Decoupling of the position and angular errors in laser pointing with a neural network method

Xia, L.; Hu, Yuanzhang; Chen, Wenyu; Li, Xiaoguang

doi:10.1017/hpl.2020.29

Cited by 15 publications

(5 citation statements)

References 20 publications

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“…Electromagnetic waves propagate in ZIMs with almost no phase delay, because the wavelength and phase velocity approach infinity in ZIMs. In addition, ZIMs has many interesting electromagnetic characteristics, such as: super coupling effect [45,46], wavefront shaping effect [47,48], field enhancement effect [49], etc. Many special electromagnetic characteristics of ZIMs can be used in antennas [50,51], lenses [52,53], laser beam combiners [54,55] and invisibility cloaks [56][57][58][59][60][61].…”

Section: Introductionmentioning

confidence: 99%

A three-dimensional polarization independent invisibility cloak by using multiband zero refraction metamaterials

Luo¹,

Jing²,

Gan³

et al. 2021

Laser Phys.

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In this paper, we propose a zero-refractive index metamaterial (ZIM) with a simple structure, which is composed of dielectric spheres. The effective medium theory was used to invert the equivalent electromagnetic parameters of metamaterials. Metamaterials have nearly zero refractive index in the three bands of 17. GHz, respectively. Through monitoring the electric field and phase distribution at three zero frequencies, the zero refractive index characteristic is verified. Based on the proposed ZIM, a three-dimensional polarization-independent invisibility cloak within three wavebands was designed and numerically demonstrated.

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Section: Introductionmentioning

confidence: 99%

A three-dimensional polarization independent invisibility cloak by using multiband zero refraction metamaterials

Luo¹,

Jing²,

Gan³

et al. 2021

Laser Phys.

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“…Back-propagation neural networks (BPNNs), a subset of machine learning, have shown potential for mapping the relationship between experimental parameters and material properties [13,14] . This approach can identify underlying regularities in the training data by updating the internal weight parameters [15,16] . In recent years, researchers have begun to study the application of neural networks in the field of thin films to predict the growth rate [17][18][19][20] , hydrophobicity [21] , permeate flux and foulant rejection [22] .…”

Section: Introductionmentioning

confidence: 99%

Neural network modeling and prediction of HfO₂ thin film properties tuned by thermal annealing

Gao,

Yin,

Shao

et al. 2024

High Pow Laser Sci Eng

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Plasma-enhanced atomic layer deposition (PEALD) is gaining interest in thin films for laser applications, and postannealing treatments are often used to improve thin film properties. However, research to improve thin film properties is often based on an expensive and time-consuming trial-and-error process. In this study, PEALD-HfO 2 thin film samples were deposited and treated under different annealing atmospheres and temperatures. The samples were characterized in terms of their refractive indices, layer thicknesses and O/Hf ratios. The collected data were split into training and validation sets and fed to multiple back-propagation neural networks with different hidden layers to determine the best way to construct the process-performance relationship. The results showed that the three-hidden-layer back-propagation neural network (THL-BPNN) achieved stable and accurate fitting. For the refractive index, layer thickness and O/Hf ratio, the THL-BPNN model achieved accuracy values of 0.99, 0.94 and 0.94, respectively, on the training set and 0.99, 0.91 and 0.90, respectively, on the validation set. The THL-BPNN model was further used to predict the laserinduced damage threshold of PEALD-HfO 2 thin films and the properties of the PEALD-SiO 2 thin films, both showing high accuracy. This study not only provides quantitative guidance for the improvement of thin film properties but also proposes a general model that can be applied to predict the properties of different types of laser thin films, saving experimental costs for process optimization.

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“…In the field of laser technology, researchers have explored several methods for distinguishing overlapping beams. Lei Xia et al introduced a technique based on the convolutional neural network (CNN) model to separate two overlapping beams [17], [18]. Nevertheless, they omitted information on the location of the beam center.…”

Section: Introductionmentioning

confidence: 99%

Accurate subpixel center determination for overlapping spots

Tao,

Nguyen,

Nguyen

et al. 2024

Preprint

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Precisely detecting laser beam locations is crucial in maximizing the performance of optical systems in manufacturing and measurement applications. There are numerous methods dealing with a single spot, but identifying overlapping spot centers is still challenging. To address this issue, we present an innovative approach that uses convolutional neural networks and image processing techniques to localize overlapping spot centers. Our method begins by utilizing the convolutional neural network to extract two crucial features: the ratio of minor to major axes and the orientation of the spots. Then, the Euclidean distance transform is employed to identify the approximate centers of the spots, which are the positions with the highest intensity in the transformed images. Finally, the gradient descent algorithm is applied to determine the precise center locations. In addition, noise was added to examine the ability to work in actual systems. The results indicate that our method performs well in noisy environments, accurately pinpointing overlapping spot centers in real-time with a 92% success rate. Furthermore, our approach strikes an excellent balance between accuracy and computational efficiency, making it suitable for use in actual laser manufacturing systems.

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Decoupling of the position and angular errors in laser pointing with a neural network method

Cited by 15 publications

References 20 publications

A three-dimensional polarization independent invisibility cloak by using multiband zero refraction metamaterials

A three-dimensional polarization independent invisibility cloak by using multiband zero refraction metamaterials

Neural network modeling and prediction of HfO₂ thin film properties tuned by thermal annealing

Accurate subpixel center determination for overlapping spots

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Decoupling of the position and angular errors in laser pointing with a neural network method

Cited by 15 publications

References 20 publications

A three-dimensional polarization independent invisibility cloak by using multiband zero refraction metamaterials

A three-dimensional polarization independent invisibility cloak by using multiband zero refraction metamaterials

Neural network modeling and prediction of HfO 2 thin film properties tuned by thermal annealing

Accurate subpixel center determination for overlapping spots

Contact Info

Product

Resources

About

Neural network modeling and prediction of HfO₂ thin film properties tuned by thermal annealing