Strict industry demands necessitate better penetration classification in gas tungsten arc welding of aluminum alloys. An innovative classification method, namely time-frequency spectrogram based convolutional neural network (TF-CNN), has been developed in this article. The logarithmic time-frequency spectrograms are used to interpret the raw arc sound data in the time-frequency domain and served as the input of an optimized convolutional neural network (CNN) model for welding quality classification. The leaky rectified linear unit activation function and root mean square prop optimizer are utilized to improve the property of CNN. Effective arc sound features are extracted automatically by the CNN model. The proposed method is experimentally verified to recognize four penetration states with an average accuracy of 98.2%, which significantly outperforms the four typical comparison models. The generation mechanism and characteristics of arc sound are discussed as the adaptive feature extraction of TF-CNN. These studies expand the application scope of CNN to intelligent welding and achieve notable effects.

This paper exploits the inter beam handover(IBH) mechanism for 5G mmWave mobile communication system in high speed rail(HSR) scenario. Considering higher speed and narrower coverage of serving beam in mmWave system, more frequent handover(HO) will happen, so in order to reduce inter beam handover failure rate of the network, a novel jointly optimized dynamic inter beam handover(JOD-IBH) scheme based on multiple beams cooperation was proposed in this paper, where handover failure rate and resource occupation rate were jointly optimized. Simulation results show that the proposed JOD-IBH brings improvements to the inter beam handover performance for HSR scenario, which can be observed in reducing handover failure rate and achieving the balance between handover failure rate and resource occupation rate.

Online monitoring and diagnosis of welding quality is essential for intelligent welding manufacturing. The recognition performance of penetration for aluminum alloy in gas tungsten arc welding (GTAW) still needs to be improved to meet the strict industry demands. This paper proposed a novel recognition method, time-frequency image based convolution neural network (TF-CNN), for GTAW penetration recognition. Time-frequency images were calculated from arc sound signals using short time Fourier transform and applied to analyze the non-stationarity of arc sound. The logarithm of time-frequency image was taken to construct the appropriate input matrix of CNN, which was optimized to improve its recognition performance, including the activation function, learning rate and architecture of network. The experimental results show that the proposed TF-CNN achieved an excellent recognition performance with 98.2% recognition accuracy and 0.21 accuracy variance for GTAW seam penetration recognition and outperformed the traditional methods. This paper provides some guidance for the application of CNN to other monitoring signals of intelligent manufacturing.

This paper studies the fault tolerant safe control of nonlinear systems. The proposed theory ensures that the state of the system is away from the unsafe set, and asymptotically stable as well in the presence of faults. The theoretical results are applied to an attitude dynamics of a hypersonic vehicle with actuator faults.