Uncertainty-based design and optimization is becoming one of the research focuses for high safety of complex engineering systems. Evaluating failure probability is essential and necessary in uncertainty-based design and optimization. Reliability analysis method using original Monte Carlo simulation usually shows good performance in evaluation accuracy. However, in situations for computing small failure probabilities, the calculation efficiency of Monte Carlo simulation is low generally. In this study, an enhanced Monte Carlo simulation method is utilized to solve the above challenge for assessment of the probability of rare failure events in uncertainty-based design and optimization. A mathematic example and a speed reducer design problem are given to illustrate the utilization of the proposed approach.

Model stealing attack may happen by stealing useful data transmitted from embedded end to server end for an artificial intelligent systems. In this paper, we are interested in preventing model stealing of neural network for resource-constrained systems. We propose an Image Encryption based on Class Activation Map (IECAM) to encrypt information before transmitting in embedded end. According to class activation map, IECAM chooses certain key areas of the image to be encrypted with the purpose of reducing the model stealing risk of neural network. With partly encrypted information, IECAM can greatly reduce the time overheads of encryption/decryption in both embedded and server ends, especially for big size images. The experimental results demonstrate that our method can significantly reduce time overheads of encryption/decryption and the risk of model stealing compared with traditional methods.

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