9783843368438 - classifying damages in engineering material using bayesian networks: naïve bayes classifiers di mohamed addin, addin osman; s. salit, m.; othman, m. (6 risultati)
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Classifying Damages in Engineering Material Using Bayesian Networks | Naïve Bayes Classifiers | Addin Osman Mohamed Addin (u. a.) | Taschenbuch | 152 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843368438 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Land…str. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Since its foundation in 1980s, Bayesian networks have been widely and successfully implemented in many research and industrial areas. Nevertheless, they have not been thoroughly investigated and implemented for damage detection in…engineering materials. This book provides a through introduction to Bayesian networks as a competitive probabilistic graphical model in general and as a classification tool (the Naïve bayes classifier) in particular for damage detection in engineering material. Since the feature extraction is essential for the classifiers, the book introduces the f -folds feature extraction algorithm. The derivation of the algorithm is based on empirical study on a data set, which represents voltage amplitudes of Lamb-waves produced and collected by sensors and actuators mounted on the surface of quasi-isotropic graphite/epoxy laminates contain different artificial damages. 152 pp. Englisch.
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Da: moluna, Greven, , Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mohamed Addin Addin OsmanNajran University, Kingdom of Saudi Arabia and University Putra Malaysia, Malaysia.Autor/Autorin: S. Salit M.Najran University, Kingdom of Saudi Arabia and University Putra Mal…aysia, Malaysia.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 59,00
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Since its foundation in 1980s, Bayesian networks have been widely and successfully implemented in many research and industrial areas. Nevertheless, they have not been thoroughly investigated and implemented for damage detection in engi…neering materials. This book provides a through introduction to Bayesian networks as a competitive probabilistic graphical model in general and as a classification tool (the Naïve bayes classifier) in particular for damage detection in engineering material. Since the feature extraction is essential for the classifiers, the book introduces the f -folds feature extraction algorithm. The derivation of the algorithm is based on empirical study on a data set, which represents voltage amplitudes of Lamb-waves produced and collected by sensors and actuators mounted on the surface of quasi-isotropic graphite/epoxy laminates contain different artificial damages.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Since its foundation in 1980s, Bayesian networks have been widely and successfully implemented in many research and industrial areas. Nevertheless, they have not been thoroughly investigated and implemented for damage detection in engin…eering materials. This book provides a through introduction to Bayesian networks as a competitive probabilistic graphical model in general and as a classification tool (the Naïve bayes classifier) in particular for damage detection in engineering material. Since the feature extraction is essential for the classifiers, the book introduces the f -folds feature extraction algorithm. The derivation of the algorithm is based on empirical study on a data set, which represents voltage amplitudes of Lamb-waves produced and collected by sensors and actuators mounted on the surface of quasi-isotropic graphite/epoxy laminates contain different artificial damages.
