• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1214-1221
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• 2006

Header compression scheme is suggested as a solution to reduce the inefficient overhead of general packet stream data. Especially, it is shown that there are more overhead rate for real-time media stream links such as voice because of its short payload size, and it is possible to get higher bandwidth efficiency using the header compression scheme. There are two kinds of error recovery in header compression such as Periodic Header Refresh(PHR) and Header Request(HR) schemes. In this paper, we analyze the performance of these two compression recovery schemes, and some results such as the overhead rate, bandwidth gain and bandwidth efficiency(BE) are presented.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.33-40
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• 2002

In this paper, we proposed a lossless compression algorithm of medical images which is essential technique in picture archive and communication system. Mammographic image and magnetic resonance image in among medical images used in this study, proposed a region growing segmentation algorithm for compression of these images. A proposed algorithm was partition by three sub region which error image, discontinuity index map, high order bit data from original image. And generated discontinuity index image data and error image which apply to a region growing algorithm are compressed using JBIG(Joint Bi-level Image experts Group) algorithm that is international hi-level image compression standard and proper image compression technique of gray code digital Images. The proposed lossless compression method resulted in, on the average, lossless compression to about 73.14% with a database of high-resolution digital mammography images. In comparison with direct coding by JBIG, JPEG, and Lempel-Ziv coding methods, the proposed method performed better by 3.7%, 7.9% and 23.6% on the database used.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Geomechanics and Engineering
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• v.30 no.5
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• pp.437-448
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• 2022

Alluvial soil is challenging to work with due to its high compressibility. Thus, consolidation settlement of this type of soil should be accurately estimated. Accurate estimation of the consolidation settlement of alluvial soil requires accurate prediction of compressibility parameters. Geotechnical engineers usually use empirical correlations to estimate these compressibility parameters. However, no attempts have been made to develop correlations to estimate compressibility parameters of alluvial soil. Thus, this paper aims to develop new models to predict the compression and recompression indices (Cc and Cr) of alluvial soils. As part of the study, geotechnical laboratory tests have been conducted on large number of undisturbed samples of local alluvial soil. The obtained results from these tests in addition to available results from the literature from different parts in the world have been compiled to form the database of this study. This database is then employed to examine the accuracy of the available empirical correlations of the compressibility parameters and to develop the new models to estimate the compressibility parameters using the nonlinear regression analysis. The accuracy of the new models has been accessed using mean absolute error, root mean square error, mean, percentage of predictions with error range of ±20%, percentage of predictions with error range of ±30%, and coefficient of determination. It was found that the new models outperform the available correlations. Thus, these models can be used by geotechnical engineers with more confidence to predict Cc and Cr.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.5
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• pp.354-360
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• 2003

A new ROM compression method for continuous data is proposed. The ROM compression method is based on two proposed ROM compression algorithms. The first one is a region select ROM compression algorithm that stores only regions including data after dividing data into many small regions by magnitude and address. The second is a quantization ROM and error ROM compression algorithm that divides data into quantized data and their errors. Using these algorithms, 40~60% ROM size reductions aye achieved for various continuous data.

• Proceedings of the Korean Geotechical Society Conference
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• 2007.09a
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• pp.363-372
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• 2007

The residual settlement due to difference between predicted and observed settlement is one of the social problems during reclaiming construction in the soft ground having a deep depth such as Busan and Gwangyang province. Prediction error for the secondary compression settlement makes the construction much harder. To examine characteristics of the secondary compression settlement, the secondary compression index is the most important factor. In this study, various empirical methods for determining the secondary compression index are evaluated. And errors applied to the design case practically are also explained. The pre loading method is the only way to reduce the secondary compression settlement and reduction ratio of the secondary compression should be investigated correctly. Hence, research results on the reduction ratio of the secondary compression are analyzed in this paper. Moreover, decrement of the secondary compression index due to over consolidation ratio is examined closely by laboratory consolidation test using clay in the Gwangyang area.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.53-62
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• 2004

A single layer latticed dome is one of the most efficient structures because of its low specivic gravity. For easily analyzing of a single layer latticed dome, joint system is assumed to be pin or rigid joint. However, its joint uses ball whose system has intermediate properties of pin and rigid joint. Therefore this study has a grasp of bending rigidity, stress and mechanical properties through experimental and analyzing method of the bolt inserted ball joint. To analyze the stress of bolt and sleeve, this study uses through 3D elastic contact and cubic element, and then the ball and the bolt are perfectly connected for easily analyzing Compared experimental results to F.E.M, each specimen has an error of less than 12 percent. In the results of stress distribution through F.E.M, stress occurs from bottom of bolt to top of sleeve, and most of tension appears on the bolt, also compression occurs from upper parts of the bolt to the sleeve. The assumption of bending stiffness in ball joint is well known that bolt resists only tension and upper sleeve resiss compression. The results of experiment and analysis have $7{\sim}56%$ error, assuring that upper part of bolt occurs of partial compression. In the result of modified assumption have $4{\sim}20%$ error.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.2
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• pp.148-154
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• 2016

In recent year, the interests of PQ is increase due to the increasing of non-linear load and distributed power sources in power system. For the parameters detection and feature extraction of PQ, and the PQ improvement method, continuous power signal monitering is needed. In this paper, the power signal compression and reconstruction method is suggested for power signal monitering. The power signal is compressed using DCT that has good compression performance, and the compressed signal is reconstructed through IDCT. And for the higher compression rate, DCT coefficients are arranged by magnitude in compression process, and in recouction process DCT coefficients are rearranged to original frequency position. The synthesized signal according to the IEC standard is used used in compression and reconstruction simulations. The performances of the proposed method are verified by comparing the error between synthesized signal and reconstructed signal.

• Textile Coloration and Finishing
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• v.18 no.5 s.90
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• pp.88-93
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• 2006

A data compression system has been developed by combining adaptive wavelets and optimization technique. The adaptive wavelets were made by optimizing the coefficients of the wavelet matrix. The optimization procedure has been performed by criteria of minimizing the reconstruction error. The resulting adaptive basis outperformed such conventional basis as Daubechies-5 by 5-10%. It was also shown that the yarn density profiles could be compressed by over 95% without a significant loss of information.

• Journal of Biomedical Engineering Research
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• v.12 no.3
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• pp.191-202
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• 1991

This paper describes ECG data compression algorithm using neural network. As a learning method, we use back error propagation algorithm. ECG data compression is performed using learning ability of neural network. CSE database, which is sampled 12bit digitized at 500samp1e/sec, is selected as a input signal. In order to reduce unit number of input layer, we modify sampling ratio 250samples/sec in QRS complex, 125samples/sec in P & T wave respectively. hs a input pattern of neural network, from 35 points backward to 45 points forward sample Points of R peak are used.