Many transfer learning methods have been proposed to implement fault transfer diagnosis, and their loss functions are usually composed of task-related losses, distribution distance losses, and correlation regularization losses. The intrinsic parameters and trade-off parameters between losses, however, need to be tuned according to the specific diagnosis tasks; thus, the generalization abilities of these methods in multiple tasks are limited. Besides, the alignment goal of most domain adaptation (DA) mechanisms dynamically changes during the training process, which will result in loss oscillation, slow convergence and poor robustness. To overcome the above-mentioned issues, a novel and simple transfer learning diagnosis method named adaptive intermediate class-wise distribution alignment (AICDA) model is proposed, and it is established via the proposed AICDA mechanism, dynamic intermediate alignment (DIA) adaptive layer and AdaSoftmax loss. The AICDA mechanism develops an adaptive intermediate distribution as the alignment goal of multiple source domains and target domains, and it can simultaneously align the global and class-wise distributions of these domains. The DIA layer is designed to adaptively achieve domain confusion without the distribution distance loss and the correlation regularization loss. Meanwhile, to ensure the classification performance of the AICDA mechanism, AdaSoftmax loss is proposed for boosting the separability of Softmax loss. Finally, in order to evaluate the effectiveness and universality of the AICDA diagnosis model to the most degree, various multisource mixed fault transfer diagnosis tasks of wind turbine planetary gearboxes, including DA and domain generalization (DG), are implemented, and the experimental results indicate that our proposed AICDA model has a higher diagnosis accuracy and a stronger generalization ability than other state-of-the-art transfer learning methods.

Controlling topological phases of light allows the observation of abundant topological phenomena and the development of robust photonic devices. The prospect of more sophisticated control with topological photonic devices for practical implementations requires high-level programmability. Here we demonstrate a fully programmable topological photonic chip with large-scale integration of silicon photonic nanocircuits and microresonators. Photonic artificial atoms and their interactions in our compound system can be individually addressed and controlled, allowing the arbitrary adjustment of structural parameters and geometrical configurations for the observation of dynamic topological phase transitions and diverse photonic topological insulators. Individual programming of artificial atoms on the generic chip enables the comprehensive statistical characterization of topological robustness against relatively weak disorders, and counterintuitive topological Anderson phase transitions induced by strong disorders. This generic topological photonic chip can be rapidly reprogrammed to implement multifunctionalities, providing a flexible and versatile platform for applications across fundamental science and topological technologies.

Generally, 360-degree images have absolute senses of reality and three-dimensionality, providing a wide range of immersive interactions. Due to the novel rendering and display technology of 360-degree images, they have more complex perceptual characteristics than other images. It is challenging to perform comprehensive image quality assessment (IQA) learning by simply stacking multichannel neural network architectures for pre/postprocessing, compression, and rendering tasks. To thoroughly learn the global and local features in 360-degree images, reduce the complexity of multichannel neural network models and simplify the training process, this paper proposes a joint architecture with user perception and an efficient transformer dedicated to 360-degree no-reference (NR) IQA. The input of the proposed method is a 360-degree cube map projection (CMP) image. Furthermore, the proposed 360-degree NRIQA method includes a saliency map-based non-overlapping self-attention selection module and a U-shaped transformer (U-former)-based feature extraction module to account for perceptual region importance and projection distortion. The transformer-based architecture and the weighted average technique are jointly utilized for predicting local perceptual quality. Experimental results obtained on widely used databases show that the proposed model outperforms other state-of-the-art methods in NR 360-degree image quality evaluation cases. Furthermore, a cross-database evaluation and an ablation study also demonstrate the inherent robustness and generalization ability of the proposed model.

Simultaneous Localization and Mapping (SLAM) technology is essential for robots to navigate unfamiliar environments. It utilizes the sensors the robot carries to answer the question "Where am I?" Of the available sensors, cameras are commonly used. Compared to other sensors like LiDARs, the method based on cameras, known as Visual SLAM has been extensively explored by researchers due to the affordability and rich image data cameras provide. Although conventional Visual SLAM algorithms have been able to accurately build a map in static environments, dynamic environments present a significant challenge for Visual SLAM in practical robotics scenarios. While efforts have been made to address this issue, such as adding semantic segmentation to conventional algorithms, a comprehensive literature review is still lacking. This paper discusses the challenges and approaches of Visual SLAM with a focus on dynamic objects and their impact on feature extraction and mapping accuracy. First, two classical approaches of conventional Visual SLAM are reviewed, then the paper explores the application of deep learning in the front-end and back-end of Visual SLAM. Next, Visual SLAM in dynamic environments is analyzed and summarized, and insights into future developments are elaborated upon. This paper provides effective inspiration for researchers on how to combine deep learning and semantic segmentation with Visual SLAM to promote its development.