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

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.12 no.1
• /
• pp.36-41
• /
• 2012

We tested a single pendulum simulation and observed the influence of several situation space segmentation types in reinforcement learning processes in order to propose a new adaptive automation for situation space segmentation. Its segmentation is performed by the Contraction Algorithm and the Cell Division Approach. Also, its automation is performed by "entropy," which is defined on action values’ distributions. Simulation results were shown to demonstrate the influence and adaptability of the proposed method.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.3
• /
• pp.97-104
• /
• 2016

In this paper, we propose spatial information based simulator for user experience optimization and minimize real space complexity. We focus on developing simulator how to design virtual space model and to implement virtual character using real space data. Especially, we use expanded events-driven inference model for SVM based on machine learning. Our simulator is capable of feature selection by k-fold cross validation method for optimization of data learning. This strategy efficiently throughput of executing inference of user behavior feature by virtual space model. Thus, we aim to develop the user experience optimization system for people to facilitate mapping as the first step toward to daily life data inference. Methodologically, we focus on user behavior and space modeling for implement virtual space.

• Journal of the Korean Institute of Educational Facilities
• /
• v.21 no.6
• /
• pp.25-32
• /
• 2014

Korean academic libraries are facing serious space shortage problems due to an inability to accommodate for the rapidly increasing number of printed materials. Despite the current situation, Korean academic libraries have largely focused on impractical applications of the new library paradigm, using technology or management programs to improve the quality of research and learning environments of the university. However, such improvements would be fruitless without first resolving the space shortage crisis. In order to make realistic improvements to the quality of academic libraries, this study used questionnaires to employ the opinions of librarians currently practicing at such Korean libraries. Survey questionnaires about types and causes of space shortage problems, library facility expansion plans, expected effects of expansion, frequency of and reasons for furniture relocation, and tight spaces needing improvement were distributed to selected 4-year college librarians through Google Drive. Analysis of survey responses indicated that library space shortage was largely responsible for the hindrance of research and learning environments. Furthermore, it reflected the urgency to secure book storage space.

• The Journal of Korean Association of Computer Education
• /
• v.4 no.1
• /
• pp.175-181
• /
• 2001

WBI provides new opportunities to realize the flexible learning environment based on hypermedia and to support distance learning with a diverse interaction. The instructors or learners in WBI claim to be able to resolve reluctant fluctuations such as disorientation and cognitive overload. To overcome these phenomena, a supplementary tool able to manage a learning space organized by the instructor's or learner's own way and offer effective navigation techniques is presented in this paper. A learning space management and navigation tool called HyperMap dynamically represents the learning space in the form of a two-dimensional labeled graph. This HyperMap also can be used for an instruction design tool, learners portfolio for the exchange of learning experiences, and the assessment of WBI.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1996.10a
• /
• pp.27-34
• /
• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12
• /
• pp.137-142
• /
• 2021

Distance learning universities provide online course content. The main methods of providing class contents are on-demand and live-streaming. This means that students are not restricted by time or space. The advantage is that students can take the course anytime and anywhere. Therefore, unlike commuting students, there is no commuting time to the campus, and there is no natural process required to take classes. However, despite this convenient situation, the attendance rate and graduation rate of distance learning universities tend to be lower than that of commuting universities. Although the course environment is not the only factor, students cannot obtain a bachelor's degree unless they fulfill the graduation requirements. In both commuter and distance learning universities, taking classes is an important factor in earning credits. There are fewer time and space constraints for distance learning students than for commuting students. It is also easy for distance learning students to take classes at their own timing. There should be more ease of learning than for students who commute to school with restrictions. However, it is easier to take a course at a commuter university that conducts face-to-face classes. I thought that the reason for this was that commuting to school was a part of the process of taking classes for commuting students. Commuting to school was thought to increase the willingness and motivation to take classes. Therefore, I thought that the inconvenient constraints might encourage students to take the course. In this research, I focused on the act of commuting to school by students. These situations are also applied to the distance learning environment. The students have physical time constraints. To achieve this goal, I will implement a course restriction method that aims to promote the willingness and attitude of students. Therefore, in this paper, I have implemented a virtual school system called "virtual go to school (VG2S)" that reflects the actual route to school.

• The Bulletin of The Korean Astronomical Society
• /
• v.44 no.1
• /
• pp.52.1-52.1
• /
• 2019

The Helioseismic and Magnetic Imager (HMI) is the instrument of Solar Dynamics Observatory (SDO) to study the magnetic field and oscillation at the solar surface. The HMI image is not enough to analyze very small magnetic features on solar surface since it has a spatial resolution of one arcsec. Super resolution is a technique that enhances the resolution of a low resolution image. In this study, we use a method for enhancing the solar image resolution using a Deep-learning model which generates a high resolution HMI image from a low resolution HMI image (4 by 4 binning). Deep learning networks try to find the hidden equation between low resolution image and high resolution image from given input and the corresponding output image. In this study, we trained a model based on a very deep residual channel attention networks (RCAN) with HMI images in 2014 and test it with HMI images in 2015. We find that the model achieves high quality results in view of both visual and measures: 31.40 peak signal-to-noise ratio(PSNR), Correlation Coefficient (0.96), Root mean square error (RMSE) is 0.004. This result is much better than the conventional bi-cubic interpolation. We will apply this model to full-resolution SDO/HMI and GST magnetograms.

• Journal of KIISE:Software and Applications
• /
• v.36 no.11
• /
• pp.960-966
• /
• 2009

A manifold is used to represent a relationship between high-dimensional data samples in low-dimensional space. In human pose estimation, it is created in low-dimensional space for processing image and 3D body configuration data. Manifold learning is to build a manifold. But it is vulnerable to silhouette variations. Such silhouette variations are occurred due to view-change, person-change, distance-change, and noises. Representing silhouette variations in a single manifold is impossible. In this paper, we focus a silhouette variation problem occurred by view-change. In previous view invariant pose estimation methods based on manifold learning, there were two ways. One is modeling manifolds for all view points. The other is to extract view factors from mapping functions. But these methods do not support one by one mapping for silhouettes and corresponding body configurations because of unsupervised learning. Modeling manifold and extracting view factors are very complex. So we propose a method based on triple manifolds. These are view manifold, pose manifold, and body configuration manifold. In order to build manifolds, we employ biased manifold learning. After building manifolds, we learn mapping functions among spaces (2D image space, pose manifold space, view manifold space, body configuration manifold space, 3D body configuration space). In our experiments, we could estimate various body poses from 24 view points.

• Journal of Korean Library and Information Science Society
• /
• v.51 no.1
• /
• pp.201-225
• /
• 2020

In order to respond to changes in college's educational goals and talents, university libraries need to be transformed into spaces that support creative and collaborative learning. Apart from overseas cases, it is necessary to discuss the development of creative and collaborative learning spaces appropriate to the situation of Korean university libraries. This purpose of this study is to provide an effective guide to university libraries planning to develop creative and cooperative learning spaces. To this end, this study conducted a literature review to define the concept of a creative and collaborative learning space in a university library, collected and analyzed cases of creative and collaborative learning space of Korean university libraries, and proposed a model for developing creative and collaborative learning space for university library. Case collection was carried out through field visits to 12 university libraries. In addition, in-depth interviews were conducted with the staffs in charge of the construction and operation of five university libraries selected in consideration of the operating entity, region, and scale. This study is meaningful in that it intensively researched and analyzed the advanced cases of creative and collaborative learning spaces in Korea to derive suggestions that can be used in the field of domestic university libraries.