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    Smart Electrical and Mechanical Systems

    Smart Electrical and Mechanical Systems

    - An Application of Artificial Intelligence and Machine Learning

    Forfatter: info mangler
    Bog
    • Format
    • Bog, paperback
    • Engelsk

    Beskrivelse

    Smart Electrical and Mechanical Systems: An Application of Artificial Intelligence and Machine Learning is an international contributed work with the most up-to-date fundamentals and conventional methods used in smart electrical and mechanical systems. Detailing methods and procedures for the application of ML and AI, it is supported with illustrations of the systems, process diagrams visuals of the systems and/or their components, and supportive data and results leading to the benefits and challenges of the relevant applications. The multidisciplinary theme of the book will help researchers build a synergy between electrical and mechanical engineering systems. The book guides readers on not only how to effectively solve problems but also provide high accuracy needed for successful implementation. Interdisciplinary in nature, the book caters to the needs of the electrical and mechanical engineering industry by offering details on the application of AI and ML in robotics, design and manufacturing, image processing, power system operation and forecasting with suitable examples.

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    Detaljer
    • SprogEngelsk
    • Sidetal314
    • Udgivelsesdato21-06-2022
    • ISBN139780323907897
    • Forlag Academic Press Inc
    • FormatPaperback
    Størrelse og vægt
  • Vægt500 g
    • coffee cup img
    10 cm
    book img
    15,2 cm
    22,9 cm

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    Integration Classification Data collection Data mining Engineering Forecasting Image processing Information management Information systems Mechanical engineering. Neural networks Signal processing. Computer vision Data processing Computational intelligence Computer science Control systems Machine learning Robotics Renewable energy sources Process control Uncertainty Pattern recognition Data visualization Big data Artificial intelligence Phasor Measurement Units Condition Monitoring Process engineering Image segmentation Energy Storage Devices Smart-Grid Decision tree Data preprocessing Empirical Mode Decomposition Levenberg-Marquardt algorithm Microgrid Smart Manufacturing Condition-based Maintenance Fluidics Structured data Distributed energy resources Critical Rocket Propulsion Object Detection Digital signal processing Automation engineering Voltage Stability Probabilistic forecasting Genetic algorithm Grid Deep learning: Control engineering CNN Discrete Wavelet Transform Energy Systems Contingency analysis Predictive Maintenance Fault Diagnosis Data-Science Short-term load forecasting DC microgrid Volume of data Diagnostic procedure Computer in society DenseNet-161Image recognition Computational neural network (CNN)Deep learning Computational pattern recognition Different load conditions distributed generators Droplet vaporization Fault detection/classification Gel fuels Ensemble subspace discriminant Continuous Stirred Tank Reactor Convolutional neural network (CNN)Data science Droplet Combustion Linear discriminant analysis (LDA)Section identification Extremum center interpolation Fully connected networks Microgrid centralized controller Organic gellants Next-generation fuels Optimal Sensor Placement powersystem Predictive Controller Point of common coupling Semi critical Severity prediction Reciprocating compressor Set-point tracking Machine learning (ML) algorithms Superconducting magnetic energy storage SteelDefect detection Testing Data Set Valve fault detection Training Data Set power system forecasting Renewable Generation Voltage stability index
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