• 전기학회논문지
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• 제62권7호
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• pp.1003-1008
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• 2013

This paper proposes a compression scheme based on the modified reversible integer transform (MRIT) and forward adaptive prediction for lossless image compression. JPEG XR is the newest image coding standard with high compression ratio and that composed of the Photo Core Transform (PCT) and backward adaptive prediction. To improve the efficiency and quality of compression, we substitutes the PCT and backward adaptive prediction for the modified reversible integer transform (MRIT) and forward adaptive prediction, respectively. Experimental results indicate that the proposed method are superior to the previous method of JPEG XR in terms of lossless compression efficiency and computational complexity.

• 한국대기환경학회지
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• 제2권3호
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• pp.52-56
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• 1986

In this paper a adaptive LMS(least mean-square) lattice predictor, which is composed of the adaptive lattice algorithm and LMS algorithm by Widrow-Hopf, is used to predict the future air pollution of the extraordinary levels in the environmental system. This prediction algorithm is applied to the one-step forward prediction of atmospheric CO concentration by using real observed data. Computer simulation proves that the power in the forward error sequences decreases as the number of stages in the lattice is increased.

• 품질경영학회지
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• 제26권4호
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• pp.143-150
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• 1998

We propose an adaptive monitoring a, pp.oach for serially correlated data. This algorithm uses the adaptive linear prediction lattice filter (ALPLF) which makes it compute process parameters in real time and recursively update their estimates. It involves computation of the forward and backward prediction errors. CUSUM control charts are a, pp.ied to prediction errors simulaneously in both directions as an omnibus method for detecting changes in process parameters. Results of computer simulations demonstrate that the proposed adaptive monitoring a, pp.oach has great potentials for real-time industrial a, pp.ications, which vary frequently in their control environment.

• 한국위성정보통신학회논문지
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• 제7권1호
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• pp.82-85
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• 2012

본 논문은 천리안 위성을 이용하여 운용중인 공공 테스트베드의 가용율 및 시스템 throughput 향상을 위해 포워드 링크 ACM(Adaptive Coding & Modulation) 방안에 대해 소개하고, 포워드링크에 ACM 기능을 구현하기 위한 채널 상태를 예측하기위한 알고리즘으로 기울기를 이용한 예측 기법과 LMS(Least Mean Square)를 이용한 예측 기법의 성능을 비교하였다. 시뮬레이션 결과 LMS 기법을 이용한 예측기법은 99%가 3dB 이내의 예측 오차를 보였고, 기울기를 이용한 예측 기법은 99%가 4.5dB 이내의 예측 오차를 갖음을 알 수 있다.

• Journal of electromagnetic engineering and science
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• 제16권2호
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• pp.80-86
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• 2016

This paper proposes the algorithm for forward link adaptive coding and modulation (ACM) and the detailed design for a satellite communication system to improve network reliability and system throughput. In the ACM scheme, the coding and modulation schemes are changed by as much as the channel can provide depending on the quality of the communication link. To implement the forward link ACM system in the Ka-band, channel prediction and modulation/coding decision methods are proposed and simulated. The parameters of the adaptive filter predictor based on the least mean square are optimized, the minimum mean square error of the channel predictor is 0.0608 when step size and the number of filter tap are 0.0001 and 4, respectively. A test-bed is set up to verify the forward link ACM system, and a test is performed using a Ka-band satellite (i.e., Communication, Ocean, and Meteorological Satellite [COMS]). This test verifies that the ACM scheme can increase the system throughput.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1987년도 학술발표회 논문집
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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.

• ETRI Journal
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• 제31권2호
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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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 제14회 신호처리 합동 학술대회 논문집
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• pp.869-872
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• 2001

Blind identification and equalization of communication channel is important because it does not need training sequence, nor does it require a priori channel information. So, we can increase the bandwidth efficiency. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind channel estimator and equalizer length mismatch as well as for its simple adaptive algorithms. In this paper, we propose method for fractionally spaced blind equalizer with arbitrary delay using one-step forward prediction error filter from second-order statistics of the received signals for SIMO channel. Our algorithm utilizes the forward prediction error as training sequences for data estimation and desired signal for channel estimation.

• 한국통신학회논문지
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• 제28권4C호
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• pp.384-391
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• 2003

Blind channel identification and equalization attempt to identify the communication channel and to remove the inter-symbol interference caused by a communication channel without using any known trainning sequences. In this paper, we propose a blind adaptive channel identification and equalization algorithm with phase offset compensation for single-input multiple-output (SIMO) channel. It is based on the one-step forward multichannel linear prediction error method and can be implemented by an RLS algorithm. Phase offset problem, we use a blind adaptive algorithm called the constant modulus derotator (CMD) algorithm based on condtant modulus algorithm (CMA). Moreover, unlike many known subspace (SS) methods or cross relation (CR) methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch.

• Journal of Power Electronics
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• 제13권4호
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.