• Journal of Korea Technology Innovation Society
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• v.18 no.1
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• pp.176-203
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• 2015

While the importance of adopting cloud computing service has been emerged, comparatively little research has been conducted on examining factors of an individual user's intention to switch toward cloud computing service. Hereafter, this study presents and empirically tests users' intention to switch to cloud computing. Our model posits that the characteristics of cloud computing such as effectiveness, economics, accessability, switching cost, security concern, and satisfaction toward existing IT service to cloud service affect perceived value, which in turn, influences intention to switch. An experimental study using student subjects provided empirical validation for our proposed model. Survey data from 204 respondents was used to test the model using partial least square analysis. As the result of the analysis, five hypotheses out of seven hypotheses were supported. According to our results, among the characteristics of cloud computing, effectiveness, economics, switching cost, and security concern were found to have significant impact on users' intention to switch that mediated by perceived value. Based on our research findings, we hope that this research will stimulate researchers' interest in the emerging area of cloud computing adoption.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.105-110
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• 2016

As smart devices have proliferated and network speed and accessibility of mobile have accelerated, mobile cloud services that can do the same tasks by various devices are promoted. This paper explores the analysis of use intention of mobile cloud services and influencing factors. We develop and apply a convergence technology acceptance model which combines TAM, VAM and UTAUT. The proposed model verifies some hypotheses to aware the significant factors of use intention of mobile cloud services with TAM, VAM and UTAUT including additional mobile cloud service characteristics such as mobility, high availability, easy accessibility and scalability. Eventually, this research can not only help users gain insights into mobile cloud service use intention, but also help mobile cloud service providers to develop more effective mobile cloud service and their business strategy in the future.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.327-341
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• 2022

Cloud removal is often required to construct time-series sets of optical images for environmental monitoring. In regression-based cloud removal, the selection of an appropriate regression model and the impact analysis of the input images significantly affect the prediction performance. This study evaluates the potential of Gaussian process (GP) regression for cloud removal and also analyzes the effects of cloud-free optical images and spectral bands on prediction performance. Unlike other machine learning-based regression models, GP regression provides uncertainty information and automatically optimizes hyperparameters. An experiment using Sentinel-2 multi-spectral images was conducted for cloud removal in the two agricultural regions. The prediction performance of GP regression was compared with that of random forest (RF) regression. Various combinations of input images and multi-spectral bands were considered for quantitative evaluations. The experimental results showed that using multi-temporal images with multi-spectral bands as inputs achieved the best prediction accuracy. Highly correlated adjacent multi-spectral bands and temporally correlated multi-temporal images resulted in an improved prediction accuracy. The prediction performance of GP regression was significantly improved in predicting the near-infrared band compared to that of RF regression. Estimating the distribution function of input data in GP regression could reflect the variations in the considered spectral band with a broader range. In particular, GP regression was superior to RF regression for reproducing structural patterns at both sites in terms of structural similarity. In addition, uncertainty information provided by GP regression showed a reasonable similarity to prediction errors for some sub-areas, indicating that uncertainty estimates may be used to measure the prediction result quality. These findings suggest that GP regression could be beneficial for cloud removal and optical image reconstruction. In addition, the impact analysis results of the input images provide guidelines for selecting optimal images for regression-based cloud removal.

• Journal of Environmental Science International
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• v.33 no.1
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• pp.43-57
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• 2024

The possible experimental time for cloud seeding was analyzed in South Korea. Rain gauge and radar precipitation data collected from September 2017 to August 2022 in from the three main target stations of cloud seeding experimentation (Daegwallyeong, Seoul, and Boryeong) were analyzed. In this study, the assumption that rainfall and cloud enhancement originating from the atmospheric updraft is a necessary condition for the cloud seeding experiment was applied. First, monthly and seasonal means of the precipitation duration and frequency were analyzed and cloud seeding experiments performed in the past were also reanalyzed. Results of analysis indicated that the experiments were possible during a monthly average of 7,025 minutes (117 times) in Daegwallyeong, 4,849 minutes (81 times) in Seoul, and 5,558 minutes (93 times) in Boryeong, if experimental limitations such as the insufficient availability of aircraft is not considered. The seasonal average results showed that the possible experimental time is the highest in summer at all three stations, which seems to be owing to the highest precipitable water in this period. Using the radar-converted precipitation data, the cloud seeding experiments were shown to be possible for 970-1,406 hours (11-16%) per year in these three regions in South Korea. This long possible experimental time suggests that longer duration, more than the previous period of 1 hour, cloud seeding experiments are available, and can contribute to achieving a large accumulated amount of enhanced rainfall.

• Journal of The Korean Astronomical Society
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• v.34 no.1
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• pp.1-8
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• 2001

We present a fast reduction method of survey data obtained using a single-dish radio telescope. Along with a brief review of classical method, a new method of identification and elimination of negative and positive bad channels are introduced using cloud identification code and several IRAF (Image Reduction and Analysis Facility) tasks relating statistics. Removing of several ripple patterns using Fourier Transform is also discussed. It is found that BACKGROUND task within IRAF is very efficient for fitting and subtraction of base-line with varying functions. Cloud identification method along with the possibility of its application for analysis of cloud structure is described, and future data reduction method is discussed.

• Korean Journal of Remote Sensing
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• v.31 no.1
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• pp.1-9
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• 2015

Mobility and cloud platform have become the dominant paradigm to develop web services dealing with huge and diverse digital contents for scientific solution or engineering application. These two trends are technically combined into mobile cloud computing environment taking beneficial points from each. The intention of this study is to design and implement a mobile cloud application for remotely sensed image fusion for the further practical geo-based mobile services. In this implementation, the system architecture consists of two parts: mobile web client and cloud application server. Mobile web client is for user interface regarding image fusion application processing and image visualization and for mobile web service of data listing and browsing. Cloud application server works on OpenStack, open source cloud platform. In this part, three server instances are generated as web server instance, tiling server instance, and fusion server instance. With metadata browsing of the processing data, image fusion by Bayesian approach is performed using functions within Orfeo Toolbox (OTB), open source remote sensing library. In addition, similarity of fused images with respect to input image set is estimated by histogram distance metrics. This result can be used as the reference criterion for user parameter choice on Bayesian image fusion. It is thought that the implementation strategy for mobile cloud application based on full open sources provides good points for a mobile service supporting specific remote sensing functions, besides image fusion schemes, by user demands to expand remote sensing application fields.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.181-195
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• 2014

The game Industry has been a major leader in business world with its size and volume in terms of profit and culture contents, and ever increasing at the moment. Cloud Game has appeared as a new, combined game format, playable on smart TV and smart phone with its upgraded storage size and fast spreading N-screen. This research studies the present reality of the cloud industry by focusing on three categories which are device type, Platform, and game contents consequently in order to determine the future prospect of cloud games. First, the cloud game business will thrive as devices such as smart TV and smart phone are used widely. Second, the cloud game industry will have a new era when OS systems of Platform are united effectively. Third, the previous platform holders will have to face new challenges brought up by cloud games' service providers. Forth, the gamer, developer, and service provider need each other in order to widen the spectrum of business in cloud game industry.

• Korean Journal of Remote Sensing
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• v.6 no.2
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• pp.153-164
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• 1990

A statistical correlation between cloud thickness and brightness is shown by regression analysis using the least-square method. Cloud thicknesses are obtained from radiosonde observation. Brightness values are obtained from GMS visible channel. Regression analyses are preformed on both thickness data used in conjunction with brightness data for summer season. The results are shown by the regression curve relating thickness and brightness accounting for 79% of variance. And the relationship between thickness and precipitable water in the cloud layers is analyzed. The thickness shows a positive correlation with precipitable water in cloudy layers.

• Atmosphere
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• v.22 no.4
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• pp.437-447
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• 2012

There is still large uncertainty in estimating aerosol indirect effect despite ever-escalating efforts and virtually exponential increase in published studies concerning aerosol-cloud-precipitation interactions (CAPI). Probably most uncertainty comes from a wide range of observational scales and different platforms inappropriately used, and inherent complex chains of CAPI. Therefore, well-designed field campaigns and data analysis are required to address how to attribute aerosol signals along with clouds and precipitation to the microphysical effects of aerosols. Basically, aerosol influences cloud properties at the microphysical scales, "process scale", but observations are generally made of bulk properties over a various range of temporal and spatial resolutions, "analysis scale" (McComiskey & Feingold, 2012). In the most studies, measures made within the wide range of scales are erroneously treated as equivalent, probably resulting in a large uncertainty in associated with CAPI. Therefore, issues associated with the disparities of the observational resolution particular to CAPI are briefly discussed. In addition, the dependence of CAPI on the cloud environment such as stability and adiabaticity, and observation characteristics with varying situations of CAPI are also addressed together with observation framework optimally designed for the Korean situation. Properly designed and observation-based CAPI studies will likely continue to accumulate new evidences of CAPI, to further help understand its fundamental mechanism, and finally to develop improved parameterization for cloud-resolving models and large scale models.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.40-41
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• 2020

As the capacity of the 3d scanner developed, the reverse engineering using the 3d scanner is emphasized in the construction industry to obtain the 3d geometric representation of buildings. However, big size of the indoor point cloud data acquired by the 3d scanner restricts the efficient process in the reverse engineering. In order to solve this inefficiency, several pre-processing methods simplifying and denoising the raw point cloud data by the rough standard are developed, but these non-standard methods can cause the inaccurate recognition and removal the key-points. This paper analyzes the correlation between the accuracy of wall recognition and the density of the data, thus proposes the proper method for the raw point cloud data. The result of this study could improve the efficiency of the data processing phase in the reverse engineering for indoor point cloud data.