The optical time domain reflectometer (OTDR) curve features of communication fibers exhibit subtle differences among their normal, subhealthy, and faulty operating states, making it challenging for existing machine learning‐based fault diagnosis algorithms to extract these minute features. In addition, the OTDR curve field fault data are scarce, and data‐driven deep neural network that needs a lot of data training cannot meet the requirements. In response to this issue, this paper proposes a communication fiber state diagnosis model based on fuzzy clustering and an improved ResNet. First, the pretrained residual network (ResNet) is modified by removing the classification layer and retaining the feature extraction layers. A global average pooling (GAP) layer is designed as a replacement for the fully connected layer. Second, fuzzy clustering, instead of the softmax classification layer, is employed in ResNet for its characteristic of requiring no subsequent data training. The improved model requires only a small amount of sample training to optimize the parameters of the GAP layer, thereby accommodating state diagnosis in scenarios with limited data availability. During the diagnosis process, the OTDR curves are input into the network, resulting in 512 features outputted in the GAP layer. These features are used to construct a feature vector matrix, and a dynamic clustering graph is formed using fuzzy clustering to realize the fiber state diagnosis. Through on‐site data detection and validation, it has been demonstrated that the improved ResNet can effectively identify the full cycle of fiber states.

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this article, a novel data-synthesized spatiotemporally convolutional encoder-decoder network (DST-CedNet) method is proposed for ESI. The DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a CedNet to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and epilepsy EEG dataset demonstrate that the DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.

Keeping an appropriate level of trust in automated driving (AD) is critical to safe driving. Although ample studies have investigated factors affecting trust in AD, few studies have investigated whether the personality of drivers influences the trust in AD system. Considering that trust measured at a given point in time lies on a continuum between dispositional and history-based trust, the current research investigated the relationship between driver’s personality and dispositional as well as history-based trust. We revealed that personality affected the two types of trust in different ways: A significant negative correlation emerged between Neuroticism and dispositional trust of AD (Study 1), whereas a significant negative correlation between Openness and history-based trust was found when participants interacted with the AD system (Study 2). These results suggest that drivers’ personality has an impact on the trust in AD, which is further modulated by the experience of driver’s interaction with the AD system.