• Korean Institute of Interior Design Journal
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• v.23 no.4
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• pp.94-102
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• 2014

Nowadays when the social and cultural paradigm is changing, the incomplete space is becoming a matter of controversy. In order to figure out the solutions to it, are being held a variety of spatial discourses for spatial essence and meaning to be cleared. Accordingly, this study has tried to seek for any probability to interpret the ontology shown at any traditional space on the ground of Heidegger's Ontological Thinking Structure which has a considerable impact on Modern Space, whose conclusions are the followings. First, Heidegger's ontological space theory, which provided a foundation of Placeness concept, includes not only the character of interdisciplinary learning among philosophy, arts and any related studies but also that of mutual oriental and occidental cultures. Second, between the thoughts of Heidegger and Lao-tzu are considerable similarities from the methodical viewpoint that materializes the meaning of existence as an essence. Third, for a convenient interpretation, the ontological spatial concept of Lao-tzu's philosophy shown at traditional spaces have been categorized into Typology-Incident, Morphology-situation and Topology-meaning generation with Schultz's Existential Spatial Concept based on Heidegger's Ontology as a medium. In particular, the meaning generation which materializes the placeness has the trait of being clarified as the product of interactions between incidents and situations.

• Journal of Ginseng Research
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• v.20 no.2
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• pp.139-148
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• 1996

This study was designed to examine the effects of red ginseng total saponin and extract on spatial working memory in mice using Morris water maze. Two kinds of red ginseng saponin (No. 1 and No. 2) and three kinds of red ginseng extract (No. 1, No. 2 and No. 3) to have different PD/ PT ratio (No. 1=1.24, No.2=1.47 No.3=2.41) were prepared by mixing the different parts of red ginseng In different ratio. In acute administration of total saponin No. 1 or No. 2, escape time to reach to a hidden platform In a fixed location for training trials was significantly decreased as compared with control group and swimming time in the quadrant that had contained the platform was also significantly increased as compared with control group. In acute treatment of extract No. 1 or 1 No. 2, swimming time in the platformless quadrant was increased dose dependently as compared with control group, especially at dose of 200 mg/kg,bw swimming time was significantly Increased. Oral treatment of extract No. 1 (100 mg/kg, bw) for 7 days produced an increase of swimming time In the platformless quadrant but a decrease of swimming time in No.3-treated group (100 mg/kg, bw). These results show that red ginseng may improve spatial discrimination learning and spatial working memory of mice

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.556-570
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• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Journal of Environmental Impact Assessment
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• v.29 no.5
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• pp.377-390
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• 2020

URBAN ECOLOGICAL MAPS must be created by local governments by NATURAL ENVIRONMENT CONSERVATION ACT, and the maps are generally called biotope map. So far, biotope maps study was a tendency to focus on the type of vegetation, naturalness, land use, landscape ecology theories. However, biotope related studies have not reflected the concept of animal habitat, which is a component of biotope, and that is the limitation of biotope map research. This study suggest a methodology to predict potential habitats for fauna using machine learning to quantify habitat values. The potential habitats of fauna were predicted by spatial statistics using machine learning, and the results were converted into species richness. For biotope type assessments, we classified biotope values into vegetation value and habitat value and evaluated them using a matrix for value summation. The vegetation value was divided into 5 stages based on vegetation nature and land use, and the habitat value was classified into five stages by predicting the species richness predicted by machine learning. This is meaningful because our research can positively reflect the results of field surveys of fauna that were negatively reflected in the evaluation of biotope types in the past. Therefore, in the future, if the biotope map manual is revised, our methodology should be applied.

• Journal of Korea Game Society
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• v.6 no.3
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• pp.33-42
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• 2006

This study is aimed to develop adventure-game style learning program for offering different levels curriculum in mathematics and figure areas in elementary schools. The 7th mathematics curriculum introduced different levels curriculum considering learners' ability, aptitude, requirement, interest so that it could improve learners' growth potential and educational efficiency. But in reality, it is quite difficult to increase educational efficiency by conducting individual learning classes according to students' ability due to the big differences among students' levels in addition to high population in each classroom. The purpose of this study is to offer different levels curriculum based on van Hiele theory and develop adventure-game style learning program to increase interests of the learners. This program can improve students' academic achievement by offering differentiated curriculums to learners who need advanced or supplementary learning materials. And it also enhances leaners' spatial-perceptual ability by offering various operating activities in figures learning.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1075-1082
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• 2007

In this paper, we propose the method of multiple human action recognition on video clip. For being invariant to the change of speed or size of actions, Spatial-Temporal Pyramid method is applied. Proposed method can minimize the complexity of the procedures owing to select Motion Gradient Histogram (MGH) based on statistical approach for action representation feature. For multiple action detection, Motion Energy Image (MEI) of binary frame difference accumulations is adapted and then we detect each action of which area is represented by MGH. The action MGH should be compared with pre-learning MGH having pyramid method. As a result, recognition can be done by the analyze between action MGH and pre-learning MGH. Ten video clips are used for evaluating the proposed method. We have various experiments such as mono action, multiple action, speed and site scale-changes, comparison with previous method. As a result, we can see that proposed method is simple and efficient to recognize multiple human action with stale variations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.77-83
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• 2020

The technologies of 3D spatial information, such as Smart City and Digital Twins, are developing rapidly for managing land and solving urban problems scientifically. In this construction of 3D spatial information, an object using aerial photo images is built as a digital DB. Realistically, the task of extracting a texturing image, which is an actual image of the object wall, and attaching an image to the object wall are important. On the other hand, occluded areas occur in the texturing image. In this study, the ResNet algorithm in deep learning technologies was tested to solve these problems. A dataset was constructed, and the street tree was detected using the ResNet algorithm. The ability of the ResNet algorithm to detect the street tree was dependent on the brightness of the image. The ResNet algorithm can detect the street tree in an image with side and inclination angles.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.182-189
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• 2023

CT is a medical device that acquires medical images based on Attenuation coefficient of human organs related to X-rays. In addition, using this theory, it can acquire sagittal and coronal planes and 3D images of the human body. Then, CT is essential device for universal diagnostic test. But Exposure of CT scan is so high that it is regulated and managed with special medical equipment. As the special medical equipment, CT must implement quality control. In detail of quality control, Spatial resolution of existing phantom imaging tests, Contrast resolution and clinical image evaluation are qualitative tests. These tests are not objective, so the reliability of the CT undermine trust. Therefore, by applying an artificial intelligence classification model, we wanted to confirm the possibility of quantitative evaluation of the qualitative evaluation part of the phantom test. We used intelligence classification models (VGG19, DenseNet201, EfficientNet B2, inception_resnet_v2, ResNet50V2, and Xception). And the fine-tuning process used for learning was additionally performed. As a result, in all classification models, the accuracy of spatial resolution was 0.9562 or higher, the precision was 0.9535, the recall was 1, the loss value was 0.1774, and the learning time was from a maximum of 14 minutes to a minimum of 8 minutes and 10 seconds. Through the experimental results, it was concluded that the artificial intelligence model can be applied to CT implements quality control in spatial resolution and contrast resolution.