• Journal of Information Processing Systems
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• v.17 no.2
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• pp.284-296
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• 2021

The cloud has already become the core infrastructure of information systems, and government institutions are rapidly migrating information systems to the cloud. Government institutions in several countries use private clouds in their closed networks. However, because of the advantages of public clouds over private clouds, the demand for public clouds is increasing, and government institutions are expected to gradually switch to public clouds. When all data from government institutions are managed in the public cloud, the biggest concern for government institutions is the leakage of confidential data. The public-private-partnership (PPP) cloud provides a solution to this problem. PPP cloud is a form participation in a public cloud infrastructure and the building of a closed network data center. The PPP cloud prevents confidential data leakage and leverages the benefits of the public cloud to build a cloud quickly and easily maintain the cloud. In this paper, based on the case of the PPP cloud applied to the Korean government, the concept, architecture, operation model, and contract method of the PPP cloud are presented.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.71-80
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• 2012

Much research has been recently accomplished for standardization in cloud computing. However, little research on standardization of data sharing and exchanging has been studied. Most of all, the corresponding standardization organization suggests no specific standardization items and reference model. This paper defines the current standardization problems and proposes a set of specific standardization items and a reference model for supporting the semantic interoperability. To achieve the goal of this paper, the overall standardization trend in cloud computing is first analyzed. Especially, this paper describes the status of standard development for addressing the semantic interoperability of data. This paper also defines the potential standardization items based on the concepts of standards in the data exchanging and management field, which are used for developing standards in various fields. Finally, the reference model is describe to show the relationships between items and overall semantic interoperability process. This paper can be used as a guideline for development of standards and also can facilitate standardization of cloud computing.

• Convergence Security Journal
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• v.24 no.2
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• pp.31-38
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• 2024

Recent expansion in cloud service usage has heightened the importance of cloud security. The purpose of this study is to analyze current research trends in the field of cloud security and to derive implications. To this end, R&D project data provided by the National Science and Technology Knowledge Information Service (NTIS) from 2010 to 2023 was utilized to analyze trends in cloud security research. Fifteen core topics in cloud security research were identified using LDA topic modeling and ARIMA time series analysis. Key areas identified in the research include AI-powered security technologies, privacy and data security, and solving security issues in IoT environments. This highlights the need for research to address security threats that may arise due to the proliferation of cloud technologies and the digital transformation of infrastructure. Based on the derived topics, the field of cloud security was divided into four categories to define a technology reference model, which was improved through expert interviews. This study is expected to guide the future direction of cloud security development and provide important guidelines for future research and investment in academia and industry.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.13-21
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• 2023

Recently, due to the collapse of boundaries between fields caused by COVID-19, the government's goal of connecting all data and making it accessible to people, businesses, and governments has garnered attention. To achieve this goal, cloud technology is consistently mentioned, and since the use of cloud technology inevitably raises security concerns, various studies are being conducted on the topic. This paper analyzes the use of cloud technology in public and administrative institutions in Korea and presents a model that applies the U.S. government's TIC 3.0 concept to mitigate potential security issues. The objective is to provide a secure cloud service utilization model for public and administrative institutions, with reference to TIC 3.0.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.346-353
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• 2023

In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.825-828
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• 2022

In this paper, we propose an efficient image size selection method for video-based point cloud compression. The current MPEG video-based point cloud compression reference encoding process configures a threshold on the size of images while converting point cloud data into images. Because the converted image is compressed and restored by the legacy video codec, the size of the image is one of the main components in influencing the compression efficiency. If the image size can be made smaller than the image size determined by the threshold, compression efficiency can be improved. Here, we studied how to improve the compression efficiency by selecting the best-fit image size generated during video-based point cloud compression. Experimental results show that the proposed method can reduce the encoding time by 6 percent without loss of coding performance compared to the test model 15.0 version of video-based point cloud encoder.

• Journal of KIISE
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• v.42 no.9
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• pp.1090-1099
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• 2015

The concept of Cloud Service Brokerage (CSB) has been introduced as a result of the expansion of the cloud-computing paradigm. Cloud services that provide similar functionality are registered with a CSB. A CSB intermediates cloud services between cloud users and providers. However, there are differences in the price and performance offered by each of the cloud providers. Thus, cloud users have difficulty in finding suitable services to use. Therefore, a CSB is required in order to provide an approach for cloud services to fulfill the requirements of cloud users. In this paper, we propose a virtual cloud bank architecture that includes both a Service Analysis Model (SAM) that can be used to specify and analyze various cloud services and a requirement analysis method that can be used to collect and analyze the cloud user requirements. The VCB architecture that is herein proposed can be used as a reference architecture to provide user-centric cloud services.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.353-363
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• 2019

This paper presents a comprehensive simulation and assessment of gas dispersion above sea from a subsea release using a Computational Fluid Dynamics (CFD) approach. A 3D CFD model is established to evaluate the behavior of flammable gas above sea, and a jack-up drilling platform is included to illustrate the effect of flammable gas cloud on surface vessels. The simulations include a matrix of scenarios for different surface release rates, distances between surface gas pool and offshore platform, and wind speeds. Based on the established model, the development process of flammable gas cloud above sea is predicted, and the dangerous area generated on offshore platform is assessed. Additionally, the effect of some critical factors on flammable gas dispersion behavior is analyzed. The simulations produce some useful outputs including the detailed parameters of flammable gas cloud and the dangerous area on offshore platform, which are expected to give an educational reference for conducting a prior risk assessment and contingency planning.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.1-8
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• 2024

This paper employs stochastic processing techniques to analyze explosion risks in plant facilities based on explosion return periods. Release probability is calculated using data from the Health and Safety Executive (HSE), along with annual leakage frequency per plant provided by DNV. Ignition probability, derived from various researchers' findings, is then considered to calculate the explosion return period based on the release quantity. The explosion risk is assessed by examining the volume, radius, and blast load of the vapor cloud, taking into account the calculated explosion return period. The reference distance for the design blast load model is determined by comparing and analyzing the vapor cloud radius according to the return period, historical vapor cloud explosion cases, and blast-resistant design guidelines. Utilizing the multi-energy method, the blast load range corresponding to the explosion return period is presented. The proposed return period serves as a standard for the design blast load model, established through a comparative analysis of vapor cloud explosion cases and blast-resistant design guidelines. The outcomes of this study contribute to the development of a performance-based blast-resistant design framework for plant facilities.