the data of solar system differ greatly from celestial bodies to probes and to payloads of each probe. Therefore, it may be difficult to apply it to various data with the deep-learning model, but we expect that it will be able to reduce human errors or compensate for missing parts. We have implemented a model that detects craters on the lunar surface. A model was created using the Lunar Reconnaissance Orbiter Camera (LROC) image and the provided shapefile as input values, and applied to the lunar surface image. Although the result was not satisfactory, it will be applied to the image of the permanently shadow regions of the Moon, which is finally acquired by ShadowCam through image pre-processing and model modification. In addition, by attempting to apply it to Ceres and Mercury, which have similar the lunar surface, it is intended to suggest that deep-learning is another method for the study of the solar system.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.626-637
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• 2018

Human exchanges information not only through words, but also through body gesture or hand gesture. And they can be used to build effective interfaces in mobile, virtual reality, and augmented reality. The past 2D gesture recognition research had information loss caused by projecting 3D information in 2D. Since the recognition of the gesture in 3D is higher than 2D space in terms of recognition range, the complexity of gesture recognition increases. In this paper, we proposed a real-time gesture recognition deep learning model and application in 3D space using deep learning technique. First, in order to recognize the gesture in the 3D space, the data collection is performed using the unity game engine to construct and acquire data. Second, input vector normalization for learning 3D gesture recognition model is processed based on deep learning. Thirdly, the SELU(Scaled Exponential Linear Unit) function is applied to the neural network's active function for faster learning and better recognition performance. The proposed system is expected to be applicable to various fields such as rehabilitation cares, game applications, and virtual reality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.792-812
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• 2022

Multi-agent systems often need to achieve the goal of learning more effectively for a task through coordination. Although the introduction of deep learning has addressed the state space problems, multi-agent learning remains infeasible because of the joint action spaces. Large-scale joint action spaces can be sparse according to implicit or explicit coordination structure, which can ensure reasonable coordination action through the coordination structure. In general, the multi-agent system is dynamic, which makes the relations among agents and the coordination structure are dynamic. Therefore, the explicit coordination structure can better represent the coordinative relationship among agents and achieve better coordination between agents. Inspired by the maximization of social group utility, we dynamically construct a factor graph as an explicit coordination structure to express the coordinative relationship according to the utility among agents and estimate the joint action values based on the local utility transfer among factor graphs. We present the application of such techniques in the scenario of multiple intelligent vehicle systems, where state space and action space are a problem and have too many interactions among agents. The results on the multiple intelligent vehicle systems demonstrate the efficiency and effectiveness of our proposed methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.572-578
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• 2008

We analyze the search space considered by the previously proposed R-CORE method for learning Bayesian network structures of large scale. Experimental analysis on the search space of the method is also shown. The R-CORE method reduces the search space considered for Bayesian network structures by recursively clustering the random variables and restricting the orders between clusters. We show the R-CORE method has a similar search space with the previous method in the worst case but has a much less search space in the average case. By considering much less search space in the average case, the R-CORE method shows less tendency of overfitting in learning Bayesian network structures compared to the previous method.