• 한국지구과학회지
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• 제40권4호
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• pp.406-413
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• 2019

Red-Green-Blue (RGB) imagery techniques are useful for both forecasters and public users because they are intuitively understood, have advantageous visualization, and do not lose observational information. This study presents a novel RGB convective cloud product and its application to tropical cyclone analysis using Communication, Oceanography, and Meteorology (COMS) satellite observations. The RGB convective cloud product was developed using the brightness temperature differences between WV ($6.75{\mu}m$) and IR1 ($10.8{\mu}m$), and IR2 ($12.0{\mu}m$) and IR1 ($10.8{\mu}m$) as well as the brightness temperature in the IR1 bands of the COMS, with the threshold values estimated from the Korea Meteorological Administration (KMA) radar observations and the EUMETSAT RGB recipe. To verify the accuracy of the convective cloud RGB product, the product was applied to the center positions analysis of two typhoons in 2013. Thus, the convective cloud RGB product threshold values were estimated for WV-IR1 (-20 K to 15 K), IR1 (210 K to 300 K), and IR1-IR2 (-4 K to 2 K). The product application in typhoon analysis shows relatively low bias and root mean square errors (RMSE)s of 23 and 28 km for DANAS in 2013, and 17 and 22 km for FRANCISCO in 2013, as compared to the best tracks data from the Regional Specialized Meteorological Center (RSMC) in Tokyo. Consequently, our proposed RGB convective cloud product has the advantages of high accuracy and excellent visualization for a variety of meteorological applications.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.680-681
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• 2015

This study develops an algorithm that automatically performs reverse engineering on three-dimensional (3D) sweeping shapes using a user's pre-defined feature templates and 3D point cloud data (PCD) of sweeping shapes. Existing methods extract 3D sweeping shapes by extracting points on a PCD cross section together with the center point in order to perform curve fitting and connect the center points. However, a drawback of existing methods is the difficulty of creating a 3D sweeping shape in which the user's preferred feature center points and parameters are applied. This study extracts shape features from cross-sectional points extracted automatically from the PCD and compared with pre-defined feature templates for similarities, thereby acquiring the most similar template cross-section. Fitting the most similar template cross-section to sweeping shape modeling makes the reverse engineering process automatic.

• International journal of advanced smart convergence
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• 제1권2호
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• pp.52-58
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• 2012

Green cloud computing refers to the green ambient benefits that information technology services delivered over the Internet can offer for the society. The green meaning environment friendly and cloud computing is a traditional symbol for the Internet and a type of service provider. Cloud computing has drastically increased the number of datacenters and the energy consumption of data centers and that has become a critical issue which is extremely important in green ambience. These days the cloud data center needs high energy resources that leads to high operational cost and also maximizes CO2 - carbon footprint that pollutes the ambience which is not to be considered as green ambience. So we need to provide a way that leads us to green ambience. Cloud computing for the green ambience should be designed in a way which will utilize less energy resources and to minimize the CO2 -carbon footprint, known as green cloud. In this paper we discuss various elements of Clouds which contributes to minimize the total energy consumption and the carbon emission so as to enable green ambience through green cloud computing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1362-1376
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• 2016

A cloud computing system can be characterized by the provision of resources in the form of services to third parties on a leased, usage-based basis, as well as the private infrastructures maintained and utilized by individual organizations. To attain the desired reliability and energy efficiency in a cloud data center, trade-offs need to be carried out between system performance and power consumption. Resolving these conflicting goals is often the major challenge encountered in the design of optimization strategies for cloud data centers. The work presented in this paper is directed towards the development of an Energy-efficient and Performance-aware Cloud System equipped with strategies for dynamic switching of optimization approach. Moreover, a platform is also provided for the deployment of a Wind Farm CMS (Condition Monitoring System) which allows ubiquitous access. Due to the geographically-dispersed nature of wind farms, the CMS can take advantage of the cloud's highly scalable architecture in order to keep a reliable and efficient operation capable of handling multiple simultaneous users and huge amount of monitoring data. Using the proposed cloud architecture, a Wind Farm CMS is deployed in a virtual platform to monitor and evaluate the aging conditions of the turbine's major components in concurrent, yet isolated working environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권12호
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• pp.4759-4775
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• 2015

The reduction of power consumption in large-scale datacenters is highly-dependent on the use of virtualization to consolidate multiple workloads. However, these consolidation strategies must also take into account additional important parameters such as performance, reliability, and profitability. Resolving these conflicting goals is often the major challenge encountered in the design of optimization strategies for cloud data centers. In this paper, we put forward a data center monitoring strategy which dynamically alters its approach depending on the cloud system's current state. Results show that our proposed scheme outperformed strategies which only focus on a single metric such as SLA-Awareness and Energy Efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권4호
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• pp.860-877
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• 2013

Cloud computing has become one of the most important technologies for reducing cost and increasing productivity by efficiently using IT resources in various companies. The cloud computing system has mainly been built for private enterprise, but public institutions, such as governments and national institutes, also plans to introduce the system in Korea. Various researches have pointed to security problems as a critical factor to impede the vitalization of cloud computing services, but they only focus on the security threats and their correspondents for addressing the problems. There are no studies that analyze major security issues with regard to introducing the cloud computing system. Accordingly, it is necessary to research the security factors in the cloud computing given to public institutions when adopting cloud computing. This research focuses on the priority of security solutions for the stepwise adoption of cloud computing services in enterprise environments. The cloud computing security area is classified into managerial, physical and technical area in the research, and then derives the detailed factors in each security area. The research derives the influence of security priorities in each area on the importance of security issues according to the identification of workers in private enterprise and public institutions. Ordered probit models are used to analyze the influences and marginal effects of awareness for security importance in each area on the scale of security priority. The results show workers in public institutions regard the technical security as the highest importance, while physical and managerial security are considered as the critical security factors in private enterprise. In addition, the results show workers in public institutions and private enterprise have remarkable differences of awareness for cloud computing security. This research compared the difference in recognition for the security priority in three areas between workers in private enterprise, which use cloud computing services, and workers in public institutions that have never used the services. It contributes to the establishment of strategies, with respect to security, by providing guidelines to enterprise or institutions that want to introduce cloud computing systems.

• 한국시뮬레이션학회논문지
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• 제20권1호
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• pp.1-8
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• 2011

클라우드 컴퓨팅은 장소나 장비에 제한 없이 네트워크를 통해 IT자원을 서비스 형태로 제공받을 수 있는 새로운 패러다임이다. 클라우드 컴퓨팅 환경은 데이터센터에 많은 IT자원이 집약된 형태로 효율적인 인프라구조를 위한 기술과 정책을 적용하여 시스템을 설계해야 한다. 즉, 클라우드 서비스를 효율적으로 제공하여 사용자의 요구를 만족시켜야 하며, 사업자는 불필요하게 낭비되는 자원으로 인한 손해가 없어야 한다. 그러나 최적 시스템을 구축하기 위해서는 서비스를 배포하기 전에 서비스 제공 성능과 자원 사용의 효율성을 예측할 수 있어야 한다. 본 논문에서는 이러한 클라우드 컴퓨팅 시스템 설계과정의 문제를 해결하기 위해 네트워크 환경에서의 클라우드 서비스 모델을 모델링하고 클라우드 서비스의 가용성 최적화를 위해 가용성 평가 지표를 산출하였다. 또한 클라우드 환경이 적용된 CloudSim 시뮬레이터를 이용해 클라우드 컴퓨팅 서비스 요구와 데이터센터 성능에 대한 가용성을 최적화하는 방법을 모색하였다.

• 대한원격탐사학회지
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• 제38권1호
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Geomechanics and Engineering
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• 제30권5호
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• pp.413-422
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• 2022

TBM is widely used in the construction of various underground projects in the current world, and has the unique advantages that cannot be compared with traditional excavation methods. However, due to the high cost of TBM, the damage is even greater when geological disasters such as collapse occur during excavation. At present, there is still a shortage of research on various types of risk prediction of TBM tunnel, and accurate and reliable risk prediction model is an important theoretical basis for timely risk avoidance during construction. In this paper, a prediction model is proposed to evaluate the risk level of tunnel collapse by establishing a reasonable risk index system, using analytic hierarchy process to determine the index weight, and using the normal cloud model theory. At the same time, the traditional analytic hierarchy process is improved and optimized to ensure the objectivity of the weight values of the indicators in the prediction process, and the qualitative indicators are quantified so that they can directly participate in the process of risk prediction calculation. Through the practical engineering application, the feasibility and accuracy of the method are verified, and further optimization can be analyzed and discussed.

• 대한원격탐사학회지
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• 제38권6_1호
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• pp.1025-1034
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• 2022

본 논문에서는 건물의 포인트 클라우드를 추출할 때 발생하는 홀 영역의 보간을 통한 후처리 방안을 제안한다. 스테레오 영상 데이터에서 영상 매칭을 수행할 경우 차폐 및 건물 벽면 등의 영향으로 홀이 발생한다. 이런 영역은 추후 포인트 클라우드를 기반으로 하는 부가 산출물의 생성에 장애 요인이 될 수 있으므로, 이에 대한 효과적인 처리 기법의 적용이 필요하다. 먼저 영상 매칭을 적용하여 생성된 시차맵을 기반으로 초기 포인트 클라우드를 추출한다. 포인트 클라우드를 격자화 시키면 차폐영역 및 건물 벽면의 영향으로 발생하는 홀 영역을 확인할 수 있다. 홀 영역에 삼각망을 생성하고 삼각망 내부 값을 영역의 최소값으로 처리하는 과정을 반복하는 것으로 건물 주변의 지표면과 건물 간에 어색함 없는 보간의 수행이 가능하다. 격자화 된 데이터에서 보간 된 영역에 해당하는 위치정보를 포인트로 추가하여 새로운 포인트 클라우드를 생성한다. 보간과정 중 불필요한 점의 추가를 최소화하기 위해 초기 포인트 클라우드 영역에서 벗어나는 영역으로 보간 된 데이터는 처리하지 않았으며, 보간 된 포인트 클라우드에 적용되는 RGB 밝기값은 매칭에 사용된 스테레오 영상 중 촬영중심과 해당 픽셀이 가장 근접한 영상으로 설정하여 처리하였다. 실험 결과 제안 기법을 통해 대상영역의 포인트 클라우드 생성 후 발생하는 음영 영역이 효과적으로 처리되는 것을 확인할 수 있었다.