• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1357-1358
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• 2007

본 연구에서는 상변화 메모리 소자의 상변화 재료의 두께에 따른 열전달 현상과 지우기 전류의 변화량을 3차원 유한요소 해석 도구를 이용하여 해석하였다. 상변화 메모리의 하부전극과 상변화 소자의 접촉 부분에서 발생한 주울열은 상변화재료를 통해 상부전극 텅스텐으로 전달되어 외부로 빠져나간다. 상변화 재료 박막의 두께가 $200[{\AA}]$인 경우는 상부전극을 통해 빠져나가는 열이 커지게 되어 상전이를 일으키는 지우기 전류가 크게 증가하는 특성을 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04d
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• pp.512-514
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• 2003

본 논문은 모바일 환경에서 제한된 메모리를 가지고 있는 수신자에 의 한 TCP흐름 제어 방법을 제안한다. TCP 흐름 제어는 송신자에서 수신자에게 전달되는 Advertised 윈도우 크기를 조정하여 수행된다. 수신자는 무선 대역폭과 종단간 패킷 왕복 시간을 동적으로 측정하며 최적의 Advertised 윈도우 크기를 계산하고 송신자의 전송률을 무선 대역폭으로 제한한다. 제안된 흐름 제어 기법은 제한된 메모리를 가진 수신자를 고려하였으며 무선 네트웍의 특성을 고려 한 효율적 인 TCP 흐름 제어로 TCP의 전송 성능 향상과 종단간 패킷 왕복 시간의 지연을 줄일 수 있도록 하였다. 제안된 흐름 제어 기 법의 효율성과 성능을 구현과 실험을 통해 검증한다.

• Journal of IKEEE
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• v.24 no.3
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• pp.797-802
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• 2020

In this paper, we presents the design and implementation results of the FFT processor, which supports 4096 points of operation with less memory by sharing several memory used in the base-4 systolic array FFT processor into one memory. Sharing memory provides the advantage of reducing the area, and also simplifies the flow of data as I/O of the data progresses in one memory. The presented FFT processor was implemented and verified on the FPGA device. The implementation resulted in 51,855 CLB LUTs, 29,712 CLB registers, 8 block RAM tiles and 450 DSPs, and confirmed that the memory area could be reduced by 65% compared to the existing base-4 systolic array structure.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.11a
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• pp.135-136
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• 2008

이 실험에서는 비휘발성 메모리에서의 블로킹 층으로 $SiN_x$ 박막을 사용하였다. ELA (poly-Si) 기판위에 $SiO_xN_y$ 박막을 성장하기 전에 BHF를 이용해 자연 산화막을 제거하였다. 터널 층을 위해 2.7nm두께의 $SiO_xN_y$를 ICP-CVD 장비를 이용해 유리기판위에 증착하였다. 다음으로 $SiH_4/H_2$기체를 이용， ICP-CVD장비를 이용해 전하 저장을 위한 a-Si 박막을 증착하고, 마지막으로 a-Si층 위에 $SiN_x$ 층을 형성하였다. $SiN_x$ 박막을 형성하는데 최적의 조건을 찾기 위해 가스의 구성 비율 및 증착시간을 변화시키고 온도와 RF power도 바꿔주었다. 굴절률이 1.79 고 두께가 30 nm 인 $SiN_x$는 블로킹 층으로 사용하기 위한 것이다. 제작된 NSO-NVM 소자의 전기적 메모리 특성은 on current가 약 $10^{-5}$ A 이고 off current가 약 $5\times10^{-13}$ A로 전류 점멸비$(I_{ON}/I_{OFF})$는 약 $1\times10^7$ 이고 Swing 값은 0.53V/decade 이다. 1ms 동안의 programming/erasing 결과 약 3.5 V의 넓은 메모리 윈도우 크기를 가진다는 것을 확인할 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.624-626
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• 2000

대칭형 멀티프로세서 시스템으로 구성된 클러스터의 메시지 전송 방법은 인트라노드인 프로세서 통신과 인터노드인 시스템 통신을 동시에 수행하므로, 노드들간의 통신 성능을 위한 메모리 버퍼의 사용과 버퍼 사이의 데이터 중복 복사가 인트라와 인터노드 사이의 통신 불균형을 가져온다. 푸쉬-풀 메시지의 버퍼 사용 기법을 제한하고 메시지 전송 수행단계를 수정하여 고속 네트웍을 위한 인터노드의 통신 불균형을 감소시켰고, 주소 전환과 전송-승인 신호 중첩 기법을 고속 네트웍에 적합하도록 변형하여 기존의 푸쉬-풀 메시지 기법과 비교, 분석하였다. 제안된 기법은 인터노드 사이의 통신 지연을 약 7~18% 감소시켰다.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.35 no.10
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• pp.52-57
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• 2017

• Journal of KIISE:Information Networking
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• v.32 no.4
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• pp.443-451
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• 2005

Stateful inspection devices must maintain flow information. These devices create the flow information also for network attack packets, and it can fatally inflate the dynamic memory allocation on stateful inspection devices under network attacks. The memory inflation leads to memory overflow and subsequent performance degradation. In this paper, we present a guideline to set the flow entry timeout for a stateful inspection device to remove harmful embryonic entries created by network attacks. Considering Transmission Control Protocol (TCP) if utilized by most of these attacks as well as legitimate traffic, we propose a parsimonious memory management guideline based on the design of the TCP and the analysis of real-life Internet traces. In particular, we demonstrate that for all practical purposes one should not reserve memory for an embryonic TCP connection with more than (R+T) seconds of inactivity where R=0, 3, 9 and $1\leqq{T}\leqq{2}$ depending on the load level.

• Journal of KIISE
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• v.42 no.7
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• pp.852-859
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• 2015

As the amount of knowledge information significantly increases, a lot of progress has been made in the studies focusing on how to reason large scale ontology effectively at the level of RDFS or OWL. These reasoning methods are divided into TBox classifications and ABox realizations. A TBox classification mainly deals with integrity and dependencies in schema, whereas an ABox realization mainly handles a variety of issues in instances. Therefore, the ABox realization is very important in practical applications. In this paper, we propose a realization method for analyzing the constraint of the specified class, so that the reasoning system automatically infers the classes to which instances belong. Unlike conventional methods that take advantage of the object oriented language based distributed file system, we propose a large scale ontology reasoning method using spark, which is a functional programming-based in-memory system. To verify the effectiveness of the proposed method, we used instances created from the Wine ontology by W3C(120 to 600 million triples). The proposed system processed the largest 600 million triples and generated 951 million triples in 51 minutes (696 K triple / sec) in our largest experiment.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.29-36
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• 2017

In this paper, we implement a spatial big data analysis prototype based on Spark which is an in-memory system and compares the performance by the spatial split algorithm on this basis. In cluster computing environments, big data is divided into blocks of a certain size order to balance the computing load of big data. Existing research showed that in the case of the Hadoop based spatial big data system, the split method by spatial is more effective than the general sequential split method. Hadoop based spatial data system stores raw data as it is in spatial-divided blocks. However, in the proposed Spark-based spatial analysis system, there is a difference that spatial data is converted into a memory data structure and stored in a spatial block for search efficiency. Therefore, in this paper, we propose an in-memory spatial big data prototype and a spatial split block storage method. Also, we compare the performance of existing spatial split algorithms in the proposed prototype. We presented an appropriate spatial split strategy with the Spark based big data system. In the experiment, we compared the query execution time of the spatial split algorithm, and confirmed that the BSP algorithm shows the best performance.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.127-132
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• 2006

K-NN (k-Nearest Neighbors), which is a well-known instance-based learning algorithm, simply stores entire training patterns in memory, and uses a distance function to classify a test pattern. K-NN is proven to show satisfactory performance, but it is notorious formemory usage and lengthy computation. Various studies have been found in the literature in order to minimize memory usage and computation time, and NGE (Nested Generalized Exemplar) theory is one of them. In this paper, we propose RPA (Recursive Partition Averaging) and IRPA (Incremental RPA) which is an incremental version of RPA. RPA partitions the entire pattern space recursively, and generates representatives from each partition. Also, due to the fact that RPA is prone to produce excessive number of partitions as the number of features in a pattern increases, we present IRPA which reduces the number of representative patterns by processing the training set in an incremental manner. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.