Buch, Englisch, 444 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1028 g
Reihe: ICT in Asset Management
Theories, Applications and Case Studies
Buch, Englisch, 444 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1028 g
Reihe: ICT in Asset Management
ISBN: 978-1-032-07764-2
Verlag: CRC Press
This book provides insights into how to approach and utilise data science tools, technologies, and methodologies related to artificial intelligence (AI) in industrial contexts. It explains the essence of distributed computing and AI technologies and their interconnections. It includes descriptions of various technology and methodology approaches and their purpose and benefits when developing AI solutions in industrial contexts. In addition, this book summarises experiences from AI technology deployment projects from several industrial sectors.
Features:
- Presents a compendium of methodologies and technologies in industrial AI and digitalisation.
- Illustrates the sensor-to-actuation approach showing the complete cycle, which defines and differentiates AI and digitalisation.
- Covers a broad range of academic and industrial issues within the field of asset management.
- Discusses the impact of Industry 4.0 in other sectors.
- Includes a dedicated chapter on real-time case studies.
This book is aimed at researchers and professionals in industrial and software engineering, network security, AI and machine learning (ML), engineering managers, operational and maintenance specialists, asset managers, and digital and AI manufacturing specialists.
Zielgruppe
Academic and Professional Practice & Development
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
- Technische Wissenschaften Technik Allgemein Industrial Engineering
Weitere Infos & Material
1. Introduction. 2. Digital Twins. 3. Hypes and Trends in Industry. 4. Data Analytics. 5. Data-Driven Decision-Making. 6. Fundamental in Artificial Intelligence. 7. Systems Thinking and Systems Engineering. 8. Software Engineering. 9. Distributed Computing. 10. Case Studies. 11. AI Factory: A Roadmap for AI Transformation. 12. In Industrial AI We Believe.
