This paper focuses on the Continual Test-Time Adaptation (CTTA) task, aiming to enable an agent to continuously adapt to evolving target domains while retaining previously acquired domain knowledge for effective reuse when those domains reappear. Existing shared-parameter paradigms struggle to balance adaptation and forgetting, leading to decreased efficiency and stability. To address this, we propose a frequency-aware shared and self-adaptive expert framework, consisting of two key components: (i) a dual-branch expert architecture that extracts general features and dynamically models domain-specific representations, effectively reducing cross-domain interference and repetitive learning cost; and (ii) an online Frequency-aware Domain Discriminator (FDD), which leverages the robustness of low-frequency image signals for online domain shift detection, guiding dynamic allocation of expert resources for more stable and realistic adaptation. Additionally, we introduce a Continual Repeated Shifts (CRS) benchmark to simulate periodic domain changes for more realistic evaluation. Experimental results show that our method consistently outperforms existing approaches on both classification and segmentation CTTA tasks under standard and CRS settings, with ablations and visualizations confirming its effectiveness and robustness.

Alternating current field measurement (ACFM) is widely used in the quantitative detection of crack due to its advantages of noncontact measurement and high accuracy. However, the noncontact measurement introduces signal interference including constant and random lift-off. Both lift-offs bring challenges to the accurate quantification of cracks. It is difficult to obtain bothBxandBzsignals effectively. In this article, a new 1D-AE-PINN framework to accurately quantify the crack under the lift-off interference is proposed. A novel insight feature ofBxsignal with physical information about the crack size is studied and integrated into loss functions of the 1D-AE-PINN. The features encoded by 1D-AE-PINN are used as input to the quantization network. The advantages of 1D-AE-PINN in accuracy are proved by comparative experiments. The results show that the length and depth of the crack can be measured by onlyBxsignal. The mean squared errors of length and depth are 0.66 and 0.39 mm2.

As the ocean engineering structure serves in the critical underwater environment, a variety of defects, such as cracks and corrosions, always cause damage to the structure. It is still a key challenge to identify and evaluate these defects accurately in the underwater environment. In this article, the visual and intelligent identification methods are presented for the inspection of defects in underwater structures using the alternating current field measurement (ACFM) technique. The current perturbation theory is developed to analyze the disturbed current field and the distorted magnetic field caused by defects. The gradient imaging algorithm is presented as an image preprocessing method to highlight the visual morphology of defects. The underwater intelligent ACFM system is set up. The experiments are carried out to verify the gradient imaging algorithm. The convolutional neural network (CNN) deep learning algorithm is presented to identify the grey-scale map samples preprocessed by the gradient imaging algorithm. The results show that the current perturbation theory clarifies the relationship between the characteristic signal and the morphology of various defects. TheBzimage reflects the surface morphology of defects. The gradient imaging algorithm can achieve visual detection of defects. The single crack, the irregular crack, and the corrosion can be identified intelligently by the CNN deep learning algorithm. These defects can be evaluated accurately after classification.

To improve the security of network in big data era, the improved clustering algorithm is applied to carry out network security defence. Firstly, application of large data clustering algorithms in network security defence is analysed. Secondly, network security defence model is studied, and corresponding mathematical model is designed. Thirdly, the improved clustering algorithm based on big data is established through analysis text requirements and data characteristics. Finally, simulation analysis is carried out, and the effectiveness of the proposed algorithm is verified. The theory analysis results show that the proposed model can provide the theoretical basis for designing network information security defence system.