Projects per year
Personal profile
Research Interests
Big data, intelligent algorithms, cloud computing, process planning
Biography
Sheng Wang received her BSc and MSc in Elecronics engineering from Xidian University China in 1992 and 1995 respectively. She then worked at the National Key Laboratory of Antennas at Xidian University as a research engineer. After that, she worked at Singapore National Institute of High Performance Computing as a senior research engineer before moving to the UK for her PhD study. She gained her PhD in electronics engineering at Queen Mary University of London, after which she was appointed as a research assitant at Queen Mary University of London. In 2013, she joined the Coventry University as a senior research assistant. She participated in numbers of projects in the UK, EU, Singapore and industry, with 20 years of research and product development experience. Mainly engaged in software and algorithm development, antenna design, optimisation of process planning, cloud platform development and design.
Vision Statement
The national manufacturing research has set one priority on using emerging ICT tools and information methodologies for future manufacturing technologies. My research will focus on employing cyber physical systems, cloud computing, big data and artifical intelligence on manufacturing processes, to enhance the manufacturing intelligence and increase efficiency.
Education/Academic qualification
Doctorate, Queen Mary University of London
Award Date: 13 Oct 2009
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Projects
- 1 Finished
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Assured Parking: Assured Parking - Trusted Autonomous Parking
Kanarachos, S., Haas, O., Christensen, J., Rajbhandari, S., Wang, S., Silverio Fernandez, M., Kouh Daragh, V., Horri, N., Ahmed, H., Foo, M. & Amin, S.
1/03/19 → 31/08/20
Project: Research
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Mode Switching Control Using Lane Keeping Assist and Waypoints Tracking for Autonomous Driving in a City Environment
Horri, N., Haas, O., Wang, S., Foo, M. & Silverio Fernandez, M., 13 Nov 2021, (E-pub ahead of print) In: Transportation Research Record. (In-press), p. (In-press) 16 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile12 Downloads (Pure) -
Prediction of the Remaining Useful Life of Cutting Tool using the Hurst Exponent and CNN-LSTM
Zhang, X., Lu, X., Li, W. & Wang, S., Feb 2021, In: The International Journal of Advanced Manufacturing Technology. 112, p. 2277-2299 23 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile5 Citations (Scopus) -
Data Driven Anomaly Diagnosis for Machining Processes
Liang, Y., Wang, S., Li, W. & Lu, X., Aug 2019, In: Engineering. 5, 4, p. 646-652 7 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile12 Citations (Scopus)32 Downloads (Pure) -
Fog computing and convolutional neural network enabled prognosis for machining process optimization
Liang, Y., Li, W., Lu, X. & Wang, S., Jul 2019, In: Journal of Manufacturing Systems. 52, Part A, p. 32-42 11 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile25 Citations (Scopus)99 Downloads (Pure) -
Sustainable Manufacturing and Remanufacturing Management: Process planning, optimization and applications
Li, W. (ed.) & Wang, S. (ed.), 2019, 1 ed. Springer. 346 p.Research output: Book/Report › Book › peer-review
1 Citation (Scopus)