• 한국환경과학회지
• /
• 제1권2호
• /
• pp.15-23
• /
• 1992

Development of cumulus is studied by numerically integrating the equation of motion equations of conservation for water vapor mixing ratio, and the thermodynamic energy equuation. We use the terrain-following coordinate system called z'-coordinate system, in which we can easily treat any calculation domain with terrain configuration such as mountains. The model domain of calculation is restricted vertically to 4.Skin and horizontally to 100 km, has a bell-type mountain in the centeral part. Four cases are considered, one in a neutral environment, second in a slightly stable environment, third in a environment decreasing water content with low value of initial water vapor mixing ratio, the fourth in a case with higher vapor gradient. The more the atmosphere is unstable, the more cumulus develops easily and the more water vapors is abundant, the more cumulus develops easily too. More detailed cloud microphysics parameterizations and wet deposition must be conridered to use in air pollutants prediction model.

• 한국융합학회논문지
• /
• 제11권11호
• /
• pp.293-300
• /
• 2020

4차 산업혁명시대 정부의 꾸준한 법 규제 완화로 클라우드 도입 저해 요소들에 대한 해결 방안이 생기면서 교육환경에도 큰 변화를 가져 오고 있다. 대학에서도 클라우드 컴퓨팅 도입에 대한 관심은 점점 커져가고 있지만 인식 확산 및 분위기 조성 단계에 머무르는 수준이다. 본 연구는 클라우드 컴퓨팅 도입 효과에 영향을 미치는 요인 분석을 통해 확장성, 민첩성, 호환성, 경제성, 보안성, 안정성, 제도적 지원 요인을 "K사이버대학교의 클라우드 기반 원격 교육 시스템 구축" 성공사례의 도입효과에 대한 실증 요인을 분석하였다. 이를 통해 도출된 요인은 성공적인 클라우드 컴퓨팅 도입을 위한 전략 및 방향성 제시의 실증적인 연구의 의의가 있다. 향후 연구에서는 클라우드 컴퓨팅 도입 이전의 다양한 요인 분석에 대한 모델을 구체화 및 확대하여 실증적 요인을 근간으로 하는 연구의 배경으로 활용 될 수 있을 것이다.

• 한국IT서비스학회지
• /
• 제14권3호
• /
• pp.99-116
• /
• 2015

The concept of Cloud computing has been introduced in the IT field over 10 years and industry has been expanding constantly. However, compare to the maturity of global market, Korea cloud computing industry is only in the early stage. Even the Korea has advantages in technology infrastructure; the pace of Korea cloud computing market growth is taking a serious downturn. Under these circumstances, it is needed to be discussing that strategy for expanding the cloud computing market size and for sustaining global competitiveness of local companies. Previous studies on plans for Korea cloud computing market has been conducted since 2009 and most of them are tend to examined in demand perspective. Thus, this study aims at identifying the priority of business challenges for making better performance in the market with service provider aspects. To analyze the important factors in the providing cloud computing service, ANP methodology was applied in this study. The network model including five clusters, security, stability, performance, consumer, and institution, was defined through literature review and expert survey was conducted to collect data. As a result of ANP analysis, 'Securing service reliability' was analyzed as the most important factor and followed by 'Preparing the range of legal liability', 'Preventing personal information leakage' and 'Preventing confidential information data leakage.' The priority of result indicates that service provider needs to focus on to make the secured service environment. This study has significance on analyzing the priority of business challenges in the service provider perspective. This study will provide useful guidelines to for establishing strategies in cloud computing market.

• 한국정보시스템학회지:정보시스템연구
• /
• 제32권2호
• /
• pp.153-176
• /
• 2023

Purpose As non-face-to-face work environments become common due to COVID-19, interest in online collaboration tools that can communicate smoothly without time and space limitations is continuously increasing. Most of the prior studies are about the introduction, use intention, and satisfaction of cloud computing-based collaboration tools, and studies on the effects of collaboration tools on work-life balance and quality of life are somewhat lacking. Therefore, in this study, the characteristics of cloud computing-based collaboration tools were derived, and the effect on job satisfaction during work and job stress outside of working hours was confirmed. Design/methodology/approach This study applied the S-O-R framework and conducted an online survey of office workers who used cloud computing-based collaboration tools for more than three months. Hypotheses were tested using structural equations. Findings As a result of the analysis, among the characteristics of collaboration tools, stability, usefulness, and interoperability had higher job satisfaction as more stimuli were applied. In addition, the higher the job satisfaction during work, the higher the job performance, work-life balance, and quality of life.

• 한국대기환경학회지
• /
• 제27권2호
• /
• pp.152-167
• /
• 2011

MODIS/Terra level 3 and NCEP/NCAR Reanalysis data from 2001 to 2008 have been analyzed to understand long-term aerosol and cloud optical properties, and their relationships around Korea. Interestingly, cloud fraction(CF) has the similar annual variation to aerosol optical depth (${\tau}_a$) without any temporal significant trend. Horizontal distributions of ${\tau}_a$ showed the substantial horizontal gradient from China to Korea, especially with the strong difference over the Yellow Sea, which could represent the evidence of the anthropogenic influence from China in the perspective of long-term average. Specifically the negative correlations between ${\tau}_a$ and liquid-phase cloud effective radius ($r_e$) were shown on the monthly-average basis, only in summer with significant associations over the Yellow Sea, but not in the other seasons and/or specific regions. Relationship between ${\tau}_a$ and CF for the low-level liquid-phase clouds exhibited the overall positive correlation, being consistent with cloud lifetime effect. Meanwhile static stability showed no deterministic relationships with ${\tau}_a$ as well as CF. The dependence of aerosol-cloud relationship on the meteorological conditions should be examined more in detail with the satellite remote sensing and reanalysis data.

• 대한원격탐사학회지
• /
• 제39권5_1호
• /
• pp.507-519
• /
• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• 디지털산업정보학회논문지
• /
• 제9권2호
• /
• pp.73-79
• /
• 2013

Cloud computing is a form which provides IT resources through network and pays the cost as much as you used. And it has advantages that it doesn't need to construct infrastructure and can be offered a variety of environments. The main core of these computing is virtualization technology. Security mechanism about attacks using vulnerabilities of virtualization technology isn't provided right and existing security tools can't be applied as it is. In this paper, we proposed honeyVM structure that can cope actively by collecting the information about attacks using virtualization vulnerability. Mamdani fuzzy inference is used to adjust dynamically the number of formed honeyVM depending on the load of system. Security structure to protect actual virtual machine from attacks and threats is proposed. The performance of the proposed structure in this paper measured occurred attack detection rate and resource utilization rate.

• Journal of Information Processing Systems
• /
• 제16권5호
• /
• pp.1169-1182
• /
• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

• 디지털콘텐츠학회 논문지
• /
• 제18권1호
• /
• pp.167-173
• /
• 2017

클라우드의 사용이 보급화 됨에 따라 가상화 기술에 다양한 요구사항이 접목, 적용되고 있다. 클라우드 컴퓨팅의 대표적인 특징은 사용자가 원하는 자원 요구사항에 따라 최적화 된 환경을 구축할 수 있으며, 나아가 확장성에도 유연하게 대처할 수 있다. 이런 장점으로 인해 다양한 분산컴퓨팅 분야에 클라우드 컴퓨팅이 적용, 활용되고 있는 실정이다. 이를 위해 클라우드 환경의 성능 안정성을 보장하는 것이 무엇보다 중요하다. 본 연구에서는 구축된 클라우드 교육 시스템 테스트베드 환경에서 시스템의 성능을 보장하기 위한 다양한 요소성능(metric) 측정을 오픈소스 기반의 툴들을 이용하여 분석하였다. 이를 위해 프로세서, 메모리, 캐시, 네트워크 등 가상화 환경에 영향을 주는 요소 성능을 구분하고, 그 성능을 호스트머신(Host Machine) 및 가상머신(Virtual Machine)에서 각각 측정하였다. 이로서 시스템의 상태를 명확하게 파악할 수 있으며, 문제점을 빠르게 진단하여 가용성을 증대시키고 나아가 클라우드 컴퓨팅의 SLA(Service Level Agreement) 수준을 보장할 수 있다.

• 한국시뮬레이션학회논문지
• /
• 제23권4호
• /
• pp.111-119
• /
• 2014

클라우드 환경은 빅데이터의 이슈와 데이터 분석을 가능하게 하는 기술로서, 이를 위한 자원 관리 기법이 필요하다. 현재까지의 자원관리 기법은 한정된 계산 방법을 이용하여 자원의 편중의 문제점이 있으며, 이를 해결하기 위해서 자원관리는 자원이력 정보를 활용한 학습기반의 스케줄링이 필요하다. 본 논문에서는 ART(Adaptive Resonance Theory)기반의 적응형 자원관리 기법을 제안한다. 제안하는 기법은 클라우드환경에서 모니터링 및 자원이력을 이용하여 작업의 적합한 할당이 가능하다. 제안하는 방법은 무감독 학습방법을 사용하며, 적응형 자원 관리를 통하여 서비스의 안정성과 데이터 처리성능을 향상시키는 것을 목적으로 한다. 제안하는 방법은 체계적인 자원관리가 가능하고 가용자원을 효율적으로 활용하여 요구 성능을 향상시킬 수 있다는 장점이 있다.