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Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning
2021
Artificial intelligence for engineering design, analysis and manufacturing
AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance
doi:10.1017/s089006042100010x
fatcat:w3amwshbing4hoparcg3cqc2sy