A Review on Zernike Coefficient-Solving Algorithms (CSAs) Used for Integrated Optomechanical Analysis (IOA)

Hu, Motong; Pan, Yue; Zhang, Ning; Xu, Xiping

doi:10.3390/photonics10020177

Cited by 4 publications

(4 citation statements)

References 85 publications

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“…The integrated optomechanical analysis method involves a variety of disciplines such as optics, mechanics, and thermodynamics [25]. The transfer of data between different disciplines is complex.…”

Section: Methodsmentioning

confidence: 99%

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Optical and Mechanical Performance and Feasibility Analysis of Meter-Level Corrector Lenses for Survey Telescope

Xue

Liu

2023

Photonics

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A prime-focus optical system is a common type of large aperture survey telescope with a small obscuration ratio, high transmittance, and simple structure. As the detection capabilities and search efficiency of the telescope are improved, the aperture of the primary mirror becomes larger. The primary mirror aperture of a high-performance survey telescope has a four-meter scale, and the aperture of the corrector lenses of a corresponding prime-focus optical system can reach a one-meter scale. In this study, optical surface shape changes due to gravity were studied via an integrated optomechanical analysis. The Zernike polynomial was used to characterize the changed optical surface. The influence of optical surface variations on the wavefront and imaging quality of the optical system was analyzed by calculating the optical path differences. From the perspective of optical aberration, the optical properties of meter-level corrector lenses were quantitatively analyzed. The effect of the meter-level corrector lenses on imaging was explained in principle, and the feasibility of this optical scheme was verified.

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Section: Methodsmentioning

confidence: 99%

“…In this paper, the of meter-level corrector lenses with a prime focus system integrated optomechanical analysis. The integrated optomechanical analysis method involves a variety of disciplines such as optics, mechanics, and thermodynamics [25]. The transfer of data between different disciplines is complex.…”

mentioning

confidence: 99%

Optical and Mechanical Performance and Feasibility Analysis of Meter-Level Corrector Lenses for Survey Telescope

Xue

Liu

2023

Photonics

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“…The traditional optomechanical system usually adopts the iterative design method of Line-Of-Sight pointing recalculation after the completion of component design [2], which cannot create a balanced impact assessment of optical, mechanical, thermal, and other factors [3]. At present, the mainstream method to ensure the Line-Of-Sight pointing of a space camera domestically and abroad is to use the integrated comprehensive design of a star sensor and camera [4,5], integrated optomechanical analysis technology that can also be used for simulation research.…”

Section: Introductionmentioning

confidence: 99%

Integrated Analysis of Line-Of-Sight Stability of Off-Axis Three-Mirror Optical System

Lu,

Sun,

Mei

et al. 2024

Photonics

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As a space camera works in orbit, the stress rebound caused by gravity inevitably results in the deformation of its optomechanical structure, and the relative position change between different optical components will affect the Line-Of-Sight pointing of the camera. In this paper, the optical sensitivity calculation of a space camera’s Line-Of-Sight pointing is realized based on the optomechanical constraint equations, and the Line-Of-Sight equations are constructed using the second type of response (DRESP2) method to realize an optomechanical integrated analysis of the camera’s Line-Of-Sight stability at the structural finite element solver level. The verification results show that the Line-Of-Sight stability error is 6.38%, meaning that this method can identify the sensitive optical elements of the optical system efficiently and quickly. Thus, the method in this paper has important significance as a reference for the analysis of the Line-Of-Sight stability of complex optical systems.

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“…Deep learning models, especially 1D CNN models, have gained popularity in various scientific fields [21]. 1D CNN models have the capability of representational learning, which allows them to perform translation invariant classification through the hierarchical structure of the input information [22]. They can be utilised for lossless maize yield prediction and are well-suited for feature extraction and classification [23].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Models Outperform Generalized Machine Learning Models in Predicting Winter Wheat Yield Based on Multispectral Data from Drones

Chen

Qian

et al. 2023

Drones

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Timely and accurate monitoring of winter wheat yields is beneficial for the macro-guidance of agricultural production and for making precise management decisions throughout the winter wheat reproductive period. The accuracy of crop yield prediction can be improved by combining unmanned aerial vehicle (UAV)-based multispectral data with deep learning algorithms. In this study, 16 yield-sensitive vegetation indices were constructed, and their correlations were analyzed based on UAV multispectral data of winter wheat at the heading, flowering, and filling stages. Seven input variable sets were obtained based on the combination of data from these three periods, and four generalized machine learning algorithms (Random Forest (RF), K-Nearest Neighbor (KNN), Bagging, and Gradient Boosting Regression (GBR)) and one deep learning algorithm (1D Convolutional Neural Network (1D-CNN)) were used to predict winter wheat yield. The results showed that the RF model had the best prediction performance among the generalised machine learning models. The CNN model achieved the best prediction accuracy based on all seven sets of input variables. Generalised machine learning models tended to underestimate or overestimate yields under different irrigation treatments, with good prediction performance for observed yields < 7.745 t·ha−1. The CNN model showed the best prediction performance based on most input variable groups across the range of observed yields. Most of the differences between observed and predicted values (Yi) for the CNN models were distributed between −0.1 t·ha−1 and 0.1 t·ha−1, and the model was relatively stable. Therefore, the CNN model is recommended in this study for yield prediction and as a reference for future precision agriculture research.

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A Review on Zernike Coefficient-Solving Algorithms (CSAs) Used for Integrated Optomechanical Analysis (IOA)

Cited by 4 publications

References 85 publications

Optical and Mechanical Performance and Feasibility Analysis of Meter-Level Corrector Lenses for Survey Telescope

Optical and Mechanical Performance and Feasibility Analysis of Meter-Level Corrector Lenses for Survey Telescope

Integrated Analysis of Line-Of-Sight Stability of Off-Axis Three-Mirror Optical System

Deep Learning Models Outperform Generalized Machine Learning Models in Predicting Winter Wheat Yield Based on Multispectral Data from Drones

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