Smart Electrical and Mechanical Systems
-10%
portes grátis
Smart Electrical and Mechanical Systems
An Application of Artificial Intelligence and Machine Learning
Kumaran, Vigna; Sharma, Mukund Dutt; Tomar, Anuradha; Sehgal, Rakesh; Gupta, Neeraj
Elsevier Science & Technology
06/2022
314
Mole
Inglês
9780323907897
15 a 20 dias
450
Descrição não disponível.
1. Basics of Artificial intelligence and Machine learning
2. Artificial Intelligence and machine learning based Robot Control
3. Smart Mechanical Design
4. Smart Manufacturing
5. Autonomous vehicles
6. Image processing for electrical and mechanical engineering
7. Machine learning in smart electric power systems
8. Renewable energy integration with ML
9. Machine learning applications in optimal power flow
10. Machine learning applications in power system control
11. Machine learning applications in power system forecasting
2. Artificial Intelligence and machine learning based Robot Control
3. Smart Mechanical Design
4. Smart Manufacturing
5. Autonomous vehicles
6. Image processing for electrical and mechanical engineering
7. Machine learning in smart electric power systems
8. Renewable energy integration with ML
9. Machine learning applications in optimal power flow
10. Machine learning applications in power system control
11. Machine learning applications in power system forecasting
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Artificial intelligence; Automation engineering; Big data; Classification; CNN; Computational intelligence; Computational neural network (CNN)Deep learning; Computational pattern recognition; Computer in society; Computer science; Computer vision; Condition based maintenance; Condition monitoring; Contingency analysis; Continuous stirred tank reactor; Control engineering; Control systems; Convolutional neural network (CNN)Data science; Critical; Data collection; Data mining; Data preprocessing; Data processing; Data science; Data visualization; DC Microgrid; Decision tree; Deep learning; DenseNet-161Image recognition; Diagnostic procedure; Different load conditions; Digital signal processing; Discrete wavelet transform; Distributed energy resources; Distributed generators; Droplet combustion; Droplet vaporization; Empirical Mode decomposition; Energy storage devices; Energy systems; Engineering; Ensemble subspace discriminant; Extremum center interpolation; Fault detection/classification; Fault diagnosis; Fluidics; Forecasting; Fully connected networks; Gel fuels; Genetic algorithm; Grid; Image processing; Image segmentation; Information management; Information systems; Integration; Levenberg-Marquardt algorithm; Linear discriminant analysis (LDA)Section identification; Machine learning (ML) algorithms; Machine learning; Mechanical engineering; Microgrid centralized controller; Microgrid; Neural networks; Next-generation fuels; Non critical; Object detection; Optimal sensor placement; Organic gellants; Pattern recognition; Phasor measurement units; Photo voltaic; Point of common coupling; Power system forecasting; Powersystem; Predictive controller; Predictive maintenance; Probabilistic forecasting; Process control; Process engineering; Reciprocating compressor; Renewable energy sources; Renewable generation; Robotics; Rocket propulsion; Semi critical; Set-point tracking; Severity prediction; Short-term load forecasting; Signal processing; Smart grid; Smart manufacturing; SteelDefect detection; Structured data; Superconducting magnetic energy storage; Testing data set; Training data set; Uncertainty; Valve fault detection; Voltage stability index; Voltage stability; Volume of data
1. Basics of Artificial intelligence and Machine learning
2. Artificial Intelligence and machine learning based Robot Control
3. Smart Mechanical Design
4. Smart Manufacturing
5. Autonomous vehicles
6. Image processing for electrical and mechanical engineering
7. Machine learning in smart electric power systems
8. Renewable energy integration with ML
9. Machine learning applications in optimal power flow
10. Machine learning applications in power system control
11. Machine learning applications in power system forecasting
2. Artificial Intelligence and machine learning based Robot Control
3. Smart Mechanical Design
4. Smart Manufacturing
5. Autonomous vehicles
6. Image processing for electrical and mechanical engineering
7. Machine learning in smart electric power systems
8. Renewable energy integration with ML
9. Machine learning applications in optimal power flow
10. Machine learning applications in power system control
11. Machine learning applications in power system forecasting
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Artificial intelligence; Automation engineering; Big data; Classification; CNN; Computational intelligence; Computational neural network (CNN)Deep learning; Computational pattern recognition; Computer in society; Computer science; Computer vision; Condition based maintenance; Condition monitoring; Contingency analysis; Continuous stirred tank reactor; Control engineering; Control systems; Convolutional neural network (CNN)Data science; Critical; Data collection; Data mining; Data preprocessing; Data processing; Data science; Data visualization; DC Microgrid; Decision tree; Deep learning; DenseNet-161Image recognition; Diagnostic procedure; Different load conditions; Digital signal processing; Discrete wavelet transform; Distributed energy resources; Distributed generators; Droplet combustion; Droplet vaporization; Empirical Mode decomposition; Energy storage devices; Energy systems; Engineering; Ensemble subspace discriminant; Extremum center interpolation; Fault detection/classification; Fault diagnosis; Fluidics; Forecasting; Fully connected networks; Gel fuels; Genetic algorithm; Grid; Image processing; Image segmentation; Information management; Information systems; Integration; Levenberg-Marquardt algorithm; Linear discriminant analysis (LDA)Section identification; Machine learning (ML) algorithms; Machine learning; Mechanical engineering; Microgrid centralized controller; Microgrid; Neural networks; Next-generation fuels; Non critical; Object detection; Optimal sensor placement; Organic gellants; Pattern recognition; Phasor measurement units; Photo voltaic; Point of common coupling; Power system forecasting; Powersystem; Predictive controller; Predictive maintenance; Probabilistic forecasting; Process control; Process engineering; Reciprocating compressor; Renewable energy sources; Renewable generation; Robotics; Rocket propulsion; Semi critical; Set-point tracking; Severity prediction; Short-term load forecasting; Signal processing; Smart grid; Smart manufacturing; SteelDefect detection; Structured data; Superconducting magnetic energy storage; Testing data set; Training data set; Uncertainty; Valve fault detection; Voltage stability index; Voltage stability; Volume of data
