Description

Volume 35 first provides a classification scheme of assembly line balancing problems according to characteristic practical settings, highlighting relevant model extensions which are required to reflect real-world problems. Additionally, an assembly line balancing problem is introduced through designing an integrated assembly line and addressing the number of workstations and simultaneous assignments of skilled and unskilled workers. The authors describe an analogy between the methods of adaptive control used in classical control theory and practice on the one hand, and the methods of self-learning used in artificial intelligence systems on the other hand. In one study, a long short-term memory (LSTM) and a Bi-LSTM are proposed to use for classifying the activities of daily living. The accuracy of the proposed approach is evaluated against the current state-of-the-art methods. Two questions regarding very large-scale integration (VSLI) implementation of the X11 algorithm are addressed: how such algorithms are efficiently implemented at once, as well as whether it is possible to use the methods applied in such a VLSI in the implementation of more powerful VLSIs. The concluding study illustrates the azimuth concept in synthetic aperture radar through an analytical description of basic state of the art azimuth signal processing performed to generate synthetic aperture radar images.

Advances in Engineering Research: Volume 35

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Hardback by Victoria M. Petrova

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Short Description:

Volume 35 first provides a classification scheme of assembly line balancing problems according to characteristic practical settings, highlighting relevant model... Read more

    Publisher: Nova Science Publishers Inc
    Publication Date: 03/06/2020
    ISBN13: 9781536178517, 978-1536178517
    ISBN10: 1536178519

    Number of Pages: 214

    Non Fiction , Technology, Engineering & Agriculture , Education

    Description

    Volume 35 first provides a classification scheme of assembly line balancing problems according to characteristic practical settings, highlighting relevant model extensions which are required to reflect real-world problems. Additionally, an assembly line balancing problem is introduced through designing an integrated assembly line and addressing the number of workstations and simultaneous assignments of skilled and unskilled workers. The authors describe an analogy between the methods of adaptive control used in classical control theory and practice on the one hand, and the methods of self-learning used in artificial intelligence systems on the other hand. In one study, a long short-term memory (LSTM) and a Bi-LSTM are proposed to use for classifying the activities of daily living. The accuracy of the proposed approach is evaluated against the current state-of-the-art methods. Two questions regarding very large-scale integration (VSLI) implementation of the X11 algorithm are addressed: how such algorithms are efficiently implemented at once, as well as whether it is possible to use the methods applied in such a VLSI in the implementation of more powerful VLSIs. The concluding study illustrates the azimuth concept in synthetic aperture radar through an analytical description of basic state of the art azimuth signal processing performed to generate synthetic aperture radar images.

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