A principal component feedforward algorithm for active noise control: flight test results

Cabell, Randolph H.; Palumbo, Daniel L.; Vipperman, Jeffrey S.

doi:10.1109/87.896748

Cited by 26 publications

(11 citation statements)

References 17 publications

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“…PCA is widely used in face recognition [8][9][10]. It is also used in vehicle sound signature recognition [11], speech recognition [12], speaker recognition [13], medical applications [14][15][16], signal noise reduction [17], and active noise control [18], among others.…”

Section: Pcamentioning

confidence: 99%

Wavelet-based PCA defect classification and quantification for pulsed eddy current NDT

Tian

Sophian

Taylor

et al. 2005

IEE Proceedings - Science, Measurement and Technology

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A new approach for defect classification and quantification by using pulsed eddy current sensors and integration of principal component analysis and wavelet transform for feature based signal interpretation is presented. After reviewing the limitation of current parameters of peak value and its arrival time from pulsed eddy current signals, a two-step framework for defect classification and quantification is proposed by using adopted features from principal component analysis and wavelet analysis. For defect classification and quantification, different features have been extracted from the pulsed eddy current signals. Experimental tests have been undertaken for ferrous and non-ferrous metal samples with manufactured defects. The results have illustrated the new approach has better performance than the current approaches for surface and sub-surface defect classification. The defect quantification performance, which is difficult by using current approaches, is impressive.

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Section: Pcamentioning

confidence: 99%

Wavelet-based PCA defect classification and quantification for pulsed eddy current NDT

Tian

Sophian

Taylor

et al. 2005

IEE Proceedings - Science, Measurement and Technology

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“…Several preconditioning methods have been published for feedforward control ͑Cabell and Fuller, 1999; Cabell et al, 2001;Elliott, 2001Elliott, , 2000Bai and Elliott, 2004͒. Preconditioning using plant decoupling has been treated using principal component LMS ͑PC-LMS͒ algorithm, based on SVD, for tonal disturbances ͑Cabell and Fuller, 1999͒ or, equivalently, SVD controller for tonal disturbances ͑Elliott, 2000͒.…”

Section: Awfs With Independent Radiation Mode Controlmentioning

confidence: 99%

Adaptive wave field synthesis for broadband active sound field reproduction: Signal processing

Gauthier

Berry²

2008

The Journal of the Acoustical Society of America

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Sound field reproduction is a physical approach to the reproduction of the natural spatial character of hearing. It is also useful in experimental acoustics and psychoacoustics. Wave field synthesis (WFS) is a known open-loop technology which assumes that the reproduction environment is anechoic. A real reflective reproduction space thus reduces the objective accuracy of WFS. Recently, adaptive wave field synthesis (AWFS) was defined as a combination of WFS and active compensation. AWFS is based on the minimization of reproduction errors and on the penalization of departure from the WFS solution. This paper focuses on signal processing for AWFS. A classical adaptive algorithm is modified for AWFS: filtered-reference least-mean-square. This modified algorithm and the classical equivalent leaky algorithm have similar convergence properties except that the WFS solution influences the adaptation rule of the modified algorithm. The paper also introduces signal processing for independent radiation mode control of AWFS on the basis of plant decoupling. Simulation results for AWFS are introduced for free-field and reflective spaces. The two algorithms effectively reproduce the sound field and compensate for the reproduction errors at the error sensors. The independent radiation mode control allows a more flexible tuning of the algorithm.

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“…• Additive noises. The additive noises frequently observed in the real-world engineering practice include maneuvering target tracking [46], distributed sensing [47,48], flight control [49,50], and UAV [51,52]. In a noisy environment, the dynamics of MASs can naturally be modeled by differential/difference equations subject to additive stochastic noises.…”

Section: Stochasticity In the Process Equations Of Agentsmentioning

confidence: 99%

Consensus control of stochastic multi-agent systems: a survey

Wang

Han

et al. 2017

Sci. China Inf. Sci.

152

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In this article, we provide a review of the consensus control problem for stochastic multi-agent systems (MASs). Recent advances are surveyed according to the method of occurrence of the stochasticity of the MASs. First, the consensus problem is discussed for MASs, wherein individual agents are corrupted by random noises, i.e., the dynamics of agents involve stochasticity in process and/or measurement equations. Both additive noises and multiplicative noises are surveyed in detail and special attention is paid to the MASs whose dynamics are governed by Itô differential equations. Moreover, particular effort is devoted to presenting the latest progress on the consensus problem for a special type of stochastic MAS with Markovian jump parameters. Subsequently, the relevant research is summarized for MASs with noisy communication environments and stochastic sampling. Further, we provide a systematic review of the consensus problems for MASs whose communication topology varies randomly in the process of data propagation among agents. Finally, conclusions are drawn and several potential future research directions are outlined.

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A principal component feedforward algorithm for active noise control: flight test results

Cited by 26 publications

References 17 publications

Wavelet-based PCA defect classification and quantification for pulsed eddy current NDT

Wavelet-based PCA defect classification and quantification for pulsed eddy current NDT

Adaptive wave field synthesis for broadband active sound field reproduction: Signal processing

Consensus control of stochastic multi-agent systems: a survey

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