Efficient vigilance estimation in driving scenarios requires a balance between model performance and practicality. Electroencephalography (EEG), which can directly reflect brain activity, is widely used for vigilance estimation, but its acquisition process is complicated and difficult to apply to real-world driving. In contrast, physiological signals such as electrooculogram, electrodermal activity, and photoplethysmography have more advantages for practical deployment, but the information they provide is relatively limited. To address the above issues, we propose a delay-aware cross-modal knowledge distillation method. EEG signals are only used to train the teacher model. Then, an information-theoretic criterion based on mutual information and response delay is employed to determine which physiological signals are suitable as student modality for knowledge distillation from the EEG-based teacher model. On this basis, considering the inherent temporal differences caused by different physiological signals with varying sensitivities to cognitive responses, a delay-aware soft alignment mechanism (DASA) is proposed, which handles the temporal misalignment of different physiological signals and captures the asynchronous dynamics of the EEG and other physiological signals through the introduction of learnable delay and spread parameters at the patch level, to achieve soft, temporally-aligned supervision from the teacher to the student model. Finally, an objective function incorporating cross-modal consistency, patch level alignment, and smooth regularization is designed to support the effective training of the proposed cross-modal knowledge distillation method. Extensive experiments on MMV and SEED-VIG datasets validates that the proposed method outperforms existing methods in terms of estimation accuracy and temporal alignment while maintaining the real-time performance required for edge deployment.

Convolutional neural networks (CNNs) have gained widespread adoption in fault diagnosis applications owing to their exceptional ability to automatically learn discriminative features from complex data patterns. However, most existing methods based on CNNs use network frameworks that stack small-sized convolutional kernels. Such network models focus on extracting local features, thus limiting the ability to extract cross-domain invariant features, and perform poorly with small sample sizes. We propose an effective channel attention depth separable CNN (ECA-DSCNN). The method designs a feature extraction module based on a large convolutional kernel to extract high-quality cross-domain invariant features. By combining depth-separable convolution with an effective channel attention mechanism, the network parameters are reduced while compensating for the cross-channel interaction capability of depth-separable convolution. ECA-DSCNN achieves high-precision fault diagnosis under finite sample conditions without relying on domain adaptive methods and is robust to fluctuations in the amount of training data.

With the development of digital technology, tourists have higher expectations for their experiences with digital cultural heritage (CH). However, there is limited research and design guidelines on how to effectively convey expert knowledge and transfer the value and connotations of digital CH to users in a virtual environment. Based on existing literature, we proposed a systematic design strategy for immersive virtual reality (IVR) systems, incorporating multimodal interaction, gamification, and storytelling for CH presentation, development, and promotion to the public. Accordingly, taking the virtual restoration of the Dunhuang murals as an example, we developed an IVR system that provided users with learning, interactive, and entertaining experiences, facilitating their transition from mere visitors to active learners while engaging with digital CH. We conducted a between-subjects user study involving 42 participants. The results demonstrated that our IVR system motivated users interests in CH, promoted CH preservation awareness, and could be applied in the domain of digital CH experiences.

Reliability allocation is a significant link in product design. To solve the problems of poor rationality of data use, considerable difficulty of calculation using existing methods, low accuracy of results, and weak ability of experts to express and process fuzzy information, this article proposes a reliability allocation method for the initial stage of product design based on the 2-tuple linguistic weighted Muirhead mean (2TLWMM) operator and 2-tuple linguistic best-worst method (2TLBWM). The 2TLBWM introduces 2-tuple linguistic to enhance the experts’ ability to express fuzzy information. The 2TLWMM operator is used to judge the reliability index ranking of each subsystem, thereby improving the rationality of scoring data, providing a basis for experts to establish the comparison vector table, and reducing the influence of experts’ subjective factors. Experts use 2TLBWM, refer to the ranking results of subsystem reliability index, only need to establish a contrast vector table, and can calculate the weight of the subsystem, thereby reducing the number of comparisons between elements and computational complexity. The advantages of the proposed method are illustrated by a specific case.