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
As COVID-19 poses a major threat to people’s health and economy,there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently.In non-stationary time series forecasting jobs,ther...As COVID-19 poses a major threat to people’s health and economy,there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently.In non-stationary time series forecasting jobs,there is frequently a hysteresis in the anticipated values relative to the real values.The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network(MDTCNet)for COVID-19 prediction to address this problem.In particular,it is possible to record the deep features and temporal dependencies in uncertain time series,and the features may then be combined using a feature fusion network and a multilayer perceptron.Last but not least,the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty,realizing the short-term and long-term prediction of COVID-19 daily confirmed cases,and verifying the effectiveness and accuracy of the suggested prediction method,as well as reducing the hysteresis of the prediction results.展开更多
Due to the low spatial resolution of images taken from the Chang＇e-1 （CE-I） orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD...Due to the low spatial resolution of images taken from the Chang＇e-1 （CE-I） orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution （SR） algorithm is employed to obtain high- resolution （HR） images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.展开更多
Chang＇e-3 （CE-3） landed on the Mare Imbrium basin in the east part of Sinus Iridum （19.51°W, 44.12°N）, which was China＇s first soft landing on the Moon and it started collecting data on the lunar surfa...Chang＇e-3 （CE-3） landed on the Mare Imbrium basin in the east part of Sinus Iridum （19.51°W, 44.12°N）, which was China＇s first soft landing on the Moon and it started collecting data on the lunar surface environment. To better understand the environment of this region, this paper utilizes the available high-resolution topography data, image data and geological data to carry out a detailed analysis and research on the area surrounding the landing site （Sinus Iridum and 45 km×70 km of the landing area） as well as on the topography, landform, geology and lunar dust of the area surrounding the landing site. A general topographic analysis of the surrounding area is based on a digital elevation model and digital elevation model data acquired by Chang＇e-2 that have high resolution; the geology analysis is based on lunar geological data published by USGS; the study on topographic factors and distribution of craters and rocks in the surrounding area covering 4km^4km or even smaller is based on images from the CE-3 landing camera and images from the topographic camera; an analysis is done of the effect of the CE-3 engine plume on the lunar surface by comparing images before and after the landing using data from the landing camera. A comprehensive analysis of the results shows that the landing site and its surrounding area are identified as typical lunar mare with flat topography. They are suitable for maneuvers by the rover, and are rich in geological phenomena and scientific targets, making it an ideal site for exploration.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 Kehdolan area is located at 20 kilometers to the?south-east of Dozdozan Town (Eastern Azarbaijan Province). According to structural geology, volconic rocks are situated in Alborz-Azarbyjan zone, and faults?are?obs...The Kehdolan area is located at 20 kilometers to the?south-east of Dozdozan Town (Eastern Azarbaijan Province). According to structural geology, volconic rocks are situated in Alborz-Azarbyjan zone, and faults?are?observed?in?the?same direction to this system with SE-NW trend. The results show that kaolinite alteration trend with Argilic and propylitic veins?is the?same direction with SW-NE faults in this area. Therefore, these faults with these trends can be considered as the mineralization control for determination of the alterations. Different image processing techniques,?such as false color composite?(FCC), band ratios, color ratio composite?(CRC), principal component?analysis?(PCA), Crosta technique, supervised spectral angle mapping?(SAM), are used for?identification of the alteration zones associated with copper mineralization. In this project ASTER?data are process and spectral analysis to fit for recognizing intensity and kind of argillic, propylitic,?philic, and ETM+ data?which?are process and to fit for iron oxide and relation to metal mineralization of the area. For recognizing different alterations of the study area, some chemical and mineralogical analysis data from the samples showed that ASTER data and ETM+ data were?capable of hydrothermal alteration mapping with copper mineralization.?Copper mineralization in the region is in agreement with argillic alteration. SW-NE trending faults controlled the mineralization process.展开更多
An astronomical observatory is the core component of any astronomical research facility that connects astronomers with their lab: the Cosmos. The research quality of an astronomical facility is rooted in the precision...An astronomical observatory is the core component of any astronomical research facility that connects astronomers with their lab: the Cosmos. The research quality of an astronomical facility is rooted in the precision of data, collected by its observatory. For optimal performance, an observatory is sited while considering certain astronomical, environmental, geological and social parameters. This study aims to identify the potential sites in Pakistan for locating an optical-astronomical observatory using the Multicriteria Decision Analysis(MCDA) technique. The study uses the Analytic Hierarchy Process(AHP) for deriving the influence weights of nine evaluation criteria: Photometric Night Fraction;Night-time Sky Brightness;Sky Transparency;Aerosol Concentration;Altitude;Terrain Slope;Accessibility;Seismic Vulnerability;and Landuse/Land Cover. On the basis of experts’ opinions and previous studies, the evaluation criteria have been ordered in two possible preference sequences for identifying their influence weights with respect to each other for taking part in MCDA. Consequently, the process of MCDA identified certain areas with respect to each preference sequence, whereas some areas were found to be suitable according to both preference sequences. The study synchronizes the required eclectic data into an evaluation matrix that augments the process of astronomical site selection. In the future, this study will be useful for astronomical societies and for furthering astronomical research in the country.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
Sunspots are the most striking and easily observed magnetic structures of the Sun,and statistical analysis of solar historical data could reveal a wealth of information on the long-term variation of solar activity cyc...Sunspots are the most striking and easily observed magnetic structures of the Sun,and statistical analysis of solar historical data could reveal a wealth of information on the long-term variation of solar activity cycle.The hand-drawn sunspot records of Yunnan Observatories,Chinese Academy of Sciences have been accumulating for more than 60 years,and nearly 16000 images have been preserved.In the future,the observation mode of recording sunspots by hand-drawing will be replaced inevitably by digital images observed either at ground or in space.To connect the hand-drawn sunspot data and the purely digital sunspot data in future,it is necessary to analyze the systematic errors of the data which are observed by the two observation modes in the period of transition.In this paper,we choose 268 round sunspots(Htype in modified Zurich sunspot classification)from the drawing of Yunnan Observatories to compare their positions and areas with the CCD observations made by Helioseismic and Magnetic Imager(HMI)on board Solar Dynamic Observatory(SDO)and Global Oscillation Network Group(GONG).We find that the latitude and longitude accuracy of hand-drawn sunspot are within-0.127 and 2.29 degree respectively,and the area accuracy is about 16.36 sunspot unit(μHem).Systematic errors apparently decrease with large sunspot.展开更多
Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on galaxy.Edge-on galaxies pose a problem to classification due to their less overall brightness levels...Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on galaxy.Edge-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of pixels.In the current work,a novel technique for the classification of edge-on galaxies has been developed.This technique is based on the mathematical treatment of galaxy brightness data from their images.A special treatment for galaxies’brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds as well as adverse effects of background bright stars.A novel slimness weighting factor is developed to classify edge-on galaxies based on their slimness.The technique has the capacity to be optimized for different catalogs with different brightness levels.In the current work,the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this catalog.Upon classification of the full set of 4458 galaxies from the EFIGI catalog,an accuracy of 97.5% has been achieved,with an average processing time of about 0.26 seconds per galaxy on an average laptop.展开更多
In this study the medium-term response of beach profiles was investigated at two sites： a gently sloping sandy beach and a steeper mixed sand and gravel beach. The former is the Duck site in North Carolina, on the ea...In this study the medium-term response of beach profiles was investigated at two sites： a gently sloping sandy beach and a steeper mixed sand and gravel beach. The former is the Duck site in North Carolina, on the east coast of the USA, which is exposed to Atlantic Ocean swells and storm waves, and the latter is the Milford-on-Sea site at Christchurch Bay, on the south coast of England, which is partially sheltered from Atlantic swells but has a directionally bimodal wave exposure. The data sets comprise detailed bathymetric surveys of beach profiles covering a period of more than 25 years for the Duck site and over 18 years for the Milford-on-Sea site. The structure of the data sets and the data-driven methods are described. Canonical correlation analysis （CCA） was used to find linkages between the wave characteristics and beach profiles. The sensitivity of the linkages was investigated by deploying a wave height threshold to filter out the smaller waves incrementally. The results of the analysis indicate that, for the gently sloping sandy beach, waves of all heights are important to the morphological response. For the mixed sand and gravel beach, filtering the smaller waves improves the statistical fit and it suggests that low-height waves do not play a primary role in the medium-term morohological resoonse, which is primarily driven by the intermittent larger storm waves.展开更多
The extraction of high-temperature regions in active regions(ARs)is an important means to help understand the mechanism of coronal heating.The important observational means of high-temperature radiation in ARs is the ...The extraction of high-temperature regions in active regions(ARs)is an important means to help understand the mechanism of coronal heating.The important observational means of high-temperature radiation in ARs is the main emission line of Fe XVⅢin the 94?of the Atmospheric Imaging Assembly.However,the diagnostic algorithms for Fe XVⅢ,including the differential emission measure(DEM)and linear diagnostics proposed by Del based on the DEM,have been greatly limited for a long time,and the results obtained are different from the predictions.In this paper,we use the outlier detection method to establish the nonlinear correlation between 94?and 171,193,211?based on the former researches by others.A neural network based on 171,193,211?is constructed to replace the low-temperature emission lines in the ARs of 94?.The predicted results are regarded as the low-temperature components of 94?,and then the predicted results are subtracted from 94?to obtain the outlier component of 94?,or Fe XVⅢ.Then,the outlier components obtained by neural network are compared with the Fe XVⅢobtained by DEM and Del's method,and a high similarity is found,which proves the reliability of neural network to obtain the high-temperature components of ARs,but there are still many differences.In order to analyze the differences between the Fe XVⅢobtained by the three methods,we subtract the Fe XVⅢobtained by the DEM and Del's method from the Fe XVⅢobtained by the neural network to obtain the residual value,and compare it with the results of Fe XIV in the temperature range of 6.1-6.45 MK.It is found that there is a great similarity,which also shows that the Fe XVⅢobtained by DEM and Del's method still has a large low-temperature component dominated by Fe XIV,and the Fe XVⅢobtained by neural network is relatively pure.展开更多
In order to search for intensity fluctuations on the HCN（1-0） and HCO＋（1-0） line pro- files, which could arise due to possible small-scale inhomogeneous structure, long-term observations of high-mass star-forming...In order to search for intensity fluctuations on the HCN（1-0） and HCO＋（1-0） line pro- files, which could arise due to possible small-scale inhomogeneous structure, long-term observations of high-mass star-forming cores S140 and S199 were carried out. The data were processed by the Fourier filtering method. Line temperature fluctuations that exceed the noise level were detected. Assuming the cores consist of a large number of randomly moving small thermal fragments, the total number of frag- ments is - 4 × 106 for the region with linear size - 0.1 pc in S140 and - 106 for the region with linear size - 0.3 pc in S 199. Physical parameters of fragments in S 140 were obtained from detailed modeling of the HCN emission in the framework of the clumpy cloud model.展开更多
Olivine and pyroxene are important mineral end-members for studying the surface material compositions of mafic bodies.The profiles of visible and near-infrared spectra of olivine-orthopyroxene mixtures systematically ...Olivine and pyroxene are important mineral end-members for studying the surface material compositions of mafic bodies.The profiles of visible and near-infrared spectra of olivine-orthopyroxene mixtures systematically vary with their composition ratios.In our experiments,we combine the RELAB spectral database with new spectral data obtained from some assembled olivine-orthopyroxene mixtures.We found that the commonly-used band area ratio(BAR,Cloutis et al.)does not work well on our newly obtained spectral data.To investigate this issue,an empirical procedure based on fitted results by a modified Gaussian model is proposed to analyze the spectral curves.Following the new empirical procedure,the endmember abundances can be estimated with a 15%accuracy with some prior mineral absorption features.In addition,the mixture samples configured in our experiments are also irradiated by pulsed lasers to simulate and investigate the space weathering effects.Spectral deconvolution results confirm that low-content olivine on celestial bodies is difficult to measure and estimate.Therefore,the olivine abundance of space weathered materials may be underestimated from remote sensing data.This study may be utilized to quantify the spectral relationship of olivine-orthopyroxene mixtures and further reveal their correlation between the spectra of ordinary chondrites and silicate asteroids.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
Deconvolution in radio interferometry faces challenges due to incomplete sampling of the visibilities in the spatial frequency domain caused by a limited number of antenna baselines,resulting in an ill-posed inverse p...Deconvolution in radio interferometry faces challenges due to incomplete sampling of the visibilities in the spatial frequency domain caused by a limited number of antenna baselines,resulting in an ill-posed inverse problem.Reconstructing dirty images into clean ones is crucial for subsequent scientific analysis.To address these challenges,we propose a U-Net based method that extracts high-level information from the dirty image and reconstructs a clean image by effectively reducing artifacts and sidelobes.The U-Net architecture,consisting of an encoder-decoder structure and skip connections,facilitates the flow of information and preserves spatial details.Using simulated data of radio galaxies,we train our model and evaluate its performance on the testing set.Compared with the CLEAN method and the visibility and image conditioned denoising diffusion probabilistic model,our proposed model can effectively reconstruct both extended sources and faint point sources with higher values in the structural similarity index measure and the peak signal-to-noise ratio.Furthermore,we investigate the impact of noise on the model performance,demonstrating its robustness under varying noise levels.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
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
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
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supported by the major scientific and technological research project of Chongqing Education Commission(KJZD-M202000802)The first batch of Industrial and Informatization Key Special Fund Support Projects in Chongqing in 2022(2022000537).
文摘As COVID-19 poses a major threat to people’s health and economy,there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently.In non-stationary time series forecasting jobs,there is frequently a hysteresis in the anticipated values relative to the real values.The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network(MDTCNet)for COVID-19 prediction to address this problem.In particular,it is possible to record the deep features and temporal dependencies in uncertain time series,and the features may then be combined using a feature fusion network and a multilayer perceptron.Last but not least,the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty,realizing the short-term and long-term prediction of COVID-19 daily confirmed cases,and verifying the effectiveness and accuracy of the suggested prediction method,as well as reducing the hysteresis of the prediction results.
基金funded by the National Natural Science Foundation of China (Grant No. 51575388)
文摘Due to the low spatial resolution of images taken from the Chang＇e-1 （CE-I） orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution （SR） algorithm is employed to obtain high- resolution （HR） images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.
基金Supported by the National Natural Science Foundation of China
文摘Chang＇e-3 （CE-3） landed on the Mare Imbrium basin in the east part of Sinus Iridum （19.51°W, 44.12°N）, which was China＇s first soft landing on the Moon and it started collecting data on the lunar surface environment. To better understand the environment of this region, this paper utilizes the available high-resolution topography data, image data and geological data to carry out a detailed analysis and research on the area surrounding the landing site （Sinus Iridum and 45 km×70 km of the landing area） as well as on the topography, landform, geology and lunar dust of the area surrounding the landing site. A general topographic analysis of the surrounding area is based on a digital elevation model and digital elevation model data acquired by Chang＇e-2 that have high resolution; the geology analysis is based on lunar geological data published by USGS; the study on topographic factors and distribution of craters and rocks in the surrounding area covering 4km^4km or even smaller is based on images from the CE-3 landing camera and images from the topographic camera; an analysis is done of the effect of the CE-3 engine plume on the lunar surface by comparing images before and after the landing using data from the landing camera. A comprehensive analysis of the results shows that the landing site and its surrounding area are identified as typical lunar mare with flat topography. They are suitable for maneuvers by the rover, and are rich in geological phenomena and scientific targets, making it an ideal site for exploration.
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.
文摘The Kehdolan area is located at 20 kilometers to the?south-east of Dozdozan Town (Eastern Azarbaijan Province). According to structural geology, volconic rocks are situated in Alborz-Azarbyjan zone, and faults?are?observed?in?the?same direction to this system with SE-NW trend. The results show that kaolinite alteration trend with Argilic and propylitic veins?is the?same direction with SW-NE faults in this area. Therefore, these faults with these trends can be considered as the mineralization control for determination of the alterations. Different image processing techniques,?such as false color composite?(FCC), band ratios, color ratio composite?(CRC), principal component?analysis?(PCA), Crosta technique, supervised spectral angle mapping?(SAM), are used for?identification of the alteration zones associated with copper mineralization. In this project ASTER?data are process and spectral analysis to fit for recognizing intensity and kind of argillic, propylitic,?philic, and ETM+ data?which?are process and to fit for iron oxide and relation to metal mineralization of the area. For recognizing different alterations of the study area, some chemical and mineralogical analysis data from the samples showed that ASTER data and ETM+ data were?capable of hydrothermal alteration mapping with copper mineralization.?Copper mineralization in the region is in agreement with argillic alteration. SW-NE trending faults controlled the mineralization process.
文摘An astronomical observatory is the core component of any astronomical research facility that connects astronomers with their lab: the Cosmos. The research quality of an astronomical facility is rooted in the precision of data, collected by its observatory. For optimal performance, an observatory is sited while considering certain astronomical, environmental, geological and social parameters. This study aims to identify the potential sites in Pakistan for locating an optical-astronomical observatory using the Multicriteria Decision Analysis(MCDA) technique. The study uses the Analytic Hierarchy Process(AHP) for deriving the influence weights of nine evaluation criteria: Photometric Night Fraction;Night-time Sky Brightness;Sky Transparency;Aerosol Concentration;Altitude;Terrain Slope;Accessibility;Seismic Vulnerability;and Landuse/Land Cover. On the basis of experts’ opinions and previous studies, the evaluation criteria have been ordered in two possible preference sequences for identifying their influence weights with respect to each other for taking part in MCDA. Consequently, the process of MCDA identified certain areas with respect to each preference sequence, whereas some areas were found to be suitable according to both preference sequences. The study synchronizes the required eclectic data into an evaluation matrix that augments the process of astronomical site selection. In the future, this study will be useful for astronomical societies and for furthering astronomical research in the country.
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supported by the National Natural Science Foundation of China(Grant Nos.U1731124,U1531247,11427901 and 11873089)the special foundation work of the Ministry of Science and Technology of China(Grant No.2014FY120300)+1 种基金the 13th Five-year Informatization Plan of Chinese Academy of Sciences(Grant No.XXH13505–04)the Youth Innovation Promotion Association CAS.The hand-drawing historic。
文摘Sunspots are the most striking and easily observed magnetic structures of the Sun,and statistical analysis of solar historical data could reveal a wealth of information on the long-term variation of solar activity cycle.The hand-drawn sunspot records of Yunnan Observatories,Chinese Academy of Sciences have been accumulating for more than 60 years,and nearly 16000 images have been preserved.In the future,the observation mode of recording sunspots by hand-drawing will be replaced inevitably by digital images observed either at ground or in space.To connect the hand-drawn sunspot data and the purely digital sunspot data in future,it is necessary to analyze the systematic errors of the data which are observed by the two observation modes in the period of transition.In this paper,we choose 268 round sunspots(Htype in modified Zurich sunspot classification)from the drawing of Yunnan Observatories to compare their positions and areas with the CCD observations made by Helioseismic and Magnetic Imager(HMI)on board Solar Dynamic Observatory(SDO)and Global Oscillation Network Group(GONG).We find that the latitude and longitude accuracy of hand-drawn sunspot are within-0.127 and 2.29 degree respectively,and the area accuracy is about 16.36 sunspot unit(μHem).Systematic errors apparently decrease with large sunspot.
文摘Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on galaxy.Edge-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of pixels.In the current work,a novel technique for the classification of edge-on galaxies has been developed.This technique is based on the mathematical treatment of galaxy brightness data from their images.A special treatment for galaxies’brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds as well as adverse effects of background bright stars.A novel slimness weighting factor is developed to classify edge-on galaxies based on their slimness.The technique has the capacity to be optimized for different catalogs with different brightness levels.In the current work,the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this catalog.Upon classification of the full set of 4458 galaxies from the EFIGI catalog,an accuracy of 97.5% has been achieved,with an average processing time of about 0.26 seconds per galaxy on an average laptop.
基金supported by the UK Natural Environment Research Council(Grant No.NE/J005606/1)the UK Engineering and Physical Sciences Research Council(Grant No.EP/C005392/1)the Ensemble Estimation of Flood Risk in a Changing Climate(EFRa CC)project funded by the British Council under its Global Innovation Initiative
文摘In this study the medium-term response of beach profiles was investigated at two sites： a gently sloping sandy beach and a steeper mixed sand and gravel beach. The former is the Duck site in North Carolina, on the east coast of the USA, which is exposed to Atlantic Ocean swells and storm waves, and the latter is the Milford-on-Sea site at Christchurch Bay, on the south coast of England, which is partially sheltered from Atlantic swells but has a directionally bimodal wave exposure. The data sets comprise detailed bathymetric surveys of beach profiles covering a period of more than 25 years for the Duck site and over 18 years for the Milford-on-Sea site. The structure of the data sets and the data-driven methods are described. Canonical correlation analysis （CCA） was used to find linkages between the wave characteristics and beach profiles. The sensitivity of the linkages was investigated by deploying a wave height threshold to filter out the smaller waves incrementally. The results of the analysis indicate that, for the gently sloping sandy beach, waves of all heights are important to the morphological response. For the mixed sand and gravel beach, filtering the smaller waves improves the statistical fit and it suggests that low-height waves do not play a primary role in the medium-term morohological resoonse, which is primarily driven by the intermittent larger storm waves.
基金supported by the National Natural Science Foundation of China under Grant Nos.U2031140,11873027,and 12073077。
文摘The extraction of high-temperature regions in active regions(ARs)is an important means to help understand the mechanism of coronal heating.The important observational means of high-temperature radiation in ARs is the main emission line of Fe XVⅢin the 94?of the Atmospheric Imaging Assembly.However,the diagnostic algorithms for Fe XVⅢ,including the differential emission measure(DEM)and linear diagnostics proposed by Del based on the DEM,have been greatly limited for a long time,and the results obtained are different from the predictions.In this paper,we use the outlier detection method to establish the nonlinear correlation between 94?and 171,193,211?based on the former researches by others.A neural network based on 171,193,211?is constructed to replace the low-temperature emission lines in the ARs of 94?.The predicted results are regarded as the low-temperature components of 94?,and then the predicted results are subtracted from 94?to obtain the outlier component of 94?,or Fe XVⅢ.Then,the outlier components obtained by neural network are compared with the Fe XVⅢobtained by DEM and Del's method,and a high similarity is found,which proves the reliability of neural network to obtain the high-temperature components of ARs,but there are still many differences.In order to analyze the differences between the Fe XVⅢobtained by the three methods,we subtract the Fe XVⅢobtained by the DEM and Del's method from the Fe XVⅢobtained by the neural network to obtain the residual value,and compare it with the results of Fe XIV in the temperature range of 6.1-6.45 MK.It is found that there is a great similarity,which also shows that the Fe XVⅢobtained by DEM and Del's method still has a large low-temperature component dominated by Fe XIV,and the Fe XVⅢobtained by neural network is relatively pure.
基金support of the RFBR grants(projects 15–02–06098,16–02–00761 and18–02–00660)support of the Russian Science Foundation grant(project 17–12–01256)
文摘In order to search for intensity fluctuations on the HCN（1-0） and HCO＋（1-0） line pro- files, which could arise due to possible small-scale inhomogeneous structure, long-term observations of high-mass star-forming cores S140 and S199 were carried out. The data were processed by the Fourier filtering method. Line temperature fluctuations that exceed the noise level were detected. Assuming the cores consist of a large number of randomly moving small thermal fragments, the total number of frag- ments is - 4 × 106 for the region with linear size - 0.1 pc in S140 and - 106 for the region with linear size - 0.3 pc in S 199. Physical parameters of fragments in S 140 were obtained from detailed modeling of the HCN emission in the framework of the clumpy cloud model.
基金supported by the Foundation of the State Key Laboratory of Lunar and Planetary Sciences, Macao University of Science and Technology, Macao, Chinafunded by The Science and Technology Development Fund, Macao SAR (No. 0073/2019/A2)+2 种基金the support from The Science and Technology Development Fund, Macao SAR (No. 0007/2019/A)supported by Beijing Municipal Science and Technology Commission (No. Z181100002918003)supported by the National Natural Science Foundation of China (NSFC, Nos. 11773023, 11941001, 12073024 and U1631124)
文摘Olivine and pyroxene are important mineral end-members for studying the surface material compositions of mafic bodies.The profiles of visible and near-infrared spectra of olivine-orthopyroxene mixtures systematically vary with their composition ratios.In our experiments,we combine the RELAB spectral database with new spectral data obtained from some assembled olivine-orthopyroxene mixtures.We found that the commonly-used band area ratio(BAR,Cloutis et al.)does not work well on our newly obtained spectral data.To investigate this issue,an empirical procedure based on fitted results by a modified Gaussian model is proposed to analyze the spectral curves.Following the new empirical procedure,the endmember abundances can be estimated with a 15%accuracy with some prior mineral absorption features.In addition,the mixture samples configured in our experiments are also irradiated by pulsed lasers to simulate and investigate the space weathering effects.Spectral deconvolution results confirm that low-content olivine on celestial bodies is difficult to measure and estimate.Therefore,the olivine abundance of space weathered materials may be underestimated from remote sensing data.This study may be utilized to quantify the spectral relationship of olivine-orthopyroxene mixtures and further reveal their correlation between the spectra of ordinary chondrites and silicate asteroids.
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 National SKA Program of China(2020SKA0110300,2020SKA0110201)the National Natural Science Foundation of China(NSFC,grant Nos.12433012 and 12373097)+1 种基金the Guangdong Province Project of the Basic and Applied Basic Research Foundation(2024A1515011503)the Guangzhou Science and Technology Funds(2023A03J0016).
文摘Deconvolution in radio interferometry faces challenges due to incomplete sampling of the visibilities in the spatial frequency domain caused by a limited number of antenna baselines,resulting in an ill-posed inverse problem.Reconstructing dirty images into clean ones is crucial for subsequent scientific analysis.To address these challenges,we propose a U-Net based method that extracts high-level information from the dirty image and reconstructs a clean image by effectively reducing artifacts and sidelobes.The U-Net architecture,consisting of an encoder-decoder structure and skip connections,facilitates the flow of information and preserves spatial details.Using simulated data of radio galaxies,we train our model and evaluate its performance on the testing set.Compared with the CLEAN method and the visibility and image conditioned denoising diffusion probabilistic model,our proposed model can effectively reconstruct both extended sources and faint point sources with higher values in the structural similarity index measure and the peak signal-to-noise ratio.Furthermore,we investigate the impact of noise on the model performance,demonstrating its robustness under varying noise levels.
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.
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.
