Earticle

There has been a snappy forward advancement in cloud. With the developing measures of affiliations, turning number of affiliations are being utilized as assets in the cloud. Issues arise regarding the certainties of various clients utilizing these assets like round measure of room associations which helps in keeping from the cost. Putting away these associations evades the high cost while posting information during the building of machine orders. This collection of information gives better execution whilst less “far point cost” and ready to make prepared modifications. Cloud can yield better possibilities through net where security weaknesses are restricted. Despite of “wellbeing” being one of the brimming with risk weakness, it adjusts unmistakable connection to go into assumed control handling general condition. This paper uses “MapReduce” library that parallelizes the figuring, and handles perplexed issues like data spread, stack modifying and adjustment to non-basic disappointment. Enormous information, spread across finished many machines, need to parallelize. Moves the data, and gives booking, adjustment to non-basic disappointment. In this paper, a graph of MapReduce programming model along with the applications are explored. The maker has delineated here the work procedure of MapReduce process. Some fundamental issues, like adjustment to non-basic disappointment, are considered in more detail. Without a doubt, even the outline of working of Map Reduce is given.

In a cloud computing environment, frequent data interactions require high information security. This paper briefly overviews encrypted data storage in the cloud computing environment. A dual encryption scheme that combined identity attributes and security devices was proposed. The scheme was evaluated through simulation experiments conducted on a laboratory server. The results indicated that an increase in the size of the storage file led to an increase in the time overhead of the encryption scheme. Furthermore, the dual encryption scheme exhibited slightly higher time overhead than a single security device encryption scheme, but was similar to a single identity attribute-based encryption scheme. For cloud storage data, the successful queries of encrypted data and effective resistance against third-party attacks were only achieved when the number of overlapping identity attributes was 5, which was the same as the storage user.

Data deduplication technology improves data storage efficiency while storing and managing large amounts of data. It reduces storage requirements by determining whether replicated data is being added to storage and omitting these uploads. Data deduplication technologies require data confidentiality and integrity when applied to cloud storage environments, and they require a variety of security measures, such as encryption. However, because the source data cannot be transformed, common encryption techniques generally cannot be applied at the same time as data deduplication. Various studies have been conducted to solve this problem. This white paper describes the basic environment for data deduplication technology. It also analyzes and compares multiple proposed technologies to address security threats.

Cloud storage is provided as a service in order to keep pace with the increasing use of digital information. It can be used to store data via networks and various devices and is easy to access. Unlike existing removable storage, many users can use cloud storage because it has no storage capacity limit and does not require a storage medium. Cloud storage reliability has become a topic of importance, as many users employ it for saving great volumes of data. For protection against unethical administrators and attackers, a variety of cryptography systems, such as searchable encryption and proxy re-encryption, are being applied to cloud storage systems. However, the existing searchable encryption technology is inconvenient to use in a cloud storage environment where users upload their data. This is because this data is shared with others, as necessary, and the users with whom the data is shared change frequently. In this paper, we propose a searchable re-encryption scheme in which a user can safely share data with others by generating a searchable encryption index and then re-encrypt it.

On-device machine learning is fundamentally constrained by limited storage, especially in continuous data collection scenarios where sensor or vision streams accumulate rapidly. This paper empirically investigates storage-aware learning, characterizing the trade-off between data quantity and data quality under lossy compression. Using the CIFAR-10 dataset as a controlled benchmark, we systematically vary both the amount and the fidelity of training data to understand their joint impact on model performance. Our results reveal that (1) neither maximizing quantity nor quality alone yields optimal accuracy, emphasizing that the optimal trade-off between them depends nonlinearly on the available storage budget, and (2) data samples exhibit differential sensitivity to compression, motivating a sample-wise adaptive compression policy. These findings challenge uniform data-retention strategies such as naive data dropping or fixed-rate compression, and establish a foundation for adaptive, storage-efficient learning systems on resource-limited devices. This work opens new directions toward generalizable, storage-aware on-device intelligence.

사물인터넷(IoT) 기술은 최근 의료사물인터넷(IoMT)으로 정의된 대량의 의료 데이터를 처리하여 발전을 위해 개발된 의료분야에서 많이 활용되고 있다. 수집된 광범위한 의료 데이터는 수집된 의료 데이터를 처리하기 위해 구조화된 방식으로 클라우드에 저장된다. 그러나 방대한 양의 의료 데이터를 효과적으로 처리하는 것은 쉽지 않기 때문에 의료분야 구조 데이터를 개발하는 것이 필요하다. 본 논문에서는 IoMT에서 수집된 구조화된 건강 관리 데이터를 처리하기 위한 기계 학습 모드를 개발 하였다. 광범위한 의료 데이터를 처리하기 위해 본 논문에서는 의료 데이터 처리를 위한 MTGPLSTM 모델을 제안하였다. 제안 된 모델은 의료 정보 처리를 위한 선형 회귀 모델을 통합한다. 개발된 모델 이상치 모델은 IoMT에서 수집된 COVID-19 의료 데이터들의 평가 및 예측을 위해 FinTech 모델을 기반으로 구현되었다. 제안된 MTGPLSTM 모델은 감염 확산 방지를 위한 계획 계획을 예측하고 평가하기 위한 회귀 모델로 구성된다. 개발된 모델 성능은 LR, SVR, RFR, LSTM 및 제안된 MTGPLSTM 모델과 같은 서로 다른 분류기를 고려하였으며 1GB, 2GB, 3GB 등 데이터 크기가 다르다는 점도 주요하게 고려되었다. 제안된 MTGPLSTM 모델이 전 세계 데이터에 대해 최대 4% 감소된 MAPE 및 RMSE 값을 달성하였고 중국의 경우 기존 분류기보다 최대 6% 최소인 최소 MAPE(0.97)이 달성되었다.

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

현재 대용량의 데이터는 다양한 분야에 활용됨에 따라 스토리지 시스템에서 확장성을 제공해야 한다. NAS와 SAN은 일반적으로 비교적 대규모 스토리지 시스템을 구축하는 데 사용된다. 네트워크 기반의 DAS는 네 트워크에 직접 연결되지만 NAS와 달리 TCP/IP를 사용하지 않고 이더넷 계층에서 통신하므로 TCP/IP 네트워크 스택으로 인한 오버헤드가 없다. 본 논문에서는 NDAS를 효과적으로 활용하는 데 필요한 다양한 시스템 매개변수 를 조사하고 실험하였다. 또한 일반적으로 사용되는 분산 시스템에 네트워크 기반의 DAS를 적용하여 성능 평가 를 수행하였다. 여러 개의 네트워크 기반 DAS를 이용하여 단일 저장소 풀을 구성하여 실제 워크로드를 수행했을 때 확장성을 확인하였다. 본 연구에서 제안하는 스토리지 시스템은 기존의 확장성이 필요한 저장 장치에 바로 활 용이 가능하다.

As large amounts of data are currently being utilized in various fields, storage systems must provide scalability. NAS and SAN are generally used to build a relatively large-scale storage system. NDAS is directly connected to the network, but unlike NAS, it does not use TCP/IP and communicates at the Ethernet layer, so there is no overhead due to the TCP/IP network stack. Based on these characteristics, NDAS is an effective solution for building cost-effective storage devices. In this paper, various system parameters needed to utilize NDAS effectively are investigated and tested. Also, performance evaluation is performed by applying NDAS to a commonly used distributed system. It also showed scalability when a single storage pool was configured with multiple NDAS storages to perform real workloads. The storage system proposed in this study can be directly applied to existing storage devices requiring scalability.

Technology of data storage device has developed noticeably as demands and needs of new media increase, Huge data can be conveniently handled using removable type optical disk. In the present paper, the trend and current issue of development for optical disk media are introduced. Standardization of next generation optical disk media, technology of recording and reading, and applications of magneto-optical devices are also discussed. Finally, a methodology of process optimization for design and fabrication of high density optical disk substrate is proposed.

In this study, a probe array has been developed for use in a data storage device that is based on scanning probe microscope (SPM) and MEMS technology. When recording data bits by poling the PZT thin layer and reading them by sensing its piezoresponse, commercial probes of which the tip heights are typically shorter than $3{\mu}m$ raise a problem due to the electrostatic forces occurring between the probe body and the bottom electrode of a medium. In order to reduce this undesirable effect, a poly-silicon probe with a high aspect-ratio tip was fabricated using a molding technique. Poly-silicon probes fabricated by the molding technique have several features. The tip can be protected during the subsequent fabrication processes and have a high aspect ratio. The tip radius can be as small as 15 nm because sharpening oxidation process is allowed. To drive the probe, electrostatic actuation mechanism was employed since the fabrication process and driving/sensing circuit is very simple. The natural frequency and DC sensitivity of a fabricated probe were measured to be 18.75 kHz and 16.7 nm/V, respectively. The step response characteristic was investigated as well. Overshoot behavior in the probe movement was hardly observed because of large squeeze film air damping forces. Therefore, the probe fabricated in this study is considered to be very useful in probe-based data storages since it can stably approach toward the medium and be more robust against external shock.

본 논문은 원 데이터를 암호화하여 URL(Uniform Resource Locator)정보 형식으로 변형하고, URL정보를 QR코드로 인코딩함으로써 원 데이터의 저장없이 QR코드를 매개로 데이터를 기록하고 해독할 수 있도록 하는 스마 트한 QR코드에 의한 비저장식 데이터 기록 시스템 및 비저장식 데이터 제공방법을 제시하는 데 목적이 있다. 연구 의 방법은 선행기술연구와 문헌조사를 통해 연구의 차별성을 두기 위한 방법을 제시했으며 분석결과, 온라인 관리 서버에 구축되는 시스템으로서, 데이터 입력 신호별 매칭 암호가 저장된 암호DB, QR코드 생성 명령에 따라 입력 데이터를 암호DB에서 매칭되는 암호정보로 치환하여 관리서버 도메인네임의 하위 로케이터로 결합시킨 URL정보 로 변환시키고, URL정보를 QR코드로 인코딩하는 코드 생성부, 그리고 URL접속신호 수신에 따라, URL정보를 입력 데이터로 변환 제공하는 암호 해독부를 포함하는 QR코드에 의한 비저장식 데이터 기록시스템을 제공한다. 따라서 스마트한 QR방식의 데이터관리방식(기록과 제공)은 사용의 편리성과 공간의 제약이나 용량사용의 장애가 없다는 점을 밝히고 있다.

The purpose of this paper is to present a smart QR code recording system and a method of nonstorage data delivery that transforms the original data into a form of URL information by encrypting it and encoding the URL information into a QR code so that the QR code can be written and decrypted in a medium without storing the original data. The method of the study was presented by the prior art study and the literature research. Analysis results show that the system is built on the online administration server. The data input signal matching secret code is stored in DB, the QR code generation command converts input data from the password DB to the password information combined into the subordinate locator of the admin server's domain name, URL code. Therefore, the smart QR method of data management (recording and providing) indicates that there are no limitations in the ease and space of use or obstacles to capacity use.

분산 클라우드 환경에서는 다양한 이기종 장치의 정보들이 꾸준하게 증가하고 있다. 이 같은 이유는 고속의 네트워크의 속도와 대용량의 멀티미디어 데이터가 사용되고 있기 때문이다. 그러나, 이기종의 장치에서 송․수신되는 빅 데이터의 정보 오류를 최소화하기 위한 방법은 여전히 연구가 진행되고 있다. 본 논문에서는 클라우드 환경에서 송․수 신되는 정보들에 의해 발생되는 네트워크의 대역폭과 데이터 오류 최소화를 위한 딥러닝 기반의 비대칭적 저장 관리 기법을 제안한다. 제안 기법은 각각의 디바이스에서 생성되는 빅 데이터정보를 비대칭적으로 해시 처리한 후 로드 밸런 스를 최적화하기 위해서 딥러닝 기술을 적용하고 있다. 제안 기법은 각 디바이스에서 수집된 빅 데이터의 오류를 허용하 는 동시에 빅 데이터의 연계 정보를 개의 클러스터 그룹으로 그룹핑함으로써 빅 데이터의 연결성을 확보한 것이 특징 이다. 특히, 제안 기법은 빅 데이터간의 유사 값을 시드로 추출한 손실 함수를 사용하였기 때문에 비대칭적으로 빅 데이 터를 저장 관리 할때의 정보 오류를 최소화하였다.

Information from various heterogeneous devices is steadily increasing in distributed cloud environments. This is because high-speed network speeds and high-capacity multimedia data are being used. However, research is still underway on how to minimize information errors in big data sent and received by heterogeneous devices. In this paper, we propose a deep learning-based asymmetric storage management technique for minimizing bandwidth and data errors in networks generated by information sent and received in cloud environments. The proposed technique applies deep learning techniques to optimize the load balance after asymmetric hash of the big data information generated by each device. The proposed technique is characterized by allowing errors in big data collected from each device, while also ensuring the connectivity of big data by grouping big data into groups of clusters of dogs. In particular, the proposed technique minimizes information errors when storing and managing big data asymmetrically because it used a loss function that extracted similar values between big data as seeds.

Block-chain technology is rapidly evolving into ICT technology. In order to write data to a block in a blockchain system, a new block must be created in the lockchain system. However, in order for a new block to be created, it takes time for proof of work.(PoW). During the block generation process, It is impossible to provide a services using the data. IoT devices transmit huge amounts of data in real-time. Therefore, a method that can guarantee real time is needed. In this paper, we propose a real-time guarantee method of distributed database using blockchain to solve the above problem.

사물인터넷 기반의 헬스케어 서비스는 다양한 사물인터넷 디바이스를 통해서 사용자의 생체신호 측정, 질병 진단 및 예방을 포함한 건강관리 및 의료 서비스를 제공하고 있다. 그러나, 사물인터넷 기반의 헬스케어 서비스는 여러 가지 요소 기술들이 통합되어 서비스를 제공하기 때문에 각 요소 기술 자체의 보안 취약성과 연동 시 새로운 보안 취약성이 발생할 수 있는 문제점이 존재한다. 본 논문에서는 모바일 환경에서 IoT 기반의 웨어러블 장비를 이 용한 사용자의 헬스케어 정보를 서버에 전달할 때 제 3자로부터 사용자의 헬스케어 정보를 안전하게 처리할 수 있 는 사용자 프라이버시 보호 모델을 제안한다. 제안 모델은 사용자의 헬스케어 정보를 안전하게 처리, 보관, 저장할 수 있도록 헬스케어 센서 정보 별로 속성 값을 부여하여 사용자의 프라이버시를 계층적으로 통합 관리한다. 성능평 가 결과, 제안모델은 기존모델보다 IoT 장치의 처리율은 평균 10.5% 향상되었고, 서버의 오버헤드는 기존 모델에 비 해 평균 9.9% 낮은 결과를 얻었다.

Objects Internet-based healthcare services provide healthcare and healthcare services, including measurement of user's vital signs, diagnosis and prevention of diseases, through a variety of object internet devices. However, there is a problem that new security vulnerability can occur when inter-working with the security weakness of each element technology because the internet service based on the object Internet provides a service by integrating various element technologies. In this paper, we propose a user privacy protection model that can securely process user's healthcare information from a third party when delivering healthcare information of users using wearable equipment based on IoT in a mobile environment to a server. The proposed model provides attribute values for each healthcare sensor information so that the user can safely handle, store, and store the healthcare information, thereby managing the privacy of the user in a hierarchical manner. As a result of the performance evaluation, the throughput of IoT device is improved by 10.5% on average and the server overhead is 9.9% lower than that of the existing model.

국토교통부 공간정보 오픈플랫폼으로 인해 3차원 공간정보자료에 대한 관심이 증가하고 있다. 본 연구는 3차원 공간정보자료 중 텍스쳐 자료를 효율적으로 관리 및 저장에 관한 것으로, 다방향 항공 카메라 및 수직 항공 카메라에 의해 획득된 원시영상을 이용하여 항공삼각측량 및 3차원 저작을 통해 제작된 3차원 공간자료를 추출하고, 이 자료로부터 해당 텍스쳐 자료를 최적 저장 공간에 정렬 및 배치하여 단일 텍스쳐 자료를 생성하는 방안을 연구하고 관련 기법을 개발하였다. 실험 결과, 기존 방안인 단일 파일 저장 방식에 비해 최대 1/8배에 상응하는 용량 축소 효과가 있었으며, 다중 파일 저장 방식과 비교하였을 때 파일 관리 효율성을 증진시킬 수 있음을 최종 확인하였다. 본 연구 성과는 다수의 3차원 공간정보자료를 효율적으로 관리하기 위한 초석이 될 것으로 판단되며, 향후 알고리즘의 개선 및 추가 연구를 통해 활용 방안이 증대될 것으로 사료된다.

Recently, interests in space information data are increasing due to the initiation of spatial information open platform service by the Ministry of Land, Infrastructure and Transport. The purpose of this study is optimizing management and storing of the texture data, one kinds of 3D-spatial data. First, extract 3D-spatial data through the aerial triangulation and 3D-writing using raw image taken with the Multi-directional aerial camera and the vertical aerial camera. And develop the method to create single texture data and related technique by align and place corresponding 3D-spatial data to optimal storage space. Through experiment, the results show effect of 8 times of storage capacity reduction compared to existing single-file storage method, additionally, new method can improve file management efficiency in comparison with multiple file storage method. The results of this study can be cornerstone of three-dimensional space information management when dealing with bulk data, and utilizations will be enhanced through the further studies and algorithm improvement.

본 연구에서는 성공적인 이산화탄소 지중저장을 위해 덮개암의 물성 평가가 필수적임에도 불구하고, 덮개암인 셰일 시료 확보가 제한적이라는 한계를 보완하기 위해 단일 3D 디지털 암석 데이터로부터 3D 데이터를 증강하는 SinGAN (Single Image GAN) 기반 방법을 제안한다. 물성 평가에필요한 최소 데이터 크기인 대표요소체적(REV)은 공극 크기 분포와 공극 구형도 분포의 수렴성을기준으로 결정하였으며, REV를 만족하는 부분 체적(sub-volume) 데이터를 학습하여 다수의 3D 디지털 암석 데이터를 생성하였다. 생성 데이터는 공극 특성·연결성 지표 및 저차원 분석을 통해검증하였고, 원본 지표가 생성 데이터 분포 범위 내에 포함되며 공극 특성 분포가 학습 데이터와유사함을 확인하였다. 본 결과는 제한된 시료 환경에서도 덮개암 셰일의 공극 구조를 대표하는 다수의 디지털 암석 데이터를 확보하여 나노 규모 물성의 통계적 분포를 구축할 수 있음을 보여주며, 향후 해당 분포를 상위 스케일 모델에 반영하는 업샘플링 절차를 통해 실험실 규모의 유효 물성 산정으로 확장할 수 있는 기반을 제공한다.

This study proposes a single-image generative adversarial network (SinGAN)-based method to augment 3D digital rock volumes from a single 3D dataset, addressing the limitation of shale core sample availability for caprock property evaluation in CO2 storage. The representative elementary volume (REV), the minimum size for reliable property evaluation, was determined based on the convergence of pore size and pore sphericity distributions. SinGAN was trained on sub-volumes satisfying the REV criterion to generate multiple three-dimensional digital rock samples. Validation through pore characteristics, connectivity metrics, and low-dimensional analysis confirmed that the metrics of the original sample fell within the generated sample distribution and that the pore-structure distributions align with the training data. These results show that, even with limited samples, multiple digital rock volumes representing shale caprock pore structures can be generated, enabling the construction of nanoscale property distributions and providing a basis for upscaling workflows to estimate laboratory-scale effective properties.

본 연구는 광동댐 유역을 대상으로 RCPs (Representative Concentration Pathways) 기후변화 시나리오의 Arc-SWAT 적용으로 평균유출량과 저유량 계열을 구축하고 경계핵함수(Boundary Kernel)를 이용하여 비매개변수적 갈수빈도 해석을 수행하였다. 분석결과, RCPs 시나리오 하에서 가까운 미래의 유출량 감소로 인한 가뭄발생빈도가 증가하였으며, RCP8.5에서 저유량 계열의 변동폭이 크게 나타났다. Median flow의 갈수량 빈도해석결과 가까운 미래(2030s)의 30년 빈도 갈수량의 경우 Historic 기간에 비하여 증가(RCP4.5: +22.4%, RCP8.5: +40.4%)하였으나, 먼 미래(2080s)에는 갈수량 감소(RCP4.5: -4.7%, RCP8.5: -52.9%)로 인한 가뭄발생빈도가 커지는 것으로 분석되었다. 또한 Quantile 25% flow 저유량 계열의 경우 먼 미래에 빈도별 갈수량이 감소(RCP4.5: -20.8% ~ -60.0%, RCP8.5: -30.4% ~ -96.0%)하여 극심한 가뭄의 발생빈도가 커질 것으로 분석되었다. RCPs 시나리오 적용에 따른 비매개변수적 갈수빈도 해석 결과는 한반도 중권역별 수자원개발계획 수립과 기후변화 대응책 마련을 위한 기초자료로 활용이 가능할 것이다.

In this study, we applied an advanced non-parametric low-flow frequency analysis using boundary kernel by Representative Concentration Pathways (RCPs) climate change scenarios through Arc-SWAT long-term runoff model simulation at the Gwangdong storage reservoir located in Taeback, Gangwondo. The results show that drought frequency under RCPs was expected to increase due to reduced runoff during the near future, and the variation of low-flow time series was appeared greatly under RCP8.5 scenario, respectively. The result from drought frequency of Median flow in the near future (2030s) compared historic period, the case of 30-year low-flow frequency was increased (the RCP4.5 shows +22.4% and the RCP8.5 shows +40.4%), but in the distant future (2080s) expected increase of drought frequency due to the reduction of low-flow (under RCP4.5: -4.7% and RCP8.5: -52.9%), respectively. In case of Quantile 25% flow time series data also expected that the severe drought frequency will be increased in the distant future by reducing low-flow (the RCP4.5 shows -20.8% to -60.0% and the RCP8.5 shows -30.4% to -96.0%). This non-parametric low-flow frequency analysis results according to the RCPs scenarios have expected to consider to take advantage of as a basis data for water resources management and countermeasures of climate change in the mid-watershed over the Korean Peninsula.

본 연구의 목적은 기상자료(강수량, 최고기온, 최저기온, 평균기온, 평균풍속) 기반의 다중선형 회귀모형을 개발하여 농업용저수지 저수율을 예측하는 것이다. 나이브 베이즈 분류를 활용하여 전국 1,559개의 저수지를 지리형태학적 제원(유효저수량, 수혜면적, 유역면적, 위도, 경도 및 한발빈도)을 기준으로 30개 군집으로 분류하였다. 각 군집별로, 기상청 기상자료와 한국농어촌공사 저수지 저수율의 13년(2002~2014) 자료를 활용하여 월별 회귀모형을 유도하였다. 저수율의 회귀모형은 결정계수($R^2$)가 0.76, Nash-Sutcliffe efficiency (NSE)가 0.73, 평균제곱근오차가 8.33%로 나타났다. 회귀모형은 2년(2015~2016) 기간의 기상청 3개월 기상전망자료인 GloSea5 (GS5)를 사용하여 평가되었다. 현재저수율과 평년저수율에 의해 산정되는 저수지 가뭄지수(Reservoir Drought Index, RDI)에 의한 ROC (Receiver Operating Characteristics) 분석의 적중률은 관측값을 이용한 회귀식에서 0.80과 GS5를 이용한 회귀식에서 0.73으로 나타났다. 본 연구의 결과를 이용해 미래 저수율을 전망하여 안정적인 미래 농업용수 공급에 대한 의사결정 자료로 사용할 수 있을 것이다.

The purpose of this study is to predict monthly agricultural reservoir storage by developing weather data-based Multiple Linear Regression Model (MLRM) with precipitation, maximum temperature, minimum temperature, average temperature, and average wind speed. Using Naïve-Bayes classification, total 1,559 nationwide reservoirs were classified into 30 clusters based on geomorphological specification (effective storage volume, irrigation area, watershed area, latitude, longitude and frequency of drought). For each cluster, the monthly MLRM was derived using 13 years (2002~2014) meteorological data by KMA (Korea Meteorological Administration) and reservoir storage rate data by KRC (Korea Rural Community). The MLRM for reservoir storage rate showed the determination coefficient ($R^2$) of 0.76, Nash-Sutcliffe efficiency (NSE) of 0.73, and root mean square error (RMSE) of 8.33% respectively. The MLRM was evaluated for 2 years (2015~2016) using 3 months weather forecast data of GloSea5 (GS5) by KMA. The Reservoir Drought Index (RDI) that was represented by present and normal year reservoir storage rate showed that the ROC (Receiver Operating Characteristics) average hit rate was 0.80 using observed data and 0.73 using GS5 data in the MLRM. Using the results of this study, future reservoir storage rates can be predicted and used as decision-making data on stable future agricultural water supply.

The development of the Web has made it easy to access large amounts of data. However, it is one of the big issues how to effectively store and improve query performance about these huge data. This paper proposes an RDF query optimization with hybrid storage structures to solve the issue. Hot-cold detection relocates data properly to improve the RDF query performance. After collecting data access information for a period of time, we identify the data with hot-cold detection. It reduces the write performance degradation of the SSD by moving all hot data at a time to the SSD.

전력 에너지 분야에 블록체인 기술이 적용되면 복잡한 거래절차가 단순화되고, 정산의 실시간성과 편리성이 확대될 수 있다. 그에 더하여, 거래 참여에 대한 인증 절차가 블록체인을 통해 간소화될 수 있다. 이러한 장점은 프로슈머들 로 하여금 참여에 대한 흥미를 불러올 수 있으며, 작은 단위의 잉여전력도 판매할 수 있어 탄소배출 절감하는 데 도 움이 된다. 하지만 앞서 언급한 새로운 비즈니스에 적용될 전력 데이터를 저장할 때 이더리움 블록체인의 성능 향상 은 반드시 필요하다. 본 연구에서는 이더리움의 저장공간을 효율적으로 활용할 수 있는 전력 데이터의 수직 결합기 법을 제안하고, 성능 실험을 통해 사용 수수료인 가스(Gas)가 최대 7.8배 이상 절감되는 것을 확인하였다.

When blockchain technology is applied to the power and energy field, complex transaction procedures can be simplified and the real-time and convenient settlement of accounts can be expanded. In addition, the authentication process for trading participation can be simplified through blockchain. These advantages can arouse interest in participation by prosumers and can be expected to help reduce carbon emissions as small units of surplus power can be sold. However, it is essential to improve the performance of the Ethereum blockchain when storing power data for the this new business. In this study, we propose a method of vertical merging power data that efficiently utilizes the storage space of Ethereum. Our experimental results show that the gas which is usage fee, is reduced by up to 7.8 times or more.