In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data be...In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.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
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
Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanal...Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanalysis database(ERA5)is used.Seeing calculated from ERA5 is compared consistently with the Differential Image Motion Monitor seeing at the height of 12 m.Results show that seeing decays exponentially with height at the Muztagh-Ata site.Seeing decays the fastest in fall in 2021 and most slowly with height in summer.The seeing condition is better in fall than in summer.The median value of seeing at 12 m is 0.89 arcsec,the maximum value is1.21 arcsec in August and the minimum is 0.66 arcsec in October.The median value of seeing at 12 m is 0.72arcsec in the nighttime and 1.08 arcsec in the daytime.Seeing is a combination of annual and about biannual variations with the same phase as temperature and wind speed indicating that seeing variation with time is influenced by temperature and wind speed.The Richardson number Ri is used to analyze the atmospheric stability and the variations of seeing are consistent with Ri between layers.These quantitative results can provide an important reference for a telescopic observation strategy.展开更多
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
Stellar classification and radius estimation are crucial for understanding the structure of the Universe and stella evolution.With the advent of the era of astronomical big data,multimodal data are available and theor...Stellar classification and radius estimation are crucial for understanding the structure of the Universe and stella evolution.With the advent of the era of astronomical big data,multimodal data are available and theoretically effective for stellar classification and radius estimation.A problem is how to improve the performance of this task by jointly using the multimodal data.However,existing research primarily focuses on using single-modal data.To this end,this paper proposes a model,Multi-Modal SCNet,and its ensemble model Multimodal Ensemble fo Stellar Classification and Regression(MESCR)for improving stellar classification and radius estimation performance by fusing two modality data.In this problem,a typical phenomenon is that the sample numbers o some types of stars are evidently more than others.This imbalance has negative effects on model performance Therefore,this work utilizes a weighted sampling strategy to deal with the imbalance issues in MESCR.Som evaluation experiments are conducted on a test set for MESCR and the classification accuracy is 96.1%,and th radius estimation performance Mean of Absolute Error andσare 0.084 dex and 0.149 R_(⊙),respectively.Moreover we assessed the uncertainty of model predictions,confirming good consistency within a reasonable deviation range.Finally,we applied our model to 50,871,534 SDSS stars without spectra and published a new catalog.展开更多
The karst mountainous area is an ecologically fragile region with prominent humanland contradictions.The resource-environment carrying capacity(RECC)of this region needs to be further clarified.The development of remo...The karst mountainous area is an ecologically fragile region with prominent humanland contradictions.The resource-environment carrying capacity(RECC)of this region needs to be further clarified.The development of remote sensing(RS)and geographic information system(GIS)provides data sources and processing platform for RECC monitoring.This study analyzed and established the evaluation index system of RECC by considering particularity in the karst mountainous area of Southwest China;processed multisource RS data(Sentinel-2,Aster-DEM and Landsat-8)to extract the spatial distributions of nine key indexes by GIS techniques(information classification,overlay analysis and raster calculation);proposed the methods of index integration and fuzzy comprehensive evaluation of the RECC by GIS;and took a typical area,Guangnan County in Yunnan Province of China,as an experimental area to explore the effectiveness of the indexes and methods.The results showed that:(1)The important indexes affecting the RECC of karst mountainous area are water resources,tourism resources,position resources,geographical environment and soil erosion environment.(2)Data on cultivated land,construction land,minerals,transportation,water conservancy,ecosystem services,topography,soil erosion and rocky desertification can be obtained from RS data.GIS techniques integrate the information into the RECC results.The data extraction and processing methods are feasible on evaluating RECC.(3)The RECC of Guangnan County was in the mid-carrying level in 2018.The midcarrying and low-carrying levels were the main types,accounting for more than 80.00%of the total study area.The areas with high carrying capacity were mainly distributed in the northern regions of the northwest-southeast line of the county,and other areas have a low carrying capacity comparatively.The coordination between regional resource-environment status and socioeconomic development is the key to improve RECC.This study explores the evaluation index system of RECC in karst mountainous area and the application of multisource RS data and GIS techniques in the comprehensive evaluation.The methods can be applied in related fields to provide suggestions for data/information extraction and integration,and sustainable development.展开更多
Space debris poses a serious threat to human space activities and needs to be measured and cataloged. As a new technology for space target surveillance, the measurement accuracy of diffuse reflection laser ranging （D...Space debris poses a serious threat to human space activities and needs to be measured and cataloged. As a new technology for space target surveillance, the measurement accuracy of diffuse reflection laser ranging （DRLR） is much higher than that of microwave radar and optoelectronic measurement. Based on the laser ranging data of space debris from the DRLR system at Shanghai Astronomical Observatory acquired in March-April, 2013, the characteristics and precision of the laser ranging data are analyzed and their applications in orbit determination of space debris are discussed, which is implemented for the first time in China. The experiment indicates that the precision of laser ranging data can reach 39 cm-228 cm. When the data are sufficient enough （four arcs measured over three days）, the orbital accuracy of space debris can be up to 50 m.展开更多
To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRD...To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRDP can perform operations such as baseband data unpacking,channel separation,coherent dedispersion,Stokes detection,phase and folding period prediction,and folding integration in GPU clusters.We tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes,and the J0332+5434 pulsar baseband data generated by the self-developed backend of the Nan Shan Radio Telescope.We obtained the pulse profiles of each baseband data.Through experimental analysis,we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy(DSPSR),which verified the effectiveness of the PSRDP algorithm.Furthermore,using the same baseband data,we compared the processing speed of PSRDP with DSPSR,and the results showed that PSRDP was not slower than DSPSR in terms of speed.The theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT(Qi Tai radio Telescope)ultra-wide bandwidth pulsar baseband data.展开更多
Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in ...Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in the received VLF signal.This study uses the VLF signal received in Qingdao City,Shandong Province,from the Russian Alpha navigation system to explore the multimode interference problem of VLF signal propagation.The characteristics of the effect of multimode interference phenomena on the phase are analyzed according to the variation of the phase of the VLF signal.However,the phase of VLF signals will also be affected by the X-ray and energetic particles that are released during the eruption of solar flares,therefore the two phenomena are studied in this work.It is concluded that the X-ray will not affect the phase of VLF signals at night,but the energetic particles will affect the phase change,and the influence of energetic particles should be excluded in the study of multimode interference phenomena.Using VLF signals for navigation positioning in degraded or unavailable GPS conditions is of great practical significance for VLF navigation systems as it can avoid the influence of multimode interference and improve positioning accuracy.展开更多
The paper considers the theoretical basics and the specific mathematical techniques having been developed for solving the tasks of the stochastic data analysis within the Rice statistical model in which the output sig...The paper considers the theoretical basics and the specific mathematical techniques having been developed for solving the tasks of the stochastic data analysis within the Rice statistical model in which the output signal’s amplitude is composed as a sum of the sought-for initial value and a random Gaussian noise. The Rician signal’s characteristics such as the average value and the noise dispersion have been shown to depend upon the Rice distribution’s parameters nonlinearly what has become a prerequisite for the development of a new approach to the stochastic Rician data analysis implying the joint signal and noise accurate evaluation. The joint computing of the Rice distribution’s parameters allows efficient reconstruction of the signal’s in-formative component against the noise background. A meaningful advantage of the proposed approach consists in the absence of restrictions connected with any a priori suppositions inherent to the traditional techniques. The results of the numerical experiments are provided confirming the efficiency of the elaborated approach to stochastic data analysis within the Rice statistical model.展开更多
Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,a...Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint changes.These limitations result in delayed diagnoses and inconsistent outcomes.Additionally,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and biomarkers.Recent advancements in artificial intelligence(AI)offer transformative potential to address these challenges.This review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics integration.By consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA research.The findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.展开更多
In this editorial,we comment on the recent article by Fei et al exploring the field of near-infrared spectroscopy(NIRS)research in schizophrenia from a bibliometrics perspective.In recent years,NIRS has shown unique a...In this editorial,we comment on the recent article by Fei et al exploring the field of near-infrared spectroscopy(NIRS)research in schizophrenia from a bibliometrics perspective.In recent years,NIRS has shown unique advantages in the auxiliary diagnosis of schizophrenia,and the introduction of bibliometrics has provided a macro perspective for research in this field.Despite the opportunities brought about by these technological developments,remaining challenges require multidi-sciplinary approach to devise a reliable and accurate diagnosis system for schizo-phrenia.Nonetheless,NIRS-assisted technology is expected to contribute to the division of methods for early intervention and treatment of schizophrenia.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
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
Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and su...Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.展开更多
Slitless spectroscopy onboard space telescopes is a powerful tool to detect emission-line objects such as emissionline galaxies(ELGs)and quasars.In this work,we present a study of ELGs observed with slitless spectrosc...Slitless spectroscopy onboard space telescopes is a powerful tool to detect emission-line objects such as emissionline galaxies(ELGs)and quasars.In this work,we present a study of ELGs observed with slitless spectroscopy by the Hubble Space Telescope(HST)in a deep field of~44 arcmin^(2).This is one of the deepest HST fields with a wealth of imaging and spectral data.In particular,previous VLT/MUSE observations have covered this field and identified a large number of ELGs.We reduce the HST spectra using the latest pipeline with a forward modeling algorithm and construct a sample of ELGs.By comparing with the MUSE spectra,we characterize our ELG detection in the HST spectra,including the impact of the line flux,line width,signal-to-noise ratio,etc.We find that the morphological broadening may affect the detection of ELGs,such that more compact sources are easier to be detected in slitless spectra.We discuss its implications to future slitless spectroscopic surveys that will be carried out by the China Space Station Telescope(CSST)and find that the CSST slitless spectroscopy has a capability comparable to that of HST in terms of the detection of emission lines.展开更多
Astronomical spectra are vital for deriving stellar properties,yet low signal-to-noise ratio(SNR)spectra often obscure key features,complicating accurate analysis.This study presents spec-Diffusion Probabilistic Model...Astronomical spectra are vital for deriving stellar properties,yet low signal-to-noise ratio(SNR)spectra often obscure key features,complicating accurate analysis.This study presents spec-Diffusion Probabilistic Models(DDPM),a novel deep learning approach based on DDPM,aimed at denoising low SNR spectra to improve stellar parameter estimation.Leveraging the LAMOST DR10 data set,we developed spec-DDPM using a tailored U-Net architecture(spec-Unet)to iteratively predict and remove noise.The model was trained on 28,500 low and high SNR spectral pairs and benchmarked against conventional methods,including Principal Component Analysis,wavelet techniques,and a modified DnCNN model.The spec-DDPM demonstrated superior performance,with reduced Mean Absolute Error,elevated Structural Similarity Index Measure,and enhanced spectral loss metrics.It effectively preserved critical spectral features and corrected continuum distortions.Validation experiments further confirmed its ability to improve stellar parameter estimation with reduced errors.These results underscore spec-DDPM’s potential to elevate spectral data quality,offering applications in restoring defective spectra and refining large-scale astronomical surveys.This work highlights the transformative role of deep learning in astronomical data processing.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
基金supported in part by the National Natural Science Foundation of China(62125306)Zhejiang Key Research and Development Project(2024C01163)the State Key Laboratory of Industrial Control Technology,China(ICT2024A06)
文摘In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.
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funded by the National Natural Science Foundation of China(NSFC)the Chinese Academy of Sciences(CAS)(grant No.U2031209)the National Natural Science Foundation of China(NSFC,grant Nos.11872128,42174192,and 91952111)。
文摘Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanalysis database(ERA5)is used.Seeing calculated from ERA5 is compared consistently with the Differential Image Motion Monitor seeing at the height of 12 m.Results show that seeing decays exponentially with height at the Muztagh-Ata site.Seeing decays the fastest in fall in 2021 and most slowly with height in summer.The seeing condition is better in fall than in summer.The median value of seeing at 12 m is 0.89 arcsec,the maximum value is1.21 arcsec in August and the minimum is 0.66 arcsec in October.The median value of seeing at 12 m is 0.72arcsec in the nighttime and 1.08 arcsec in the daytime.Seeing is a combination of annual and about biannual variations with the same phase as temperature and wind speed indicating that seeing variation with time is influenced by temperature and wind speed.The Richardson number Ri is used to analyze the atmospheric stability and the variations of seeing are consistent with Ri between layers.These quantitative results can provide an important reference for a telescopic observation strategy.
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 by the National Natural Science Foundation of China(12261141689,12273075,and 12373108)the National Key R&D Program of China No.2019YFA0405502the science research grants from the China Manned Space Project with No.CMS-CSST-2021-B05。
文摘Stellar classification and radius estimation are crucial for understanding the structure of the Universe and stella evolution.With the advent of the era of astronomical big data,multimodal data are available and theoretically effective for stellar classification and radius estimation.A problem is how to improve the performance of this task by jointly using the multimodal data.However,existing research primarily focuses on using single-modal data.To this end,this paper proposes a model,Multi-Modal SCNet,and its ensemble model Multimodal Ensemble fo Stellar Classification and Regression(MESCR)for improving stellar classification and radius estimation performance by fusing two modality data.In this problem,a typical phenomenon is that the sample numbers o some types of stars are evidently more than others.This imbalance has negative effects on model performance Therefore,this work utilizes a weighted sampling strategy to deal with the imbalance issues in MESCR.Som evaluation experiments are conducted on a test set for MESCR and the classification accuracy is 96.1%,and th radius estimation performance Mean of Absolute Error andσare 0.084 dex and 0.149 R_(⊙),respectively.Moreover we assessed the uncertainty of model predictions,confirming good consistency within a reasonable deviation range.Finally,we applied our model to 50,871,534 SDSS stars without spectra and published a new catalog.
基金the support given by the government and official in Guangnan Countyfunded by[National Natural Science Foundation of China]grant number[41361020,40961031]+3 种基金[Joint Fund of Yunnan Provincial Science and Technology Department and Yunnan University]grant number[2018FY001(-017)][Project of Innovative Talents Cultivation for Graduate Students of Yunnan University]grant number[C176230200][Project of Internationalization and Cultural Inheritance and Innovation of Yunnan University]grant number[C176250202][Science Research Fund of Yunnan Provincial Education Department in 2020:Postgraduate]grant number[2020Y0030]。
文摘The karst mountainous area is an ecologically fragile region with prominent humanland contradictions.The resource-environment carrying capacity(RECC)of this region needs to be further clarified.The development of remote sensing(RS)and geographic information system(GIS)provides data sources and processing platform for RECC monitoring.This study analyzed and established the evaluation index system of RECC by considering particularity in the karst mountainous area of Southwest China;processed multisource RS data(Sentinel-2,Aster-DEM and Landsat-8)to extract the spatial distributions of nine key indexes by GIS techniques(information classification,overlay analysis and raster calculation);proposed the methods of index integration and fuzzy comprehensive evaluation of the RECC by GIS;and took a typical area,Guangnan County in Yunnan Province of China,as an experimental area to explore the effectiveness of the indexes and methods.The results showed that:(1)The important indexes affecting the RECC of karst mountainous area are water resources,tourism resources,position resources,geographical environment and soil erosion environment.(2)Data on cultivated land,construction land,minerals,transportation,water conservancy,ecosystem services,topography,soil erosion and rocky desertification can be obtained from RS data.GIS techniques integrate the information into the RECC results.The data extraction and processing methods are feasible on evaluating RECC.(3)The RECC of Guangnan County was in the mid-carrying level in 2018.The midcarrying and low-carrying levels were the main types,accounting for more than 80.00%of the total study area.The areas with high carrying capacity were mainly distributed in the northern regions of the northwest-southeast line of the county,and other areas have a low carrying capacity comparatively.The coordination between regional resource-environment status and socioeconomic development is the key to improve RECC.This study explores the evaluation index system of RECC in karst mountainous area and the application of multisource RS data and GIS techniques in the comprehensive evaluation.The methods can be applied in related fields to provide suggestions for data/information extraction and integration,and sustainable development.
基金Supported by the National Natural Science Foundation of China
文摘Space debris poses a serious threat to human space activities and needs to be measured and cataloged. As a new technology for space target surveillance, the measurement accuracy of diffuse reflection laser ranging （DRLR） is much higher than that of microwave radar and optoelectronic measurement. Based on the laser ranging data of space debris from the DRLR system at Shanghai Astronomical Observatory acquired in March-April, 2013, the characteristics and precision of the laser ranging data are analyzed and their applications in orbit determination of space debris are discussed, which is implemented for the first time in China. The experiment indicates that the precision of laser ranging data can reach 39 cm-228 cm. When the data are sufficient enough （four arcs measured over three days）, the orbital accuracy of space debris can be up to 50 m.
基金supported by the National Key R&D Program of China Nos.2021YFC2203502 and 2022YFF0711502the National Natural Science Foundation of China(NSFC)(12173077 and 12003062)+5 种基金the Tianshan Innovation Team Plan of Xinjiang Uygur Autonomous Region(2022D14020)the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095)the Scientific Instrument Developing Project of the Chinese Academy of Sciences,grant No.PTYQ2022YZZD01China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRDP can perform operations such as baseband data unpacking,channel separation,coherent dedispersion,Stokes detection,phase and folding period prediction,and folding integration in GPU clusters.We tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes,and the J0332+5434 pulsar baseband data generated by the self-developed backend of the Nan Shan Radio Telescope.We obtained the pulse profiles of each baseband data.Through experimental analysis,we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy(DSPSR),which verified the effectiveness of the PSRDP algorithm.Furthermore,using the same baseband data,we compared the processing speed of PSRDP with DSPSR,and the results showed that PSRDP was not slower than DSPSR in terms of speed.The theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT(Qi Tai radio Telescope)ultra-wide bandwidth pulsar baseband data.
基金supported by the National Natural Science Foundation of China(U1704134)。
文摘Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in the received VLF signal.This study uses the VLF signal received in Qingdao City,Shandong Province,from the Russian Alpha navigation system to explore the multimode interference problem of VLF signal propagation.The characteristics of the effect of multimode interference phenomena on the phase are analyzed according to the variation of the phase of the VLF signal.However,the phase of VLF signals will also be affected by the X-ray and energetic particles that are released during the eruption of solar flares,therefore the two phenomena are studied in this work.It is concluded that the X-ray will not affect the phase of VLF signals at night,but the energetic particles will affect the phase change,and the influence of energetic particles should be excluded in the study of multimode interference phenomena.Using VLF signals for navigation positioning in degraded or unavailable GPS conditions is of great practical significance for VLF navigation systems as it can avoid the influence of multimode interference and improve positioning accuracy.
文摘The paper considers the theoretical basics and the specific mathematical techniques having been developed for solving the tasks of the stochastic data analysis within the Rice statistical model in which the output signal’s amplitude is composed as a sum of the sought-for initial value and a random Gaussian noise. The Rician signal’s characteristics such as the average value and the noise dispersion have been shown to depend upon the Rice distribution’s parameters nonlinearly what has become a prerequisite for the development of a new approach to the stochastic Rician data analysis implying the joint signal and noise accurate evaluation. The joint computing of the Rice distribution’s parameters allows efficient reconstruction of the signal’s in-formative component against the noise background. A meaningful advantage of the proposed approach consists in the absence of restrictions connected with any a priori suppositions inherent to the traditional techniques. The results of the numerical experiments are provided confirming the efficiency of the elaborated approach to stochastic data analysis within the Rice statistical model.
基金supported by the National Natural Science Foundation of China(82302757)Shenzhen Science and Technology Program(JCY20240813145204006,SGDX20201103095600002,JCYJ20220818103417037,KJZD20230923115200002)+1 种基金Shenzhen Key Laboratory of Digital Surgical Printing Project(ZDSYS201707311542415)Shenzhen Development and Reform Program(XMHT20220106001).
文摘Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint changes.These limitations result in delayed diagnoses and inconsistent outcomes.Additionally,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and biomarkers.Recent advancements in artificial intelligence(AI)offer transformative potential to address these challenges.This review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics integration.By consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA research.The findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.
文摘In this editorial,we comment on the recent article by Fei et al exploring the field of near-infrared spectroscopy(NIRS)research in schizophrenia from a bibliometrics perspective.In recent years,NIRS has shown unique advantages in the auxiliary diagnosis of schizophrenia,and the introduction of bibliometrics has provided a macro perspective for research in this field.Despite the opportunities brought about by these technological developments,remaining challenges require multidi-sciplinary approach to devise a reliable and accurate diagnosis system for schizo-phrenia.Nonetheless,NIRS-assisted technology is expected to contribute to the division of methods for early intervention and treatment of schizophrenia.
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.
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.
文摘Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.
基金support from the National Key R&D Program of China(2022YFF0503401)the China Manned Space Project with No.CMS-CSST-2021-A05the National Natural Science Foundation of China(12225301)。
文摘Slitless spectroscopy onboard space telescopes is a powerful tool to detect emission-line objects such as emissionline galaxies(ELGs)and quasars.In this work,we present a study of ELGs observed with slitless spectroscopy by the Hubble Space Telescope(HST)in a deep field of~44 arcmin^(2).This is one of the deepest HST fields with a wealth of imaging and spectral data.In particular,previous VLT/MUSE observations have covered this field and identified a large number of ELGs.We reduce the HST spectra using the latest pipeline with a forward modeling algorithm and construct a sample of ELGs.By comparing with the MUSE spectra,we characterize our ELG detection in the HST spectra,including the impact of the line flux,line width,signal-to-noise ratio,etc.We find that the morphological broadening may affect the detection of ELGs,such that more compact sources are easier to be detected in slitless spectra.We discuss its implications to future slitless spectroscopic surveys that will be carried out by the China Space Station Telescope(CSST)and find that the CSST slitless spectroscopy has a capability comparable to that of HST in terms of the detection of emission lines.
基金study was Foundation of China(NSFC)under grant Nos.11873037 and 11803016the science research grants from the China Manned Space Project with Nos.CMS-CSST-2021-B05 and CMSCSST-2021-A08+1 种基金the Natural Science Foundation of Shandong Province under grant Nos.ZR2022MA076,ZR2022MA089 and ZR2024MA063the Young Scholars Program of Shandong University,Weihai,under grant No.2016WHWLJH09 and GHfund A(202202018107).
文摘Astronomical spectra are vital for deriving stellar properties,yet low signal-to-noise ratio(SNR)spectra often obscure key features,complicating accurate analysis.This study presents spec-Diffusion Probabilistic Models(DDPM),a novel deep learning approach based on DDPM,aimed at denoising low SNR spectra to improve stellar parameter estimation.Leveraging the LAMOST DR10 data set,we developed spec-DDPM using a tailored U-Net architecture(spec-Unet)to iteratively predict and remove noise.The model was trained on 28,500 low and high SNR spectral pairs and benchmarked against conventional methods,including Principal Component Analysis,wavelet techniques,and a modified DnCNN model.The spec-DDPM demonstrated superior performance,with reduced Mean Absolute Error,elevated Structural Similarity Index Measure,and enhanced spectral loss metrics.It effectively preserved critical spectral features and corrected continuum distortions.Validation experiments further confirmed its ability to improve stellar parameter estimation with reduced errors.These results underscore spec-DDPM’s potential to elevate spectral data quality,offering applications in restoring defective spectra and refining large-scale astronomical surveys.This work highlights the transformative role of deep learning in astronomical data processing.
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.
