Recently, the attention mechanism combining spatial and channel information has been widely used in various deep convolutional neural networks (CNNs), proving its great potential in improving model performance. However, this usually uses 2D global pooling operations to compress spatial information or scaling methods to reduce the computational overhead in channel attention. These methods will result in severe information loss. Therefore, we propose a Spatial channel attention mechanism that captures cross-dimensional interaction, which does not involve dimensionality reduction and brings significant performance improvement with negligible computational overhead. The proposed attention mechanism can be seamlessly integrated into any convolutional neural network since it is a lightweight general module. Our method achieves a performance improvement of 2.08% on ResNet and 1.02% on MobileNetV2 in top-one error rate on the ImageNet dataset.

In this article, a new intelligent fault-tolerant control (FTC) is designed to control a flexible-link manipulator with uncertain dead-zone and intermittent actuator faults. Initially, a smooth dead-zone inverse model using a hyperbolic tangent function is introduced to handle dead-zone nonlinearity and suppress input chattering. An adaptive law is proposed to estimate an unknown coupling item, combining the upper bounds of compensation error and floating bias faults, achieving robust adaptive control of the system. A new FTC strategy is subsequently developed to address intermittent actuator faults. Finally, the bounded convergence of system state errors is proven using direct Lyapunov methods, and the effectiveness and superiority of the proposed controller are demonstrated through numerical simulation and experiment.