Data Driven Science And Engineering

This comprehensive textbook introduces students to the wide-ranging responsibilities of computing, science and engineering professionals by laying strong transdisciplinary foundations and by highlighting ethical issues that may arise during their careers. The work is well illustrated, and makes extensive use of both activities, and ethical dilemmas which are designed to stimulate reader engagement. A number of memorable case studies are also included and frequently draw on the demanding aerospace industry.

This comprehensive textbook introduces students to the wide-ranging responsibilities of computing, science and engineering professionals by laying strong transdisciplinary foundations and by highlighting ethical issues that may arise during their careers. The work is well illustrated, and makes extensive use of both activities, and ethical dilemmas which are designed to stimulate reader engagement. A number of memorable case studies are also included and frequently draw on the demanding aerospace industry.

"Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering" provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners.

This book sets the stage for understanding how the exponential escalation of digital ubiquity in the contemporary environment is being absorbed, modulated, processed and actively used for enhancing the performance of our built environment. S.M.A.R.T., in this context, is thus used as an acronym for Systems & Materials in Architectural Research and Technology, with a specific focus on interrogating the intricate relationship between information systems and associative material, cultural and socioeconomic formations within the built environment.

This text covers all of the data science, machine learning, and deep learning topics relevant to materials science and engineering, accompanied by numerous examples and applications. Almost all methods and algorithms introduced are implemented “from scratch” using Python and NumPy.

Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis.

This book systematically discusses the intelligent scheduling problem of complex semiconductor manufacturing systems from theory to method and then to application. The main contents include data-driven scheduling framework of semiconductor manufacturing system, data preprocessing of semiconductor manufacturing system, correlation analysis of performance index of semiconductor production line, intelligent release control strategy, dynamic dispatching rules simulating pheromone mechanism, and load balancing dynamic scheduling of semiconductor production line, performance index-driven dynamic scheduling method of semiconductor production line, scheduling trend of semi-conductor manufacturing system in big data environment.