• Journal of the Korean earth science society
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• v.40 no.4
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• pp.406-413
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• 2019

Red-Green-Blue (RGB) imagery techniques are useful for both forecasters and public users because they are intuitively understood, have advantageous visualization, and do not lose observational information. This study presents a novel RGB convective cloud product and its application to tropical cyclone analysis using Communication, Oceanography, and Meteorology (COMS) satellite observations. The RGB convective cloud product was developed using the brightness temperature differences between WV ($6.75{\mu}m$) and IR1 ($10.8{\mu}m$), and IR2 ($12.0{\mu}m$) and IR1 ($10.8{\mu}m$) as well as the brightness temperature in the IR1 bands of the COMS, with the threshold values estimated from the Korea Meteorological Administration (KMA) radar observations and the EUMETSAT RGB recipe. To verify the accuracy of the convective cloud RGB product, the product was applied to the center positions analysis of two typhoons in 2013. Thus, the convective cloud RGB product threshold values were estimated for WV-IR1 (-20 K to 15 K), IR1 (210 K to 300 K), and IR1-IR2 (-4 K to 2 K). The product application in typhoon analysis shows relatively low bias and root mean square errors (RMSE)s of 23 and 28 km for DANAS in 2013, and 17 and 22 km for FRANCISCO in 2013, as compared to the best tracks data from the Regional Specialized Meteorological Center (RSMC) in Tokyo. Consequently, our proposed RGB convective cloud product has the advantages of high accuracy and excellent visualization for a variety of meteorological applications.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.21-26
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• 2018

Recently, the emergence of 3D cameras, 3D scanners and various cameras including Lidar is expected to be applied to applications such as AR, VR, and autonomous mobile vehicles that deal with 3D data. In Particular, the 3D point cloud data consisting of tens to hundreds of thousands of 3D points is rapidly increased in capacity compared with 2D data, Efficient encoding / decoding technology for smooth service within a limited bandwidth, and efficient service provision technology for differentiating the area of interest and the surrounding area are needed. In this paper, we propose a new quality parameter considering characteristics of 3D point cloud instead of quality change based on assumed video codec in MPEG V-PCC used in 3D point cloud compression, 3D Grid division method and representation for selectively transmitting 3D point clouds according to user's area of interest, and propose a new 3D Manifesto. By using the proposed technique, it is possible to generate more bitrate images, and it is confirmed that the efficiency of network, decoder, and renderer can be increased while selectively transmitting as needed.

• Smart Media Journal
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• v.11 no.3
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• pp.39-53
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• 2022

Cloud of Things (CoT) can provide IoT applications with unlimited storage functions and processing power supported by cloud services. However, in a centralized cloud of things, it can create a single point of failure that can lead to bottleneck problems, outages of the CoT network. In this paper, to solve the problem of centralized cloud of things and interoperate between different service domains, we propose an IoT service interoperability system using distributed fog computing and blockchain technology. Distributed fog is used to provide real-time data processing and services in fog systems located at a geographically close distance to IoT devices, and to enable service interoperability between each fog using smart contracts and distributed ledgers of the blockchain. The proposed system provides services within a region close to the distributed fog entrusted with the service from the cloud, and it is possible to access the services of other fogs without going through the cloud even between fogs. In addition, by sharing a service right token issuance information between the cloud and fog nodes using a blockchain network, the integrity of the token can be guaranteed and reliable service interoperability between fog nodes can be performed.

• Journal of Information Technology Services
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• v.10 no.3
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• pp.213-236
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• 2011

Mobile devices are widely accepted as a convenient machine which provides computing capability as well we cell phone capability. Because of limited resources on mobile devices, complex applications could not be deployed on the devices. Service-based mobile applications (SMAs) can provide a solution to overcome the limitation by subscribing cloud services. Since SMAs have complex structures than standalone applications, it is challenging to develop high quality SMAs, to manage both services and mobile applications, and to implement automated billing for subscribed services. Therefore, there is a great demand for a platform for super mobile computing, which supports all key activities in managing life cycle of SMAs. In this paper, we present technical aspects of a platform which is under development; Super Mobile Autonomous Reliable plaTform (SMART). We believe that it provides a number of practical features which are essential in supporting life-cycle of SMAs; development, deployment, management, and operation.

• Journal of Computing Science and Engineering
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• v.9 no.3
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• pp.155-162
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• 2015

The last decade has seen a rapid growth in the use of mobile devices all over the world. With an increasing use of mobile devices, mobile applications are becoming more diverse and complex, demanding more computational resources. However, mobile devices are typically resource-limited (i.e., a slower-speed CPU, a smaller memory) due to a variety of reasons. Mobile users will be capable of running applications with heavy computation if they can offload some of their computations to other places, such as a desktop or server machines. However, mobile users are typically subject to dynamically changing network environments, particularly, due to user mobility. This makes it hard to choose good offloading decisions in mobile environments. In general, users' mobility can provide some hints for upcoming changes to network environments. Motivated by this, we propose a mobility model of each individual user taking advantage of the regularity of his/her mobility pattern, and develop an offloading decision-making technique based on the mobility model. We evaluate our technique through trace-based simulation with real log data traces from 14 Android users. Our evaluation results show that the proposed technique can help boost the performance of mobile devices in terms of response time and energy consumption, when users are highly mobile.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.347-357
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• 2019

The usefulness of processing and analysis systems of GIS-based agricultural climate data is affected by the reliability and availability of computing infrastructures such as cloud, on-premises, and hybrid. Cloud technology has grown in popularity. However, various reference cases accumulated over the years of operational experiences point out important features that make on-premises technology compatible with cloud technology. Both cloud and on-premises technologies have their advantages and disadvantages in terms of operational time and cost, reliability, and security depending on cases of applications. In this study, we have described characteristics of four general computing platforms including cloud, on-premises with hardware-level virtualization, on-premises with operating system-level virtualization and hybrid environments, and compared them in terms of advantages and disadvantages when a huge amount of GIS-based agricultural climate data were stored and processed to provide public services of agro-meteorological and climate information at high spatial and temporal resolutions. It was found that migrating high-resolution agricultural climate data to public cloud would not be reasonable due to high cost for storing a large amount data that may be of no use in the future. Therefore, we recommended hybrid systems that the on-premises and the cloud environments are combined for data storage and backup systems that incur a major cost, and data analysis, processing and presentation that need operational flexibility, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.543-548
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• 2015

Recently top-k query processing has been extremely important along with the explosion of data produced by a variety of applications. Top-k queries return the best k results ordered by a user-provided monotone scoring function. As cloud computing service has been getting more popular than ever, a hot attention has been paid to cloud-based data outsourcing in which clients' data are stored and managed by the cloud. The cloud-based data outsourcing, though, exposes a critical secuity concern of sensitive data, resulting in the misuse of unauthorized users. Hence it is essential to encrypt sensitive data before outsourcing the data to the cloud. However, there has been little attention to efficient top-k processing on the encrypted cloud data. In this paper we propose a novel top-k processing algorithm that can efficiently process a large amount of encrypted data in the cloud. The main idea of the algorithm is to prune unpromising intermediate results at the early phase without decrypting the encrypted data by leveraging an order-preserving encrypted technique. Experiment results show that the proposed top-k processing algorithm significantly reduces the overhead of client systems from 10X to 10000X.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.291-298
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• 2024

Despite its wide application, cloud computing raises privacy leakage concerns because users should send their private data to the cloud. Homomorphic encryption (HE) can resolve the concerns by allowing cloud servers to compute on encrypted data without decryption. However, due to the huge computation overhead of HE, simply executing an entire cloud program with HE causes significant computation. Manually partitioning the program and applying HE only to the partitioned program for the cloud can reduce the computation overhead. However, the manual code partitioning and HE-transformation are time-consuming and error-prone. This work proposes a new homomorphic encryption enabled annotation-guided code partitioning compiler, called Heapa, for privacy preserving cloud computing. Heapa allows programmers to annotate a program about the code region for cloud computing. Then, Heapa analyzes the annotated program, makes a partition plan with a variable list that requires communication and encryption, and generates a homomorphic encryptionenabled partitioned programs. Moreover, Heapa provides not only two region-level partitioning annotations, but also two instruction-level annotations, thus enabling a fine-grained partitioning and achieving better performance. For six machine learning and deep learning applications, Heapa achieves a 3.61 times geomean performance speedup compared to the non-partitioned cloud computing scheme.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.151-155
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• 2017

With the rapid growth of the up-to-date wireless network and Internet technologies, huge and various types of things around us are connected to the Internet and build the hyper-connected society, and lots of smart applications using these technologies are actively developed recently. IoT connects human, things, space, and data with various types of networks to construct the hyper-connected network that can create, collect, share and appling realtime information. Furthermore, most of the smart applications are concentrated on the service that can collect and store realtime contexts using various sensors and cloud technology, and provide intelligence by making inferences and decisions from them nowadays. In this paper, we design a smart application that can accurately control and process the current state of the specific context in realtime by using the state-of-the-art ICT techniques such as various sensors and cloud technologies on the IoT based mobile computing environment.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.131.1-131.1
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• 2011

We present applications of clustering methods to detect variability in massive astronomical time series data. Focusing on variability of bright stars, we use clustering methods to separate possible variable sources from other time series data, which include intrinsically non-variable sources and data with common systematic patterns. We already finished the analysis of the Northern Sky Variability Survey data, which include about 16 million light curves, and present candidate variable sources with their association to other data at different wavelengths. We also apply our clustering method to the light curves of bright objects in the SuperWASP Data Release 1. For the analysis of the SuperWASP data, we exploit a elastically configurable Cloud computing environments that the KISTI Supercomputing Center is deploying. Two quite different configurations are incorporated in our Cloud computing test bed. One system uses the Hadoop distributed processing with its distributed file system, using distributed processing with data locality condition. Another one adopts the Condor and the Lustre network file system. We present test results, considering performance of processing a large number of light curves, and finding clusters of variable and non-variable objects.