• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1100-1105
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• 2022

Denoising, registering, and detecting changes of 3D digital map are generally conducted by skilled technicians, which leads to inefficiency and the intervention of individual judgment. The manual post-processing for analyzing 3D point cloud data of construction sites requires a long time and sufficient resources. This study develops automation technology for analyzing 3D point cloud data for construction sites. Scanned data are automatically denoised, and the denoised data are stored in a specific storage. The stored data set is automatically registrated when the data set to be registrated is prepared. In addition, regions with non-homogeneous densities will be converted into homogeneous data. The change detection function is developed to automatically analyze the degree of terrain change occurred between time series data.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2014년도 춘계 종합학술대회 논문집
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• pp.469-470
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• 2014

• International Journal of Contents
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• 제10권1호
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• pp.62-67
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• 2014

Usually, cloud storage systems are developed based on DFS (Distributed File System) for scalability and reliability reasons. DFSs are designed to improve throughput than IO response time, and therefore, they are appropriate for batch processing jobs. Recently, cloud storage systems have been used for update intensive applications such as OLTP and so on. However, in DFSs, in-place update operations are not carefully considered. Therefore, when updates are frequent, I/O performance of DFSs are degraded significantly. DFSs with RAID techniques have been proposed to improve their performance and reliability. Their performance degradation caused by frequent update operations can be more significant. In this paper, we propose an in-place update method for DFS RAID exploiting a differential logging technique. The proposed method reduces the I/O costs, network traffic and XOR operation costs for RAID. We demonstrate the efficiency of our proposed in-place update method through various experiments.

• 융복합기술연구소 논문집
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• 제6권2호
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• pp.29-33
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• 2016

Recently, the authors of this paper proposed newly the correlation model to calculate the horizontal global solar radiation in Korea based the Zhang-Huang (ZH) model proposed in 2002 for China. Previous study was pronounced the correlation with a new term of the duration of sunshine proved as being closely related with the hourly solar radiation in Korea into ZH model. And then another modified correlation for the regions without measuring cloud cover was proposed and evaluated the accuracy and validity for those regions. So, this study was performed to propose modified correlation to calculate the horizontal global solar irradiance of non-measuring cloud-cover regions. Finally, this study proposed the new correlation that could well predict hourly and daily total solar radiation for all regions, various seasons, and various weather conditions including overcast and clear, with higher accuracy and lower error than other models proposed ever before in Korea for non-measuring cloud-cover regions.

• International Journal of Contents
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• 제12권1호
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• pp.60-64
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• 2016

As new applications of cloud data management system (CDMS) such as online games, cooperation edit, social network, and so on, are increasing, transaction processing capabilities for CDMS are required. Several transaction processing methods for cloud data management system (CDMS) have been proposed. However, existing transaction processing methods have some problems. Some of them provide limited transaction processing capabilities. Some of them are hard to be integrated with existing CDMSs. In this paper, we proposed a new concurrency control method to support transaction processing capability for CDMS to solve these problems. The proposed method was designed and implemented based on Spark, an in-memory distributed processing framework. It uses RDD (Resilient Distributed Dataset) model to provide fault tolerant to data in the main memory. In our proposed method, database stored in CDMS is loaded to main memory managed by Spark. The loaded data set is then transformed to RDD. In addition, we proposed a multi-version concurrency control method through immutable characteristics of RDD. Finally, we performed experiments to show the feasibility of the proposed method.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.238-240
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.166-173
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• 2022

Recently, studies have been conducted on intelligent transportation systems (ITS) that provide safety and convenience to humans. Systems that compose the ITS adopt architectures that applied the cloud computing which consists of a high-performance general-purpose processor or graphics processing unit. However, an architecture that only used the cloud computing requires a high network bandwidth and consumes much power. Therefore, applying edge computing to ITS is essential for solving these problems. In this paper, we propose an edge artificial intelligence (AI) device based ITS. Edge AI which is applicable to various systems in ITS has been applied to license plate recognition. We implemented edge AI on a field-programmable gate array (FPGA). The accuracy of the edge AI for license plate recognition was 0.94. Finally, we synthesized the edge AI logic with Magnachip/Hynix 180nm CMOS technology and the power consumption measured using the Synopsys's design compiler tool was 482.583mW.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.721-723
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• 2015

최근 클라우드 컴퓨팅 기술에 대한 중요성과 확장성이 급부상하면서 다양한 정보 시스템에 적용하고 있는 추세이다. 현재 모바일 클라우드 환경에서 많은 서비스가 이루어지고 있는데 의료정보 서비스도 빠른 적용이 요구되고 있다. 의료정보는 개인의 중요한 정보가 노출될 경우 심각한 문제가 발생할 수 있으므로 안전한 인증 절차 문제가 해결되어야만 한다. 따라서 본 논문에서는 모바일 클라우드 환경에서 의료정보시스템을 보다 효율적으로 관리 할 수 있는 인증 기법을 제안하려 한다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 춘계학술대회
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• pp.603-605
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• 2014

의료정보 기술의 빠른 발전으로 의료 산업은 의료 정보 활용 및 통합에 관한 연구가 활발히 진행되고 있다. 현재 의료정보 통합을 위해서는 각 의료기관 시스템의 전반적인 변화가 있어야 하므로 의료기관의 부담이 적지 않다. 이에 최근 클라우드 기반에 EMR 시스템을 도입하는 환경으로 발전하고 있다. 클라우드 환경에서 사용자의 모든 의료정보를 하나로 통합하여 관리하는 중앙집중적 시스템은 의료정보 노출등 많은 문제점이 있을 수 있다. 따라서 본 논문에서는 멀티클라우드 환경에서 중개기관을 이용한 각 의료기관의 의료정보 통합 및 인증에 관한 기법을 제안하려 한다.