• ETRI Journal
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• v.25 no.1
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• pp.1-8
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• 2003

This paper introduces an efficient iterative decoding method for high-dimensional block turbo codes. To improve the decoding performance, we modified the soft decision Viterbi decoding algorithm, which is a trellis-based method. The iteration number can be significantly reduced in the soft output decoding process by applying multiple usage of extrinsic reliability information from all available axes and appropriately normalizing them. Our simulation results reveal that the proposed decoding process needs only about 30% of the iterations required to obtain the same performance with the conventional method at a bit error rate range of $10^{-5}\;to\;10^{-6}$.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.663-666
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• 2004

In this paper, a modified-SOYA (soft output viterbi algorithm) of turbo codes is proposed for quantized channel receiver filter outputs. We derive optimum branch values for the Viterbi algorithm. Here we assume that received filter outputs are quantized and the channel is additive white Gaussian noise. The optimum non-uniform quantizer is used to quantize channel receiver filter outputs. To compare the BER (bit error rate) performance we perform simulations for the modified SOYA algorithm and the general SOYA

• Journal of the Korean Geotechnical Society
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• v.33 no.6
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• pp.27-36
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• 2017

Gravel compaction pile method has been widely used to improve the soft ground on the land or sea as one of the soft ground improvement technique. The ultimate bearing capacity of the ground reinforced by gravel compaction piles is affected by the soil strength, the replacement ratio of pile, construction conditions, and so on, and various prediction equations have been proposed to predict this. However, the prediction of the ultimate bearing capacity using the existing models has a very large error and variation, and it is not suitable for practical design. In this study, multiple regression analysis was performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by gravel compaction pile, and the most efficient input variables are selected through evaluation of error by leave one out cross validation, and a multiple regression equation for the prediction of ultimate bearing capacity was proposed. In addition, the prediction error was evaluated by applying artificial neural network using the selected input variables, and the results were compared with those of the existing model.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.3
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• pp.111-116
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• 2013

With technology scaling, soft error rate has greatly increased in embedded systems. Due to high performance and low power consumption, VLIW (Very Long Instruction Word) architectures have been widely used in embedded systems and thus many researches have been studied to improve the reliability of a system by duplicating instructions in VLIW architectures. However, existing studies have ignored the feature, called VLES (Variable Length Execution Set), which is adopted in most modern VLIW architectures to reduce code size. In this paper, we propose how to support instruction duplication in VLIW architecture with VLES. Our experimental results demonstrate that a VLIW architecture with VLES shows 64% code size decrement on average at the cost of about 4% additional cell area as compared to the case of a VLIW architecture without VLES when instruction duplication is applied to both architectures. Also, it is shown that the case with VLES does not cause extra execution time compared to the case without VLES.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.12
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• pp.1216-1222
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• 2010

Based on the MacDonald's analytic model for the diffracted sound field of a semi-infinite noise barrier, computer simulations were performed for various positions of error microphones for an active noise barrier system. The simulation process also included the effects of floor reflections on both sides of the barrier. The results were also compared with Niu's simulation results and showed a straight line arrangement of sensors and actuators, in the order of primary source, secondary source and error microphone is better than over the top arrangement of the error microphones.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.1
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• pp.15-23
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• 2009

Microprocessors, which need correct arithmetic operations, have been the subject of in-depth research in relation to soft errors. Of the existing microprocessor devices, the memory cell is the most vulnerable to soft errors. Moreover, when soft errors emerge in a memory cell, the processes and operations are greatly affected because the memory cell contains important information and instructions about the entire process or operation. Users do not realize that if soft errors go undetected, arithmetic operations and processes will have unexpected outcomes. In the field of architectural design, the tool that is commonly used to detect and correct soft errors is the error check and correction code. The Itanium, IBM PowerPC G5 microprocessors contain Hamming and Rasio codes in their level-2 cache. This research, however, focuses on huge server devices and does not consider power consumption. As the operating and threshold voltage is currently shrinking with the emergence of high-density and low-power embedded microprocessors, there is an urgent need to develop ECC (error check correction) circuits. In this study, the in-output data of the level-2 cache were analyzed using SimpleScalar-ARM, and a 32-bit H-matrix for the level-2 cache of an embedded microprocessor is proposed. From the point of view of power consumption, the proposed H-matrix can be implemented using a schematic editor of Cadence. Therefore, it is comparable to the modified Hamming code, which uses H-spice. The MiBench program and TSMC 0.18 um were used in this study for verification purposes.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.10a
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• pp.147-158
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• 2001

In general, two methods have been used to predict settlement of soft ground. One method is Terzaghi's one dimensional consolidation theory which gives time-settlement relationship using the standard consolidation test results. The other is forecasting method of ground settlement to be occured in the future using in-situ monitoring data. The above both methods have some defects in application manner or in itself especially in very deep and soft clayey ground. In view of the lessons and experiences of soft ground improvement projects, several techniques were proposed for more accurate theorectical calculation of consolidation settlement as follows ; ① Subdivision of soft ground, ② Consideration of secondary compression, ③ Using the modified compression index, etc. And also, revised hyperbolic fitting method was suggested to minimize the error of predicted future settlement. In addition, revised De-Beer equation of immediate settlement of loose sandy soil was proposed to overcome the tendency to show too small settlement calculation results by original De-Deer equation. And also, considering the various effects of settlement delay in the improved ground by vertical drains, time-settlement caculation equation(Onoue method) was revised to match the tendency of settlement delay by using the characteristics of discharge capacity decreases of vertical drain with time elapse by the pattern of hyperbolic equation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.896-900
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• 2010

We investigate the bit error rate against signal-to-noise ratio performance corresponding to track mis-registration for patterned media storage. The patterned media channels with and without soft underlayer are implemented, and we simulate using one-dimensional Viterbi detector and two-dimensional soft output Viterbi detector (SOVA) when the track mis-registration is 0% (on-track), 10%, 20%, 30%, and 40%. While the BER performance degrades approximate 0.3 ~ 0.5 dB at 10% track mis-registration, it degrades severe over 10% track mis-registration.