• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.35-41
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• 2020

This paper proposes an algorithm to improve the compression performance of the adaptive multi-rate (AMR) speech coding using the context tree weighting (CTW) method. AMR is the voice encoding standard adopted by IMT-2000, and supports 8 transmission rates from 4.75 kbit/s to 12.2 kbit/s to cope with changes in the channel condition. CTW as a kind of the arithmetic coding, uses a variable-order Markov model. Considering that CTW operates bit by bit, we propose an algorithm that re-orders AMR data and compresses them with CTW. To verify the validity of the proposed algorithm, an experiment is conducted to compare the proposed algorithm with existing compression methods including ZIP in terms of compression ratio. Experimental results indicate that the average additional compression rate in AMR data is about 3.21% with ZIP and about 9.10% with the proposed algorithm. Thus our algorithm improves the compression performance of AMR data by about 5.89%.

• Journal of the Korea Computer Industry Society
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• v.5 no.5
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• pp.737-746
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• 2004

Two-channel multiwavelet system is investigated for image compression application in this paper. Generally, multiwavelets are known for their superb capability of compressing non-stationary signals like voice. However, multivavelet system have a critical problem in processing and compressing image data due to mesh-grid visual artifacts. In our two-channel multiwavelet system we have investigated incorporation of pre and post filtering to the multiwavelet transform and compression system for alleviating those ingerent visual artifacts due to multiwavelet effect. In addition, to quantify the image data compression performance of proposed multiwavelet system, computer simulations have been performed using various image data. For bit allocation and quantization, the Lagrange multiplier technique considering data rate vs. distortion rate along with a nonlinear companding method are applied equallly to all systems considered, here. The simulation results have yielded 1 ~ 2 dB compression enhancement over the scalar savelet systems. If the more advanced compression methods like SPIHT and run-length channel coding were adopted for the proposed multiwavelet system, a much higher compression gain could be obtained.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.640-643
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• 2004

기하급수적으로 증가하고 있는 방대한 양의 데이터를 효율적으로 저장하고, 검색하기 위한 방법으로 압축된 인덱스 자료구조(compressed index data structure)가 제안되었고 현재 활발히 연구되고 있다. 압축된 인덱스 자료구조란 데이터를 적절한 방법으로 색인화(indexing)하여 이를 압축(compression)된 자료구조로 저장하여, 데이터 검색의 성능저하 없이, 저장 공간을 줄일 수 있는 방법이다. 본 논문에서는 대표적인 방법으로 Ferragina와 Manzini가 제시한 FM-index를 다룬다. 이 방법을 구현하여 전체적인 성능에 영향을 미치는 요소들을 실험을 통해 분석하였다. 이를 통하여 각 파라미터들의 상관관계를 분석하고 이상적인 설정 값을 실험하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.98-99
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• 2020

최근 다양한 응용 분야에서 딥러닝을 적용한 사례가 나오고 있으며, 딥러닝 네트워크 경량화 또는 압축 기법을 적용해 정확도는 최대한 유지하면서 에너지 효율을 개선하려는 연구도 활발하게 이루어지고 있다. 이에 본 논문에서는 딥러닝 추론 과정에서 중간 데이터로 도출되는 활성화 데이터의 압축을 위해 연속 길이 부호화 방법을 적용해보고 압축률과 개선점에 대해 분석 한다.

• Spatial Information Research
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• v.19 no.1
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• pp.107-116
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• 2011

Due to development of environment of wireless Internet, location based services on the basis of spatial data have been increased such as real time traffic information as well as CNS(Car Navigation System) to provide mobile user with route guidance to the destination. However, the current application adopting the file-based system has limitation of managing and storing the huge amount of spatial data. In order to supplement this challenge, research which is capable of managing large amounts of spatial data based on embedded database system is surely demanded. For this reason, this study aims to suggest the lossless compression technique by using the hybrid approach of BytePacking and Lempel-Ziv which can be applicable in DBMS so as to save a mass spatial data efficiently. We apply the proposed compression technique to actual the Seoul and Inchcon metropolitan area and compared the existing method with suggested one using the same data through analyzing the query processing duration until the reconstruction. As a result of comparison, we have come to the conclusion that suggested technique is far more performance on spatial data demanding high location accuracy than the previous techniques.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.1-9
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• 2020

In this paper, we propose Dynamic Rank Subsetting (DRAS) technique that enhances the energy-efficiency and the performance of memory system through the data compression. The goal of this technique is to enable a partial chip access by storing data in a compressed format within a subset of DRAM chips. To this end, a memory rank is dynamically configured to two independent sub-ranks. When writing a data block, it is compressed with a data compression algorithm and stored in one of the two sub-ranks. To service a memory request for the compressed data, only a sub-rank is accessed, whereas, for a memory request for the uncompressed data, two sub-ranks are accessed as done in the conventional memory systems. Since DRAS technique requires minimal hardware modification, it can be used in the conventional memory systems with low hardware overheads. Through experimental evaluation with a memory simulator, we show that the proposed technique improves the performance of the memory system by 12% on average and reduces the power consumption of memory system by 24% on average.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.4
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• pp.51-56
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• 2011

In this paper, we propose a CAN message compression method using bit rearrangement to reduce the CAN bus load and the error probability during the transmission of CAN messages. In conventional CAN message compression methods, message compression is accomplished by sending only the differences between the previous data and the current data. In the proposed method, the difference bits are rearranged to further increase the compression efficiency. By simulations in car applications, it is shown that the CAN transmission data is further reduced up to 26% by the proposed method, compared with the conventional method.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.5
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• pp.457-467
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• 2000

본 논문에서는 분산 메모리 구조를 갖는 병렬 컴퓨터 상에서 방대한 크기를 갖는 볼륨 데이터의 효과적인 가시화를 위한 병렬 광선 투사법을 제안한다. 데이터의 압축을 기반으로 하는 본 기법은 다른 프로세서의 메모리로부터 데이터를 읽기보다는 자신의 지역 메모리에 존재하는 압축된 데이터를 빠르게 복원함으로써 병렬 렌더링 성능을 향상시키는 것을 목표로 한다. 본 기법은 객체-순서와 영상-순서 탐색 알고리즘 모두의 정점을 이용하여 성능을 향상시켰다. 즉, 블록 단위의 최대-최소 팔진트리의 탐색과 각 픽셀의 불투명도 값을 동적으로 유지하는 실시간 사진트리를 응용함으로써 객체-공간과 영상-공간 각각의 응집성을 이용하였다. 본 논문에서 제안하는 압축 기반 병렬 볼륨 렌더링 방법은 렌더링 수행 중 발생하는 프로세서간의 통신을 최소화하도록 구현되었는데, 이러한 특징은 프로세서 사이의 상당히 높은 데이터 통신 비용을 감수하여야 하는 PC 및 워크스테이션의 클러스터와 같은 더욱 실용적인 분산 환경에서 매우 유용하다. 본 논문에서는 Cray T3E 병렬 컴퓨터 상에서 Visible Man 데이터를 이용하여 실험을 수행하였다.

• Journal of the Korean Institute of Gas
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• v.4 no.4 s.12
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• pp.58-64
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• 2000

Data compression and retrieval method are investigated for the effective utilization of measured process data. In this paper, a new data compression method, Clustering Compression(CC), which is based on the k-means clustering algorithm and piecewise linear approximation method is suggested. Case studies on industrial data set showed the superior performance of clustering based techniques compared to other conventional methods and showed that CC could handle the compression of multi-dimensional data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.6
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• pp.49-54
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• 2008

Recently, with the development of wireless communications and GIS, interest about mobile computing is rising. In this case, the GIS needs to be placed in a constricted environment than the environment of server computing. For this reason large amounts of spatial data must be compressed into the mobile device. The compression technique of the spatial data is difficult and must be processed in the correct order because the size of the data is unpredictable. Therefore, in this paper, the cell-based compression technique of the spatial data for mobile GIS is presented. This paper includes the process of transformation of spatial data from a certain server to a mobile device by cell-based compression technique. In this paper, the cell-based compression technique of the spatial data has been proven practically efficient.