Optimizing a Multi-Channel ANC System for Broadband Noise Cancellation in a Telephone Kiosk Using Genetic Algorithms

Montazeri, Allahyar; Poshtan, Javad

doi:10.1155/2009/943240

Cited by 17 publications

(18 citation statements)

References 11 publications

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“…The parameters of the quantum cross, variation, and rotation gate can be determined by experiences on the premise of fast searching optimization. The initialization parameters range of DFNN can be obtained with multiple algorithm running tests [22][23][24][25]. In this paper, parameters of the QGA are selected as follows [26,27]: population scale is 40, the number of the iterations is 100, the initialization parameters interval of DFNN is [0, 3], cross probability is 0.3 while variation probability is 0.1, maximum rotating angle max is 0.15 and minimum rotating angle min is 0.01 , and expansion coefficient is 2.…”

Section: The Experiments Resultsmentioning

confidence: 99%

Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network

Zhang

Gao

Huang

2015

Shock and Vibration

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To evaluate the performance of ball screw, screw performance degradation assessment technology based on quantum genetic algorithm (QGA) and dynamic fuzzy neural network (DFNN) is studied. The ball screw of the CINCINNATIV5-3000 machining center is treated as the study object. Two Kistler 8704B100M1 accelerometers and a Kistler 8765A250M5 three-way accelerometer are installed to monitor the degradation trend of screw performance. First, screw vibration signal features are extracted both in time domain and frequency domain. Then the feature vectors can be obtained by principal component analysis (PCA). Second, the initialization parameters of the DFNN are optimized by means of QGA. Finally, the feature vectors are inputted to DFNN for training and then get the screw performance degradation model. The experiment results show that the screw performance degradation model could effectively evaluate the performance of NC machine screw.

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Section: The Experiments Resultsmentioning

confidence: 99%

Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network

Zhang

Gao

Huang

2015

Shock and Vibration

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“…is the lkth component of the matrix ) (ω Z expressed in equation (28). By combining the result obtained from the expression in (38) with equation 37, it is possible to derive a closed form for the acoustic pressure in modal space as a function of the control input voltages applied to the secondary sources as follows:…”

Section: Formulation Of Coupling For An Active Noise Control Systemmentioning

confidence: 99%

Modeling and analysis of secondary sources coupling for active sound field reduction in confined spaces

Montazeri

Taylor

2017

Mechanical Systems and Signal Processing

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This article addresses the coupling of acoustic secondary sources in a confined space in a sound field reduction framework. By considering the coupling of sources in a rectangular enclosure, the set of coupled equations governing its acoustical behavior are solved. The model obtained in this way is used to analyze the behavior of multi-input multi-output (MIMO) active sound field control (ASC) systems, where the coupling of sources cannot be neglected. In particular, the article develops the analytical results to analyze the effect of coupling of an array of secondary sources on the sound pressure levels inside an enclosure, when an array of microphones is used to capture the acoustic characteristics of the enclosure. The results are supported by extensive numerical simulations showing how coupling of loudspeakers through acoustic modes of the enclosure will change the strength and hence the driving voltage signal applied to the secondary loudspeakers. The practical significance of this model is to provide a better insight on the performance of the sound reproduction/reduction systems in confined spaces when an array of loudspeakers and microphones are placed in a fraction of wavelength of the excitation signal to reduce/reproduce the sound field. This is of particular importance because the interaction of different sources affects their radiation impedance depending on the electromechanical properties of the loudspeakers.

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“…Due to advantages of vibration signal analysis, in this paper, using vibration signals, we propose a strategy for detecting and classifying such rectifier bridge faults in brushless synchronous generator with the enhanced classification accuracy utilizing the wavelet transform and a modified sequential forward subset selection approach applied to the time-domain (statistical) features. Here the search is done over all parameters, nevertheless in case of a large number of parameters evolutionary algorithm can be used at this stage [21,22]. In this approach, both subset selection and MSVM parameter tuning are performed simultaneously which improves the classification performance.…”

Section: Introductionmentioning

confidence: 99%

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Rahnama

Vahedi

Mohammad‐Alikhani

et al. 2019

IET Science, Measurement & Technology

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In order to maintain continuous production and to avoid the maintenance cost increment in power plants, it is important to monitor the condition of equipment, especially the generator. Regarding the impossibility of direct access to rotating diodes in brushless synchronous generators, the condition monitoring of these elements is very important. In this paper, a novel fault detection method is proposed for the diode rectifier of brushless synchronous generator. At the first stage of this method, the vibration signals are recorded and feature extraction is performed by calculating the relative energy of discrete wavelet transform components. Multiclass support vector machine (MSVM) is used for classification, and the best mother wavelet and number of decomposition level are chosen based on classification performance. To enhance the performance of the classification, a modified sequential forward subset selection approach is included by which the best statistical features are selected. In this approach, besides selecting the best subset of statistical features, the classification parameter is tuned according to the selected subset to achieve the best performance. The result of the proposed method is eventually compared with those results of classification performance using conventional subset selection. Experimental results show that the proposed method can detect rectifier faults effectively.

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Optimizing a Multi-Channel ANC System for Broadband Noise Cancellation in a Telephone Kiosk Using Genetic Algorithms

Cited by 17 publications

References 11 publications

Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network

Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network

Modeling and analysis of secondary sources coupling for active sound field reduction in confined spaces

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

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