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Hydrogels with good flexibility and strong hydrophilicity can be candidates for excellent flexible electrolyte materials. However, the poor structural stability, uncontrollable swelling, and lower potential window of hydrogel electrolytes need to be improved. This work combined quaternized gelatin with cross-linked poly(acrylic acid-co-acrylamide) to form a semi-interpenetrating network and gelatinized in situ in a flexible porous wood skeleton. The flexible wood (FW) skeleton enhances the hydrogel and limits the swelling of the hydrogel. In addition, quaternary ammonium groups and FW act synergistically to provide the composite hydrogel electrolyte with a high ionic conductivity of 5.57 × 10

The modular power electronic transformer (PET) faces difficulty carrying out microsecond-level electromagnetic transient (EMT) simulations. This paper provides a high-speed and high-precision simulation method capable of eliminating the internal nodes and reducing the order of the nodal admittance matrix. Meanwhile, the parallel computing is integrated into the whole solution process, which achieves a significant simulation speedup. A physical prototype is established to prove that the detailed model is sufficient to reflect the dynamics of physical devices. Moreover, simulations in PSCAD/EMTDC are carried out to compare the proposed method with the detailed model in terms of accuracy and time efficiency. Simulation results show that the proposed method is accurate to simulate the external and internal dynamics of PET with hundreds of times simulation speed acceleration.

Currently, Deep Neural Networks has achieved excellent results on many tasks. But recent studies have shown that these networks are easily influenced by adversarial examples, which are artificially crafted by adding perturbation to original image. Moreover, most of the models to which we can access are black-box, we don’t know the internal structure and parameters of the model. Thus, it is more practical and more challenging to study how to attack these models. In this article, we propose a cam(class activation map)-guided data augmentation attack method, which can improve the transferability of adversarial examples. Specifically, first use the trained network to get the class activation maps for an input image, then binarize the cam to get the mask, finally implement the data augmentation attack method on the masked area of the image. Experiments based on ImageNet prove that our proposed method can generate more transferable adversarial examples, and the attack success rates of our method have a certain improvement compared with the latest methods.