Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

Industry 4.0: A Survey on Technologies, Applications and Open Research Issues

Rapid advances in industrialisation and informatisation methods have spurred tremendous progress in developing the next generation of manufacturing technology. Today, we are on the cusp of the Fourth Industrial Revolution. In 2013, amongst one of 10 ‘Future Projects’ identified by the German government as part of its High-Tech Strategy 2020 Action Plan, the Industry 4.0 project is considered to be a major endeavour for Germany to establish itself as a leader of integrated industry. In 2014, China’s State Council unveiled their ten-year national plan, Made-in-China 2025, which was designed to transform China from the world’s workshop into a world manufacturing power. Made-in-China 2025 is an initiative to comprehensively upgrade China’s industry including the manufacturing sector. In Industry 4.0 and Made-in-China 2025, many applications require a combination of recently emerging new technologies, which is giving rise to the emergence of Industry 4.0. Such technologies originate from different disciplines ...

Industry 4.0: state of the art and future trends

The expected contribution of Industry 4.0 technologies for industrial performance

https://cyberleninka.org/article/n/663976.pdf

A categorical framework of manufacturing for industry 4.0 and beyond

https://pcwww.liv.ac.uk/~robpoole/PAPERS/POOLE_44.pdf

Non-dimensional scaling of tidal stream turbines

Wave-current interaction effects on tidal stream turbine performance and loading characteristics

This paper reviews the evolution of intelligent, distributed, microcontroller based machine and process monitoring systems. It considers basic building blocks that support such systems through the development of fault diagnostic methods, intelligent system based approaches and sensor-based machine monitoring. It then identifies three approaches that may be utilised when designing monitoring system architecture: integrated, distributed and embedded. Current applications of these approaches, when deployed individually and in combination with each other, are then discussed. In particular, the paper considers the efficacy of microcontroller based embodiments, in both current and future monitoring activities.

A review of the evolution of microcontroller-based machine and process monitoring

This paper reports on a study into comparisons of different methods of tool wear assessment in turning. There is still a great deal of controversy about the usefulness and sensitivity of the different types of device which are commercially available.

Roger Ivor Grosvenor

Papers

A categorical framework of manufacturing for industry 4.0 and beyond

Non-dimensional scaling of tidal stream turbines

Wave-current interaction effects on tidal stream turbine performance and loading characteristics

A review of the evolution of microcontroller-based machine and process monitoring

A Comparison of in-Process Tool Wear Measurement Methods in Turning