Buabeng albert (8 resultados)
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 106,01
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Librería: moluna, Greven, Alemaniamoluna
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EUR 64,70
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 70,95
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Taschenbuch. Condición: Neu. Intelligent Predictive Maintenance Frameworks for Fault Classification | Hybridising Machine Learning Techniques | Albert Buabeng (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206752738 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landst…r. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 84,90
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -With the rising demand for complex, integrated and autonomous systems in the field of engineering, efficient and versatile Predictive Maintenance (PdM) frameworks have become a requirement for monitoring the health status of these s…ystems since safety, reliability and optimum asset utilisation, are key issues. However, due to the continuously changing dynamics of industrial operations, the data recorded for developing PdM frameworks are often high-dimensional and characterised by undesirable features such as high level of uncertainty, class imbalance and multiclass among others. These undesirables limit the efficiency of existing PdM frameworks in producing desirable results. For these reasons, this book has proposed three hybrid and novel PdM frameworks capable of handling such undesirable features through the hybridisation of machine learning techniques. The proposed hybrid frameworks advance the field of PdM by improving the accuracy of fault diagnosis as the issue of undesirable features impedes the ability of machine learning algorithms to produce desired results. 244 pp. Englisch.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 108,00
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 107,64
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 84,90
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -With the rising demand for complex, integrated and autonomous systems in the field of engineering, efficient and versatile Predictive Maintenance (PdM) frameworks have become a requirement for monitoring the health status of these syste…ms since safety, reliability and optimum asset utilisation, are key issues. However, due to the continuously changing dynamics of industrial operations, the data recorded for developing PdM frameworks are often high-dimensional and characterised by undesirable features such as high level of uncertainty, class imbalance and multiclass among others. These undesirables limit the efficiency of existing PdM frameworks in producing desirable results. For these reasons, this book has proposed three hybrid and novel PdM frameworks capable of handling such undesirable features through the hybridisation of machine learning techniques. The proposed hybrid frameworks advance the field of PdM by improving the accuracy of fault diagnosis as the issue of undesirable features impedes the ability of machine learning algorithms to produce desired results.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 244 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 85,92
Envío por EUR 61,91Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With the rising demand for complex, integrated and autonomous systems in the field of engineering, efficient and versatile Predictive Maintenance (PdM) frameworks have become a requirement for monitoring the health status of these system…s since safety, reliability and optimum asset utilisation, are key issues. However, due to the continuously changing dynamics of industrial operations, the data recorded for developing PdM frameworks are often high-dimensional and characterised by undesirable features such as high level of uncertainty, class imbalance and multiclass among others. These undesirables limit the efficiency of existing PdM frameworks in producing desirable results. For these reasons, this book has proposed three hybrid and novel PdM frameworks capable of handling such undesirable features through the hybridisation of machine learning techniques. The proposed hybrid frameworks advance the field of PdM by improving the accuracy of fault diagnosis as the issue of undesirable features impedes the ability of machine learning algorithms to produce desired results.
