• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.4
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• pp.337-342
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• 2016

In this paper, we studied a modified linear prediction method to estimate target signal correctly. Linear prediction method estimate direction-of-arrival to linear combination for any antenna element and other antenna elements. Modified linear prediction used optimal weight and posterior probability method. Through simulation, we are comparative analysis about the performance of proposed, bartlett and MUSIC method. From simulation, Bartlett and MUSIC method was estimation 3 targets signal, and proposed method estimated 4 targets. We showed the superior performance of the proposed algorithm relative to the classical method in order to estimate of target signals.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.477-482
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• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.

• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• Computers and Concrete
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• v.5 no.3
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• pp.261-277
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• 2008

This paper presents methodologies for remaining life prediction of plain concrete structural components considering tension softening effect. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. A methodology to account for tension softening effects in the computation of SIF and remaining life prediction of concrete structural components has been presented. The tension softening effects has been represented by using any one of the models mentioned above. Numerical studies have been conducted on three point bending concrete structural component under constant amplitude loading. Remaining life has been predicted for different loading cases and for various tension softening models. The predicted values have been compared with the corresponding experimental observations. It is observed that the predicted life using bi-linear model and power curve model is in close agreement with the experimental values. Parametric studies on remaining life prediction have also been conducted by using modified bilinear model. A suitable value for constant of modified bilinear model is suggested based on parametric studies.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.146-153
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• 2009

One dimensional site response analysis is widely used in prediction of the ground motion that is induced by earthquake. Equivalent linear analysis is the most widely used method due to its simplicity and ease of use. However, the equivalent linear method has been known to be unreliable since it approximates the nonlinear soil behavior within the linear framework. To consider the nonlinearity of the ground at frequency domain, frequency dependent algorithms that can simulate shear strain - frequency dependency have been proposed. In this study, the results of the modified equivalent linear analysis are compared to evaluate the degree of improvement and the applicability of the modified algorithms. Results show the novel smoothed curve that is proposed by this study indicates the most stable prediction and can enhance the accuracy of the prediction.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.196-203
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• 2000

On the assumption that the voices of the cows are produced by the linear prediction filter, we characterized the cows' voices. The order of this filter is determined by examining the voices characteristics both in time and frequency domains. The proposed order of the linear prediction filter is 15 for modeling voice production of the cow. The combination of the two parameters of the fundamental frequency, the slope of the straight line regressed from the log-log spectra of the amplitude-envelope and the only one coefficient involved in the linear prediction filter can differentiate the two cows.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.9
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• pp.1348-1357
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• 1993

In this paper, we present code-excited linear prediction coding using trellis coded vector excitation, termed trellis code-excited linear prediction coding (TCELP), for an efficient 8 kbps digital cellular vocoder. A training sequence-based algorithm is developed for designing an optimized codebook subject to the TCELP structure. Also, we discuss the encoding complexity of the TCELP system and trellis symbol release rules that avoid excessive encoding delay. finally, simulation results results for the TCELP coder are given at the bit rate of 8 kbps.

• Journal of Communications and Networks
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• v.1 no.3
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• pp.153-157
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• 1999

In this paper, we derive finite-dimensional non-linear fil-ters for optimally reconstructing speech signals in Switched Predic-tion vocoders, Code Excited Linear Prediction(CELP) and Differ-ential Pulse Code Modulation (DPCM). Our filter is an extension of the Hidden Markov filter.

• ETRI Journal
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• v.31 no.2
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• pp.162-172
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• 2009

A linear-prediction-based blind equalization algorithm for single-input single-output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second-order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single-input multiple-output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum-mean-square-error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.5
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• pp.52-59
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• 2008

The accuracy of the machine tools is degraded because of thermal error of structure due to thermal variation. To improve the accuracy of a machine tools, measurement and prediction of thermal error is very important. The main part of thermal source is spindle due to high speed with friction. The thermal error of spindle is very important because it is over 10% in total thermals errors. In this paper, the suitable thermal error prediction technology for machine tools with open architecture controller is developed and implemented to machine tools. Two thermal error prediction technologies, neural network and multi-linear regression, are investigated in several methods. The multi-linear regression method is more effective for implementation to CNC. The developed thermal error prediction technology is implemented on the internal function of CNC.