Mr. Xiaowei Chen Profile

Xiaowei Chen

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Proceedings Article | 30 April 2024 Paper

Uncertainty analysis of propagation efficiency of high-energy laser horizontal propagation in the atmosphere

Lingyun Min, Xiaowei Chen, Ningquan Weng, Pengfei Wu, Wenyue Zhu, Xianmei Qian, Xun Cui

Proceedings Volume 13157, 1315711 (2024) https://doi.org/10.1117/12.3018181

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Due to variations of influencing factors and atmospheric effects, the propagation efficiency (transmittance, thermal distortion parameter and 63.2% encircled average power density) of high-energy laser propagating in the atmosphere is uncertain. In this paper, aiming to evaluate the uncertainty of propagation efficiency and identify the main influencing factors, the following research is made. (1) The scaling law is established through numerical simulation, which is suitable for the Gaussian waveform laser and considers the interaction between different effects. (2) The probability distribution characteristics and uncertainty of propagation efficiency are evaluated in the horizontal propagation scenario by the Latin hypercube sampling method. (3) The Elementary Effect Test is applied, with the aim to give the parameters prioritization and identify the crucial parameters affecting encircled average power density. The results show that the uncertainty and parameters prioritization of propagation efficiency vary with the propagation distance. Considering the results of the Elementary Effect Test at different distances, the crucial parameters for 63.2% encircled average power density are transverse wind speed, absorption coefficient, power, and initial beam quality. This research is of great significance for the application of laser systems.

Proceedings Article | 30 April 2024 Paper

Transformer model for evaluating Gaussian beam propagation through turbulent atmosphere

Xun Cui, Xiaowei Chen, Xianmei Qian, Wenyue Zhu, Pengfei Wu, Lingyun Min, Ruizhong Rao

Proceedings Volume 13157, 131570T (2024) https://doi.org/10.1117/12.3017798

KEYWORDS: Transformers, Atmospheric propagation, Turbulence, Gaussian beams, Laser beam propagation

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Scaling law and artificial intelligence model are two methods to quickly evaluate the far-field spot characteristics of laser propagation through turbulence. On the one hand, it is necessary to compare the evaluation accuracy of both models. On the other hand, the comparison between different models is only meaningful if each has their best accuracy. For specific scaling law model and artificial intelligence model, scaling exponents and hyperparameters determine the evaluation accuracy of the model to a certain extent. This paper discusses how to search better scaling exponents and hyperparameters to construct each model and compare the evaluation accuracy of both models. This paper first introduces the MRSS (Modified-Radius-Square-Sum) scaling law model and FT-Transformer (Feature Tokenizer + Transformer) model, and 3 hyperparameter (scaling exponent) optimization algorithms. Then, the accuracy of scaling exponents and hyperparameters obtained by different optimization algorithms is compared. Finally, the best scaling exponents and hyperparameters are used to construct each model. The results show that the TPE algorithm achieves better search results in fewer iterations for the FT-Transformer model, and the CmaEs algorithm achieves higher accuracy in more iterations for the scaling law model. The FT-Transformer model has better accuracy compared to the scaling law model, with the mean relative error of the far field effective radius and mean intensity is 1.32% and 2.66%, while those of scaling law model is 1.97% and 3.91% respectively.

Proceedings Article | 24 May 2023 Paper

Research on atmospheric turbulence-degraded image restoration based on generative adversarial networks

Jiuming Cheng, Jianyu Li, Congming Dai, Yichong Ren, Gang Xu, Shuai Li, Xiaowei Chen, Wenyue Zhu

Proceedings Volume 12706, 1270607 (2023) https://doi.org/10.1117/12.2681805

KEYWORDS: Image restoration, Turbulence, Image processing, Image quality, Convolution, Atmospheric optics, Optical transfer functions, Atmospheric turbulence

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The imaging equipment working in the atmosphere will not only be limited by the performance of the imaging system, but also be affected by turbulence. In the fields of astronomical observation, ground-based remote sensing and remote monitoring, there is an urgent need for corresponding methods and technologies to eliminate the impact of atmospheric turbulence and obtain clear images. With the development of computer technology, atmospheric optics theory and image processing technology, more and more researchers hope to combine deep learning technology with atmospheric turbulence theory to reduce the impact of turbulence on imaging and obtain clear and stable images. In this paper, a turbulence image restoration technique based on Generative Adversarial Networks (GAN) is proposed, which is divided into generator network and discriminator network. The generator network is used to convert blurred images affected by turbulence into clear images. The discriminator network is used to compare the converted image with the real clear image to determine whether the image is real or generated. After the whole GAN is optimized and trained, the image transformed by the generator and the real and clear image cannot be distinguished from each other. Because the training of the GAN requires a large number of corresponding samples, it is difficult to obtain the images affected and unaffected by turbulence at the same time in real life, so this paper uses the statistical characteristics of turbulence to simulate a large number of images affected by turbulence. We used the trained GAN model to simulate turbulence image restoration and got some achievements.

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