Calibrations

Modern human societies rely on measurements. Measurements are essential for understanding and description of the world around us and the overall nature. Ensuring measurement accuracy, traceability, and consistency is important in scientific research, trade, manufacturing, health and medicine, and every day running of modern societies. One of the most essential components of measurement is calibration. This book walks the reader through fundamental principles of calibrations, traditional calibration methods, the related standards, and procedures, as well as fast developing and diverse range of artificial intelligence-based calibration methods. Calibration complies by strict regulations and legislation by governments, relevant authorities, and concerned parties. New and effective calibration techniques are evolving in parallel to the traditional methods. As explained here, new techniques that are based on artificial intelligence (AI) offer instantaneous, continuous calibrations in standalone devices as well as in complex and large systems. Continuous and large-scale calibration are possible due to the new generation of computers and advanced digital systems, intelligent devices and sensors, communication networks, advanced mathematical methods, and the newly developed areas such as the cyber-physical systems.

- Clarification of understanding of classic and emerging calibrations and related concepts

- Highlights the use of AI in calibrations and discussing the methods

- Explains the recent progresses calibrations R&D, and new applications of calibrations

- Discusses and verifies the possible future progresses in calibration methods

- Explains the dynamics and changes in calibration practices as the supporting technologies change and evolve

This book highlights that advanced technological and AI methods are applied for calibration of distributed cyber-physical and data-driven systems. AI-based calibrations find applications in large-scale systems that traditional methods become almost impossible to implement or can be costly. Typical examples are Internet of Things (IoTs), self-driving vehicles, intelligent transportation systems, unmanned arial vehicles, drones and drone clusters, industrial processes, communications networks, health and medicine, environmental monitoring, pollution control, remote and inaccessible devices, smart cities, space explorations, management systems, economics, and finance.