• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1662-1665
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• 2010

그린 컴퓨팅의 일환으로 데이터 센터(Data Center)의 전력 효율을 고려한 소프트웨어/하드웨어 기술연구가 활발하게 진행되고 있다. 데이터 센터에서는 부하 분산(Load Balancing) 서비스의 마이그레이션(Migration) 기법을 통해 서비스의 품질을 높이고 데이터 센터의 효율성을 극대화 시킨다. 본 논문에서는 데이터 센터에서 냉각으로 소모되는 전력 절감을 위해 데이터 센터의 환경과 시스템 부하(Work Load)량에 기반을 둔 프로세스 마이그레이션(Process Migration) 기법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.619-620
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• 2011

MAC 프레임 결합 기법은 다수의 MPDU (MAC protocol data units)를 하나의 PPDU (PHY protocol data units)로 결합시켜 네트워크의 데이터 전송 효율을 높이는 방법이다. 본 논문에서는 프레임 결합 기법이 애드혹 네트워크에서 TCP 성능에 미치는 영향을 살펴본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.930-932
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• 2021

영상처리에서 데이터 증강(Data augmentation)은 단순히 사진을 편집하여 사진의 개수를 증강하는 것이다. 단순 데이터 증강은 동물의 반점이나 다양한 색깔을 반영하지 못하는 한계가 있다. 본 논문에서는 GAN을 통한 데이터 증강 기법을 제안한다. 제안하는 방법은 CycleGAN을 사용하여 GAN 이미지를 생성한 뒤, 데이터 증강을 거쳐 동물의 종 분류 정확도를 측정한다. 정확도 비교를 위해 일반 사진으로만 구성한 집단과 GAN 사진을 추가한 두 집단으로 나누었다. ResNet50을 사용하여 종 분류 정확도를 측정한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.75-77
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• 2008

Nowadays, as the indices of the major search engines grow to a tremendous proportion, vertical search services can help customers to find what they need. Search Engine is one of the reasons for Internet shopping success in today's world. The import one part of search engine is clustering data. The objective of this paper is to explore a k-means++ algorithm to calculate the clustering data which in the Internet shopping environment. The experiment results shows that the k-means++ algorithm is a faster algorithm to achieved a good clustering.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.240-257
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• 2023

The industrial Internet of Things (IIoT) is characterized by intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. The heterogeneous IIoT devices require a high data rate, high reliability, high coverage, and low delay, thus posing a significant challenge to information security. High-performance edge and cloud servers are a good backup solution for IIoT devices with limited capabilities. However, privacy leakage and network attack cases may occur in heterogeneous IIoT environments. Cloud-based multi-party computing is a reliable privacy-protecting technology that encourages multiparty participation in joint computing without privacy disclosure. However, the default cloud selection method does not meet the heterogeneous IIoT requirements. The server can be dishonest, significantly increasing the probability of multi-party computation failure or inefficiency. This paper proposes a blockchain and smart contract-based optimized cloud node selection framework. Different participants choose the best server that meets their performance demands, considering the communication delay. Smart contracts provide a progressive request mechanism to increase participation. The simulation results show that our framework improves overall multi-party computing efficiency by up to 44.73%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3826-3851
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• 2016

With traditional data storage solutions becoming too expensive and cumbersome to support Big Data processing, enterprises are now starting to outsource their data requirements to third parties, such as cloud service providers. However, this outsourced initiative introduces a number of security and privacy concerns. In this paper, homomorphic encryption is suggested as a mechanism to protect the confidentiality and privacy of outsourced data, while at the same time allowing third parties to perform computation on encrypted data. This paper also discusses the challenges of Big Data processing protection and highlights its differences from traditional data protection. Existing works on homomorphic encryption are technically reviewed and compared in terms of their encryption scheme, homomorphism classification, algorithm design, noise management, and security assumption. Finally, this paper discusses the current implementation, challenges, and future direction towards a practical homomorphic encryption scheme for securing outsourced Big Data computation.

• International Journal of Internet, Broadcasting and Communication
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• v.5 no.1
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• pp.14-17
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• 2013

RDF data is widely used for exchanging data nowadays to enable semantic web era. This leads to the need for storing and retrieving these data efficiently and effectively. Recently, the structure index in graph-based perspective is considered as a promising approach to deal with issues of complex query graphs. However, even though there are many researches based on structure indexing, there can be a better architectural approach instead of addressing the issue as a part. In this research, we propose architecture for storing, query processing and retrieving RDF data in efficient manner using structure indexing. Our research utilizes research results from iStore and 2 relation-based approaches and we focus on improving query processing to reduce the time of loading data and I/O cost.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.864-867
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• 2005

In this paper we introduce a design of parallel architecture for wavelet transformation on FPGA. We implement wavelet transforms though lifting scheme and apply Daubechies4 transform equations. This technique has an advantage that we can obtain perfect reconstruction of the data. We divide our process to high pass filter and low pass filter. With this division, we can find coefficients from low and high pass filters simultaneously using parallel processing properties of FPGA to reduce processing time. From the equations, we have to design real number computation module, referred to IEEE754 standard. We choose 32 bit computation that is fine enough to reconstruct data. After that we arrange the real number module according to Daubechies4 transform though lifting scheme.

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.25-32
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• 2016

In this paper, we design a middleware for context-awareness which provides compound contexts from diverse sensors on a mobile device. Until now, most of context-aware application developers have taken responsibility for context processing from sensing data. Such application-level context processing causes heavily redundant data processing and leads to significant resource waste in energy as well as computing. In the proposed scheme, we define primitive and compound context map which consists of relavant sensors and features. Based on the context definition, each application demands a context of interest to the middleware, and thus similar context-aware applications inherently share context information and procesing within the middleware. We show that the proposed scheme significantly reduces the resource amounts of cpu, memory, and battery, and that the performance gain gets much more when multiple applications which need similar contexts are running.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.1183-1186
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• 2014

Our research is different form traditional to have some large LED screen grouping together to constitute multi-vision technique. In this paper, we purpose a method of using k-path diffusion method to build connect between the devices and find an optimal data transmission path. In second half of this paper, through practical application, we using this technique transmitting data successfully and achieving a simple Multi-vision effect. This technique possess smart devices and Wifi P2P's features, these features improve system's dynamic and decentralized processing ability make our technique has high scalability.