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Libros sobre el tema "Machine Diagnostic"

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Autor: Grafiati
Publicado: 14 de marzo de 2023

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1

Naidenova, Xenia y Viktor Shagalov. Diagnostic test approaches to machine learning and commonsense reasoning systems. Hershey, PA: Information Science Reference, 2013.

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2

Miller, Richard Kendall. Survey on X-ray machine vision and compute [sic] tomography. Madison, GA: Future Technology Surveys, 1989.

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3

Machine learning in computer-aided diagnosis: Medical imaging intelligence and analysis. Hershey: Medical Information Science Reference, 2011.

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4

1970-, Gonzalez Fabio A. y Romero Eduardo 1963-, eds. Biomedical image analysis and machine learning technologies: Applications and techniques. Hershey, PA: Medical Information Science Reference, 2010.

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5

1970-, Gonzalez Fabio A. y Romero Eduardo 1963-, eds. Biomedical image analysis and machine learning technologies: Applications and techniques. Hershey, PA: Medical Information Science Reference, 2010.

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6

MLMI 2010 (2010 Beijing, China). Machine learning in medical imaging: First International Workshop, MLMI 2010, held in conjunction with MICCAI 2010, Beijing, China, September 20, 2010 : proceedings. Berlin: Springer, 2010.

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7

Sibikin, Mihail, A. N. Chernenko y Yuriya Voronkin. Technological equipment. Metal cutting machines. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1061257.

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The textbook discusses and describes the technological equipment of machine tool construction: metalworking machines; standard mechanisms and devices for machine tools; purpose, device, kinematics, adjustment of machines of various groups and types; multipurpose and aggregate machines; precision equipment; automatic lines; flexible production module; flexible production system; testing of machines; indicators of the technical level and reliability of technological equipment; diagnostics of machine systems. Meets the requirements of the federal state educational standards of secondary vocational education of the latest generation. For students of secondary vocational education institutions. It can be useful for professional training of technicians and craftsmen.
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8

Trigeassou, Jean-Claude. Electrical Machines Diagnosis. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2011. http://dx.doi.org/10.1002/9781118601662.

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9

Electrical machines diagnosis. London: ISTE, 2011.

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10

Williams, J. Hywel. Condition-based maintenance and machine diagnostics. London: Chapman & Hall, 1994.

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11

Diagnostic prédictif de l'état des machines. Paris: Masson, 1996.

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12

V, Frolov K. y Menyailov A. I, eds. Vibroacoustical diagnostics for machines and structures. Taunton, Somerset, England: Research Studies Press, 1991.

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13

A, Balter M., ed. Fraktografii͡a︡--sredstvo diagnostiki razrushennykh detaleĭ. Moskva: "Mashinostroenie", 1987.

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14

Wien), Internationales Seminar Wasserkraftanlagen (7th 1992 TU. Instandhaltung, Schwachstellenanalyse, Diagnostik. Wien: Technische Universität Wien, 1992.

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15

Suzuki, Kenji. Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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16

Lyon, R. H. Machinery noise and diagnostics. Cambridge,Ma: Massachusetts Institute of Technology, 1989.

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17

Machinery noise and diagnostics. Boston: Butterworths, 1987.

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18

Diana, G., ed. Diagnostics of Rotating Machines in Power Plants. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3.

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19

Kanarchuk, Vadim Evgenʹevich. Termometricheskai͡a︡ diagnostika mashin. Kiev: Gol. izd-vo izdatelʹskogo obʺedinenii͡a︡ "Vyshcha shkola", 1985.

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20

In-service lubricant and machine analysis, diagnostics, and prognostics. West Conshohocken, PA: ASTM International, 2011.

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21

Guli͡aev, Vasiliĭ Anatolʹevich. Vychislitelʹnai͡a diagnostika. Kiev: Izd-vo "Nauk. dumka", 1992.

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22

Kuz'min, Nikolay y Aleksandr Kustikov. Diagnostics of modern cars. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1078766.

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The textbook contains the main provisions of the theory of automobile diagnostics, methods and characteristic technological processes for diagnosing automobile engines, structural elements of the transmission, chassis, bodywork, and active safety systems. The theoretical material is supported by the description of practical technologies for conducting diagnostics of modern car systems. Meets the requirements of the federal state educational standards of higher education of the latest generation. Recommended for undergraduates and bachelors of all forms of education in the areas of training 23.04.03 and 23.03.03 "Operation of transport and technological machines and complexes".
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23

Yang, Bo-Suk. Introduction to intelligent machine fault diagnosis and prognosis. New York: Nova Science Publishers, 2009.

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24

Achmad, Widodo, ed. Introduction of intelligent machine fault diagnosis and prognosis. New York: Nova Science Publishers, 2009.

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25

Wu, Guorong, Dinggang Shen y Mert Sabuncu. Machine Learning and Medical Imaging. Elsevier Science & Technology, 2016.

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26

Wu, Guorong, Dinggang Shen y Mert Sabuncu. Machine Learning and Medical Imaging. Elsevier Science & Technology Books, 2016.

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27

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging. Elsevier, 2019. http://dx.doi.org/10.1016/c2017-0-02827-6.

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28

Dey, Nilanjan, Amira S. Ashour, Surekha Borra y Fuqian Shi. Machine Learning in Bio-Signal Analysis and Diagnostic Imaging. Elsevier Science & Technology Books, 2018.

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29

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging. Academic Press, 2018.

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30

Dey, Nilanjan, K. C. Santosh, D. S. Guru y Sameer Antani. Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. Taylor & Francis Group, 2019.

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31

Dey, Nilanjan, K. C. Santosh, D. S. Guru y Sameer Antani. Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. Taylor & Francis Group, 2019.

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32

Dey, Nilanjan, K. C. Santosh, D. S. Guru y Sameer Antani. Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. Taylor & Francis Group, 2019.

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33

Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. Taylor & Francis Group, 2019.

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34

Zhou, Kevin. Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches. Elsevier Science & Technology Books, 2015.

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35

Bhatia, Kanwal y Herve Lombaert. Machine Learning Meets Medical Imaging: First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers. Springer London, Limited, 2015.

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36

Bhatia, Kanwal y Herve Lombaert. Machine Learning Meets Medical Imaging: First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, ... Springer, 2015.

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37

Paul, Sudip y Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.

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38

Paul, Sudip y Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.

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39

Paul, Sudip y Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.

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40

Paul, Sudip y Sanjay Saxena. Deep Learning Applications in Medical Imaging. IGI Global, 2020.

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41

Santosh, K. C. Medical Imaging. Taylor & Francis Group, 2019.

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42

Bruno, Michael A. Error and Uncertainty in Diagnostic Radiology. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190665395.001.0001.

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Diagnostic radiology is a medical specialty that is primarily devoted to the diagnostic process, centered on the interpretation of medical images. This book reviews the high level of uncertainty inherent to radiological interpretation and the overlap that exists between the uncertainty of the process and what might be considered “error.” There is also a great deal of variability inherent in the physical and technological aspects of the imaging process itself. The information in diagnostic images is subtly encoded, with a broad range of “normal” that usually overlaps the even broader range of “abnormal.” Image interpretation thus blends technology, medical science, and human intuition. To develop their skillset, radiologists train intensively for years, and most develop a remarkable level of expertise. But radiology itself remains a fallible human endeavor, one involving complex neurophysiological and cognitive processes employed under a range of conditions and generally performed under time pressure. This book highlights the human experience of error. A taxonomy of error is presented, along with a theoretical classification of error types based on the underlying causes and an extensive discussion of potential error-reduction strategies. The relevant perceptual science, cognitive science, and imaging science are reviewed. A chapter addresses the issue of accountability for error, including peer review, regulatory oversight/accreditation, and malpractice litigation. The potential impact of artificial intelligence, including the use of machine learning and deep-learning algorithms, to reduce human error and improve radiologists’ efficiency is also explored.
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43

Zhang, Daoqiang, Luping Zhou y Guorong Wu. Machine Learning in Medical Imaging: 5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 14, 2014, Proceedings. Springer London, Limited, 2014.

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44

Machine Learning in Medical Imaging: 5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 14, 2014, ... Springer, 2014.

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45

Lin, Hong, E. Priya, Venkatesan Rajinikanth y Fuhua Lin. Hybrid Image Processing Methods for Medical Image Examination. Taylor & Francis Group, 2021.

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46

Hybrid Image Processing Methods for Medical Image Examination. Taylor & Francis Group, 2021.

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47

Lin, Hong, E. Priya, Venkatesan Rajinikanth y Fuhua Lin. Hybrid Image Processing Methods for Medical Image Examination. Taylor & Francis Group, 2020.

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48

Lin, Hong, E. Priya, Venkatesan Rajinikanth y Fuhua Lin. Hybrid Image Processing Methods for Medical Image Examination. Taylor & Francis Group, 2021.

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49

Lin, Hong, E. Priya, Venkatesan Rajinikanth y Fuhua Lin. Hybrid Image Processing Methods for Medical Image Examination. Taylor & Francis Group, 2021.

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50

Electric Machines: Fault Diagnosis and Condition Monitoring. CRC, 2008.

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