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    • Title:Progress in Engineering Technology III [electronic resource] / edited by Muhamad Husaini Abu Bakar, Mohd Nurhidayat Zahelem, Andreas Öchsner.
    • ISBN:9783030677503
    • Edition:1st ed. 2021.
    • Publication:Cham : Springer International Publishing : Imprint: Springer, 2021.
    • Physical Description:1 online resource (XVII, 369 p.) 275 illus., 220 illus. in color.
    • Links:Online book
    • Yale Holdings

       
    • Local Notes:Access is available to the Yale community.
    • Access and use:Access restricted by licensing agreement.
    • Summary:This book contains the selected, peer-reviewed manuscripts presented at the Conference on Multidisciplinary Engineering and Technology (COMET 2019), held at the University Kuala Lumpur Malaysian Spanish Institute (UniKL MSI), Kedah, Malaysia, from September 18 to 19, 2019. This event presented research being carried out in the field of mechanical, manufacturing, electrical and electronics for engineering and technology. This book also contains the manuscripts from the System Engineering and Energy Laboratory (SEELAB) research cluster, UniKL, which is actively doing research mainly focused on artificial intelligence, Internet of things, metal air batteries, advanced battery materials and energy material modelling fields. This book is the fourth edition of the progress in engineering technology, Advanced Structured Materials which provides in-depth ongoing research activities among academia of UniKL MSI.
    • In_:Springer Nature eBook
    • Variant and related titles:Springer ENIN.
    • Other formats:Printed edition:
      Printed edition:
      Printed edition:
    • Format:Book
    • Series:Advanced Structured Materials, 148
      Advanced Structured Materials, 148
    • Contents:1. Vibration Response of Magnetorheological Elastomer Sandwich Plate -- 2. Nonlinear Control of Magnetorheological Fluid Electrohydraulic Positioning System -- 3. Development of Magnetorheological Fluid (MRF) Powertrain for Electric Vehicle Application -- 4. GPU Accelerated Vehicle Detection for Roads -- 5. Anomaly Vehicle Detection using Deep Neural Network.
    • Subjects:Mechanical engineering.
    • Also listed under:Abu Bakar, Muhamad Husaini. editor.
      Nurhidayat Zahelem, Mohd. editor.
      Öchsner, Andreas. editor.
      SpringerLink (Online service)