Smart Innovations in Energy and Mechanical Systems
Proceedings of SIEMS-2026
Series: Lecture Notes in Networks and Systems;
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Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 2 September 2026
- ISBN 9783032315564
- Binding Paperback
- No. of pages345 pages
- Size 235x155 mm
- Language English
- Illustrations V, 345 p. 700
Categories
Long description:
This book offers a comprehensive review of smart innovations and provides perspectives on their applications in energy and mechanical engineering. It covers a wide range of topics, from intelligent manufacturing and aerospace technologies to renewable energy systems and artificial intelligence in engineering, offering readers new visions and ideas for future research and interdisciplinary collaborations.
The book presents selected papers from the 2nd International Conference on Smart Innovations in Energy and Mechanical Systems (SIEMS-2026), held in Zaporizhzhia, Ukraine on May 11–13, 2026. The conference was organized by the National University Zaporizhzhia Polytechnic, in partnership with University of Stuttgart (Germany), Gdańsk University of Technology (Poland), Offenburg University of Applied Sciences (Germany), International Association for Technological Development and Innovations (IATDI) (Ukraine), GPMS International Inc (USA), JSC “Ivchenko-Progress”(Ukraine), JSC “Motor Sich” (Ukraine) and Cardiff University (the UK). The volume offers a comprehensive overview of recent progress and research in mechanical engineering, energy systems, artificial intelligence, smart manufacturing, and sustainable technologies. It covers a broad range of topics including digital manufacturing, aerospace technologies, renewable energy, providing valuable insights and fostering collaboration amongst researchers, academics, and industry professionals in the fields of mechanical and electrical engineering.
Table of Contents:
Enhancing Cyber-Physical System Resilience: Machine Learning Analysis of Credential Reliability in Smart Manufacturing.- Modeling and Optimal Neural Network Parameter Selection for Predicting Leakage Current of 35 kV Insulators under Nonlinear Environmental Effects.- Research of the SMART Greenhouse Automatic Control System.
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