• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.99-107
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• 2013

Controller area network (CAN) was designed for multiplexing communication between electronic control units (ECUs) in vehicles and thus for decreasing the overall wire harness. The increasing number of ECUs causes the CAN bus overloaded and consequently the error probability of data transmission increases. Since the time duration for the data transmission is proportional to CAN frame length, it is desirable to reduce the frame length. In this paper, a CAN message compression method is proposed using Data Length Code (DLC) and compression area selection algorithm to reduce the CAN frame length and the error probability during the transmission of CAN messages. By the proposed method, it is not needed to predict the maximum value of the difference in successive CAN messages as opposed to other compression methods. Also, by the use of DLC, we can determine whether the received CAN message has been compressed or not without using two ID's as in conventional methods. By simulations using actual CAN data, it is shown that the CAN transmission data is reduced up to 52 % by the proposed method, compared with conventional methods. By using an embedded test board, it is shown that 64bit EMS CAN data compression can be performed within 0.16ms and consequently the proposed algorithm can be used in automobile applications without any problem.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.171-180
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• 2002

The effective and accurate data flow problem analysis uses the dominator tree and DJ graphs. The data flow problem solving is to safely reduce the flow graph to the dominator tree. The flow graph replaces a parse tree and used to accurately reduce either reducible or irreducible flow graph to the dominator tree. In this paper, in order to utilize Tarian's path compress algorithm, the Top node finding algorithm is suggested and the existing delay reduction algorithm is improved using Path compression. The delayed reduction a1gorithm using path compression actually compresses the pathway of the dominator tree by hoisting the node while reducing to delay the DJ graph. Realty, the suggested algorithm had hoisted nodes in 22% and had compressed path in 20%. The compressed dominator tree makes it possible to analyze the effective data flow analysis and brings the improved effect for the complexity of code optimization process with the node hoisting effect of code optimization process.

• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.624-631
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• 2012

For many medical imaging systems, volume datasets are stored as a compressed form, so that the dataset has to be decompressed before it is visualized. Since the decompression process takes quite a long time, we present an acceleration method for medical volume decompression using GPU. Our method supports that both lossy and lossless compression and progressive refinement is possible to satisfy variable user requirements. Moreover, our decompression method is well parallelized for GPU so that the decompression takes a very short time. Finally, we designed that the decompression and volume rendering work in one framework so that the selective decompression is available. As a result, we gained additional improvement in volume decompression.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.18-28
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• 1997

This paper presents image data compression using a classified vector quantization (CVQ) which categories edge blocks according to the energy distribution of subimages in the discrete cosine transform domain. Classifying the edge blocks enhances visual quality of the compressed images while maintaining a high compression ratio. The proposed classification method categories subimages into eight lypes of edge features according to an energy distribution. A neural network, trained with the data generated from the proposed classification method, can successfully classify subimages to eight edge categories. Experimental results are given to show how the (1VQ method incorporatd with a neural network can produce faithful compressed image quality for high compression ratios.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.3
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• pp.30-36
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• 2012

block truncation coding(BTC) image compression is known as a simple and efficient technology for image compression algorithm. In this paper, we propose RMC-BTC algorithm(RMC : reduction method chrominace data) for color image compression. To compress chrominace data, in every BTC block, the RMC-BTC coding employs chrominace data expressed with average of chrominace data and using method of luminance data bit-map to represented chrominance data bit-map. Experimental results shows efficiency of proposed algorithm, as compared with PSNR and compression ratio of the conventional BTC method.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.5
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• pp.16-22
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• 2000

This paper presents a compression algorithm applicable for transmitting a HPO hologram data. The proposed algorithm exploits a modulating function to compress the bandwidth of the hologram pattern, resulting in decimation due to relaxed Nyquist sampling constraints. At the receiver, the compressed data will be interpolated and compensated via being divided by the modulating function. We also present compression rate and analyze the resolution of a reconstructed image and the periodicity of harmonic interferences. Finally, we shows the validity of the proposed algorithm by simulation where a reconstructed image from undersampled data is compared with a reconstructed image obtained through decimatioin by modulating function, interpolation and compensation.

• Convergence Security Journal
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• v.7 no.2
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• pp.57-63
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• 2007

The security of data in network is provided by encryption. Selective encryption is a recent approach to reduce the computational cost and complexity for large file size data such as image and video. This paper describes techniques to encrypt Huffman code and discusses the performance of proposed scheme. We propose a simple encryption technique applicable to the Huffman code and study effectiveness of encryption against insecure channel. Our scheme combine encryption process and compression process, and it can reduce processing time for encryption and compression by combining two processes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.10
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• pp.901-913
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• 1991

Data compression has important application in the areas of file storage and distributed computer systems. The universal data compression achieves asymptotically optimum cimpression ratio for strings generated by any stationary ergodic source without a priori source probabilities.The paper describes the principle and the recent research trends on universal data compression. And its applications.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.268-271
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• 2022

단어 의미 모호성 해소는 동형이의어의 의미를 문맥에 맞게 결정하는 일이다. 최근 연구에서는 희소 데이터 처리를 위해 시소러스를 사용해 의미 어휘를 압축하고 사용하는 방법이 좋은 성능을 보였다[1]. 본 연구에서는 시소러스 없이 군집화 알고리즘으로 의미 어휘를 압축하는 방법의 성능 향상을 위해 두 가지 방법을 제안한다. 첫째, 의미적으로 유사한 의미 어휘 집합인 범주(category) 정보를 군집화를 위한 초기 군집 생성에 사용한다. 둘째, 다양하고 많은 문맥 정보를 학습해 만들어진 품질 좋은 벡터를 군집화에 사용한다. 영어데이터인 SemCor 데이터를 학습하고 Senseval, Semeval 5개 데이터로 평가한 결과, 제안한 방법의 평균 성능이 기존 연구보다 1.5%p 높은 F1 70.6%를 달성했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.389-392
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• 2021

문장 압축은 원본 문장의 중요한 의미를 보존하는 짧은 길이의 압축 문장을 생성하는 자연어처리 태스크이다. 문장 압축은 사용자가 텍스트로부터 필요한 정보를 빠르게 획득할 수 있도록 도울 수 있어 활발히 연구되고 있지만, 기존 연구들은 사람이 직접 정의한 압축 규칙이 필요하거나, 모델 학습을 위해 대량의 데이터셋이 필요하다는 문제점이 존재한다. 사전 학습된 언어 모델을 통한 perplexity 기반의 문장 점수 측정을 통해 문장을 압축하여 압축 규칙과 모델 학습을 위한 데이터셋이 필요하지 않은 연구 또한 존재하지만, 문장 점수 측정에 문장에 속한 단어들의 의미적 중요도를 반영하지 못하여 중요한 단어가 삭제되는 문제점이 존재한다. 본 논문은 언어 정보 중 품사 정보, 의존관계 정보, 개체명 정보의 중요도를 수치화하여 perplexity 기반의 문장 점수 측정에 반영하는 방법을 제안한다. 또한 제안한 문장 점수 측정 방법을 활용하였을 때 문장 점수 측정 기반 문장 압축 모델의 문장 압축 성능이 향상됨을 확인하였으며, 이를 통해 문장에 속한 단어의 언어 정보를 문장 점수 측정에 반영하는 것이 의미적으로 적절한 압축 문장을 생성하는 데 도움이 될 수 있음을 보였다.