• Journal of the Korea Furniture Society
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• v.28 no.4
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• pp.294-304
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• 2017

The purpose of this study is to analyse and derive the color characteristics of childhood children's learning spaces and furniture for improving the color design of those and to present the direction of design and to build up the DB. The study covers products from domestic and overseas infant furniture manufacturers and 11 preschool learning rooms. The study method uses the collected furniture and spatial images to derive a palette of colors and compare color characteristics. The analysis results show that, the domestic infant furniture tend to use a variety of colors with pale tone or use consistent hard wood furniture according to the requirements of adult taste and marketability. Also, it attempts to create a very negative color scheme, but it looks like disorganized, and there is not much color design integrated with the architecture. Thus, color designs ensure that the ordering elements of the color scheme are clearly recognized by applying preferred color tones of childhood, like vivid and bright tone with high chroma and middle or hight Brightness value. Lastly, it should establish guidelines for specific palettes and color scheme, which can be utilized in the design of childhood children's learning spaces and furniture.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.443-453
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• 2020

The automatic land category extraction method was proposed, and the accuracy was evaluated by learning the aerial photo characteristics by land category in the border area with various restrictions on the acquisition of geospatial data. As experimental data, this study used four years' worth of published aerial photos as well as serial cadastral maps from the same time period. In evaluating the results of land category extraction by learning features from different temporal and spatial ranges of aerial photos, it was found that land category extraction accuracy improved as the temporal and spatial ranges increased. Moreover, the greater the diversity and quantity of provided learning images, the less the results were affected by the quality of images at a specific time to be extracted, thus generally demonstrating accurate and practical land category feature extraction.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.178-185
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• 2021

Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.41-48
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• 2021

A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.73-82
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• 2023

Optimization of Construction Site Layout Planning (CSLP) heavily relies on workers' travel paths. However, traditional path generation approaches predominantly focus on the shortest path, often neglecting critical variables such as individual wayfinding tendencies, the spatial arrangement of site objects, and potential hazards. These oversights can lead to compromised path simulations, resulting in less reliable site layout plans. While Deep Reinforcement Learning (DRL) has been proposed as a potential alternative to address these issues, it has shown limitations. Despite presenting more realistic travel paths by considering these variables, DRL often struggles with efficiency in complex environments, leading to extended learning times and potential failures. To overcome these challenges, this study introduces a refined model that enhances spatial navigation capabilities and learning performance by integrating workers' visibility into the reward functions. The proposed model demonstrated a 12.47% increase in the pathfinding success rate and notable improvements in the other two performance measures compared to the existing DRL framework. The adoption of this model could greatly enhance the reliability of the results, ultimately improving site operational efficiency and safety management such as by reducing site congestion and accidents. Future research could expand this study by simulating travel paths in dynamic, multi-agent environments that represent different stages of construction.

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.1-10
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• 2020

The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.95-105
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• 2003

This purpose of this study was to investigate learning effects of Web-based 3-dimensional VR(Virtual Reality) program and a 2-dimensional HTML program, and to find out interaction effects between the types of web-based instruction program(3-dimensional VR program and 2-dimensional HTML program) and the levels of spatial perception of the learners on learning achievement and satisfaction in an educational aspect. In conclusion, there was a significant difference in learning achievement and general satisfaction of the learners between the group providing 3-dimensional VR program and the group 2-dimensional HTML program(F=4.85, p<.05). The 3-dimensional VR program was found to be more effective. And, there was a significant interaction effect in learning achievement effects between the types of web-based instruction program and the level of spatial perception of the learners(F=32.48, p<.05).

• Research in Vestibular Science
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• v.17 no.4
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• pp.134-141
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• 2018

Objectives: We aimed to study the role of vestibular input on spatial memory performance in mice that had undergone bilateral surgical labyrinthectomy, semicircular canal (SCC) occlusion and 4G hypergravity exposure. Methods: Twelve to 16 weeks old ICR mice (n=30) were used for the experiment. The experimental group divided into 3 groups. One group had undergone bilateral chemical labyrinthectomy, and the other group had performed SCC occlusion surgery, and the last group was exposed to 4G hypergravity for 2 weeks. The movement of mice was recorded using camera in Y maze which had 3 radial arms (35 cm long, 7 cm high, 10 cm wide). We counted the number of visiting arms and analyzed the information of arm selection using program we developed before and after procedure. Results: The bilateral labyrinthectomy group which semicircular canal and otolithic function was impaired showed low behavioral performance and spacial memory. The semicircular canal occlusion with $CO_2$ laser group which only semicircular canal function was impaired showed no difference in performance activity and spatial memory. However the hypergravity exposure group in which only otolithic function impaired showed spatial memory function was affected but the behavioral performance was spared. The impairment of spatial memory recovered after a few days after exposure in hypergravity group. Conclusions: This spatial memory function was affected by bilateral vestibular loss. Space-related information processing seems to be determined by otolithic organ information rather than semicircular canals. Due to otolithic function impairment, spatial learning was impaired after exposure to gravity changes in animals and this impaired performance was compensated after normal gravity exposure.

• Journal of Korean Association for Spatial Structures
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• v.17 no.4
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• pp.4-9
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• 2017

• Journal of Korean Association for Spatial Structures
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• v.17 no.4
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• pp.20-26
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• 2017