• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.375-380
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• 2022

Virtual and augmented reality technologies provide access to learning materials and improve the organization of a modern library's media space. This article aims to identify the significance and role of virtual and augmented reality technologies in the modern library's media space organization. Methodology. The research uses a university library case study methodology to empirically investigate virtual and augmented reality technologies. Results. Virtual and augmented reality technologies provide research and improve learning outcomes by engaging students and learners with significant interest in such technologies. Libraries offer users the opportunity to create their VR content through available software. Students can test their VR content in the libraries' labs. Libraries support access to a variety of virtual and augmented reality content. The content is accessed using "virtual reality headsets" for viewing and workstations with "authoring software and loanable 360 cameras" for creating. The library lab is a space to support students' digital creativity and research through virtual and augmented reality. There are 3D Design Labs within the libraries as a medium to large group design learning spaces with virtual reality technology. Libraries form a media space where users can create videos, podcasts, portfolios, edit media, and book tours, and students and researchers can explore different scientific knowledge. In this way, technology ensures that risks in learning are minimized as opposed to hands-on seminars and classes.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.2
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• pp.153-176
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• 1994

In this paper we introduce machine learning theory to business domains for business decisions. First, we review machine learning in general. We give a new look on a previous framework, version space approach, and we introduce PAC (probably approximately correct) learning paradigm which has been developed recently. We illustrate major results of PAC learning with business examples. And then, we give a theoretical analysis is decision tree induction algorithms by the frame work of PAC learning. Finally, we will discuss implications of learning theory toi business domains.

• Journal of the Korean Institute of Educational Facilities
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• v.20 no.4
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• pp.3-10
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• 2013

Basic precondition for effective curriculum on learning activities to take place, the internal and external environment of the school facilities, improve the environment of the classroom space and etc. Specifically, the use of classroom space, hardly learners improve their academic motivation to achievement tend to concentrate within the party regularly scheduled class hours. Physical environment surrounding them is giving considerable impact for behavioral psychological and bodily change of the learners. In this study, we are focused on the form of the learner in the general classroom space and classroom environment that can increase the learning effect will be examined. Consequently, What is appropriate classroom environment for learning increase the concentration of elements are presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.60-64
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• 2001

In this paper, a new learning methodology for Kernel Methods is suggested that results in a sparse representation of kernel space from the training patterns for classification problems. Among the traditional algorithms of linear discriminant function(perceptron, relaxation, LMS(least mean squared), pseudoinverse), this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epochs. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.48-55
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• 1998

Structural learning methods of MLP classifiers for a given application using genetic algorithms have been studied. In the methods, however, the search space for an optimal structure is increased exponentially for the physical application of high diemension-multi calss. In this paperwe propose a method of MLP classifiers using species genetic algorithm(SGA), a modified GA. In SGA, total search space is divided into several subspaces according to the number of hidden units. Each of the subdivided spaces is called "species". We eliminate low promising species from the evoluationary process in order to reduce the search space. experimental results show that the proposed method is more efficient than the conventional genetic algorithm methods in the aspect of the misclassification ratio, the learning rate, and the structure.structure.

• Smart Media Journal
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• v.10 no.3
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• pp.9-22
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• 2021

Basically, machine learning models use input data to produce results. Sometimes, the input data is too complicated for the models to learn useful patterns. Therefore, feature engineering is a crucial data preprocessing step for constructing a proper feature set to improve the performance of such models. One of the most efficient methods for automating feature engineering is the autoencoder, which transforms the data from its original space into a latent space. However certain factors, including the datasets, the machine learning models, and the number of dimensions of the latent space (denoted by k), should be carefully considered when using the autoencoder. In this study, we design a framework to compare two data preprocessing approaches: with and without autoencoder and to observe the impact of these factors on autoencoder. We then conduct experiments using autoencoders with classifiers on popular datasets. The empirical results provide a perspective regarding the best suited autoencoder for these factors.

• Current Optics and Photonics
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• v.8 no.3
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• pp.239-245
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• 2024

This paper proposes single logarithmic amplification (single-LA) and deep learning (DL)-based fixed-threshold on-off keying (OOK) detection for free-space optical (FSO) communication. Multilevel LAs (MLAs) can be used to mitigate intensity fluctuations in the received OOK signal by their nonlinear gain characteristics; however, it is ineffective in the case of high scintillation, owing to degradation of the OOK signal's extinction ratio. Therefore, a DL technique is applied to realize effective scintillation compensation in single-LA applications. Fully connected (FC) networks and fully connected neural networks (FCNN), which have nonlinear modeling characteristics, are deployed in this work. The performance of the proposed method is evaluated through simulations under various scintillation effects. Simulation results show that the proposed method outperforms the conventional adaptive-threshold-decision, single-LA-based, MLA-based, FC-based, and FCNN-based OOK detection techniques.

• Journal of the Korean Institute of Educational Facilities
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• v.16 no.3
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• pp.49-58
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• 2009

This study is carried out to understand application type and change of learning space and to find out actual situation of management in open planned elementary schools. The twelve elementary schools were selected based on case studies and fundamental data of the agencies performing basic plan, the actual situation of management was studied by interviews of principals and teachers. By the results, open planned elementary schools have been notably reduced after the year of 2004 and corridor expending type was broadly chosen as a plane type for open space. It was also shown that learning space was transformed to the independent type integrating open space to the unit classroom in most part of twelve cases. In addition, whole sliding doors fixed in open classrooms by some Provincial Office of Education didn't need certain physical shut-offs, and it was considered as one of useful alternatives to manage open space.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.82.3-82.3
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• 2019

In this study, we generate future full disk magnetograms in 12, 24, 36 and 48 hours advance from SDO/HMI images using deep learning. To perform this generation, we apply the convolutional generative adversarial network (cGAN) algorithm to a series of SDO/HMI magnetograms. We use SDO/HMI data from 2011 to 2016 for training four models. The models make AI-generated images for 2017 HMI data and compare them with the actual HMI magnetograms for evaluation. The AI-generated images by each model are very similar to the actual images. The average correlation coefficient between the two images for about 600 data sets are about 0.85 for four models. We are examining hundreds of active regions for more detail comparison. In the future we will use pix2pix HD and video2video translation networks for image prediction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.7
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• pp.3247-3252
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• 2013

The convergence of ICT and cloud computing is treated as a main issue all over the fields including education. This development leads to change from e-learning to u-learning and smart education. Therefore, we need to study in term of the systematic and a long-term viewpoint how smart education environment have an influence on the practical space. And we need a concrete study for smart education space based on property of space. Under these critical mind, this study understands the smart education space in terms of the convergence of computing space and physical space. As a Result, smart education space have major property such as flexibility, communication, polyvalence.