• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.213-225
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• 2018

For constructing three-dimensional (3D) spatial information occlusion region problem arises in the process of taking the texture of the building. In order to solve this problem, it is necessary to investigate the automation method to automatically recognize the occlusion region, issue it, and automatically complement the texture. In fact there are occasions when it is possible to generate a very large number of structures and occlusion, so alternatives to overcome are being considered. In this study, we attempt to apply an approach to automatically create an occlusion region based on learning by patterning the blocked region using the recently emerging deep learning algorithm. Experiment to see the performance automatic detection of people, banners, vehicles, and traffic lights that cause occlusion in building walls using two advanced algorithms of Convolutional Neural Network (CNN) technique, Faster Region-based Convolutional Neural Network (R-CNN) and Mask R-CNN. And the results of the automatic detection by learning the banners in the pre-learned model of the Mask R-CNN method were found to be excellent.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.1
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• pp.18-22
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• 2022

To investigate effects of cognitive function improvement whether against Taegeuk ginseng on scopolamine-induced memory impairment in rats. All experiments were conducted in three groups: the control group (CTR), the scopolamine 0.4mg/kg (SCP), and the scopolamine (SCP+T) treated with Taegeuk ginseng 100 mg/kg. Taegeuk ginseng 100 mg/kg daily was orally administered for one month and treated with scopolamine was only for 7 consecutive days on the Morris water maze task. 3 weeks after oral administration of Taegeuk ginseng, subjects were performed the Morris water maze test for 8 days and then the open-field exploration test which to assessed for cognitive function improvement. After behavioral testing, subjects were sacrificed and microdissected brains for neurochemical analysis. In the cognitive-behavioral test, long-term administration of Taegeuk ginseng improved spatial navigation learning task compared with the impeded by scopolamine treatment. In neurochemistry, the expression of the synaptic marker PSD95 (postsynaptic density protein 95) was increased in the hippocampus compared to the scopolamine group. Also, brain-derived neurotrophic factor (BDNF) expression was significantly increased in the taegeuk ginseng administration group. These data suggested that long-term administration of taegeuk ginseng might improve cognitive-behavioral functions on hippocampal related spatial learning memory, and it was correlated with neurotropic and synaptic reinforcement. In conclusion, treatment with taegeuk ginseng may positive outcome on learning and memory deficit disorders.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.3
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• pp.291-300
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• 2016

The emergence of smart devices and IoT leads to the appearance of O2O service to blur the difference between online and offline. As online services' merits were added to the offline market, it caused a change in the dynamics of the offline industry, which means the offline-space's digitization. Unlike these changing aspects of the offline market, exhibition industry grows steadily in the industry, however it is also possible to create a new value added by combining O2O service. We conducted a survey targeting 20 spectators in '2015 Seoul Design Festival' at COEX. The survey was used to analysis of the spatial structure and generate the dataset for machine learning. We identified problems with the analysis study of the existing spatial structure, and based on this investigation we propose a new method for analyzing a spatial structure. Also by processing a machine learning technique based on the generated dataset, we propose a novel evaluation model of exhibition-space cells for O2O service layout.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Smart Media Journal
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• v.13 no.5
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• pp.45-51
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• 2024

Recently, realistic contents such as virtual reality(VR) and augmented reality (AR) are widely used for education to provide beneficial learning environments with thee-dimensional(3D) information and interactive technology. Specially, AR technology will be helpful to intuitively understand by adding virtual objects registered in the real learning environment with effective ways. In this paper, we developed an AR learning system using 3D spatial information in the 2D based textbook for studying math related to geometry. In order to increase spatial learning effect, we applied to solid shapes such as prisms and pyramids in mathematics education process. Also, it allows participants to use various shapes and expression methods (e.g., wireframe mode) with interaction. We conducted the experiment with our AR system, evaluated achievement and interest. Our experimental study showed positive results, our results are expected to provide effective learning methods in various classes through realistic visualization and interaction methods.

• School Mathematics
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• v.7 no.3
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• pp.269-286
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• 2005

The purpose of this paper is to study the spatial sense that is introduced for the first time in our 7th mathematics curriculum. For this purpose, we first investigated the factors of the spatial sense ability and with this factors, we analyze the errors those was founded in elementary school students' carrying out tasks related to the spatial sense, and the contents of elementary mathematics textbook. From the analysis, we found that the teaching topics in the spatial sense is disagreed with the students' learning level and for each similar topics is cut off into not adjacent grades, connecting these topics to each other and to the other traditional geo-metric topics is not easy. we must consider this findings in the future revision of mathematics curriculum.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.31-37
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• 2021

In this paper proposed a feature fusion and spatial attention-based modified YOLOv4S for small and occluded detection. Conventional YOLOv4S is a lightweight network and lacks feature extraction capability compared to the method of the deep network. The proposed method first combines feature maps of different scales with feature fusion to enhance semantic and low-level information. In addition expanding the receptive field with dilated convolution, the detection accuracy for small and occluded objects was improved. Second by improving the conventional spatial information with spatial attention, the detection accuracy of objects classified and occluded between objects was improved. PASCAL VOC and COCO datasets were used for quantitative evaluation of the proposed method. The proposed method improved mAP by 2.7% in the PASCAL VOC dataset and 1.8% in the COCO dataset compared to the Conventional YOLOv4S.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1737-1741
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• 2022

Active fire monitoring using high-resolution drone images and deep learning technologies is now an initial stage and requires various approaches for research and development. This letter examined the detection of active fire objects using You Look Only Once Version 7 (YOLOv7), a state-of-the-art (SOTA) model that has rarely been used in fire detection with drone images. Our experiments showed a better performance than the previous works in terms of multiple quantitative measures. The proposed method can be applied to continuous monitoring of wide areas, with an integration of additional development of new technologies.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• Educational Technology International
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• v.20 no.2
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• pp.195-221
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• 2019

This study explored middle and high school students' learning experiences using e-textbooks in online learning courses. Data were collected from in-depth interviews. The interviewees for this study were 19 students who enrolled voluntarily in an online mathematics and science inquiry program, actively participated in the online learning. The students generally have high academic achievement and motivation for learning in science and mathematics. Data were analyzed based on a grounded theory approach. As a result, the characteristics of the online learning environment using e-textbooks were conceptualized via three different categories including temporal, spatial, and technical. Such characteristics of the learning environment were able to provoke self-directed learning, extended learning, interactive learning, in-depth learning, improved ICT literacy, and formation of positive emotions and learning habits. Most of the learners showed positive feedback towards the use of e-textbooks, while some mentioned the technical limitations compared to conventional paper-based learning. This study suggested that e-textbooks are likely to induce positive experiences for learners in the context of online learning, so it is necessary to design contents that utilize various functions and advantages of electronic teaching materials in order to use e-textbooks effectively.