In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers a...In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.展开更多
This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert ...This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert triggering and light curve generation for transient sources.By applying the STRIP pipeline to both simulated and real observational data of the Mini-Si Tian survey,it successfully identified various types of variable sources,including stellar flares,supernovae,variable stars,and asteroids,while meeting requirements of reduction speed within 5 minutes.For the real observational data set,the pipeline detected one flare event,127 variable stars,and14 asteroids from three monitored sky regions.Additionally,two data sets were generated:one,a real-bogus training data set comprising 218,818 training samples,and the other,a variable star light curve data set with 421instances.These data sets will be used to train machine learning algorithms,which are planned for future integration into STRIP.展开更多
Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometri...Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.展开更多
The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatia...The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatial resolution than expected.In this paper,we developed the SDI point-spread function(PSF)and Image Bivariate Optimization Algorithm(SPIBOA)to improve the quality of SDI images.The bivariate optimization method smartly combines deep learning with optical system modeling.Despite the lack of information about the real image taken by SDI and the optical system function,this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data.We use the estimated PSF to conduct deconvolution correction to observed SDI images,and the resulting images show that the spatial resolution after correction has increased by a factor of more than three with respect to the observed ones.Meanwhile,our method also significantly reduces the inherent noise in the observed SDI images.The SPIBOA has now been successfully integrated into the routine SDI data processing,providing important support for the scientific studies based on the data.The development and application of SPIBOA also paves new ways to identify astronomical telescope systems and enhance observational image quality.Some essential factors and precautions in applying the SPIBOA method are also discussed.展开更多
In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical...In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical observation data,and the target source classification information in vast photometric data has not been accurately measured.To address this,we propose a novel deep learning-based algorithm,YL8C4Net,for the automatic detection and classification of target sources in photometric images.This algorithm combines the YOLOv8 detection network with the Conv4Net classification network.Additionally,we propose a novel magnitude-based labeling method for target source annotation.In the performance evaluation,the YOLOv8 achieves impressive performance with average precision scores of 0.824 for AP@0.5 and 0.795 for AP@0.5:0.95.Meanwhile,the constructed Conv4Net attains an accuracy of 0.8895.Overall,YL8C4Net offers the advantages of fewer parameters,faster processing speed,and higher classification accuracy,making it particularly suitable for large-scale data processing tasks.Furthermore,we employed the YL8C4Net model to conduct target source detection and classification on photometric images from 20 sky regions in SDSS-DR17.As a result,a catalog containing about 9.39 million target source classification results has been preliminarily constructed,thereby providing valuable reference data for astronomical research.展开更多
The China Space Station Telescope(CSST)is a 2 m three-mirror anastigmat equipped with a Fast Steering Mirror(FSM),which is part of its precision image stabilization system.The FSM is used to compensate for residuals f...The China Space Station Telescope(CSST)is a 2 m three-mirror anastigmat equipped with a Fast Steering Mirror(FSM),which is part of its precision image stabilization system.The FSM is used to compensate for residuals from the previous stage of the image stabilization system.However,a new type of image stabilization residual caused by image rotation and projection distortion is introduced when the FSM performs tip-tilt adjustments,reducing both the image stabilization accuracy and the absolute pointing accuracy of the CSST.In this paper,we propose a scheme to compute the image stabilization residuals across the full field of view(FOV)by using a reference star as the target for stabilization control,which can be utilized for subsequent image position correction.To achieve this,we developed a linear optical model for image point displacement by simplifying an existing image point displacement model and incorporating more readily available parameters.The computational accuracy of the new model is equivalent to that of the original,with computational differences of less than 0.03μm.Based on this linear model,we established a calculation model for image stabilization residuals,including those due to image rotation and projection distortion caused by FSM tip-tilt adjustments.This model provides a theoretical foundation for quantifying such residuals during the image stabilization process.Finally,the results of testing using this scheme are provided.Experimental results demonstrate that within the observation FOV of the CSST,when the FSM tilts by(1″,1″),the maximum absolute value of the image stabilization residuals accounts for 20%of the total image stabilization accuracy requirement.This finding underscores the necessity of computing and correcting these residuals to meet performance requirements.展开更多
As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the...As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the precise data processing pipeline designed for wide-field,CMOS-based devices,including the removal of instrumental effects,astrometry,photometry,and flux calibration.When applying this pipeline to approximately3000 observations taken in the Field 02(f02)region by MST,the results demonstrate a remarkable astrometric precision of approximately 70–80 mas(about 0.1 pixel),an impressive calibration accuracy of approximately1 mmag in the MST zero points,and a photometric accuracy of about 4 mmag for bright stars.Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs,highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys,like the SiTian project.展开更多
The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear...The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.展开更多
We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing ...We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function(PSF)deconvolution,resulting in enhanced restoration of extended sources,the highest peak signal-to-noise ratio,and reduced ringing artefacts.To test our method,we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/the VLT Survey Telescope(VST)and compared our results to those obtained using previous algorithms.The simulation showed that our method outperforms previous approaches in several ways,such as restoring the profile of extended sources and minimizing ringing artefacts.Additionally,because our method relies on the inherent advantages of least squares fitting,it is more versatile and does not depend on the local uniformity hypothesis for the PSF.However,the new method consumes much more computation than the other approaches.展开更多
Obtaining high precision is an important consideration for astrometric studies using images from the Narrow Angle Camera(NAC)of the Cassini Imaging Science Subsystem(ISS).Selecting the best centering algorithm is key ...Obtaining high precision is an important consideration for astrometric studies using images from the Narrow Angle Camera(NAC)of the Cassini Imaging Science Subsystem(ISS).Selecting the best centering algorithm is key to enhancing astrometric accuracy.In this study,we compared the accuracy of five centering algorithms:Gaussian fitting,the modified moments method,and three point-spread function(PSF)fitting methods(effective PSF(ePSF),PSFEx,and extended PSF(x PSF)from the Cassini Imaging Central Laboratory for Operations(CICLOPS)).We assessed these algorithms using 70 ISS NAC star field images taken with CL1 and CL2 filters across different stellar magnitudes.The ePSF method consistently demonstrated the highest accuracy,achieving precision below 0.03 pixels for stars of magnitude 8-9.Compared to the previously considered best,the modified moments method,the e PSF method improved overall accuracy by about 10%and 21%in the sample and line directions,respectively.Surprisingly,the xPSF model provided by CICLOPS had lower precision than the ePSF.Conversely,the ePSF exhibits an improvement in measurement precision of 23%and 17%in the sample and line directions,respectively,over the xPSF.This discrepancy might be attributed to the xPSF focusing on photometry rather than astrometry.These findings highlight the necessity of constructing PSF models specifically tailored for astrometric purposes in NAC images and provide guidance for enhancing astrometric measurements using these ISS NAC images.展开更多
The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small e...The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity.However,one of the most significant challenges lies in ultra-long-distance data transmission,particularly for the Magnetic and Helioseismic Imager(MHI),which is the most important payload and generates the largest volume of data in SPO.In this paper,we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data.The background out of the solar disk is removed to decrease the pixel number of an image under compression.Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation(space,spectrum,and polarization)in data set,improving the compression ratio.Experimental results demonstrate that our method achieves an average compression ratio of 3.67.The compression time is also less than the general observation period.The method exhibits strong feasibility and can be easily adapted to MHI.展开更多
The Mini-SiTian(MST)project is a pathfinder for China's next-generation large-scale time-domain survey,SiTian,aimed at discovering variable stars,transients,and explosive events.MST generates hundreds of thousands...The Mini-SiTian(MST)project is a pathfinder for China's next-generation large-scale time-domain survey,SiTian,aimed at discovering variable stars,transients,and explosive events.MST generates hundreds of thousands of transient alerts every night,approximately 99%of which are false alarms,posing a significant challenge to its scientific goals.To mitigate the impact of false positives,we propose a deep learning–based solution and systematically evaluate 13 convolutional neural networks.The results show that ResNet achieves exceptional specificity(99.70%),EfficientNet achieves the highest recall rate(98.68%),and DenseNet provides balanced performance with a recall rate of 94.55%and specificity of 98.66%.Leveraging these complementary strengths,we developed a bagging-based ensemble classifier that integrates ResNet18,DenseNet121,and EfficientNet_B0 using a soft voting strategy.This classifier achieved the best AUC value(0.9961)among all models,with a recall rate of95.37%and specificity of 99.25%.It has now been successfully deployed in the MST real-time data processing pipeline.Validation using 5000 practically processed samples with a classification threshold of 0.798 showed that the classifier achieved 88.31%accuracy,91.89%recall rate,and 99.82%specificity,confirming its effectiveness and robustness under real application conditions.展开更多
Lunar wrinkle ridges are an important stress geological structure on the Moon, which reflect the stress state and geological activity on the Moon. They provide important insights into the evolution of the Moon and are...Lunar wrinkle ridges are an important stress geological structure on the Moon, which reflect the stress state and geological activity on the Moon. They provide important insights into the evolution of the Moon and are key factors influencing future lunar activity, such as the choice of landing sites. However, automatic extraction of lunar wrinkle ridges is a challenging task due to their complex morphology and ambiguous features. Traditional manual extraction methods are time-consuming and labor-intensive. To achieve automated and detailed detection of lunar wrinkle ridges, we have constructed a lunar wrinkle ridge data set, incorporating previously unused aspect data to provide edge information, and proposed a Dual-Branch Ridge Detection Network(DBR-Net) based on deep learning technology. This method employs a dual-branch architecture and an Attention Complementary Feature Fusion module to address the issue of insufficient lunar wrinkle ridge features. Through comparisons with the results of various deep learning approaches, it is demonstrated that the proposed method exhibits superior detection performance. Furthermore, the trained model was applied to lunar mare regions, generating a distribution map of lunar mare wrinkle ridges;a significant linear relationship between the length and area of the lunar wrinkle ridges was obtained through statistical analysis, and six previously unrecorded potential lunar wrinkle ridges were detected. The proposed method upgrades the automated extraction of lunar wrinkle ridges to a pixel-level precision and verifies the effectiveness of DBR-Net in lunar wrinkle ridge detection.展开更多
The SiTian project,with its vast field of view,will become an ideal platform for scientific research on asteroids.In this study,we develop a pipeline to analyze the photometry of asteroids and derive their periods fro...The SiTian project,with its vast field of view,will become an ideal platform for scientific research on asteroids.In this study,we develop a pipeline to analyze the photometry of asteroids and derive their periods from the data collected by the SiTian pathfinder project Mini-SiTian(MST).The pipeline is applied to the MST f02 region,an MST test region with a sky area of 2°.29×1°.53.Rotation periods of 22 asteroids are derived by the obtained light curve analysis.Among them,there are eight asteroids available in the Asteroid Lightcurve Photometry Database(ALCDEF),and six of them with more photometric points(>200)that have similar period parameters as the ones in ALCDEF.Additionally,the periods for 14 of these asteroids are newly obtained and are not listed in ALCDEF.This study demonstrates the feasibility of asteroid photometric research by the SiTian project.It shows that future observations from the SiTian project will provide even more photometry of asteroids,significantly increasing the number of available light curves.The potential vast photometric data on asteroids will help us to further understand the physics of asteroids,their material composition,and the formation and evolution of the solar system.展开更多
BFOSC and YFOSC are the most frequently used instruments in the Xinglong 2.16 m telescope and Lijiang 2.4 m telescope,respectively.We developed a software package named“BYSpec”(BFOSC and YFOSC Spectra Reduction Pack...BFOSC and YFOSC are the most frequently used instruments in the Xinglong 2.16 m telescope and Lijiang 2.4 m telescope,respectively.We developed a software package named“BYSpec”(BFOSC and YFOSC Spectra Reduction Package)dedicated to automatically reducing the long-slit and echelle spectra obtained by these two instruments.The package supports bias and flat-fielding correction,order location,background subtraction,automatic wavelength calibration,and absolute flux calibration.The optimal extraction method maximizes the signal-to-noise ratio and removes most of the cosmic rays imprinted in the spectra.A comparison with the 1D spectra reduced with IRAF verifies the reliability of the results.This open-source software is publicly available to the community.展开更多
The Square Kilometre Array(SKA)has the potential to revolutionize astronomical research through its unparalleled precision.A critical aspect of SKA imaging is the computation of the UVW coordinates,which must be accur...The Square Kilometre Array(SKA)has the potential to revolutionize astronomical research through its unparalleled precision.A critical aspect of SKA imaging is the computation of the UVW coordinates,which must be accurate and reliable for the development of the SKA scientific data processor.Katpoint is the current method used to calculate UVW in Meer KAT.Using a pseudo-source,we employ a simple cross-product method to determine UVWs.In this study,we explore the applicability of Katpoint for SKA1-low and SKA1-mid and evaluate its precision.The conventional method,CALC/Omni UV,and Katpoint were quantitatively assessed through simulations.The results indicate that Katpoint exhibits substantial accuracy with MeerKAT compared to traditional techniques.However,its precision is slightly inadequate for the long baselines of SKA1.We improved the precision of Katpoint by identifying optimal offset values for pseudo-sources on the SKA1 telescope through simulation,finding a 0°.11 offset suitable for SKA1-Mid and a 0°.045 offset for SKA1-Low.Final result validations demonstrate that these adjustments render the computational accuracy fully comparable to the standard CALC/Omni UV method,which would meet the requirements of SKA high-precision imaging and offer a solution for high-precision imaging in radio interferometers.展开更多
As massive amounts of image data are generated by large-scale sky survey projects, the importance of research on the morphological classification of galaxy images is growing increasingly. Deep learning, with the capab...As massive amounts of image data are generated by large-scale sky survey projects, the importance of research on the morphological classification of galaxy images is growing increasingly. Deep learning, with the capability of automatic feature extraction, exhibits remarkable performance in image classification algorithms. In the past, most of the excellent algorithm models proposed by astronomers focused on the classification of major categories and often ignored the subtle differences between galaxy categories. For this purpose, based on the DenseNet-121model, this paper attempts to introduce a variety of improvement strategies such as dynamic multi-scale convolution, learnable grouped convolution, and the squeeze-and-excitation module to optimize the performance of the model. After numerous exhaustive experimental comparisons, the DenseNet-SE4 network with excellent performance is proposed. Subsequently, we conduct comparative experiments between this network and multiple advanced convolutional models on the data set consisting of Galaxy10 DECaLS and GZD-5. We select the data of eight galaxy categories with similar morphologies, such as round smooth galaxies and barred spiral galaxies, to comprehensively test the classification ability of the model. The experimental results illustrate that the DenseNetSE4 network achieves an accuracy of 88.96%, a precision of 89.00%, a recall rate of 89.44%, and an F1-score of 89.21% on the test set, thus it has reached the highest level among the eight comparison algorithms. Moreover, the model was tested on data within different redshift intervals, demonstrating good robustness. Finally, the visualization method was employed to further validate the effectiveness and rationality of the DenseNet-SE4 network.展开更多
In this paper,we introduce a new algorithm,fiDrizzle-MU,to coadd multiple exposures with multiplicative updates in each iteration instead of the difference correction terms of the preceding version.We find that multip...In this paper,we introduce a new algorithm,fiDrizzle-MU,to coadd multiple exposures with multiplicative updates in each iteration instead of the difference correction terms of the preceding version.We find that multiplicative update mechanisms demonstrate superior performance in decorrelating adjacent pixels compared to additive approaches,reducing noise complexity in the final stacked images.After applying fiDrizzle-MU to the JWST-NIRCam F277W band data,we obtain a comprehensive reconstruction of a potential gravitational lensing candidate substantially blurred by the JWST pipeline’s resampling process.展开更多
Stellar classification is a fundamental task in astronomical data analysis.Photometric data offer a significant advantage over spectral data in terms of data volume.Their lower acquisition cost and broader coverage ma...Stellar classification is a fundamental task in astronomical data analysis.Photometric data offer a significant advantage over spectral data in terms of data volume.Their lower acquisition cost and broader coverage make them more suitable for stellar classification applications.This study selects photometric data from the SDSS DR18.Instead of using traditional RGB image formats,a series of preprocessing steps were applied to generate five-channel Numpy files as the data set.To enhance stellar classification performance,we propose a deep learning model based on photometric feature fusion–Stellar Photometric Features Fusion Network.Additionally,we introduce the Dynamic Enhanced Stellar Squeeze-and-Excitation module,designed to optimize the weight allocation of different photometric bands in the classification task,and investigate the impact of each band's features on classification performance.Ultimately,we found that the information from the r and z bands played a more crucial role in the stellar classification task,achieving a final classification accuracy of 87.47%,thereby demonstrating the effectiveness of photometric data in stellar classification.展开更多
The 21 cm radiation of neutral hydrogen provides crucial information for studying the early universe and its evolution.To advance this research,countries have made significant investments in constructing large lowfreq...The 21 cm radiation of neutral hydrogen provides crucial information for studying the early universe and its evolution.To advance this research,countries have made significant investments in constructing large lowfrequency radio telescope arrays,such as the Low Frequency Array and the Square Kilometre Array Phase 1 Low Frequency.These instruments are pivotal for radio astronomy research.However,challenges such as ionospheric plasma interference,ambient radio noise,and instrument-related effects have become increasingly prominent,posing major obstacles in cosmology research.To address these issues,this paper proposes an efficient signal processing method that combines wavelet transform and mathematical morphology.The method involves the following steps:Background Subtraction:Background interference in radio observation signals is eliminated.Wavelet Transform:The signal,after removing background noise,undergoes a two-dimensional discrete wavelet transform.Threshold processing is then applied to the wavelet coefficients to effectively remove interference components.Wavelet Inversion:The processed signal is reconstructed using wavelet inversion.Mathematical Morphology:The reconstructed signal is further optimized using mathematical morphology to refine the results.Experimental verification was conducted using solar observation data from the Xinjiang Observatory and the Yunnan Observatory.The results demonstrate that this method successfully removes interference signals while preserving useful signals,thus improving the accuracy of radio astronomy observations and reducing the impact of radio frequency interference.展开更多
基金supported by the National Key R&D Program of China(2017YFF0205600)the International Research Cooperation Seed Fund of Beijing University of Technology(2018A08)+1 种基金Science and Technology Project of Beijing Municipal Commission of Transport(2018-kjc-01-213)the Construction of Service Capability of Scientific and Technological Innovation-Municipal Level of Fundamental Research Funds(Scientific Research Categories)of Beijing City(PXM2019_014204_500032).
文摘In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.
基金supported from the Strategic Pioneer Program of the Astronomy Large-Scale Scientific FacilityChinese Academy of Sciences and the Science and Education Integration Funding of University of Chinese Academy of Sciences+9 种基金the supports from the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)the supports from the Strategic Priority Research Program of the Chinese Academy of Sciences under grant No.XDB0550000the National Natural Science Foundation of China(NSFC,grant Nos.12422303 and12261141690)the supports from the NSFC(grant No.12403024)supports from the NSFC through grant Nos.11988101 and 11933004the Postdoctoral Fellowship Program of CPSF under grant No.GZB20240731the Young Data Scientist Project of the National Astronomical Data Centerthe China Post-doctoral Science Foundation(No.2023M743447)supports from the New Cornerstone Science Foundation through the New Cornerstone Investigator Program and the XPLORER PRIZE。
文摘This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert triggering and light curve generation for transient sources.By applying the STRIP pipeline to both simulated and real observational data of the Mini-Si Tian survey,it successfully identified various types of variable sources,including stellar flares,supernovae,variable stars,and asteroids,while meeting requirements of reduction speed within 5 minutes.For the real observational data set,the pipeline detected one flare event,127 variable stars,and14 asteroids from three monitored sky regions.Additionally,two data sets were generated:one,a real-bogus training data set comprising 218,818 training samples,and the other,a variable star light curve data set with 421instances.These data sets will be used to train machine learning algorithms,which are planned for future integration into STRIP.
基金funded by the National Natural Science Foundation of China(NSFC,Nos.12373086 and 12303082)CAS“Light of West China”Program+2 种基金Yunnan Revitalization Talent Support Program in Yunnan ProvinceNational Key R&D Program of ChinaGravitational Wave Detection Project No.2022YFC2203800。
文摘Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.12233012,the Strategic Priority Research Program of the Chinese Academy of Sciences,grant No.XDB0560102the National Key R&D Program of China 2022YFF0503003(2022YFF0503000)。
文摘The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatial resolution than expected.In this paper,we developed the SDI point-spread function(PSF)and Image Bivariate Optimization Algorithm(SPIBOA)to improve the quality of SDI images.The bivariate optimization method smartly combines deep learning with optical system modeling.Despite the lack of information about the real image taken by SDI and the optical system function,this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data.We use the estimated PSF to conduct deconvolution correction to observed SDI images,and the resulting images show that the spatial resolution after correction has increased by a factor of more than three with respect to the observed ones.Meanwhile,our method also significantly reduces the inherent noise in the observed SDI images.The SPIBOA has now been successfully integrated into the routine SDI data processing,providing important support for the scientific studies based on the data.The development and application of SPIBOA also paves new ways to identify astronomical telescope systems and enhance observational image quality.Some essential factors and precautions in applying the SPIBOA method are also discussed.
基金supported by the National Natural Science Foundation of China (NSFC, Grant No. U1731128)
文摘In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical observation data,and the target source classification information in vast photometric data has not been accurately measured.To address this,we propose a novel deep learning-based algorithm,YL8C4Net,for the automatic detection and classification of target sources in photometric images.This algorithm combines the YOLOv8 detection network with the Conv4Net classification network.Additionally,we propose a novel magnitude-based labeling method for target source annotation.In the performance evaluation,the YOLOv8 achieves impressive performance with average precision scores of 0.824 for AP@0.5 and 0.795 for AP@0.5:0.95.Meanwhile,the constructed Conv4Net attains an accuracy of 0.8895.Overall,YL8C4Net offers the advantages of fewer parameters,faster processing speed,and higher classification accuracy,making it particularly suitable for large-scale data processing tasks.Furthermore,we employed the YL8C4Net model to conduct target source detection and classification on photometric images from 20 sky regions in SDSS-DR17.As a result,a catalog containing about 9.39 million target source classification results has been preliminarily constructed,thereby providing valuable reference data for astronomical research.
基金financially supported by the National Key R&D Program of China(2022YFB3806300)。
文摘The China Space Station Telescope(CSST)is a 2 m three-mirror anastigmat equipped with a Fast Steering Mirror(FSM),which is part of its precision image stabilization system.The FSM is used to compensate for residuals from the previous stage of the image stabilization system.However,a new type of image stabilization residual caused by image rotation and projection distortion is introduced when the FSM performs tip-tilt adjustments,reducing both the image stabilization accuracy and the absolute pointing accuracy of the CSST.In this paper,we propose a scheme to compute the image stabilization residuals across the full field of view(FOV)by using a reference star as the target for stabilization control,which can be utilized for subsequent image position correction.To achieve this,we developed a linear optical model for image point displacement by simplifying an existing image point displacement model and incorporating more readily available parameters.The computational accuracy of the new model is equivalent to that of the original,with computational differences of less than 0.03μm.Based on this linear model,we established a calculation model for image stabilization residuals,including those due to image rotation and projection distortion caused by FSM tip-tilt adjustments.This model provides a theoretical foundation for quantifying such residuals during the image stabilization process.Finally,the results of testing using this scheme are provided.Experimental results demonstrate that within the observation FOV of the CSST,when the FSM tilts by(1″,1″),the maximum absolute value of the image stabilization residuals accounts for 20%of the total image stabilization accuracy requirement.This finding underscores the necessity of computing and correcting these residuals to meet performance requirements.
基金supported by the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)+3 种基金the National Science Foundation of China 12422303,12403024,12222301,12173007,and 12261141690the Postdoctoral Fellowship Program of CPSF under grant Number GZB20240731the Young Data Scientist Project of the National Astronomical Data Center,and the China Post-doctoral Science Foundation No.2023M743447support from the NSFC through grant No.12303039 and No.12261141690.
文摘As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the precise data processing pipeline designed for wide-field,CMOS-based devices,including the removal of instrumental effects,astrometry,photometry,and flux calibration.When applying this pipeline to approximately3000 observations taken in the Field 02(f02)region by MST,the results demonstrate a remarkable astrometric precision of approximately 70–80 mas(about 0.1 pixel),an impressive calibration accuracy of approximately1 mmag in the MST zero points,and a photometric accuracy of about 4 mmag for bright stars.Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs,highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys,like the SiTian project.
文摘The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.
基金supported by the GHfund A(202302017475)supported by the Foundation for Distinguished Young Scholars of Jiangsu Province(No.BK20140050)+5 种基金the National Natural Science Foundation of China(Nos.11973070,11333008,11273061,11825303,and 11673065)the China Manned Space Project with No.CMS-CSST-2021-A01,CMSCSST-2021-A03,CMS-CSST-2021-B01the Joint Funds of the National Natural Science Foundation of China(No.U1931210)the support from Key Research Program of Frontier Sciences,CAS,grant No.ZDBS-LY-7013Program of Shanghai Academic/Technology Research Leaderthe support from the science research grants from the China Manned Space Project with CMS-CSST-2021-A04,CMS-CSST-2021-A07。
文摘We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function(PSF)deconvolution,resulting in enhanced restoration of extended sources,the highest peak signal-to-noise ratio,and reduced ringing artefacts.To test our method,we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/the VLT Survey Telescope(VST)and compared our results to those obtained using previous algorithms.The simulation showed that our method outperforms previous approaches in several ways,such as restoring the profile of extended sources and minimizing ringing artefacts.Additionally,because our method relies on the inherent advantages of least squares fitting,it is more versatile and does not depend on the local uniformity hypothesis for the PSF.However,the new method consumes much more computation than the other approaches.
基金supported by the National Natural Science Foundation of China(No.12373073,U2031104,No.12173015)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011340)。
文摘Obtaining high precision is an important consideration for astrometric studies using images from the Narrow Angle Camera(NAC)of the Cassini Imaging Science Subsystem(ISS).Selecting the best centering algorithm is key to enhancing astrometric accuracy.In this study,we compared the accuracy of five centering algorithms:Gaussian fitting,the modified moments method,and three point-spread function(PSF)fitting methods(effective PSF(ePSF),PSFEx,and extended PSF(x PSF)from the Cassini Imaging Central Laboratory for Operations(CICLOPS)).We assessed these algorithms using 70 ISS NAC star field images taken with CL1 and CL2 filters across different stellar magnitudes.The ePSF method consistently demonstrated the highest accuracy,achieving precision below 0.03 pixels for stars of magnitude 8-9.Compared to the previously considered best,the modified moments method,the e PSF method improved overall accuracy by about 10%and 21%in the sample and line directions,respectively.Surprisingly,the xPSF model provided by CICLOPS had lower precision than the ePSF.Conversely,the ePSF exhibits an improvement in measurement precision of 23%and 17%in the sample and line directions,respectively,over the xPSF.This discrepancy might be attributed to the xPSF focusing on photometry rather than astrometry.These findings highlight the necessity of constructing PSF models specifically tailored for astrometric purposes in NAC images and provide guidance for enhancing astrometric measurements using these ISS NAC images.
基金supported by the National Key R&D Program of China(grant No.2022YFF0503800)by the National Natural Science Foundation of China(NSFC)(grant No.11427901)+1 种基金by the Strategic Priority Research Program of the Chinese Academy of Sciences(CAS-SPP)(grant No.XDA15320102)by the Youth Innovation Promotion Association(CAS No.2022057)。
文摘The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity.However,one of the most significant challenges lies in ultra-long-distance data transmission,particularly for the Magnetic and Helioseismic Imager(MHI),which is the most important payload and generates the largest volume of data in SPO.In this paper,we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data.The background out of the solar disk is removed to decrease the pixel number of an image under compression.Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation(space,spectrum,and polarization)in data set,improving the compression ratio.Experimental results demonstrate that our method achieves an average compression ratio of 3.67.The compression time is also less than the general observation period.The method exhibits strong feasibility and can be easily adapted to MHI.
基金supported by the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)the National Natural Science Foundation of China under grant Nos.12273076,12133001,12422303 and12261141690。
文摘The Mini-SiTian(MST)project is a pathfinder for China's next-generation large-scale time-domain survey,SiTian,aimed at discovering variable stars,transients,and explosive events.MST generates hundreds of thousands of transient alerts every night,approximately 99%of which are false alarms,posing a significant challenge to its scientific goals.To mitigate the impact of false positives,we propose a deep learning–based solution and systematically evaluate 13 convolutional neural networks.The results show that ResNet achieves exceptional specificity(99.70%),EfficientNet achieves the highest recall rate(98.68%),and DenseNet provides balanced performance with a recall rate of 94.55%and specificity of 98.66%.Leveraging these complementary strengths,we developed a bagging-based ensemble classifier that integrates ResNet18,DenseNet121,and EfficientNet_B0 using a soft voting strategy.This classifier achieved the best AUC value(0.9961)among all models,with a recall rate of95.37%and specificity of 99.25%.It has now been successfully deployed in the MST real-time data processing pipeline.Validation using 5000 practically processed samples with a classification threshold of 0.798 showed that the classifier achieved 88.31%accuracy,91.89%recall rate,and 99.82%specificity,confirming its effectiveness and robustness under real application conditions.
文摘Lunar wrinkle ridges are an important stress geological structure on the Moon, which reflect the stress state and geological activity on the Moon. They provide important insights into the evolution of the Moon and are key factors influencing future lunar activity, such as the choice of landing sites. However, automatic extraction of lunar wrinkle ridges is a challenging task due to their complex morphology and ambiguous features. Traditional manual extraction methods are time-consuming and labor-intensive. To achieve automated and detailed detection of lunar wrinkle ridges, we have constructed a lunar wrinkle ridge data set, incorporating previously unused aspect data to provide edge information, and proposed a Dual-Branch Ridge Detection Network(DBR-Net) based on deep learning technology. This method employs a dual-branch architecture and an Attention Complementary Feature Fusion module to address the issue of insufficient lunar wrinkle ridge features. Through comparisons with the results of various deep learning approaches, it is demonstrated that the proposed method exhibits superior detection performance. Furthermore, the trained model was applied to lunar mare regions, generating a distribution map of lunar mare wrinkle ridges;a significant linear relationship between the length and area of the lunar wrinkle ridges was obtained through statistical analysis, and six previously unrecorded potential lunar wrinkle ridges were detected. The proposed method upgrades the automated extraction of lunar wrinkle ridges to a pixel-level precision and verifies the effectiveness of DBR-Net in lunar wrinkle ridge detection.
基金supports from the National Natural Science Foundation of China(NSFC,grant Nos.12203002 and 11973015)supports from the National Key Basic R&D Program of China via 2023YFA1608303 and the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)+6 种基金supports from the National Natural Science Foundation of China(NSFCgrant No.12373015)supports from the National Natural Science Foundation of China(NSFCgrant Nos.12120101003 and 12373010)National Key R&D Program of China(grant Nos.2023YFA1607800,2023YFA1607804,2022YFA1602902)Beijing Municipal Natural Science Foundation(grant No.1222028)Strategic Priority Research Program of the Chinese Academy of Science(grant Nos.XDB0550100 and XDB0550000).
文摘The SiTian project,with its vast field of view,will become an ideal platform for scientific research on asteroids.In this study,we develop a pipeline to analyze the photometry of asteroids and derive their periods from the data collected by the SiTian pathfinder project Mini-SiTian(MST).The pipeline is applied to the MST f02 region,an MST test region with a sky area of 2°.29×1°.53.Rotation periods of 22 asteroids are derived by the obtained light curve analysis.Among them,there are eight asteroids available in the Asteroid Lightcurve Photometry Database(ALCDEF),and six of them with more photometric points(>200)that have similar period parameters as the ones in ALCDEF.Additionally,the periods for 14 of these asteroids are newly obtained and are not listed in ALCDEF.This study demonstrates the feasibility of asteroid photometric research by the SiTian project.It shows that future observations from the SiTian project will provide even more photometry of asteroids,significantly increasing the number of available light curves.The potential vast photometric data on asteroids will help us to further understand the physics of asteroids,their material composition,and the formation and evolution of the solar system.
基金supported by the National Natural Science Foundation of China under grant No.U2031144partially supported by the Open Project Program of the Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciences+5 种基金supported by the National Key R&D Program of China with No.2021YFA1600404the National Natural Science Foundation of China(12173082)the Yunnan Fundamental Research Projects(grant 202201AT070069)the Top-notch Young Talents Program of Yunnan Provincethe Light of West China Program provided by the Chinese Academy of Sciencesthe International Centre of Supernovae,Yunnan Key Laboratory(No.202302AN360001)。
文摘BFOSC and YFOSC are the most frequently used instruments in the Xinglong 2.16 m telescope and Lijiang 2.4 m telescope,respectively.We developed a software package named“BYSpec”(BFOSC and YFOSC Spectra Reduction Package)dedicated to automatically reducing the long-slit and echelle spectra obtained by these two instruments.The package supports bias and flat-fielding correction,order location,background subtraction,automatic wavelength calibration,and absolute flux calibration.The optimal extraction method maximizes the signal-to-noise ratio and removes most of the cosmic rays imprinted in the spectra.A comparison with the 1D spectra reduced with IRAF verifies the reliability of the results.This open-source software is publicly available to the community.
基金supported by the China National SKA Programme(2020SKA0110300)the National Natural Science Foundation of China(NSFC,Grant Nos.12433012 and 12373097)the Guangzhou Science and Technology Funds(2023A03J0016)。
文摘The Square Kilometre Array(SKA)has the potential to revolutionize astronomical research through its unparalleled precision.A critical aspect of SKA imaging is the computation of the UVW coordinates,which must be accurate and reliable for the development of the SKA scientific data processor.Katpoint is the current method used to calculate UVW in Meer KAT.Using a pseudo-source,we employ a simple cross-product method to determine UVWs.In this study,we explore the applicability of Katpoint for SKA1-low and SKA1-mid and evaluate its precision.The conventional method,CALC/Omni UV,and Katpoint were quantitatively assessed through simulations.The results indicate that Katpoint exhibits substantial accuracy with MeerKAT compared to traditional techniques.However,its precision is slightly inadequate for the long baselines of SKA1.We improved the precision of Katpoint by identifying optimal offset values for pseudo-sources on the SKA1 telescope through simulation,finding a 0°.11 offset suitable for SKA1-Mid and a 0°.045 offset for SKA1-Low.Final result validations demonstrate that these adjustments render the computational accuracy fully comparable to the standard CALC/Omni UV method,which would meet the requirements of SKA high-precision imaging and offer a solution for high-precision imaging in radio interferometers.
基金supported by the National Natural Science Foundation of China(NSFC,grant No.U1731128).
文摘As massive amounts of image data are generated by large-scale sky survey projects, the importance of research on the morphological classification of galaxy images is growing increasingly. Deep learning, with the capability of automatic feature extraction, exhibits remarkable performance in image classification algorithms. In the past, most of the excellent algorithm models proposed by astronomers focused on the classification of major categories and often ignored the subtle differences between galaxy categories. For this purpose, based on the DenseNet-121model, this paper attempts to introduce a variety of improvement strategies such as dynamic multi-scale convolution, learnable grouped convolution, and the squeeze-and-excitation module to optimize the performance of the model. After numerous exhaustive experimental comparisons, the DenseNet-SE4 network with excellent performance is proposed. Subsequently, we conduct comparative experiments between this network and multiple advanced convolutional models on the data set consisting of Galaxy10 DECaLS and GZD-5. We select the data of eight galaxy categories with similar morphologies, such as round smooth galaxies and barred spiral galaxies, to comprehensively test the classification ability of the model. The experimental results illustrate that the DenseNetSE4 network achieves an accuracy of 88.96%, a precision of 89.00%, a recall rate of 89.44%, and an F1-score of 89.21% on the test set, thus it has reached the highest level among the eight comparison algorithms. Moreover, the model was tested on data within different redshift intervals, demonstrating good robustness. Finally, the visualization method was employed to further validate the effectiveness and rationality of the DenseNet-SE4 network.
基金supported by the Foundation for Distinguished Young Scholars of Jiangsu Province(No.BK20140050)the National Key R&D Program of China(No.2022YFF0503403)+7 种基金the National Natural Science Foundation of China(Nos.11973070,11988101,11333008,11273061,11825303,and 11673065)the China Manned Space Project(Nos.CMS-CSST-2021-A01,CMS-CSST-2021-A03,and CMS-CSST-2021-B01)the Joint Funds of the National Natural Science Foundation of China(No.U1931210).H.Y.S.acknowledges the support from the Key Research Program of Frontier Sciences,CAS,grant No.ZDBS-LY-7013Program of Shanghai Academic/Technology Research Leader.R.L.acknowledges the science research grants from the China Manned Space Project,CAS Project for Young Scientists in Basic Research(No.YSBR-062)the support from K.C.Wong Education Foundationsupport from the science research grants from the China Manned Space Project with CMS-CSST-2021-A04 and CMS-CSST-2021-A07supported by the GHfund A(202302017475).
文摘In this paper,we introduce a new algorithm,fiDrizzle-MU,to coadd multiple exposures with multiplicative updates in each iteration instead of the difference correction terms of the preceding version.We find that multiplicative update mechanisms demonstrate superior performance in decorrelating adjacent pixels compared to additive approaches,reducing noise complexity in the final stacked images.After applying fiDrizzle-MU to the JWST-NIRCam F277W band data,we obtain a comprehensive reconstruction of a potential gravitational lensing candidate substantially blurred by the JWST pipeline’s resampling process.
文摘Stellar classification is a fundamental task in astronomical data analysis.Photometric data offer a significant advantage over spectral data in terms of data volume.Their lower acquisition cost and broader coverage make them more suitable for stellar classification applications.This study selects photometric data from the SDSS DR18.Instead of using traditional RGB image formats,a series of preprocessing steps were applied to generate five-channel Numpy files as the data set.To enhance stellar classification performance,we propose a deep learning model based on photometric feature fusion–Stellar Photometric Features Fusion Network.Additionally,we introduce the Dynamic Enhanced Stellar Squeeze-and-Excitation module,designed to optimize the weight allocation of different photometric bands in the classification task,and investigate the impact of each band's features on classification performance.Ultimately,we found that the information from the r and z bands played a more crucial role in the stellar classification task,achieving a final classification accuracy of 87.47%,thereby demonstrating the effectiveness of photometric data in stellar classification.
基金funded by the National Key Research and Development Program’s intergovernmental International Science and Technology Innovation Cooperation project,titled Remote Sensing and Radio Astronomy Observation of Space Weather in Low and Middle Latitudes(project number:2022YFE0140000)Supported by International Partnership Program of Chinese Academy of Sciences,grant No.114A11KYSB20200001。
文摘The 21 cm radiation of neutral hydrogen provides crucial information for studying the early universe and its evolution.To advance this research,countries have made significant investments in constructing large lowfrequency radio telescope arrays,such as the Low Frequency Array and the Square Kilometre Array Phase 1 Low Frequency.These instruments are pivotal for radio astronomy research.However,challenges such as ionospheric plasma interference,ambient radio noise,and instrument-related effects have become increasingly prominent,posing major obstacles in cosmology research.To address these issues,this paper proposes an efficient signal processing method that combines wavelet transform and mathematical morphology.The method involves the following steps:Background Subtraction:Background interference in radio observation signals is eliminated.Wavelet Transform:The signal,after removing background noise,undergoes a two-dimensional discrete wavelet transform.Threshold processing is then applied to the wavelet coefficients to effectively remove interference components.Wavelet Inversion:The processed signal is reconstructed using wavelet inversion.Mathematical Morphology:The reconstructed signal is further optimized using mathematical morphology to refine the results.Experimental verification was conducted using solar observation data from the Xinjiang Observatory and the Yunnan Observatory.The results demonstrate that this method successfully removes interference signals while preserving useful signals,thus improving the accuracy of radio astronomy observations and reducing the impact of radio frequency interference.
