Robust Perception and Control in Prognostic Systems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 3
Special Issue Editors
Interests: machine learning; computer vision; robot learning; nonlinear dynamics and control
Special Issue Information
Dear Colleagues,
In real-world applications, noise/domain shift is an issue that cannot be avoided. Agile adaptions without sensitivity to such interferences is a crucial feature of a well-built prognostic model. The current mainstream models adopt an engineering-friendly but costly paradigm that refines/re-designs the previous model/control-principle obtained in old scenarios using newly gathered labeled data from new scenarios. Furthermore, with increasing business-orientated demands such as safety, privacy, and agility, perception/control under these constraints becomes a challenging problem that attracts the attention of both artificial intelligence and reliability communities. In this context, some techniques are proposed to address this issue in relation to domain adaptation (for transfer with full data access), unsupervised model adaptation (for safety transfer), efficient online algorithms (for agility), and fuzzy controlling (for adaptive control).
This Special Issue aims to present recent advances in robust perception and control in prognostic systems, as well as investigating their applications in real-world scenarios. The potential topics include, but are not limited to, the following:
- New transfer topic in real applications;
- Neural network in prognostic/control applications;
- Domain adaptation in multimodal perception;
- Unsupervised model adaptation;
- AI security;
- Efficient online algorithm;
- Knowledge-based algorithm;
- Control of adaptive system;
- Uncertainty estimation of models.
Dr. Song Tang
Prof. Dr. Mao Ye
Guest Editors
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Keywords
- robust perception and control
- prognostic systems
- unsupervised and semi-supervised learning
- transfer learning
- adaptive system
- AI security
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