Data-Driven Cyber Physical Systems
| AUTHOR | Han, Honggui; Li, Fangyu; Wu, Xiaolong |
| PUBLISHER | Springer (09/27/2025) |
| PRODUCT TYPE | Hardcover (Hardcover) |
This book shows the exploration and integration of data-driven approaches within cyber-physical systems (CPS), focusing on how these systems leverage data science, artificial intelligence, and machine learning to enhance performance, optimize real-time decision-making, and improve the interaction between physical and digital components.
Readers will be interested in the areas of data acquisition, integration, storage, modeling, simulation, fault detection, predictive maintenance, and cybersecurity. Because these topics highlight how data-driven approaches and advanced technologies can be applied to optimize system performance, enhance real-time decision-making, and ensure the safety and reliability of DDCPS. Additionally, practical applications across various industries demonstrate the real-world impact.
The inclusion of real-world examples and practical applications helps bridge the gap between theory and practice, making the content highly relevant for professionals and researchers. Additionally, the book covers emerging trends and technologies, offering readers insights into the future of DDCPS.
Readers will gain a comprehensive understanding of how to leverage data-driven approaches to enhance the performance and reliability of DDCPS.
This book shows the exploration and integration of data-driven approaches within cyber-physical systems (CPS), focusing on how these systems leverage data science, artificial intelligence, and machine learning to enhance performance, optimize real-time decision-making, and improve the interaction between physical and digital components.
Readers will be interested in the areas of data acquisition, integration, storage, modeling, simulation, fault detection, predictive maintenance, and cybersecurity. Because these topics highlight how data-driven approaches and advanced technologies can be applied to optimize system performance, enhance real-time decision-making, and ensure the safety and reliability of DDCPS. Additionally, practical applications across various industries demonstrate the real-world impact.
The inclusion of real-world examples and practical applications helps bridge the gap between theory and practice, making the content highly relevant for professionals and researchers. Additionally, the book covers emerging trends and technologies, offering readers insights into the future of DDCPS.
Readers will gain a comprehensive understanding of how to leverage data-driven approaches to enhance the performance and reliability of DDCPS.
This book shows the exploration and integration of data-driven approaches within cyber-physical systems (CPS), focusing on how these systems leverage data science, artificial intelligence, and machine learning to enhance performance, optimize real-time decision-making, and improve the interaction between physical and digital components.
Readers will be interested in the areas of data acquisition, integration, storage, modeling, simulation, fault detection, predictive maintenance, and cybersecurity. Because these topics highlight how data-driven approaches and advanced technologies can be applied to optimize system performance, enhance real-time decision-making, and ensure the safety and reliability of DDCPS. Additionally, practical applications across various industries demonstrate the real-world impact.
The inclusion of real-world examples and practical applications helps bridge the gap between theory and practice, making the content highly relevant for professionals and researchers. Additionally, the book covers emerging trends and technologies, offering readers insights into the future of DDCPS.
Readers will gain a comprehensive understanding of how to leverage data-driven approaches to enhance the performance and reliability of DDCPS.