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During the operation of the power transformer, the fault location often occurs in the winding, and the fault phenomenon caused by insufficient short‐circuit withstand ability of the winding is particularly prominent. Based on this problem, this article proposes an improved symplectic geometry mode decomposition (ISGMD) algorithm to evaluate the short‐circuit withstand ability of winding. The realization method is to use the algorithm to perform time‐frequency decomposition of the vibration signal of the transformer, calculate time‐domain eigenvalues of the symplectic geometry components to constitute the eigenvector, utilize principal component analysis to reduce dimensionality of the data. Finally, apply the obtained data to train and test the extreme learning machine. The correctness of the algorithm is analyzed by simulating vibration signals. At last, the experiment is designed and the evaluation standard is proposed, and the practicability of the intelligent evaluation model based on the method is verified. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

While the widespread application of biometric identification technology has provided convenient services, the security, and privacy of personal data have also attracted increasing attention. Because biometric traits are unique, once an attack occurs, the information stored in the system will be permanently leaked. Thus, ensuring the security of biometric templates is a major challenge. In order to solve this problem, we propose a finger vein template protection method based on chaotic map. First, we randomize and scramble the finger vein features after feature extraction, disturbing the location distribution of the pixels. Second, on the basis of the chaotic features of the finger veins, binary quantization is applied to further ensure the safety of the template. Finally, the compact and safe finger vein template is directly stored in the database. Finger vein matching is realized in the encryption domain, which changes the way that the traditional finger vein recognition system directly stores biometric templates. We conducted a series of detailed experiments on the public dataset. The experimental results show that our method can ensure the safety of finger vein templates while maintaining stable recognition performance. Moreover, the method has universality for different feature‐extraction methods and meets the requirements of good biometric template protection. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]