Nature-Inspired Computing Paradigms in Systems

Reliability, Availability, Maintainability, Safety and Cost (RAMS+C) and Prognostics and Health Management (PHM)
Erscheinungsjahr 2021
ISBN: 978-0-12-823749-6
Verlag: Elsevier Science Publishing Co Inc

Buch, Englisch, 144 Seiten, Format (B × H): 232 mm x 191 mm, Gewicht: 322 g

Reihe: Intelligent Data-Centric Systems

Nature-Inspired Computing Paradigms in Systems: Reliability, Availability, Maintainability, Safety and Cost (RAMS+C) and Prognostics and Health Management (PHM) covers several areas that include bioinspired techniques and optimization approaches for system dependability.

The book addresses the issue of integration and interaction of the bioinspired techniques in system dependability computing so that intelligent decisions, design, and architectures can be supported. It brings together these emerging areas under the umbrella of bio- and nature-inspired computational intelligence.

The primary audience of this book includes experts and developers who want to deepen their understanding of bioinspired computing in basic theory, algorithms, and applications. The book is also intended to be used as a textbook for masters and doctoral students who want to enhance their knowledge and understanding of the role of bioinspired techniques in system dependability.

Autoren/Hrsg.

Mellal, Mohamed Arezki

Pecht, Michael G.

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik

Inhaltsverzeichnis

Optimization 1. RAMS+C (reliability, availability, maintainability, safety and cost) 2. Optimal design 3. Diagnostic 4. Resilience and vulnerability 5. Prognostics and Health Management (PHM) 6. Risk assessment and mitigation 7. Faults 8. Obsolescence 9. Lifetime and lifecycle prediction

Methods of interest include, but are not limited to 10. Genetic algorithms 11. Particle swarm optimization 12. Differential evolution 13. Cuckoo algorithms 14. Artificial bee colony 15. Ant colony optimization 16. Artificial neural networks 17. Brain 18. Inspired computing 19. Hybrid techniques

Über Autor(innen)

Mellal, Mohamed Arezki
Mohamed Arezki Mellal is a Professor at M'Hamed Bougara University of Boumerdes, Algeria. He was ranked among the top 2% of scientists worldwide by Stanford University (single-year 2022) and placed first by the 47th National University Commission of the MESRS for the professorship in Mechanical Engineering in 2022. He served as a visiting scholar at the University of Maryland, USA, and at other universities. He has been an invited keynote speaker at numerous conferences and is an associate editor of Frontiers in Mechanical Engineering (Topic: Mechatronics) an SCIE-indexed journal. His research interests encompass AI techniques for solving engineering problems, including system design and dependability, control, energy management, robotics, and manufacturing.

Pecht, Michael G.
He is the founder and director of CALCE (Center for Advanced Life Cycle Engineering) at the University of Maryland, which is funded by over 150 of the world’s leading electronics companies at more than US$6M/year. He is a Professional Engineer, an IEEE Fellow, an ASME Fellow, an SAE Fellow, and an IMAPS Fellow. He is currently serving as editor-in-chief of Circuit World. He served as editor-in-chief of IEEE Access for 6 years, as editor-in-chief of IEEE Transactions on Reliability for 9 years, and as editor-in-chief of Microelectronics Reliability for 16 years. He has also served on three U.S. National Academy of Science studies, two U.S. Congressional investigations in automotive safety, and as an expert to the U.S. FDA. He is also a Chair Professor. He consults for 22 major international electronics companies, providing expertise in strategic planning, design, test, prognostics, IP and risk assessment of electronic products and systems.