Acoustic characterization and prediction of the cut-off dimensionless frequency of an elastic tube by neural networks.
Identifieur interne : 000271 ( PubMed/Checkpoint ); précédent : 000270; suivant : 000272Acoustic characterization and prediction of the cut-off dimensionless frequency of an elastic tube by neural networks.
Auteurs : Abdelilah Dariouchy [France] ; El Houcein Aassif ; Dominique Décultot ; Gérard MazeSource :
- IEEE transactions on ultrasonics, ferroelectrics, and frequency control [ 0885-3010 ] ; 2007.
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Abstract
A neural network is developed to predict cut-off dimensionless frequencies of the antisymmetric circumferential waves (Ai) propagating around an elastic circular cylindrical shell of different radius ratio b/a (a, outer radius; b, inner radius). The useful data to train and test the performances of the model are determinated from calculated trajectories of natural modes of resonances or extracted from time-frequency representations of Wigner-Ville of the acoustic backscattered time signal obtained from a computation. In this work, the studied tubes are made of aluminum or stainless steel. The material density, the radius ratio b/a, the index i of the antisymmetric waves, and the propagation velocities in the tube, are selected like relevant entries of the model of neural network. During the development of the network, several configurations are evaluated. The optimal model selected is a network with two hidden layers. This model is able to predict the cut-off dimensionless frequencies with a mean relative error (MRE) of about 1%, a mean absolute error (MAE) of 3.10(-3) k1a, and a standard error (SE) of 10(-3) k1a (k1a is the dimensionless frequency, k is the wave number in water).
PubMed: 17523570
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The useful data to train and test the performances of the model are determinated from calculated trajectories of natural modes of resonances or extracted from time-frequency representations of Wigner-Ville of the acoustic backscattered time signal obtained from a computation. In this work, the studied tubes are made of aluminum or stainless steel. The material density, the radius ratio b/a, the index i of the antisymmetric waves, and the propagation velocities in the tube, are selected like relevant entries of the model of neural network. During the development of the network, several configurations are evaluated. The optimal model selected is a network with two hidden layers. This model is able to predict the cut-off dimensionless frequencies with a mean relative error (MRE) of about 1%, a mean absolute error (MAE) of 3.10(-3) k1a, and a standard error (SE) of 10(-3) k1a (k1a is the dimensionless frequency, k is the wave number in water).</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">17523570</PMID><DateCreated><Year>2007</Year><Month>5</Month><Day>25</Day></DateCreated><DateCompleted><Year>2007</Year><Month>06</Month><Day>19</Day></DateCompleted><DateRevised><Year>2007</Year><Month>5</Month><Day>25</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0885-3010</ISSN><JournalIssue CitedMedium="Print"><Volume>54</Volume><Issue>5</Issue><PubDate><Year>2007</Year><Month>May</Month></PubDate></JournalIssue><Title>IEEE transactions on ultrasonics, ferroelectrics, and frequency control</Title><ISOAbbreviation>IEEE Trans Ultrason Ferroelectr Freq Control</ISOAbbreviation></Journal><ArticleTitle>Acoustic characterization and prediction of the cut-off dimensionless frequency of an elastic tube by neural networks.</ArticleTitle><Pagination><MedlinePgn>1055-64</MedlinePgn></Pagination><Abstract><AbstractText>A neural network is developed to predict cut-off dimensionless frequencies of the antisymmetric circumferential waves (Ai) propagating around an elastic circular cylindrical shell of different radius ratio b/a (a, outer radius; b, inner radius). The useful data to train and test the performances of the model are determinated from calculated trajectories of natural modes of resonances or extracted from time-frequency representations of Wigner-Ville of the acoustic backscattered time signal obtained from a computation. In this work, the studied tubes are made of aluminum or stainless steel. The material density, the radius ratio b/a, the index i of the antisymmetric waves, and the propagation velocities in the tube, are selected like relevant entries of the model of neural network. During the development of the network, several configurations are evaluated. The optimal model selected is a network with two hidden layers. This model is able to predict the cut-off dimensionless frequencies with a mean relative error (MRE) of about 1%, a mean absolute error (MAE) of 3.10(-3) k1a, and a standard error (SE) of 10(-3) k1a (k1a is the dimensionless frequency, k is the wave number in water).</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Dariouchy</LastName><ForeName>Abdelilah</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Laboratoire d'Acoustique Ultrasonore et d'Electronique Unité Mixte de Recherche Centre National de la Recherche Scientifique 6068, Université du Havre, Institut Universitaire de Technologie, Place Robert Schuman, 76610 Le Havre, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Aassif</LastName><ForeName>El Houcein</ForeName><Initials>el H</Initials></Author><Author ValidYN="Y"><LastName>Décultot</LastName><ForeName>Dominique</ForeName><Initials>D</Initials></Author><Author ValidYN="Y"><LastName>Maze</LastName><ForeName>Gérard</ForeName><Initials>G</Initials></Author></AuthorList><Language>ENG</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>IEEE Trans Ultrason Ferroelectr Freq Control</MedlineTA><NlmUniqueID>9882735</NlmUniqueID><ISSNLinking>0885-3010</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000162" MajorTopicYN="Y">Acoustics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004548" MajorTopicYN="N">Elasticity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008422" MajorTopicYN="N">Materials Testing</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016571" MajorTopicYN="Y">Neural Networks (Computer)</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010363" MajorTopicYN="N">Pattern Recognition, Automated</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013314" MajorTopicYN="N">Stress, Mechanical</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2007</Year><Month>5</Month><Day>26</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2007</Year><Month>6</Month><Day>20</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2007</Year><Month>5</Month><Day>26</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">17523570</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li></settlement><orgName><li>Université du Havre</li></orgName></list><tree><noCountry><name sortKey="Aassif, El Houcein" sort="Aassif, El Houcein" uniqKey="Aassif E" first="El Houcein" last="Aassif">El Houcein Aassif</name><name sortKey="Decultot, Dominique" sort="Decultot, Dominique" uniqKey="Decultot D" first="Dominique" last="Décultot">Dominique Décultot</name><name sortKey="Maze, Gerard" sort="Maze, Gerard" uniqKey="Maze G" first="Gérard" last="Maze">Gérard Maze</name></noCountry><country name="France"><region name="Région Normandie"><name sortKey="Dariouchy, Abdelilah" sort="Dariouchy, Abdelilah" uniqKey="Dariouchy A" first="Abdelilah" last="Dariouchy">Abdelilah Dariouchy</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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