An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments.

The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model's robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.

Fatigue Crack Growth Behaviour of Precipitate-Strengthened CuNi2Si Alloy under Different Loading Modes.

In this study, fatigue crack tests of CuNi2Si alloys using the replica technique under symmetrical tensile-compression loading, and rotational-bending loading were carried out with the same nominal stress amplitude. Observation and analysis results indicate that under different load types, the cracks display a trend of slow initiation growth and then rapid growth. The critical point is identified at the approximate value of 0.8 of the fatigue life fraction, and the crack growth rate of the sample under tensile-compression load is approximately an order of magnitude higher than that under rotational-bending load, resulting in the average life of the former being significantly shorter than the latter. Combining the observation results of the fractographical analysis and the surface-etched sample replica film, it can be seen that whether it is a tensile-compression load or a rotational-bending load, cracks mainly propagate in intergranular mode after initiation.