RESUMO
Scientific-grade cameras are frequently employed in industries such as spectral imaging technology, aircraft, medical detection, and astronomy, and are characterized by high precision, high quality, fast speed, and high sensitivity. Especially in the field of astronomy, obtaining information about faint light often requires long exposure with high-resolution cameras, which means that any external factors can cause the camera to become unstable and result in increased errors in the detection results. This paper aims to investigate the effect of displacement introduced by various vibration factors on the imaging of an astronomical camera during long exposure. The sources of vibration are divided into external vibration and internal vibration. External vibration mainly includes environmental vibration and resonance effects, while internal vibration mainly refers to the vibration caused by the force generated by the refrigeration module inside the camera during the working process of the camera. The cooling module is divided into water-cooled and air-cooled modes. Through the displacement and vibration experiments conducted on the camera, it is proven that the air-cooled mode will cause the camera to produce greater displacement changes relative to the water-cooled mode, leading to blurring of the imaging results and lowering the accuracy of astronomical detection. This paper compares the effects of displacement produced by two methods, fan cooling and water-circulation cooling, and proposes improvements to minimize the displacement variations in the camera and improve the imaging quality. This study provides a reference basis for the design of astronomical detection instruments and for determining the vibration source of cameras, which helps to promote the further development of astronomical detection.
RESUMO
This erratum reports corrections for the original publication, Appl. Opt.61, 2834 (2022)APOPAI0003-693510.1364/AO.450805.
RESUMO
Owing to the general disadvantages of traditional neural networks in gas concentration inversion, such as slow training speed, sensitive learning rate selection, unstable solutions, weak generalization ability, and an ability to easily fall into local minimum points, the extreme learning machine (ELM) was applied to sulfur hexafluoride (SF6) concentration inversion research. To solve the problems of high dimensionality, collinearity, and noise of the spectral data input to the ELM network, a genetic algorithm was used to obtain fewer but critical spectral data. This was used as an input variable to achieve a genetic algorithm joint extreme learning machine (GA-ELM) whose performance was compared with the genetic algorithm joint backpropagation (GA-BP) neural network algorithm to verify its effectiveness. The experiment used 60 groups of SF6 gas samples with different concentrations, made via a self-developed Fourier transform infrared spectroscopy instrument. The SF6 gas samples were placed in an open optical path to obtain infrared interference signals, and then spectral restoration was performed. Fifty groups were randomly selected as training samples, and 10 groups were used as test samples. The BP neural network and ELM algorithms were used to invert the SF6 gas concentration of the mixed absorbance spectrum, and the results of the two algorithms were compared. The sample mean square error decreased from 248.6917 to 63.0359; the coefficient of determination increased from 0.9941 to 0.9984; and the single running time decreased from 0.0773 to 0.0042 s. Comparing the optimized GA-ELM algorithm with traditional algorithms such as ELM and partial least squares, the GA-ELM algorithm had higher prediction accuracy and operating efficiency and better stability and generalization performance in the quantitative analysis of small samples of gas under complex noise backgrounds.
