The emission wavelength of an ultrafast laser generated by thulium (Tm)-doped fiber laser (TDFL) ranges from 1.7 to 2.1 μm, covering the water-absorbing band and atmospheric transmission window. In this study, an intelligent Tm-doped mode-locked fiber laser was experimentally demonstrated by combining a genetic algorithm (GA) with an adaptive mutation rate and a nonlinear polarization rotation mode-locked fiber oscillator. A closed-loop feedback system was set up in the experiment, including an oscilloscope, a laptop computer, an electric polarization controller, and a mode-locked fiber oscillator. Based on the aforementioned intelligent design of manual-operation-free, a stable femtosecond level noise-like-mode-locked pulse with an output power of 57.7 mW and a central wavelength of 1973 nm was automatically generated. The evolutionary dynamics of the different parameter structures of the GA-controlled ultrafast TDFL with varying mutation rates were also investigated. This study will pave the way for generating robust ultrafast lasers in the short-wave infrared region.

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In order to address the timing problem, invalid data problem and deep feature extraction problem in the current deep learning based aero-engine remaining life prediction, a remaining life prediction method based on time-series residual neural networks is proposed. This method uses a combination of temporal feature extraction layer and deep feature extraction layer to build the network model. First, the temporal feature extraction layer with multi-head structure is used to extract rich temporal features; then, the spatial attention mechanism is applied to improve the weights of important data; finally, the deep feature extraction layer is used to process the deep features of the data. To verify the effectiveness of the proposed method, experiments are conducted on the C-MAPSS dataset provided by NASA. The experimental results show that the method proposed in this paper can make accurate predictions of the remaining service life under different sub-datasets and has outstanding performance advantages in comparison with other outstanding networks.

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