• Plant Journal
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• v.12 no.4
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• pp.44-50
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• 2016

To model the compression system with more integrity, the pipeline dynamic model was applied to the compression system model. To combine the pipeline dynamic model and the compression system model, appropriate boundary conditions were selected on each end of connecting pipe with compressor, plenum and throttle valve. Simulation result illustrate the effect of pipeline dynamic model on the stability of compression system.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.668-673
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• 2008

In this paper, we developed a physically-based micromechanical model for inelastic deformation of ceramic powders. The aggregate response of ceramic particles was modeled using the two-surface yield function which considered the shear-induced dilatancy caused by friction, rolling resistance and cohesion between powder particles and consolidation caused by plastic deformation of powder themselves under high compression. The constitutive equations were implemented into the user-subroutine VUMAT of finite element program ABAQUS/Explicit. The material parameters in the constitutive model were identified by calibrating the model to reproduce data from triaxial compression tests and simple compression tests. The density distribution obtained by using the proposed model was in good quantitative agreement with the experimental results of the triaxial compression and cold isostaic compression as well.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.2
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• pp.1-12
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• 2024

In order to integrate two consolidation theories of Terzaghi's consolidation theory and Mesri's secondary compression theory and to identify a model suitable for analyzing stress-strain behavior over time, numerical analysis on consolidation tests were conducted using a modified cam-clay model and a soft soil creep model and the following conclusions were obtained. The results of numerical analysis applying the theory that a linear proportional relationship is established between the void ratio at logarithmic scale and the permeability coefficient at logarithmic scale is better agreement with the result of oedometer test than the results of applying constant hydraulic conductivity. The modified cam-clay model is a model that does not include secondary compression, but the slope of the normal consolidation line corresponding to the compression index of the standard consolidation test includes secondary compression, so the actual settlement curve over time is lower than the predicted value through numerical analysis. It always gets smaller. Other previous studies that applied Terzaghi's consolidation theory to consolidation test analysis showed the same results and were cross-confirmed. The soft soil creep model, which includes secondary compression in the theory, showed good agreement in all sections including secondary compression in the consolidation test results. It was judged appropriate to use a soft soil creep model when performing numerical analysis of soft clay ground.

• Journal of Energy Engineering
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• v.24 no.3
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• pp.61-66
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• 2015

Homogeneous charge compression ignition (HCCI) engine combines the combustion characteristics of a compression ignition engine and a spark ignition engine. HCCI engines take advantage of the high compression ratio and heat release rate and thus exhibit high efficiency found in compression ignition engines. In modern research, simulation has be come a powerful tool as it saves time and also economical when compared to experimental study. Engine simulation has been developed to predict the performance of a homogeneous charge compression ignition engine. The effects of compression ratio, cylinder pressure, rate of pressure rise, flame temperature, rate of heat release, and mass fraction burned were simulated. The simulation and analysis show several meaningful results. The objective of the present study is to develop a combustion characteristics model for a homogeneous charge compression ignition engine running with isooctane as a fuel and effect of compression ratio.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.3
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• pp.23-36
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• 1997

In tis paper, sthe new data model for the hardware implementation of lempel-ziv compression algorithm was proposed. Traditional model generates the codeword which consists of 3 bytes, the last symbol, the position and the matched length. MSB (most significant bit) of the last symbol is the comparession flag and the remaining seven bits represent the character. We confined the value of the matched length to 128 instead of 256, which can be coded with seven bits only. In the proposed model, the codeword consists of 2 bytes, the merged symbol and the position. MSB of the merged symbol is the comression flag. The remaining seven bits represent the character or the matched length according to the value of the compression flag. The proposed model reduces the compression ratio by 5% compared with the traditional model. The proposed model can be adopted to the existing hardware architectures. The incremental factors of the compression ratio are also analyzed in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1389-1408
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• 2021

Proof-of-Work (PoW) is the first and still most common consensus protocol in blockchain. But it is costly and energy intensive, aiming at addressing these problems, we propose a consensus algorithm named Proof-of-Work-and-Block-Compression (PoW-BC). PoW-BC is an improvement of PoW to compress blocks and adjust consensus parameters. The algorithm is designed to encourage the reduction of block size, which improves transmission efficiency and reduces disk space for storing blocks. The transaction optimization model and block compression model are proposed to compress block data with a smaller compression ratio and less compression/ decompression duration. Block compression ratio is used to adjust mining difficulty and transaction count of PoW-BC consensus protocol according to the consensus parameters adjustment model. Through experiment and analysis, it shows that PoW-BC improves transaction throughput, and reduces block interval and energy consumption.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.1159-1164
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• 2006

In this paper an artificial neural network model is developed to estimate the unconfined compression strength of Deep Cement Mixing(DCM) treated soil. A database which consists of a number of unconfined compression test result compiled from 9 clay sites is used to train and test of the artificial neural network model. Developed neural network model requires water content of soil, unit weight of soil, passing percent of #200 sieve, weight of cement, w-c ratio as input variables. It is found that the developed artificial neural network model can predict more precise and reliable unconfined compression strength than the conventional empirical models.

• Geomechanics and Engineering
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• v.25 no.4
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• pp.341-345
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• 2021

1D sand compression response to ko-loading experiences volume contraction from low to high effective stress regimes. Previous study suggested compressibility model with physically correct asymptotic void ratios at low and high stress levels and examined only for both remolded clays and natural clays. This study extends the validity of Enhanced Terzaghi model for different sand types complied from 1D compression data. The model involved with four parameters can adequately fit 1D sand compression data for a wide stress range. The low stress obtained from fitting parameters helps to identify the initial fabric conditions. In addition, strong correlation between compressibility and the void ratio at low stress facilitates determination of self-consistent fitting parameters. The computed tangent constrained modulus can capture monotonic stiffening effect induced by an increase in effective stress. The magnitude of tangent stiffness during large strain test should not be associated with small strain stiffness values. The use of a single continuous function to capture 1D stress-strain sand response to ko-loading can improve numerical efficiency and systematically quantify the yield stress instead of ad hoc methods.

• Steel and Composite Structures
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• v.39 no.3
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• pp.319-335
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• 2021

This study proposes a new and highly-accurate artificial intelligence model, namely ANN-IP, which combines an interior-point (IP) algorithm and artificial neural network (ANN), to improve the axial compression capacity prediction of elliptical concrete-filled steel tubular (CFST) columns. For this purpose, 145 tests of elliptical CFST columns extracted from the literature are used to develop the ANN-IP model. In this regard, axial compression capacity is considered as a function of the column length, the major axis diameter, the minor axis diameter, the thickness of the steel tube, the yield strength of the steel tube, and the compressive strength of concrete. The performance of the ANN-IP model is compared with the ANN-LM model, which uses the robust Levenberg-Marquardt (LM) algorithm to train the ANN model. The comparative results show that the ANN-IP model obtains more magnificent precision (R² = 0.983, RMSE = 59.963 kN, a20 - index = 0.979) than the ANN-LM model (R² = 0.938, RMSE = 116.634 kN, a20 - index = 0.890). Finally, a new Graphical User Interface (GUI) tool is developed to use the ANN-IP model for the practical design. In conclusion, this study reveals that the proposed ANN-IP model can properly predict the axial compression capacity of elliptical CFST columns and eliminate the need for conducting costly experiments to some extent.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.31-43
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• 2022

Traditional video compression has developed so far based on hybrid compression methods through motion prediction, residual coding, and quantization. With the rapid development of technology through artificial neural networks in recent years, research on image compression and video compression based on artificial neural networks is also progressing rapidly, showing competitiveness compared to the performance of traditional video compression codecs. In this paper, a new method capable of improving the performance of such an artificial neural network-based video compression model is presented. Basically, we take the rate-distortion optimization method using the auto-encoder and entropy model adopted by the existing learned video compression model and shifts some components of the latent information that are difficult for entropy model to estimate when transmitting compressed latent representation to the decoder side from the encoder side, and finally compensates the distortion of lost information. In this way, the existing neural network based video compression framework, MFVC (Motion Free Video Compression) is improved and the BDBR (Bjøntegaard Delta-Rate) calculated based on H.264 is nearly twice the amount of bits (-27%) of MFVC (-14%). The proposed method has the advantage of being widely applicable to neural network based image or video compression technologies, not only to MFVC, but also to models using latent information and entropy model.