Artificial Intelligence and Evolutionary Computations in Engineering Systems Computational Algorithm for AI Technology, Proceedings of ICAIECES 2020 /

Podrobná bibliografie
Korporativní autor: SpringerLink (Online service)
Další autoři: Chandramohan, S. (Editor), Venkatesh, Bala (Editor), Sekhar Dash, Subhransu (Editor), Das, Swagatam (Editor), Sharmeela, C. (Editor)
Shrnutí:XII, 402 p. 240 illus., 192 illus. in color.
text
Jazyk:angličtina
Vydáno: Singapore : Springer Nature Singapore : Imprint: Springer, 2022.
Vydání:1st ed. 2022.
Edice:Advances in Intelligent Systems and Computing, 1361
Témata:
On-line přístup:https://doi.org/10.1007/978-981-16-2674-6
Médium: Elektronický zdroj Kniha
Obsah:
  • Chapter 1. Design and Construction of a Dual Axis Solar Tracking System by Astronomical Algorithm
  • Chapter 2. Estimation of Magnetic Flux linkage in SRM using various defuzzification techniques
  • Chapter 3. Multilevel Inverter based STATCOM for Distribution System
  • Chapter 4. Sensitivity Analysis and Design Optimization of Synchronous Reluctance and Permanent Magnet Motors
  • Chapter 5. A New Heuristic algorithm for Economic Load Dispatch incorporating wind power
  • Chapter 6. Enhanced Grasshopper Optimization Algorithm For Numerical Optimization
  • Chapter 7. Eco-Routing – To Reduce Vehicle CO2 Emissions by CACC: An IoT Application
  • Chapter 8. Fuzzy Sliding Mode Control of DC-DC Boost Converter with Right-Half Plane Zero
  • Chapter 9. Liquid Level Control of Non Linear Process Using Big Bang - Big Crunch Optimization Based Controller
  • Chapter 10. Impact of PV Cells and MPPT Controller on Power System Dynamics
  • Chapter 11. Wavelet Feature Based Microcalcification Detection in Mammogram
  • Chapter 12. Reliable Radiation Hardened Memory Cells for Single-Event Multiple Effects
  • Chapter 13. Finger Vein Identification Using Deep Convolutional Generative Adversarial Networks
  • Chapter 14. Computer Aided Detection of Malignant Mass in Mammogram using U-Net Architecture
  • Chapter 15. Visualization and Evaluation of Methane Gas Leakage by Thermal Image Processing using Supervised Deep Learning Models.