Items where Subject is "TA174 Engineering design"
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- Library of Congress Subject Areas (6)
- T Technology (6)
- TA Engineering (General). Civil engineering (General) (6)
- TA174 Engineering design (6)
- TA Engineering (General). Civil engineering (General) (6)
- T Technology (6)
Article
Zhang, Ming ORCID: https://orcid.org/0000-0001-5202-5574, Lu, Yang
ORCID: https://orcid.org/0000-0002-0583-2688, Hu, Youxi, Amaitik, Nasser
ORCID: https://orcid.org/0000-0002-0962-4341 and Xu, Yuchun
ORCID: https://orcid.org/0000-0001-6388-813X
(2022)
Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization.
Sustainability, 14 (9).
p. 5177.
Book Section
Lu, Yang ORCID: https://orcid.org/0000-0002-0583-2688 and Sinnott, Richard O.
(2016)
Semantic-based Privacy Protection of Electronic Health Records for Collaborative Research.
In:
IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom).
IEEE, pp. 519-526
Wei, Yun-gang, Lu, Yang ORCID: https://orcid.org/0000-0002-0583-2688, Hu, Xiao-yan and Sun, Bo
(2014)
Research and Application of Access Control Technique in 3D Virtual Reality System OpenSim.
In:
2013 Sixth International Symposium on Computational Intelligence and Design.
IEEE
Monograph
Buil-Gil, David, Zeng, Yongyu, Lu, Yang ORCID: https://orcid.org/0000-0002-0583-2688, Limniou, Maria and Renwick, Robin
(2023)
Conceptual and Methodological Framework for a Digital Identity and Life-Course Study.
Project Report.
https://spritehub.org/2023/08/23/digital-identify-and-life-course-study-dialcs/.
Conference or Workshop Item
Lu, Yang ORCID: https://orcid.org/0000-0002-0583-2688, Sinnott, Richard, Verspoor, Karin and Parampalli, Udaya
(2017)
Effective preservation of privacy during record linkage.
In: 5th Annual CIS Doctoral Colloquium, School of Computing and Information Systems, Melbourne, Australia.
Zhang, Ming, Lu, Yang ORCID: https://orcid.org/0000-0002-0583-2688, Liu, Chao and Xu, Yuchun
(2022)
Dynamic Scheduling Method for Job-shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization.
In: Connected Everything Conference 2022, 18/05/2022, Liverpool, UK.