• Journal of Digital Convergence
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• v.17 no.8
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• pp.123-130
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• 2019

The advent of the full-fledged Internet of Things era will bring together various types of information through Internet of Things devices, and the vast amount of information collected will be generated as new information by the analysis process. To effectively store this generated information, a flexible and scalable cloud computing system is advantageous. Therefore, the main determinants for effective client system acceptance are viewed as motivator factor (economics, efficiency, etc.) and hindrance factor (transitional costs, security issues, etc.) and the purpose of this study is to determine which detailed factors play a major role in making new system acceptance decisions around harm. The factors required to determine the major priorities are defined as the system acceptance determinants from the technical point of view obtained through the literature review, and the questionnaire is prepared based on the factors derived, and the survey is conducted on the experts concerned. In addition, the AHP analysis aims to achieve a final priority by performing a bifurcation between components for measuring a decision unit. Furthermore, the results of this study will serve as an important basis for making decisions based on acceptance (enabling) of technology.

• Journal of the Korea Computer Graphics Society
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• v.24 no.2
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• pp.21-28
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• 2018

We present interactive methods for visualizing the cloud height data and the backscatter data collected from ceilometers in the three-dimensional virtual space. Because ceilometer data is high-dimensional, large-size data associated with both spatial and temporal information, it is highly improbable to exhibit the whole aspects of ceilometer data simply with static, two-dimensional images. Based on the three-dimensional rendering technology, our visualization methods allow the user to observe both the global variations and the local features of the three-dimensional representations of ceilometer data from various angles by interactively manipulating the timing and the view as desired. The cloud height data, coupled with the terrain data, is visualized as a realistic cloud animation in which many clouds are formed and dissipated over the terrain. The backscatter data is visualized as a three-dimensional terrain which effectively represents how the amount of backscatter changes according to the time and the altitude. Our system facilitates the multivariate analysis of ceilometer data by enabling the user to select the date to be examined, the level-of-detail of the terrain, and the additional data such as the planetary boundary layer height. We demonstrate the usefulness of our methods through various experiments with real ceilometer data collected from 93 sites scattered over the country.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.695-706
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• 2022

Medium and high-resolution optical satellites have proven their effectiveness in detecting wildfire areas. However, smoke plumes generated by wildfire scatter visible light incidents on the surface, thereby interrupting accurate monitoring of the area where wildfire occurs. Therefore, a technology to extract smoke in advance is required. Deep learning technology is expected to improve the accuracy of smoke extraction, but the lack of training datasets limits the application. However, for clouds, which have a similar property of scattering visible light, a large amount of training datasets has been accumulated. The purpose of this study is to develop a smoke extraction technique using deep learning, and the limits due to the lack of datasets were overcome by using a cloud dataset on transfer learning. To check the effectiveness of transfer learning, a small-scale smoke extraction training set was made, and the smoke extraction performance was compared before and after applying transfer learning using a public cloud dataset. As a result, not only the performance in the visible light wavelength band was enhanced but also in the near infrared (NIR) and short-wave infrared (SWIR). Through the results of this study, it is expected that the lack of datasets, which is a critical limit for using deep learning on smoke extraction, can be solved, and therefore, through the advancement of smoke extraction technology, it will be possible to present an advantage in monitoring wildfires.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.11-21
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• 2013

Recently operating a large scale computing resource like a data center becomes easier because of the virtualization technology that virtualize servers and enable flexible resource provision. The most of public cloud services provides automatic scaling in the form of scale-in or scale-out and these scaling approaches works well to satisfy the service level agreement (SLA) of users. However, a novel scaling approach is required to operate private clouds that has smaller amount of computing resources than vast resources of public clouds. In this paper, we propose a hybrid server scaling architecture and related algorithms using both scale-in and scale-out to achieve higher resource utilization rate for private clouds. We uses dynamic resource allocation and live migration to run our proposed algorithm. Our propose system aims to provide a fine-grain resource scaling by steps. Thus private cloud systems are able to keep stable service and to reduce server management cost by optimizing server utilization. The experiment results show that our proposed approach performs better in resource utilization than the scale-out approach based on the number of users.

• Journal of the Korean earth science society
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• v.23 no.5
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• pp.424-435
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• 2002

This study analyzes the surface ozone and meteorological data in Daegu for a period from 1997 to 1999. It also investigates the meteorological characteristics of high ozone episodes. For this study the high ozone episode has been defined as a daily maximum ozone concentration higher than 100ppb in at least one station among six air quality monitoring stations in Daegu, Korea. The frequency of episodes is 13 days. The frequency is the highest in May and September. The average value of daily maximum ozone concentration is 81.6ppb, and 8-hour average ozone concentration is 58.6ppb for the high episodes. This shows that ozone pollution is continuous and wide-ranging in Daegu. The daily maximum ozone concentration is positively correlated to solar radiation and daily maximum temperature, but negatively correlated to relative humidity, wind speed and cloud amount. The maximal correlation coefficient to solar radiation is 0.45. The differences between high ozone episode day's daily mean meteorological value and monthly mean value are +1.58hPa for sea level pressure, +3.45${\circ}$C for maximum temperature, -5.69% for relative humidity, -0.46ms$^{-1}$ for wind speed, -1.79 for cloud amount, and +3.97MJm$^{-2}$ for solar radiation, respectively. This shows that strong solar radiation, low wind speed and no precipitation between 0700${\sim}$1100LST are favorite conditions for high ozone episodes. It is related to the morning stagnant condition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1577-1585
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• 2022

As the interest in the 4th industrial revolution and metaverse increases, metaverse with multi edge structure is proposed and noted. Metaverse is a structure that can create digital doctor-like system through a large amount of image processing and data transmission in a multi edge system. Since metaverse application requires calculating performance, which can reconstruct 3-D space, edge hardware's insufficient calculating performance has been a problem. To provide streamable AI software in runtime, image processing, and data transmission, which is edge's loads, needs to be lightweight. Also lightweight at the edge leads to power consumption reduction of the entire metaverse application system. In this paper, we propose collaborative edge-cloud image processing with remote image processing method and Region of Interest (ROI) to overcome edge's power performance and build streamable and runtime executable AI software. The proposed structure was implemented using a PC and an embedded board, and the reduction of time, power, and network communications were verified.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1409-1416
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• 2015

The change of temperature influences on the various aspect, especially human health, plant and animal's growth, economics, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly temperature data at the Suwon monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). Among five meteorological variables, radiation, amount of cloud, and wind speed are more influence on the temperature. The radiation influences during spring, summer and fall, whereas wind speed influences for the winter time. Also, among four greenhouse gas variables and five pollution variables, chlorofluorocarbon, methane, and ozone are more influence on the temperature. The monthly ARE model explained about 43-69% for describing the temperature.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.795-807
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• 2014

Vehicular Ad hoc Network (VANET) has gone through a rich amount of research and currently is making its way towards the deployment. However, surprisingly it evolved to rather more applications and services-rich breed referred to as VANET-based clouds due to the advancements in the automobile and communication technologies. Security and privacy have always been the challenges for the think tanks to deploy this technology on mass scale. It is even worse that some security issues are orthogonally related to each other such as privacy, revocation and route tracing. In this paper, we aim at a specific VANET-based clouds framework proposed by Hussain et al. namely VANET using Clouds (VuC) where VANET and cloud infrastructure cooperate with each other in order to provide VANET users (more precisely subscribers) with services. We specifically target the aforementioned conflicted privacy, route tracing, and revocation problem in VANET-based clouds environment. We propose a multiple pseudonymous approach for privacy reasons and leverage the beacons stored in the cloud infrastructure for both route tracing and revocation. In the proposed scheme, revocation authorities after colluding, can trace the path taken by the target node for a specified timespan and can also revoke the identity if needed. Our proposed scheme is secure, conditional privacy preserved, and is computationally less expensive than the previously proposed schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1797-1804
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• 2015

Integrated log management system can help to predict the risk of security and contributes to improve the security level of the organization, and leads to prepare an appropriate security policy. In this paper, we have designed and implemented a Hadoop-based log analysis system by using distributed database model which can store large amount of data and reduce analysis time by automating log collecting procedure. In the proposed system, we use the HBase in order to store a large amount of data efficiently in the scale-out fashion and propose an easy data storing scheme for analysing data using a Hadoop-based normal expression, which results in improving data processing speed compared to the existing system.

• Journal of Environmental Science International
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• v.13 no.12
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• pp.1079-1088
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• 2004

In order to understand chemical characteristics and dewfall formation in western Busan area, we analysed monthly distribution of dewfall, and investigated the correlation between dewfall formation amount and meteoro­logical factors. This study used the modified teflon plate $(1m{\times}1m)$ at Silla university in Busan from August 2002 to April 2003. In order to estimate qualitatively water soluble components, IC, ICP and UV methods for water soluble ions are also used respectively. Dewfall amount of sampling periods (47 day) collected 3.8 mm. Meteorological conditions for the formation of dewfall above $50\;g/m^{2}$ showed that temperature diurnal $range(^{\circ}C)\;was\;5.6^{\circ}C$ above, cloud amounts (1/10) at dawn of the sampling day was 7/10 below, mean wind speed at dawn (0~6hr) of the sampling day was 4.4 m/sec below, and mixing ratio at 6hr of the sampling day was 3.2 g/kg above. Distribution of water soluble ions in dewfall founded the highest concentration (206.1\;{\mu}eq/{\ell}\;for\;SO_{4}^{2-},\;42.4\;{\mu}eq/{\ell}\;for\;NH_{4}^{+},\;249.2\;{\mu}eq/{\ell}\;for\;Ca^{2+},\;and\;42.0\;{\mu}eq/{\ell}\;for\;Mg^{2+})$ during the March, the lowest concentration $(73.0\;{\mu}eq/{\ell}\;for\;SO_{4}^{2-},\;4.6\;{\mu}eq/{\ell}\;for\;NH_{4}^+\;and\;72.7\;{\mu}eq/{\ell}\;for\;Ca^{2+})$ during the August. Monthly equivalent ratio of $[SO_{4}^{2-}]/[NO_{3}^-]$ showed the highest value (4.99) during the October, the lowest value (1.84) during the August, and the mean value was 3.45.