• Journal of the Korean Association of Geographic Information Studies
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• v.1 no.1
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• pp.86-98
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• 1998

In order to provide an information as input data of possible storm surges in advance, the typhoon center and maximum wind position analysis scheme must be developed for the initialization of pressure and wind field.This study proposes a semi-automatical and objective analysis method and a procedure on a real time basis using the satellite TBB data of the GMS IR1, NOAA satellite CH4 and CH5, and shows the result of an experimental analysis. It includes a simple method of determining the parameters of the typhoon using minimum top temperature of the convective cloud near the inner eyewall. The method analyzing the isotropic cross sectional variation of TBB gradient from center to environment was developed to determine the center of Rmax of typhoon. This position of intense eyewall from typhoon center can be considered as the position of maximum wind. The results of estimation of typhoon center show very good agreement to the results of synoptic analysis. It is found that the Rmax is approximately 50-200km. From the comparison of the GMS and NOAA IR TBB data, it is found that the Rmax from NOAA data tends to be longer than those from GMS data.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.39-49
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• 2019

Advances in IoT data analysis technology have made it easier to analyze situation and provide interactive services based on the context. Most of digital signage application have been used to provide information uni-directionally, but in the future it will evolve to provide personalized content according to the individual user situation and responses. However, it is not easy to modify or apply the existing interactive digital signage platforms due to their hardware dependency. The proposed platform is modularized by dividing main functions into two, the cloud and the edge, so that advertisement resources can be easily generated and registered. Thus, interactive advertisement can be rendered in a timely manner based on sensor analysis results. At the edge, personal data can be processed to minimize privacy issues, and real-time IoT sensor data can be analyzed for quick response to the signage player. The cloud is easier to access and manage by multiple users than edge. Therefore, the signage content generation module improves accessibility and flexibility by handling advertisement contents in the cloud so that multiple users can work together on the cloud platform. The proposed platform was developed and simulated in two aspects. First is the provider who provides the signage service, and second is the viewer who uses the content of the signage. Simulation results show that the proposed platform enables providers to quickly construct interactive signage contents and responses appropriately to the context changes in real-time.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.511-521
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• 2022

Cloud detection and removal from satellite images is an essential process for topographic observation and analysis. Threshold-based cloud detection techniques show stable performance because they detect using the physical characteristics of clouds, but they have the disadvantage of requiring all channels' images and long computational time. Cloud detection techniques using deep learning, which have been studied recently, show short computational time and excellent performance even using only four or less channel (RGB, NIR) images. In this paper, we confirm the performance dependence of the deep learning network according to the heterogeneous learning dataset with different resolutions. The DeepLabV3+ network was improved so that channel features of cloud detection were extracted and learned with two published heterogeneous datasets and mixed data respectively. As a result of the experiment, clouds' Jaccard index was low in a network that learned with different kind of images from test images. However, clouds' Jaccard index was high in a network learned with mixed data that added some of the same kind of test data. Clouds are not structured in a shape, so reflecting channel features in learning is more effective in cloud detection than spatial features. It is necessary to learn channel features of each satellite sensors for cloud detection. Therefore, cloud detection of heterogeneous sensors with different resolutions is very dependent on the learning dataset.

• Journal of the Korean earth science society
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• v.38 no.2
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• pp.129-140
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• 2017

Global solar radiation was calculated in this research using ground-base measurement data, meteorological satellite data, and GWNU (Gangneung-Wonju National University) solar radiation model. We also analyzed the accuracy of the GWNU model by comparing the observed solar radiation according to the total cloud cover. Our research was based on the global solar radiation of the GWNU radiation site in 2012, observation data such as temperature and pressure, humidity, aerosol, total ozone amount data from the Ozone Monitoring Instrument (OMI) sensor, and Skyview data used for evaluation of cloud mask and total cloud cover. On a clear day when the total cloud cover was 0 tenth, the calculated global solar radiations using the GWNU model had a high correlation coefficient of 0.98 compared with the observed solar radiation, but root mean square error (RMSE) was relatively high, i.e., $36.62Wm^{-2}$. The Skyview equipment was unable to determine the meteorological condition such as thin clouds, mist, and haze. On a cloudy day, regression equations were used for the radiation model to correct the effect of clouds. The correlation coefficient was 0.92, but the RMSE was high, i.e., $99.50Wm^{-2}$. For more accurate analysis, additional analysis of various elements including shielding of the direct radiation component and cloud optical thickness is required. The results of this study can be useful in the area where the global solar radiation is not observed by calculating the global solar radiation per minute or time.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.3
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• pp.7-15
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• 2019

Cloud computing markets are rapidly growing as cost savings and business innovation are being carried out through ICT worldwide. In line with this paradigm, the nation is striving to introduce cloud computing in various areas, including the public sector and defense sector, through various research. In the defense sector, DIDC was established in 2015 by integrating military, naval, air and military computing centers, and it provides cloud services in the form of IaaS to some systems in the center. In DIDC and various future cloud defense systems, It is an important issue to ensure availability in cloud defense systems in the defense sector because system failures such as network delays and system resource failures are directly linked to the results of battlefields. However, ensuring the highest levels of availability for all systems in the defense cloud can be inefficient, and the efficiency that can be gained from deploying a cloud system can be reduced. In this paper, we classify and define the level of availability of defense cloud systems step by step, and propose the strategy of introducing Erasure coding and failure acceptance systems, and disaster recovery system technology according to each level of availability acquisition.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.83-85
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• 2017

We conducted an unbiased near- to mid-infrared imaging and spectroscopic survey of the Large Magellanic Cloud (LMC) as a part of the AKARI Mission Program "Large-area Survey of the LMC" (LSLMC, PI: T. Onaka). An area of about 10 square degrees of the LMC was observed by five photometric bands (3.2, 7, 11, 15, and $24{\mu}m$) and a low-resolution slitless prism ($2-5{\mu}m$, R ~20) equipped with AKARI /IRC. We constructed and publicly released photometric and spectroscopic catalogues of point sources in the LMC based on the survey data. The catalogues provide a large number of near-infrared spectral data, coupled with complementary broadband photometric data. Combined use of the present AKARI LSLMC catalogues with other infrared point source catalogues of the LMC possesses scientific potential that can be applied to various astronomical studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.224-227
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• 2015

Data transmission via the car driver's tethered smart phone may have a volume-dependent billing in case car driver' phone transmits data in real-time to the remote data center. The on-board diagnosis data generated are temporary stored locally to mobile remote diagnosis application on the car driver's phone, and then transmit to the data center later when car driver connects to the Internet. To increase the easiest of using the remote vehicle application without blocking other tasks to be executing on the cloud, node.js stands as a suitable candidate for handling tasks of data storage on the cloud via mobile network. We demonstrate the effectiveness of the proposed architecture by simulating a preliminary case study of an android application responsible of real time analysis by using a vehicle-to- smart phones applications interface approach that considers the smart phones to act as a remote user which passes driver inputs and delivers output from external applications. In this paper, we propose a study on development of Remote Vehicle fault diagnostic system features web server architecture based event loop approach using node.js platform, and wireless communication to handle vehicle diagnostics data to a data center.

• Atmosphere
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• v.31 no.5
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• pp.489-510
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• 2021

In this paper, we propose an algorithm for detecting convective initiation (CI) using GEO-KOMPSAT-2A/advanced meteorological imager data. The algorithm identifies clouds that are likely to grow into convective clouds with radar reflectivity greater than 35 dBZ within the next two hours. This algorithm is developed using statistical and qualitative analysis of cloud characteristics, such as atmospheric instability, cloud top height, and phase, for convective clouds that occurred on the Korean Peninsula from June to September 2019. The CI algorithm consists of four steps: 1) convective cloud mask, 2) cloud object clustering and tracking, 3) interest field tests, and 4) post-processing tests to remove non-convective objects. Validation, performed using 14 CI events that occurred in the summer of 2020 in Korean Peninsula, shows a total probability of detection of 0.89, false-alarm ratio of 0.46, and mean lead-time of 39 minutes. This algorithm can be useful warnings of rapidly developing convective clouds in future by providing information about CI that is otherwise difficult to predict from radar or a numerical prediction model. This CI information will be provided in short-term forecasts to help predict severe weather events such as localized torrential rainfall and hail.

• 전자공학회논문지 IE
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• v.47 no.1
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• pp.56-60
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• 2010

The widespread development of IT, growth of Web 2.0 application, the proliferation of personal hand held devices with access to the internet, and the availability of wireless networks, each have played an important role in creating the cloud computing model. Cloud computing is a business model and new trend of web application technology. The term is often used in the same context as grid computing or utility computing. In the cloud computing environment, we are able to use the same all of hardware resources in the server and share information easily. In this paper, we aimed a study to apply e-learning part to cloud computing environment. For this purpose, we proposed an application of LMS and LCMS based e-learning in the cloud computing environment. So LMS including LCMS connected to data center of cloud computing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.793-796
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• 2012

Recently adoption of the Green IT is regarded as an essential element in order to decrease server heat and save energy in data center because of continuous increase of energy consumption and energy price. Consequently the conventional IT infrastructure is replaced with cloud computing platform. In this paper, we have implemented a Linux virtual server load balancing in open source-based cloud platform and the performance of the LVS load balancing is analyzed.