This book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type of nano material problems can be better solved by which set of data science methods. The majority of material science research is thus far carried out by domain-specific experts in material engineering, chemistry/chemical engineering, and mechanical & aerospace engineering. The book could benefit materials scientists and manufacturing engineers who were not exposed to systematic data science training while in schools, or data scientists in computer science or statistics disciplines who want to work on material image problems or contribute to materials discovery and optimization.

This book provides in-depth discussions of how data science and operations research methods can help and improve nano image analysis, automating the otherwise manual and time-consuming operations for material engineering and enhancing decision making for nano material exploration. A broad set of data science methods are covered, including the representations of images, shape analysis, image pattern analysis, and analysis of streaming images, change points detection, graphical methods, and real-time dynamic modeling and object tracking. The data science methods are described in the context of nano image applications, with specific material science case studies.

Applies Data Science and OR techniques to Nano Image Analysis Showcases specific nano material problems with the appropriate set of data science methods Includes real-time dynamic modeling and object tracking

Autorentext
Chiwoo Park is an Associate Professor of Industrial and Manufacturing Engineering and Principal Investigator of the High Performance Material Institute in the Department of Industrial and Manufacturing Engineering at Florida State University, Tallahassee, USA. His research interests include data science and computer vision with applications to quality and manufacturing engineering and materials imaging. He is a senior member of the IISE and the IEEE. He serves as the Associate Editor for the IISE Transactions and the IEEE Transactions on Automation Science and Engineering. Yu Ding is the Mike and Sugar Barnes Professor of Industrial & Systems Engineering and Professor of Electrical & Computer Engineering at Texas A&M University and Associate Director for Research Engagement at Texas A&M Institute of Data Science. His research interest is on data and quality science with applications to wind energy, and materials and manufacturing informatics. He is a Fellow of the IISE and ASME. He serves as the Editor-in-Chief for IISE Transaction for the term of 2021-2024.

Inhalt
Chapter 1. Introduction.- Chapter 2. Image Representation.- Chapter 3. Segmentation.- Chapter 4. Shape Analysis.- Chapter 5. Location and Dispersion Analysis.- Chapter 6. Lattice Pattern Analysis.- Chapter 7. Change Point Detection.- Chapter 8. State Space Modeling for Size Changes.- Chapter 9. Shape Change Tracking.- Chapter 10. Tracking Nucleation, Growth and Aggregation.- Chapter 11. Further Issues and Discussions.