• Korean Institute of Interior Design Journal
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• v.22 no.4
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• pp.113-120
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• 2013

This study aims to determine adolescents' emotional response and preferences for varying interior designs of learning spaces available at community facilities in apartment across Seoul. In particular, the subjects have been fragmented by gender and age for comparative analysis of emotional responses across different demographics of adolescents. A survey on the preferred designs of learning spaces in community facilities revealed that 'elegant,' 'cheerful,' and 'temperate' are the three main emotional words selected for image tool development. Emotional assessment verified the validity of these terms. Between the two genders, adolescent males preferred 'temperate' images more while adolescent females preferred 'cheerful.' In terms of the design of learning space, adolescent females deemed the interior atmosphere and area space to be the most important factors, while adolescent males pointed to the color of furniture and lighting to be the most important. Such results imply that there is a clear difference of emotional response between adolescent males and females. The results also imply that different atmospheres and design priorities must be considered when designing gender-specific spaces.

• Journal of Computing Science and Engineering
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• v.11 no.4
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• pp.121-129
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• 2017

Feature selection has been widely established as an efficient technique for microarray data analysis. Feature selection aims to search for the most important feature/gene subset of a given dataset according to its relevance to the current target. Unsupervised feature selection is considered to be challenging due to the lack of label information. In this paper, we propose a novel method for unsupervised feature selection, which incorporates embedded learning and $l_{2,1}-norm$ sparse regression into a framework to select genes in microarray data analysis. Local tangent space alignment is applied during embedded learning to preserve the local data structure. The $l_{2,1}-norm$ sparse regression acts as a constraint to aid in learning the gene weights correlatively, by which the proposed method optimizes for selecting the informative genes which better capture the interesting natural classes of samples. We provide an effective algorithm to solve the optimization problem in our method. Finally, to validate the efficacy of the proposed method, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method obtains quite promising performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.817-821
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• 2001

In this paper, a new learning methodology for kernel methods that results in a sparse representation of kernel space from the training patterns for classification problems is suggested. Among the traditional algorithms of linear discriminant function, 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 epoches. For sequential learning of kernel methods, extended SVM and kernel discriminant function are defined. Systematic derivation of learning algorithm is introduced. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.91-97
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• 2022

Recently, as per a representative education method to develop core capabilities (such as critical thinking, communication, collaboration, and creativity) problem based learning (PBL) has been widely adopted in universities. Two important features of PBL are 'collaboration between team members' and 'participation based self-directed learning'. These two features should be satisfied in online education, although it is difficult due to the limitation on space and time in the COVID-19 pandemic. This paper presents a new design of PBL class in Metaverse, based on improving the online PBL class operated in the previous semesters in the H university. In the proposed PBL class, students are able to display materials (e.g., image, pdf, video files) in 3D virtual space, that are related to problem solving. The 3D virtual space is called gallery in this paper. The concept of gallery allows for active participation of students. In addition, the gallery can be used as a tool for collaborative meeting or for final presentation. If possible, the new design of PBL class will be applied and its effectiveness will be analyzed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.66-69
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• 2003

This paper considers a reinforcement learning(RL) which deals with real environments. Most reinforcement learning studies have been made by simulations because real-environment learning requires large computational cost and much time. Furthermore, it is more difficult to acquire many rewards efficiently in real environments than in virtual ones. The most important requirement to make real-environment learning successful is the appropriate construction of the state space. In this paper, to begin with, I show the basic overview of the reinforcement learning under real environments. Next, 1 introduce a state-space construction method under real environmental which is State Partition Method. Finally I apply this method to a robot navigation problem and compare it with conventional methods.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.87-97
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• 2010

A fault tolerant satellite attitude control scheme with a modified iterative learning law is proposed for dealing with actuator faults. The actuator fault is modeled to reflect the degradation of actuation effectiveness, and the solar array-induced disturbance is considered as an external disturbance. To estimate the magnitudes of the actuator fault and the external disturbance, a modified iterative learning law using only the information associated with the state error is applied. Stability analysis is performed to obtain the gain matrices of the modified iterative learning law using the Lyapunov theorem. The proposed fault tolerant control scheme is applied to the rest-to-rest maneuver of a large satellite system, and numerical simulations are performed to verify the performance of the proposed scheme.

• Journal of the Korean Institute of Educational Facilities
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• v.11 no.5
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• pp.24-34
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• 2004

The purpose of this study is to analyze the planning characteristics of contemporary Japanese elementary schools. Fifteen schools, that have new planning trends or design ideas have been selected and analyzed. The planning characteristics of schools identified by plan analyses are summarized as follows. First, space programs of schools are diverse, especially in support facilities, gymnasiums and auditoriums. These spaces can be used by community members. So it is assumed that needs of communities are reflected in space programs of schools. Second, various types of unit learning spaces consisting of multipurpose spaces and classrooms embodied in case schools tell the differentiation in the structure of unit learning spaces. Third, grouped with gymnasiums or auditoriums, special instructional spaces constitute community zones where school facilities are open to public. Fourth, replacing the monotonous circulation systems by corridors, multipurpose hall-type space organization systems make surrounding spaces more activated and complex and the multipurpose hall itself becomes the central part of schools. Finally, outdoor spaces are designed to have convenient access and approach zones to school precincts are linked with city street.

• Structural Engineering and Mechanics
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• v.81 no.2
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• pp.195-203
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• 2022

This paper presents the quantification and location damage detection of plane and space truss structures in a two-phase method to reduce the computations efforts significantly. In the first phase, a proposed damage indicator based on the residual force vector concept is used to get the suspected damaged members. In the second phase, using damage quantification as a variable, a teaching-learning based optimization algorithm (TLBO) is used to obtain the damage quantification value of the suspected members obtained in the first phase. TLBO is a relatively modern algorithm that has proved distinguished in solving optimization problems. For more verification of TLBO effeciency, the classical particle swarm optimization (PSO) is used in the second phase to make a comparison between TLBO and PSO algorithms. As it is clear, the first phase reduces the search space in the second phase, leading to considerable reduction in computations efforts. The method is applied on three examples, including plane and space trusses. Results have proved the capability of the proposed method to precisely detect the quantification and location of damage easily with low computational efforts, and the efficiency of TLBO in comparison to the classical PSO.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.42.3-42.3
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• 2019

We translate Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA) ultraviolet (UV) multi-channel images into another UV single-channel image using a deep learning algorithm based on conditional generative adversarial networks (cGANs). The base input channel, which has the highest correlation coefficient (CC) between UV channels of AIA, is 193 Å. To complement this channel, we choose two channels, 1600 and 304 Å, which represent upper photosphere and chromosphere, respectively. Input channels for three models are single (193 Å), dual (193+1600 Å), and triple (193+1600+304 Å), respectively. Quantitative comparisons are made for test data sets. Main results from this study are as follows. First, the single model successfully produce other coronal channel images but less successful for chromospheric channel (304 Å) and much less successful for two photospheric channels (1600 and 1700 Å). Second, the dual model shows a noticeable improvement of the CC between the model outputs and Ground truths for 1700 Å. Third, the triple model can generate all other channel images with relatively high CCs larger than 0.89. Our results show a possibility that if three channels from photosphere, chromosphere, and corona are selected, other multi-channel images could be generated by deep learning. We expect that this investigation will be a complementary tool to choose a few UV channels for future solar small and/or deep space missions.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.70.4-70.4
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• 2019

Image super-resolution (SR) is a technique that enhances the resolution of a low resolution image. In this study, we use three SR models (RCAN, ProSRGAN and Bicubic) for enhancing solar SDO/HMI magnetograms using deep learning. Each model generates a high resolution HMI image from a low resolution HMI image (4 by 4 binning). The pixel resolution of HMI is about 0.504 arcsec. 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 three models with HMI images in 2014 and test them with HMI images in 2015. We find that the RCAN model achieves higher quality results than the other two methods in view of both visual aspects and metrics: 31.40 peak signal-to-noise ratio(PSNR), Correlation Coefficient (0.96), Root mean square error (RMSE) is 0.004. This result is also much better than the conventional bi-cubic interpolation. We apply this model to a full-resolution SDO/HMI image and compare the generated image with the corresponding Hinode NFI magnetogram. As a result, we get a very high correlation (0.92) between the generated SR magnetogram and the Hinode one.