• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• Proceedings of the Acoustical Society of Korea Conference
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• 1987.11a
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• pp.61-65
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• 1987

An algorithm for estimating incident angles of multiple broaband signals is proposed. The method adopts semicausal model for two dimensional linear prediction filter coefficients such that the arithmatic averag of the mean squared values of the forward and reverse prediction arrors is minimized. Preliminary results demonstrating the performance of the proposed method are presented. Simulation results indicate that the performance depends on signal-to-noise ratio and prediction order in spatial demension.

• Wind and Structures
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• v.31 no.6
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• pp.549-560
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• 2020

Methods for stochastic simulation of non-Gaussian wind pressure have increasingly addressed the efficiency and accuracy contents to offer an accurate description of the extreme value estimation of the long-span and high-rise structures. This paper presents a linear prediction and z-transform (LPZ) based Cumulative distribution function (CDF) mapping algorithm for the simulation of multivariate non-Gaussian fluctuating wind pressure. The new algorithm generates realizations of non-Gaussian with prescribed marginal probability distribution function (PDF) and prescribed spectral density function (PSD). The inverse linear prediction and z-transform function (ILPZ) is deduced. LPZ is improved and applied to non-Gaussian wind pressure simulation for the first time. The new algorithm is demonstrated to be efficient, flexible, and more accurate in comparison with the FFT-based method and Hermite polynomial model method in two examples for transverse softening and longitudinal hardening non-Gaussian wind pressures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.12
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• pp.1287-1296
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• 2009

It is very important to estimate the Signal to Noise Ratio(SNR) of received signal in time varying channel state. Most SNR estimation techniques derive the SNR estimates solely from the samples of the received signal after the matched filter. In the severe distorted wireless channel, the performance of these estimators become unstable and degraded. LP-based SNR estimator which can operate on data samples collected at the front-end of a receiver shows more stable performance than other SNR estimator. In this paper, we study an efficient SNR estimation algorithm based on LP and propose a new estimation method to decrease the computation complexity. Proposed algorithm accomplishes the SNR estimation process efficiently because it uses the forward prediction error and its conjugate value during the linear prediction error update. Via the computer simulation, the performance of this proposed estimation method is compared and discussed with other conventional SNR estimators in digital communication channels.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.4
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• pp.1-5
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• 1982

A new algorithm, based upon a lattice formulation, is presented for linear prediction. The output of the algorithm is the reflection coefficients that guarantee the stability of the all-pole model. The equations are derived that compute the covariance of the residuals recursively at each prediction stage, and in processing of computing that eqations, the reflection coefficients are estimated without computing the predictor coefficients. Comparing with covariance-lattice method, it can be said that the new algorithm reduce the number of computations to about half and is more efficient for fitting of the high-order model.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.84-91
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• 2008

This paper suggests the robust localization system in the application of ambient display with multiple ultrasonic range sensors. The ambient display provides the interactive image and video to improve the quality of life, especially for low mobility elders. Due to the limitation of indoor localization, this paper employs linear prediction algorithm to recover the missing information based on AR(Autoregressive) model by using Yule-Walker method. Numerous speculations from prediction error and computation load are considered to decide the optimal length of referred data and order. The results of these analyses demonstrate that the linear prediction algorithm with the 16th order and 50 reference data can improve reliability of the system in average 74.39% up to 97.97% to meet the performance of interactive system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.996-1007
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• 1996

In this paper, a blind adaptation method for a decision feedback equalizer (DFE) is proposed to deal with nominimum phase channels. This equalizer is composed of a linear transversal filter and a prediction error filter which are trained separately using constant modulus and decision feedback prediction algorithms, respectively, during the learnign time. The proposed algorithm guaranetees the DFE to converge to a suboptimal point on the condition that a linear transversal of the proposed scheme is illustrated and the performance is compared with conventional blind equlization algorithms.

• Journal of Biomedical Engineering Research
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• v.14 no.1
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• pp.1-8
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• 1993

In this study, the extractions of formants and articulatory motion trajectories for Korean complex vowels are performed by using the RLSL adaptive linear prediction filter. This enables us to extract accurate spectrum in transition of speech signal. This study shows that the RLSL algorithm is superior to the Levinson algorithm, specially in transition part of speech.