Machine learning for real-time event monitoring at industrial sites
Recent advances in Machine Learning (ML), especially deep learning, have demonstrated superior to human performance for a variety of decision and recognition tasks. Together with advances in computational hardware and hyperspectral optics, affordable real-time ML based hazard event detection has become a reality. By providing the state-of-the art Artificial intelligence(AI) and ML based solutions for event detection, Rebellion Photonics embarks on a journey to revolutionize hazard and safety monitoring at all points of the petrochemical industry and beyond.
In this talk I will cover recent ML research efforts at Rebellion Photonics, with a focus on gas, fire and intrusion detection. Though comparative studies, advantages of data-driven algorithms are presented. Results from various field studies are also presented. It is demonstrated that ML based algorithms can adapt well to different environmental conditions, while improving its performance by learning over time.
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Speakers
Patrick O'Driscoll (Rebellion Photonics)
Dr. Patrick O'Driscoll is a Machine Learning Algorithm Development Engineer for Rebellion Photonics Houston, Texas. His research background is in machine learning methods for pattern recognition in large, complex, functional and temporal datasets. He currently research and develops real-time gas, fire, and intrusion detection methods for Rebellion Photonic's Gas Cloud Imaging safety system. He holds a B.S. in Chemical and Biomolecular Engineering, and Applied Mathematics and Statistics from Johns Hopkins University, USA, and an M.S. & Ph.D. in Applied Physics from Rice University, USA.
Events
Nov 26 2024 Paris, France
Nov 27 2024 Istanbul, Turkey
H2O Accadueo International Water Exhibition
Nov 27 2024 Bari, Italy
Biogas Convention & Trade Fair 2024
Nov 27 2024 Hanover, Germany
Dec 11 2024 Shanghai, China