• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.89-98
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• 2000

This paper presents an active noise cancelling model using wavelet transform and subband filter banks based on adaptive filter. The analysis filter banks decompose input and error signals into QMF filter banks of lowpass and highpass bands. Each filter bank uses wavelet filter with dyadic tree structure. The decomposed input and error signals are iterated by adaptive filter coefficients of each subband using filtered-X LMS algorithm. The synthesis filter banks make output signal of wideband with perfect reconstruction to prepare adaptive filter output signals of each subband. The analysis and synthesis niter hants use conjugate quadrature filters for Pefect reconstruction. Also, The delayed LMS algorithm model for on-line identification of error path transfer characteristics is used gain and acoustic time delay factors. The proposed adaptive active noise cancelling modelis suggested by system retaining the computational and convergence speed advantage using wavelet subband filter banks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.172-185
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• 2019

In this paper, we investigate the performance of the diffusion adaptation strategies for parameter estimation in wireless adaptive networks, where the nodes exchange information over noisy and fading wireless channels. This paper shows the differences between the effect of Rayleigh and Rician fading over wireless adaptive networks and proves that the Rician fading is a more practical model in such kinds of networks. Simulation results imply that the effect of Rayleigh fading is more degrading for the estimation process than Rician fading. Also, the simulation results show the performance of adapt then combine (ATC) diffusion algorithm is better than the combine then adapt (CTA) algorithm by merely considering noise in wireless channels. While the performance of CTA prevails ATC over the wireless adaptive network in the presence of noise plus channel fading.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.277-282
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• 2012

The speech recognition system can not quickly adapt to varied environmental noise factors that degrade the performance of recognition. In this paper, the echo noise robust HMM learning model using average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise HMM learning model consists of the recognition performance is evaluated. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 3.1dB, recognition rate improved as 3.9%.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.43-46
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• 1995

Adaptive algorithms based on gradient adaptation have been extensively investigated and successfully jointed with active noise/vibration control applications. The Filtered-X LMS algorithm became one of the basic feedforward algorithms in such applications, but still is not fully understood. The error path model effect on the Filtered-X LMS algorithm has been under the investigation and some useful properties related stability has been discovered. We are interested in utilizing the fact that the model error caused by the way optimizing the error path model in a view point of convergence speed of Filtered-X LMS algorithm for pure tone noise suppression application without any performance loss at steady state.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.164-169
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• 2005

Microphone array techniques are being used widely in wind tunnel measurements for identification of the distributed aerodynamic noise sources on the model being tested. Depending on the frequencies and sound levels, conventional beamforming algorithm has limitation in separating two adjacent sources. Several modifications to the classical beamforming have been developed to enhance way resolution and reduce sidelobe levels. In this Paper the robust adaptive beamforming and the CLEAN algorithm are used to compare to the result of conventional beamforming method. It is found that the CLEAN algorithm is capable of pin-pointing locations of multiple sources nearby, while these sources are unidentifiable with robust adaptive or conventional beamforming techniques.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.802-805
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• 1994

There are strong ocean wave interference with big amplitude very low frenqencies are similar to the ship's hydrodynamic signals. To detect ship's hydrodynamic field will be faced various natural hydrodynamic interferences which are radom and the prior knowlege of which are not know. This paper proposes to use the adaptive noise cancelling principle and used the model of adaptive wave canceller to eliminate the ocean wave interfrence and detect the ship's hydrodynamic signals. Computer simulation results shown that signal to noise ratio can be raised from several to ten times. It shows the fact that this mathod can detect the ship's hydrodynamic signals from the strong ocean wave interferences while it is difficult for the old methods.

• The Journal of the Acoustical Society of Korea
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• v.33 no.5
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• pp.335-340
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• 2014

In this paper, we propose a new sign subband adaptive filter to improve the convergence rate of the conventional sign subband adaptive filter which has been proposed to deal with colored input signal under the environment with impulsive noise. The existing sign subband adaptive filter does not increase the convergence speed by increasing the number of subband because each subband input signal is normalized by $l_2-norm$ of all of the subband input signals. We devised a new sign subband adaptive filter that normalizes each subband input signal with $l_2-norm$ of each subband input signal and increases the convergence rate by increasing the number of subband. We carried out a performance comparison of the proposed algorithm with the existing sign subband adaptive filter using a system identification model. It is shown that the proposed algorithm has faster convergence rate than the existing sign subband adaptive filter.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.59-66
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• 1993

In this paper we propose an adaptive update rate tracking algorithm for a phased array radar, based on the interacting multiple model(IMM) algorithm. The purpose of the IMM algorithm hers is twofold: 1) to estimate and predict the target states, and 2) to estimate the level of the process noise. Using the estimate of the process noise level adapted to target dynamics, the update interval is determined to maintain a desired prediction accuracy so that the radar system load is minimized. The adaptive update rate tracking algorithm is implemented for a phased array radar and evaluated with Monte Carlo simulations on various trajectories. The evaluation results of the proposed algorithm and a standard Kalman filter without the adaptive update rate control are presented to compare.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.239-245
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• 2000

Adaptive bang-bang control algorithm has been proposed by the authors to improve peak response reduction of building structures under unexpected large earthquake. At the previous research, control performance of the proposed algorithm was experimentally confirmed by using a I-DOF test structure. As an extended research, performance tests on a multi-DOF model structure have been conducted to prove the usefulness of the adaptive bang-bang control algorithm using a hydraulic AMD. It is confirmed that the proposed adaptive bang-bang algorithm is applicable to suppress the vibration of multi-DOF structures subject to severe external excitations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.