Ce Zhang – författare

Machinery Dynamics includes recent advancements in this quickly evolving area, while also analyzing real applications, analyzing integrated systems, and including further discussions on each mechanical component. The book treats mechanisms separately, with different methods depending on the level of accuracy required. The contents of this book is made to suit the needs of MsC and PhD students, researchers and engineers in the areas of design of high speed machinery, condition monitoring of machine operation, and vibration. Addresses theoretical backgrounds on topics, including vibration and elastodynamics Introduces rigid and elastic dynamics of various mechanisms, including linkages, cams, gears and planetary gear trains Features relevant application examples

This book constitutes the refereed post-conference proceedings of the 14th BenchCouncil International Symposium on Benchmarking, Measuring, and Optimization, Bench 2022, held virtually in November 2022.The 10 revised full papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections named: Architecture and System, Algorithm and Dataset, Network and Memory.

This book explores the history of mechanical engineering since the Bronze Age.

This book explores the history of mechanical engineering since the Bronze Age.

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol.Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appealto a broad audience in the field of machine learning, artificial intelligence, big data and database management.