MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network.

Rolling bearing fault diagnosis is of great significance to the safe and reliable operation of manufacturing equipment. In the actual complex environment, the collected bearing signals usually contain a large amount of noises from the resonances of the environment and other components, resulting in the nonlinear characteristics of the collected data. Existing deep-learning-based solutions for bearing fault diagnosis perform poorly in classification performance under noises. To address the above problems, this paper proposes an improved dilated-convolutional-neural network-based bearing fault diagnosis method in noisy environments named MAB-DrNet. First, a basic model called the dilated residual network (DrNet) was designed based on the residual block to enlarge the model's perceptual field to better capture the features from bearing fault signals. Then, a max-average block (MAB) module was designed to improve the feature extraction capability of the model. In addition, the global residual block (GRB) module was introduced into MAB-DrNet to further improve the performance of the proposed model, enabling the model to better handle the global information of the input data and improve the classification accuracy of the model in noisy environments. Finally, the proposed method was tested on the CWRU dataset, and the results showed that the proposed method had good noise immunity; the accuracy was 95.57% when adding Gaussian white noises with a signal-to-noise ratio of -6 dB. The proposed method was also compared with existing advanced methods to further prove its high accuracy.

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing.

With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.

Joint Soft-Hard Attention for Self-Supervised Monocular Depth Estimation.

In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised methods can obtain dense depths, the estimation accuracy needs to be further improved for better applications in scenarios such as autonomous driving and robot perception. In this paper, we innovatively combine soft attention and hard attention with two new ideas to improve self-supervised monocular depth estimation: (1) a soft attention module and (2) a hard attention strategy. We integrate the soft attention module in the model architecture to enhance feature extraction in both spatial and channel dimensions, adding only a small number of parameters. Unlike traditional fusion approaches, we use the hard attention strategy to enhance the fusion of generated multi-scale depth predictions. Further experiments demonstrate that our method can achieve the best self-supervised performance both on the standard KITTI benchmark and the Make3D dataset.

Study of Cell Behaviors on Anodized TiO2 Nanotube Arrays with Coexisting Multi-Size Diameters.

It has been revealed that the different morphologies of anodized TiO2 nanotubes, especially nanotube diameters, triggered different cell behaviors. However, the influence of TiO2 nanotubes with coexisting multi-size diameters on cell behaviors is seldom reported. In this work, coexisting four-diameter TiO2 nanotube samples, namely, one single substrate with the integration of four different nanotube diameters (60, 150, 250, and 350 nm), were prepared by repeated anodization. The boundaries between two different diameter regions show well-organized structure without obvious difference in height. The adhesion behaviors of MC3T3-E1 cells on the coexisting four-diameter TiO2 nanotube arrays were investigated. The results exhibit a significant difference of cell density between smaller diameters (60 and 150 nm) and larger diameters (250 and 350 nm) within 24 h incubation with the coexistence of different diameters, which is totally different from that on the single-diameter TiO2 nanotube arrays. The coexistence of four different diameters does not change greatly the cell morphologies compared with the single-diameter nanotubes. The findings in this work are expected to offer further understanding of the interaction between cells and materials.