• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.103-110
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• 2008

Due to the development of the e-commerce, the shopping mall such as auction collects and manages the personal information of the customers for efficient service. However, because of the leakage of the Personal information in auction, the image of the companies as well as the information subjects is damaged. Even though the organizations and the companies store the personal information as common sentences and protect using role based access control technique, the personal information can be leaked easily in case of getting the authority of the database administrator. And also the role based access control technique is not appropriate for protecting the sensitive information of the information subject. In this paper, we encrypted the sensitive information assigned by the information subject and then stored them into the database. We propose the personal policy based access control technique which controls the access to the information strictly according to the personal policy of the information subject. Through the proposed method we complemented the problems that the role based access control has and also we constructed the database safe from the database administrator. Finally, we get the control authority about the information of the information subject.

• Journal of Climate Change Research
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• v.7 no.3
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• pp.249-256
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• 2016

Porous $Al_2O_3$ hollow fiber membranes were successfully prepared by dry-wet spinning/sintering method. The SEM image shows that the $Al_2O_3$ hollow fiber membrane consists mostly of sponge pore structure. The contact angle and the breakthrough pressure were $126^{\circ}$ and 1.91 bar, respectively. This results indicate that the $Al_2O_3$ hollow fiber membranes were successfully modified to hydrophobic surface. The hydrophobic modified $Al_2O_3$ hollow fiber membranes were assembled into a membrane contactor system to separate $CO_2$ from a model gas mixture of the flue gas at elevated gas velocity. The $CO_2$ absorption flux was enhanced when the gas velocity increased from $1{\times}10^{-3}$ to $6{\times}10^{-3}$ m/s. Whereas the $CO_2$ absorption flux was decreased with the number of hollow fiber membrane of a module because of the concentration polarization. Furthermore, we developed an lab-scale $Al_2O_3$ hollow fiber membrane contactor modules and their system (i.e., $CO_2$ absorption using the $Al_2O_3$ membrane and monoethanolamine (MEA)) that could dispose of over $0.02Nm^3/h$ mixture gas (15% $CO_2$) with the removal efficiency higher than 95%. The results can be useful in a field of the membrane contactor for $CO_2$ separation, helping to design and extend a equipment.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.441-447
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• 2018

This study aims to develop digital techniques that enable repeated practice of dental radiography using augmented reality technology. A three-dimensional object was fabricated by superimposing a photograph of an adult model and a computed tomography image of a manikin phantom. The system was structured using 106 radiographs such that one of these saved radiographs is opened when the user attempts to take a radiograph on a mobile device. This system enabled users to repeatedly practice at the pre-clinical stage without exposure to radiation. We attempt to contribute to enhancing dental hygienists' competency in dental radiography using these techniques. However, a system that enables the user to actually take a radiograph based on face recognition would be more useful in terms of practice, so additional studies are needed on the topic.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.311-318
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• 2021

Research has been ongoing to detect ships from offshore photographs for a variety of reasons, including maritime security, identifying international trends, and social scientific research. Due to the development of artificial intelligence, R-CNN models for object detection in photographs and images have emerged, and the performance of object detection has risen dramatically. Ship detection in offshore photographs using the R-CNN model has also begun to apply to satellite photography. However, satellite images project large areas, so various objects such as vehicles, landforms, and buildings are sometimes recognized as ships. In this paper, we propose a novel methodology to improve the performance of ship detection in satellite photographs using R-CNN series models. We separate land and sea via marker-based watershed algorithm and perform morphology operations to specify RoI one more time, then detect vessels using R-CNN family models on specific RoI to reduce typology. Using this method, we could reduce the misdetection rate by 80% compared to using only the Fast R-CNN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1026-1031
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• 2021

With the development of global automotive technology and the expansion of market size, demand for vehicles is increasing, which is leading to a decrease in the number of passengers on the road and an increase in the number of vehicles on the road. This causes traffic jams, and in order to solve these problems, the number of illegal vehicles continues to increase. Various technologies are being studied to crack down on these illegal activities. Previously developed systems use trigger equipment to recognize vehicles and photograph vehicles using infrared cameras to detect the number of passengers on board. In this paper, we propose a vehicle occupant detection system with deep learning model techniques without exploiting existing system-applied trigger equipment. The proposed technique proposes a system to detect vehicles by establishing triggers within images and to apply deep learning object recognition models to detect real-time boarding personnel.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.1-9
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• 2022

UML class diagrams are used to visualize the static aspects of a software system and are involved from analysis and design to documentation and testing. Software modeling using class diagrams is essential for software development, but it may be not an easy activity for inexperienced modelers. The modeling productivity could be improved with a dataset of class diagrams which are classified by domain categories. To this end, this paper provides a classification method for a dataset of class diagram images. First, real class diagrams are selected from collected images. Then, class names are extracted from the real class diagram images and the class diagram images are classified according to domain categories. The proposed classification model has achieved 100.00%, 95.59%, 97.74%, and 97.77% in precision, recall, F1-score, and accuracy, respectively. The accuracy scores for the domain categorization are distributed between 81.1% and 95.2%. Although the number of class diagram images in the experiment is not large enough, the experimental results indicate that it is worth considering the proposed approach to class diagram image classification.

• Journal of Korean Society of Rural Planning
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• v.28 no.2
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• pp.87-96
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• 2022

As part of strengthening energy security and responding to climate change, the government has promoted various renewable energy measures to increase the development of renewable energy facilities. As a result, small-scale solar installations in rural areas have increased rapidly. The number of complaints from local residents is increasing. Therefore, in this study, deep learning technology is applied to high-resolution aerial images on the internet to detect solar power plants installed in rural areas to determine whether or not solar power plants are installed. Specifically, I examined the solar facility detector generated by training the YOLO(You Only Look Once) v2 object detector and looked at its usability. As a result, about 800 pieces of training data showed a high object detection rate of 93%. By constructing such an object detection model, it is expected that it can be utilized for land use monitoring in rural areas, and it can be utilized as a spatial data construction plan for rural areas using technology for detecting small-scale agricultural facilities.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.851-859
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• 2017

Today, with the advent and development of Social Network Service (SNS), various types of information that have been difficult to observe have been pouring out. Recently, Vertical Social Networking Service (SNS), a service that shares specific interests with users' Vertical Social Networking Service) is emerging as a major research area. Especially, various human, social and spatial characteristics can be observed through geolocation data and social data collected through mobile GPS, and it is used in various studies. In this study, we analyze the social data collected through the image - based vertical SNS Instagram, and measure the user 's experience based on the social media based on the user' s spatial context. Therefore, in this study, we investigate what types of spatial patterns exist between experiential elements of sharing experiences and geographical characteristics through social data, and examine a new model of shared experience structure through extracted data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.749-757
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• 2008

Recently, flood damages have increased with heavy rainfall and typhoon influences, and it requires that visualization information to the flood inundation area of downstream in dam discharge. This study developed 3D GIS system that can visualize flood inundation area for Namgang Dam downstream. First, DEMs extracted from NGIS digital maps and IKONOS satellite images were optimized to mount in iWorld engine using TextureMaker and HeightMaker modules. And flood inundation area of downstream could be efficiently extracted with real-time flooding water level using Coordinate Operation System for Flood control In Multi-reservoir (COSFIM) and Flood Wave routing model (FLDWAV) in river cross section. This visualization information of flood inundation area can be used to examine flood weakness district needed in real time Dam operation and be applied to establish the rapid flood disaster countermeasures efficiently.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.311-316
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• 2006

Despite geospatial information systems are widely used in many different fields as a powerful tool for spatial analysis and decision-making, their capabilities to handle realistic 3-D urban environment are very limited. The objective of this work is to integrate the recent developments in 3-D modeling and visualization into GIS to enhance its 3-D capabilities. To achieve a photorealistic view, building models are collected from a pair of aerial stereo images. Roof and wall textures are respectively obtained from ortho-rectified aerial image and ground photography. This study is implemented by using ArcGIS as the work platform and ArcObjects and Visual Basic as development tools. Presented in this paper are 3-D geometric modeling and its data structure, texture creation and its association with the geometric model. As the results, photorealistic views of Purdue University campus are created and rendered with ArcScene.