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Transient pulses generated by high-energy particles can cause soft errors in circuits, resulting in spacecraft malfunctions and posing serious threats to the normal operation of spacecraft. For integrated circuits used in space applications, it is necessary to first evaluate soft errors caused by transient pulses. Conventional soft-error-rate evaluation tools are designed to simulate the generation of transient pulses using many accurate models, while the propagation of transient pulses is primarily simulated by circuit-level simulation tools. Due to the limitations of simulation tools, conventional evaluation approaches are limited to the circuit scale. The simulation runtime is unbearable for large-scale integrated circuits. This paper presents an approach for evaluating the soft error rate using machine learning. A back propagation neural network is implemented in the proposed approach. It helps to determine the probability of transient pulse propagation. Compared with the conventional soft-error-rate evaluation results, the proposed approach demonstrates a strong correlation in both trend and magnitude. The average difference between the results obtained using the proposed evaluation method and the experimental results is 23.5%, which is 7.5% higher than that between the results obtained using the conventional evaluation method and the experimental results. Compared to the conventional evaluation method, the proposed approach improves the runtime by an order of magnitude. The proposed approach also benefits the locating of highly sensitive circuit nodes in large-scale integrated circuits. Circuit design and radiation hardening are both useful applications. [ABSTRACT FROM AUTHOR]

• Data-based models with EMD are proposed for deformation monitoring of concrete dams. • The proposed models can well simulate the irreversible component of concrete dam deformation and significantly decrease the over-fitting levels. • A new model selection criterion, namely Over-fitting coefficient, is proposed for evaluating and selecting dam health monitoring models. • The Over-fitting coefficient can better reflect over-fitting levels of individual monitoring models compared to traditional model selection criteria. Data-based models are extensively applied in concrete dam health monitoring. In this paper, the Empirical Mode Decomposition (EMD) is adopted to extract and visualize the irreversible component of deformation monitoring sequence. Consequently proper mathematical formulation of the irreversible component of deformation monitoring model for concrete dams can be established, and the problem of improperly assuming the mathematical form of irreversible component when constructing data-based models is well addressed. The periodic component of the deformation time series is modelled by regression and three machine learning algorithms (i.e., Extreme learning machine, Support Vector Machine based on Grid search and Cross-Verification, and Back Propagation Neural Networks optimized by Genetic Algorithm and Simulated Annealing) respectively. The data-based models with the EMD are able to effectively improve the performance of formulating irreversible component of concrete dam deformation and decrease the over-fitting levels. A new model selection criterion, namely Over-fitting coefficient, is proposed for evaluating and selecting the optimum model among candidate monitoring models. The Over-fitting coefficient can better reflect over-fitting levels of individual monitoring models compared to traditional model selection criteria. As a case study, the displacement and strain monitoring data from the YL gravity dam are modeled by data-based models with the EMD and machine learning algorithms. It is shown that data-based models with the EMD are of higher prediction accuracy and lower rate of false alarm in deformation monitoring than traditional models. The Over-fitting coefficient can better reflect over-fitting levels of monitoring models compared to the error measurement criteria and information criteria. The case study highlights the merits of the proposed data-based models and the model selection criterion for health monitoring of concrete dams. [ABSTRACT FROM AUTHOR]