• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.1 no.2
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• pp.119-124
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• 1997

Cloud streets were successfully simulated by numerical model (RAMS) including an isolated mountain near the coast, large sensible heat flux from the sea surface, uniform stratification and wind velocity with low Froude number (0.25) in the inflow boundary. The well developed cloud streets between a pair of convective rolls are simulated at a level of 1 km over the sea. The following five results were obtained: 1) For the formation of the pair of convective rolls, both strong static instability and a topographically induced mechanical disturbance are strongly required at the same time. 2) Strong sensible heat flux from the sea surface is the main energy source of the pair of convective rolls, and the buoyancy caused by condensation in the cloud is negligibly small. 3) The pair of convective rolls is a complex of two sub-rolls. One is the outer roll, which has a large radius, but weak circulation, and the other is the inner roll, which has a small radius, but strong circulation. The outer roll gathers a large amount of moisture by convergence in the lower marine boundary, and the inner roll transfers the convergent moisture to the upper boundary layer by strong upward motion between them. 4) The pair of inner rolls form the line-shaped cloud streets, and keep them narrow along the center-line of the domain. 5) Both by non-hydrostatic and by hydrostatic assumptions, cloud streets can be simulated. In our case, non-hydrostatic processes enhanced somewhat the formation of cloud streets. The horizontal size of the topography does not seem to be restricted to within the small scale where non-hydrostatic effects are important.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.8
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• pp.599-604
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• 2012

Most air pollution and smog are a result of the burning of fossil fuels. The use of fossil fuels also causes acid rain and global warming. So the need for solar energy utilization is increased. It is essentially important to make efforts to reduce usage of fossil energy resources. In this study, we analyzed the correlation between climatic elements(Cloud cover, Duration of sunshine, Temperature) and the photovoltaic power generation. Cloud cover of the correlation coefficient was 0.87. And duration of sunshine of the correlation coefficient was 0.93. The order of the correlation coefficient was duration of sunshine, cloud cover, temperature. To accurately analyze of the degree of correlation for the photovoltaic power generation, additional research about climatic elements that show a high correlation is needed.

• KIEAE Journal
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• v.11 no.1
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• pp.15-20
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• 2011

Computer simulation of buildings and solar energy systems is being used increasingly in energy assessments and design. For the six locations (Seoul, Incheon, Daejeon, Deagu, Gwangju and Busan) in South Korea where the global hourly solar irradiation (GHSI) is currently measured, GHSI was calculated using a comparatively simple cloud cover radiation model (CRM) and sunshine fraction radiation model (SFRM). The result was that the measured and calculated values of GHSI were similar for the six regions. Results of cloud cover and sunshine fraction models have been compared with the measured data using the coefficient of determination (R2), root-mean-square error (RMSE) and mean bias error (MBE). The strength of correlation R2 varied within similar ranges: 0.886-0.914 for CRM and 0.908-0.934 for SFRM. Average MBE for the CRM and SFRM were 6.67 and 14.02 W/m2, respectively, and average RMSE 104.36 and 92.15 W/m2. This showed that SFRM was slightly accurate and used many regions as compared to CRM for prediction of GHSI.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.24-30
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• 2014

This study investigates computing offloading effect of cloud in real-time video personal broadcast service, whose server is mobile device. Mobile device does not have enough computing resource for encoding video. The computing burden is offloaded to cloud, which has abundant resources in terms of computing, power, and storage compared to mobile device. By reducing computing burden, computation energy can be saved while transmission data amount increases because of decreasing compression efficiency. This study shows that the optimal operation point can be found adaptively to time-varying LTE communication condition result of tradeoff analysis between offloaded computation burden and increase in amount of transmitted data.

• Journal of the Korean Solar Energy Society
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• v.40 no.2
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• pp.37-48
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• 2020

This study compares cloud radiation model (CRM) and sunshine fraction radiation model (SFRM) according to the solar altitude using hourly sunshine duration (SD) and cloud cover (CC) data. Solar irradiance measurements are not easy for the expensive measuring equipment and precise measuring technology. The two models with the site fitting and South Korea coefficients have been analyzed for fourteen cities of South Korea during the period (1986-2015) and evaluated using the root mean square error (RMSE) and the mean bias error (MBE). From the comparison of the results, it is found that the SFRM with the site fitting coefficients could be the best method for fourteen locations. It may be concluded that the SFRM models of South Korea coefficients generated in this study may be used reasonably well for calculating the hourly horizontal global irradiance (HGI) at any other location of South Korea.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Transactions of the Korean hydrogen and new energy society
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• v.30 no.6
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• pp.499-504
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• 2019

Due to the rapid spread and low minimum ignition energy of hydrogen, rupture is highly likely to cause fire, explosion and major accidents. The self-ignition of high-pressure hydrogen is highly likely to ignite immediately when it leaks from an open space, resulting in jet fire. Results of the diffusion and leakage simulation show that jet effect occurs from the leakage source to a certain distance. And at the end of location, the vapor cloud explosion can be occurred due to the formation of hydrogen vapor clouds by built-up. In the result, it is important that depending on the time of ignition, a jet fire or a vapor cloud explosion may occur. Therefore, it is necessary to take into account jet effect by location of leakage source and establish a damage minimizing plan for the possible jet fire or vapor cloud explosion. And it is required to any kind of measurements such as an interlock system to prevent hydrogen leakage or minimize the amount of leakage when detecting leakage of gas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3821-3841
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• 2019

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.235-243
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• 2016

Legacy data centers are transforming toward cloud data centers according to the advance of mobile and Internet of Things technology, processing of big data, and development of cloud computing technology. The goal of cloud data centers is to efficiently manage energy and facility, and to rapidly provide service demands to users by operating virtualized ICT(Information and Communication Technology) resources. Accordingly, it requires to configure and operate networks for efficiently providing virtualized ICT resources. This paper analyzes networking technologies suitable for cloud data centers and presents ways to efficiently operate the data center.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.137-141
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• 2015

Mobile cloud computing uses cloud service to solve the resource constraint problem of mobile devices. Offloading means that a task executed on the mobile device commits to cloud and many studies related to the energy consumption have been researched. In this paper, we designed a response time model considering sensing data synchronization to estimate the efficiency of the offloading scheme in terms of the response time. The proposed model considers synchronization of required sensing data to improve the accuracy of response time estimation when cloud processes the task requested from a mobile device. We found that the response time is effected by new sensing data generation rate and synchronization period through simulation results.