Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework

Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters. In this paper, we propose a novel example weighting framework called Learning to Auto Weight (LAW). The proposed framework finds step-dependent weighting policies adaptively, and can be jointly trained with target networks without any assumptions or prior knowledge about the dataset. It consists of three key components: Stage-based Searching Strategy (3SM) is adopted to shrink the huge searching space in a complete training process; Duplicate Network Reward (DNR) gives more accurate supervision by removing randomness during the searching process; Full Data Update (FDU) further improves the updating efficiency. Experimental results demonstrate the superiority of weighting policy explored by LAW over standard training pipeline. Compared with baselines, LAW can find a better weighting schedule which achieves much more superior accuracy on both biased CIFAR and ImageNet.Comment: Accepted by AAAI 202

Inversion technique for quantitative infrared thermography evaluation of delamination defects in multilayered structures

Inverse analysis is a promising tool for quantitative evaluation offering informative model-based prediction and providing accurate reconstruction results without pre-inspections for characterization criteria. For traditional defect inverse reconstruction, a large number of parameters are required to reconstruct a complex defect, and the corresponding forward modelling simulation is very time-consuming. Such issues result in ill-posed and complex inverse reconstruction results, which further reduces its practical applicability. In this paper, we propose and experimentally validate an inversion technique for the reconstruction of complexly-shaped delamination defects in a multilayered metallic structure using signals derived from infrared thermography (IRT) testing. First, we employ a novel defect parameterization strategy based on Fourier series fitting to represent the profile of a complicated delamination defect with relatively few coefficients. Secondly, the multi-medium element modelling method is applied to enhance a FEM fast forward simulator, in order to solve the mismatching mesh issue for mesh updating during inversion. Thirdly, a deterministic inverse algorithm based on a penalty conjugate gradient algorithm is employed to realize a robust and efficient inverse analysis. By reconstructing delamination profiles with both numerically-simulated IRT signals and those obtained through laser IRT experiments, the validity, efficiency and robustness of the proposed inversion method are demonstrated for delamination defects in a double-layered plate. Based on this strategy, not only is the feasibility of the proposed method in Infrared thermography NDT validated, but the practical applicability of inversion reconstruction analysis is significantly improved

An efficient electromagnetic and thermal modelling of eddy current pulsed thermography for quantitative evaluation of blade fatigue cracks in heavy-duty gas turbines

The blade surface fatigue cracks often occur during service of Heavy-Duty Gas Turbines (HDGT) in high temperature, high rotational velocity and high frequency vibration environment. These fatigue cracks seriously threaten the safe operation of heavy-duty gas turbines, which would cause significant hazard or economic loss. The quantitative evaluation of blade surface fatigue cracks is extremely significant to HDGT. Eddy current pulsed thermography (ECPT) is an emerging non-destructive testing technology and show great potential for fatigue crack evaluation. This paper proposes a novel electromagnetic and thermal modelling of ECPT to achieve fast and effective quantitative evaluation for surface fatigue cracks. First, the proposed numerical method calculates electromagnetic field using the reduced magnetic vector potential method in the frequency domain based on frequency series method. The thermal source is transformed to an equivalent and simple form according to the energy equivalent method. Second, the temperature signals of ECPT are calculated through the time-domain iteration strategy with a relatively large time step. Then the ECPT experimental setup is established and the developed simulator is validated numerically and experimentally. The developed simulator is five times faster than the previous one and can be applied to eddy current thermography (ECT) with any kind of excitation waveforms. Finally, the depth of surface fatigue crack is quantitatively evaluated by means of the developed simulator, which is not only a promising simulation progress for ECPT, but also can be an effective tool embedded HDGT though-life maintenanc