Inversion for acoustic impedance of a wall by using artificial neural network

Too, Gee-Pinn James; Chen, S. R.; Hwang, Sheng-Jye

doi:10.1016/j.apacoust.2006.03.013

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…The parameters are then arranged into a vector {k} and the corresponding space, denoted by k z , is a subspace of k. The second step consists in the correction of only the parameters selected at the localization step. The problem consists then in finding {k} ∈ k z , which minimizes modified CRE (19).…”

Section: Two-stages Updating Techniquementioning

confidence: 99%

“…At the start of the updating process, the value of r in Eqn. (19) is taken in order to have the same contribution of the errors on the model and on the measurements, ensuring a good sensitivity of both of them to a change of parameters. This value is equal to 0.76, representing in Fig.…”

Section: Updating Processmentioning

confidence: 99%

“…The first method does not provide phase information of the coefficient and the second method gives results only for normal incident waves which do not always represent the reality of practical problems. The limitations of both methods have therefore led to the development of other methods of measurements [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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A Model Updating Technique Based on the Constitutive Relation Error for In Situ Identification of Admittance Coefficient of Sound Absorbing Materials

Progneaux

Bouillard

Deraemaeker

2015

Journal of Vibration and Acoustics

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The development of new absorbing materials and the description of their acoustical properties take an important place in the current acoustical researches. This paper focuses on the identification of the admittance coefficient of sound absorbing material from in situ measurements, using the constitutive relation error (CRE)-based updating technique. This technique consists of a two-stage approach, allowing to regularize the inverse problem. Moreover, the technique allows the detection of faulty sensors and therefore the correction of the erroneous measurements before the updating process. The technique is developed, in a first part of this paper, for acoustical problems with generalized boundary conditions, and illustrated, in a second part, on a numerical and a physical two-dimensional (2D) test case.

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Section: Two-stages Updating Techniquementioning

confidence: 99%

Section: Updating Processmentioning

confidence: 99%

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A Model Updating Technique Based on the Constitutive Relation Error for In Situ Identification of Admittance Coefficient of Sound Absorbing Materials

Progneaux

Bouillard

Deraemaeker

2015

Journal of Vibration and Acoustics

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“…No ramo de acústica pesquisas utilizam redes neurais artificias para classificar a impedância acústica de um ambiente (Too, Chen, Hwang, 2007). A capacidade das redes neurais artificias em diferenciar padrões e as classificar é expressa em (Fernandes, 2009), na qual as utiliza para obtenção de fontes de harmônicos em sistemas elétricos de distribuição.…”

Section: Redes Neurais Artificiaisunclassified

Classificação de eventos de PIO utilizando Redes Neurais Artificiais

Bruschi¹

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During the certification and the flight tests of a new airplane, the verification process to prove the low susceptibility at PIO is usually done for the experience of the test pilots, thus, the result and safety operation regarding PIO is intrinsic the assessment subjectivity, in other words, the certification process of PIO is directly related with the test pilots experiences. New airplanes tend to reduce stability margins, that is, increasing PIO tendencies, and full autonomous airplanes with capacity to perform a complete flight without crew, for this reason it is necessary the reduction of the subjectivity of PIO assessment for next generation of aircraft. This project covers the development of a neural network that will be able to classify the PIO oscillations in accordance with the PIOR Scale and to the subjective assessment of PIO classification.Neural networks assertiveness was higher for PIO classification than the result done by pilots, when the choice of inputs follow the guidelines established for this work.

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