• Information and Communications Magazine
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• v.31 no.2
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• pp.105-111
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• 2014

클라우드 컴퓨팅과 빅데이터 시대의 개막으로 클라우드에 저장되는 데이터가 급속도로 증가함에 따라 최근 클라우드 컴퓨팅의 주요한 요소로 클라우드 저장 시스템이 주목받고 있다. 클라우드 저장 시스템은 크게 두 가지 목적에 의해 동작한다. 첫 번째는 사용자에게 데이터를 소실 없이 정확하게 전달해주는 것이고, 두 번째는 네트워크 상에서 소실된 데이터를 복구해 내는 것이다. 데이터 소실은 분산 노드 내 장비의 결함, 소프트웨어 업데이트 등과 같은 요인에 의해 발생하는데, 이와 같은 데이터 소실에 대응하기 위해 소실 복구 코딩 기법을 사용한다. 본 고에서는 클라우드 저장 시스템의 요구사항들을 토대로 현재 클라우드 저장 시스템에 사용되는 다양한 코딩 기법을 살펴보고 차세대 클라우드 저장 시스템을 위한 코딩 기법에 대해 논의해본다.

• The Journal of the Korea Contents Association
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• v.12 no.7
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• pp.21-28
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• 2012

The data centric storage (DCS) scheme is one of representative methods to efficiently store and maintain data generated in wireless sensor networks. In the DCS schemes, each node has the specified data range for storing data. This feature is highly vulnerable to the faults of nodes. In this paper, we propose a new recovery scheme for the lost data caused by the faults of nodes in DCS environments. The proposed scheme improves the accuracy of query results by recovering the lost data using the spatial continuity of physical data. To show the superiority of our proposed scheme, we simulate it in the DCS environments with the faults of nodes. In the result, our proposed scheme improves the accuracy by about 28% through about 2.5% additional energy consumption over the existing scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.11
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• pp.723-729
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• 2014

In this paper, we derive the average repair bandwidth and/or read cost for arbitrary repairable linear code ensembles. The repair bandwidth and read cost are the required amount of data and access number of nodes to restore a failed node, respectively. Here, the repairable linear code ensemble is given by such parameters as the number k of data symbols, the number m of parity symbols, and their degree distributions. We further assume that the code is systematic, and no other constraint is assumed, except possibly that the exact repair could be done by the parity check-sum relation with fully connected n=k+m storages. This enables one to apply the result of this paper directly to any randomly constructed codes with the above parameters, such as linear fountain codes. The final expression of the average repair read cost shows that it is highly dependent on the degree distribution of parity symbols, and also the values n and k.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.1-7
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• 2020

This paper introduces two different techniques for dealing with incomplete data and algorithms for learning this data. The first method is to process the incomplete data by assigning the missing value with equal probability that the missing variable can have, and learn this data with the SVM. This technique ensures that the higher the frequency of missing for any variable, the higher the entropy so that it is not selected in the decision tree. This method is characterized by ignoring all remaining information in the missing variable and assigning a new value. On the other hand, the new method is to calculate the entropy probability from the remaining information except the missing value and use it as an estimate of the missing variable. In other words, using a lot of information that is not lost from incomplete learning data to recover some missing information and learn using deep learning. These two methods measure performance by selecting one variable in turn from the training data and iteratively comparing the results of different measurements with varying proportions of data lost in the variable.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.474-485
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• 2017

Sungnyemun Gate, Korea's National Treasure No.1, was destroyed by fire on February 10, 2008 and has been re-opened to the public again as of May 4, 2013 after a reconstruction work. Sungnyemun Gate become a national issue and draw public attention to be a major topic on news or research. In this research, text mining and association rule mining techniques were used on keyword of newspaper articles related to Sungnyemun Gate as a cultural heritage from 2002 to 2016 to find major keywords and keyword association rule. Next, we analyzed some typical and specific keywords that appear frequently and partially depending on before and after the fire and newpaper companies. Through this research, the trends and keywords of newspapers articles related to Sungnyemun Gate could be understood, and this research can be used as fundamental data about Sungnyemun Gate to information producer and consumer.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.