• Journal of the Korea Society for Simulation
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• v.16 no.2
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• pp.9-15
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• 2007

As the Internet grows and the mass multimedia data become popular, the storage system migrates from DAS, where the storage and the server are directly connected, to SAN and NAS. SAN connects the storages with a separate network, and NAS provides only file services, connects the storages with IP network. However, SAN and NAS can not fulfill the needs for companies if used separately, thus are asked to be integrated. In this research, we propose an efficient data sharing method which employees the concept of GMC, Croup Master Cache for the integrated environment of SAN and NAS. GMC is based on MCI, Metadata server and Cluster system Integration, but tries to solve the high expansion cost problem with MCI. We introduce the basic concept of GMC, compare the performance of GMC with that of MCI using computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2960-2976
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• 2018

The limited storage and computing capacity hinder the development of cognitive radio ad hoc networks (CRAHNs). To solve the problem, a new paradigm of cloud-based CRAHN has been proposed, in which a CRAHN will make use of the computation and storage resources of the cloud. This paper envisions an integrated CRAHN-cloud network architecture. In this architecture, some cognitive radio users (CUs) who satisfy the required metrics could perform as mobile gateway candidates to connect other ordinary CUs with the cloud. These mobile gateway candidates are dynamically clustered according to different related metrics. Cluster head and time-to-live value are determined in each cluster. In this paper, the gateway advertisement and discovery issues are first addressed to propose a hybrid gateway discovery mechanism. After that, a QoS-based gateway selection algorithm is proposed for each CU to select the optimal gateway. Simulations are carried out to evaluate the performance of the overall scheme, which incorporates the proposed clustering and gateway selection algorithms. The results show that the proposed scheme can achieve about 11% higher average throughput, 10% lower end-to-end delay, and 8% lower packet drop fractions compared with the existing scheme.

• KCID journal
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• v.19 no.1
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• pp.19-29
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• 2012

The data of the agricultural water quality survey network was analyzed between from 1990 to 2010 in order to propose effective plans for water quality management by analyzing the characteristics of agricultural lakes and the change of water quality. The result of the analysis shows that there is a correlation between water quality and items that can be a function of water depth such as dam height, dam length, dam height/dam length ratio and active storage/surface area of lake ratio. This means that, Korean agricultural lakes, there is a correlation between water quality and water depth. Water quality of the lakes that have lower than 5m of active storage/surface area of lake ratio (effective water depth) especially tends to get worse rapidly. The Chl-a and COD concentration of Korean agricultural lakes have a tendency to increase between June and September. Therefore, we recommend first taking a water quality improvement project for the lakes preformed watershed management project, and taking a preventive short-term water quality improvement project for the unperformed lakes before June among lakes that have lower than 5m of effective water depth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3203-3215
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• 2015

Due to the high growth of SNS population, service scalability is one of the critical issues to be addressed. The cloud environment provides the flexible computing and storage resources for services deployment, which fits the characteristics of scalable SNS deployment. However, if the SNS related information is not properly placed, it will cause unbalance load and heavy transmission cost on the storage virtual machine (VM) and cloud data center (CDC) network. In this paper, we characterize the SNS into a graph model based on the users' associations and interest correlations. The node weight represents the degree of associations, which can be indexed by the number of friends or data sources, and the link weight denotes the correlation between users/data sources. Then, based on the SNS graph, the two-step algorithm is proposed in this paper to determine the placement of SNS related data among VMs. Two k-means based clustering schemes are proposed to allocate social data in proper VM and physical servers for pre-configured VM and dynamic VM environment, respectively. The experimental example was conducted and to illustrate and compare the performance of the proposed schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2164-2170
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• 2011

Rapid surge in date quantity lead to increase in storage demand from corporate. The existing SAN with fiber channel is being changed to IP-based SAN environment due to installment and maintenance cost. But the IP-based network still have some similar security problems as existing TCP/IP network. Also, for the security reasons of storage traffic, data are encrypted, but with the existing system, data larger than 10G can't be handled. To address security and speed issue, this paper proposes to a structure applied to IP SAN environment with Parallel Structure Word-based FSR (PS-WFSR) as hardware.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.221.1-221.1
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• 2012

The Korea Astronomy and Space Science Institute (KASI) is developing three 1.6m optical telescopes with $18k{\times}18k$ mosaic CCD cameras. These telescopes will be installed and operated at Chile, South Africa, and Australia for Korea Micro-lensing Telescope Network (KMTNet) project. The main scientific goal of the project is to discover earth-like extra-solar planets using the gravitational micro-lensing technique. To achieve the goal, each telescope at three sites will continuously monitor the specific region of Galactic bulge with 2.5 minute cadence for five years. Assuming 12 hour observation in maximum for a night, the amount of 200 GB file storage is required for one night observation at one observatory. If we consider the whole project period and the data processing procedure, a few PB class data storage, high-speed network, and high performance computers are essential. In this presentation, we introduce the KMTNet data management plan that handles gigantic data; raw image collecting, image processing, photometry pipeline, database archiving, and backup.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• ETRI Journal
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• v.43 no.5
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• pp.835-855
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• 2021

With the recent increase in data disclosure, the Comprehensive Knowledge Archive Network (CKAN), which is an open-source data distribution platform, is drawing much attention. CKAN is used together with additional extensions, such as Datastore and Datapusher for data management and Harvest and DCAT for data collection. This study derives the problems of CKAN itself and Harvest Extension. First, CKAN causes two problems of data inconsistency and storage space waste for data deletion. Second, Harvest Extension causes three additional problems, namely source deletion that deletes only sources without deleting data themselves, job stop that cannot delete job during data collection, and service interruption that cannot provide service, even if data exist. Based on these observations, we propose herein an improved CKAN that provides a new deletion function solving data inconsistency and storage space waste problems. In addition, we present an improved Harvest Extension solving three problems of the legacy Harvest Extension. We verify the correctness and the usefulness of the improved CKAN and Harvest Extension functions through actual implementation and extensive experiments.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.281-287
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• 2021

Bioinformatics is formed with a blend of biology and informatics technologies and it employs the statistical methods and approaches for attending the concerning issues in the domains of nutrition, medical research and towards reviewing the living environment. The ceaseless growth of DNA sequencing technologies has resulted in the production of voluminous genomic data especially the DNA sequences thus calling out for increased storage and bandwidth. As of now, the bioinformatics confronts the major hurdle of management, interpretation and accurately preserving of this hefty information. Compression tends to be a beacon of hope towards resolving the aforementioned issues. Keeping the storage efficiently, a methodology has been recommended which for attending the same. In addition, there is introduction of a competent algorithm that aids in exact matching of small pattern. The DNA representation sequence is then implemented subsequently for determining 2 bases to 6 bases matching with the remaining input sequence. This process involves transforming of DNA sequence into an ASCII symbols in the first level and compress by using LZ77 compression method in the second level and after that form the grid variables with size 3 to hold the 100 characters. In the third level of compression, the compressed output is in the grid variables. Hence, the proposed algorithm S_Pattern DNA gives an average better compression ratio of 93% when compared to the existing compression algorithms for the datasets from the UCI repository.

• Imaging Science in Dentistry
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• v.50 no.4
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• pp.331-337
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• 2020

Purpose: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. Materials and Methods: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. Results: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. Conclusion: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.