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Machine learning can deliver unprecedented performance. Its application domain has expanded into safety-critical cyber-physical systems such as UAVs and self-driver cars. However, the safety assurance of vehicular control has two conditions: 1) an analytical model of system behaviors such as provable stability, and 2) the software safety certification process (e.g., DO 178C) requires that the software be simple enough so that software safety can be validated by a combination of model checking and near exhaustive testing. Although ML software, as is, does not meet these two safety requirements, the real-time physics model supervised ML architecture holds the promise to 1) meet the two safety requirements and 2) enable ML software to safely improve control performance and safely learn from its experience in real-time. This talk will review the structure of the proposed architecture and some methods to embed physics into ML-enabled CPS control.
Event Date: 12/05/2022
Speaker: Prof. Lui Sha (University of Illinois Urbana-Champaign)
Hosted by: Graduate School
- Subjects:
- Aeronautical and Aviation Engineering and Computing
- Keywords:
- Machine learning Vehicles Remotely piloted Computer software -- Reliability Drone aircraft
- Resource Type:
- Video
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Video
In this video, Dr Charles Lam shares his views on how to deals with information at various stages of the investment deal. In his sharing, he covers 4 stages of the investment deal. In completing a deal -- What are the major steps involved in completing a real estate investment deal? In making decisions about real estate -- Which business information will inform his decision-making? In validating information -- What mechanism does he use to validate the information sources and data? In spotting bad information -- How does he spot out biased or even fraudulent information?
- Keywords:
- Investments Real estate investment Business -- Research -- Methodology Investment analysis
- Resource Type:
- Video