Applications of Artificial Intelligence in Process Systems Engineering, Jingzheng Ren Weifeng Shen Yi Man Lichun Dong
Автор: Arnab Chakrabarty Название: Multiscale Modeling for Process Safety Applications, ISBN: 0123969751 ISBN-13(EAN): 9780123969750 Издательство: Elsevier Science Рейтинг: Цена: 19875.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Suitable for readers interested in theoretical simulations and or computer simulations of hazardous scenarios, this book explores fundamental topics of toxic, fire, and air explosion modeling, as well as modeling jet and pool fires using computational fluid dynamics.
Автор: Xing & Gao Название: Computational Intelligence In Remanufacturing ISBN: 1466649089 ISBN-13(EAN): 9781466649088 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28413.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In attempts to reduce greenhouse gas emissions, many alternatives to manufacturing have been recommended from a number of international organisations. Although challenges will arise, remanufacturing has the ability to transform ecological and business value.Computational Intelligence in Remanufacturing introduces various computational intelligence techniques that are applied to remanufacturing-related issues, results, and lessons from specific applications while highlighting future development and research. This book is an essential reference for students, researchers, and practitioners in mechanical, industrial, and electrical engineering.
Название: Artificial Intelligence in Drug Discovery ISBN: 1788015479 ISBN-13(EAN): 9781788015479 Издательство: Royal Society of Chemistry Рейтинг: Цена: 37805.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation.
Описание: The proposed book will be divided into three parts. The chapters in Part I provide an overview of certain aspect of process retrofitting. The focus of Part II is on computational techniques for solving process retrofit problems. Finally, Part III addresses retrofit applications from diverse process industries.
Описание: In the industry of manufacturing and design, one major constraint has been enhancing operating performance using less time. As technology continues to advance, manufacturers are looking for better methods in predicting the condition and residual lifetime of electronic devices in order to save repair costs and their reputation. Intelligent systems are a solution for predicting the reliability of these components; however, there is a lack of research on the advancements of this smart technology within the manufacturing industry. AI Techniques for Reliability Prediction for Electronic Components provides emerging research exploring the theoretical and practical aspects of prediction methods using artificial intelligence and machine learning in the manufacturing field. Featuring coverage on a broad range of topics such as data collection, fault tolerance, and health prognostics, this book is ideally designed for reliability engineers, electronic engineers, researchers, scientists, students, and faculty members seeking current research on the advancement of reliability analysis using AI.
Автор: Cherry Bhargava Название: AI Techniques for Reliability Prediction for Electronic Components ISBN: 1799814645 ISBN-13(EAN): 9781799814641 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 30215.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the industry of manufacturing and design, one major constraint has been enhancing operating performance using less time. As technology continues to advance, manufacturers are looking for better methods in predicting the condition and residual lifetime of electronic devices in order to save repair costs and their reputation. Intelligent systems are a solution for predicting the reliability of these components; however, there is a lack of research on the advancements of this smart technology within the manufacturing industry.
AI Techniques for Reliability Prediction for Electronic Components provides emerging research exploring the theoretical and practical aspects of prediction methods using artificial intelligence and machine learning in the manufacturing field. Featuring coverage on a broad range of topics such as data collection, fault tolerance, and health prognostics, this book is ideally designed for reliability engineers, electronic engineers, researchers, scientists, students, and faculty members seeking current research on the advancement of reliability analysis using AI.
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