FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku prod...FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.展开更多
The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflect...The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.展开更多
Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning b...Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning based model,for the types identification.However,traditional approaches such as convolutional neural networks(CNNs)encounter difficulties in capturing global contextual information.In addition,they are computationally expensive,which restricts their usability in resource-limited environments.To tackle these issues,we present the Cloud Vision Transformer(CloudViT),a lightweight model that integrates CNNs with Transformers.The integration enables an effective balance between local and global feature extraction.To be specific,CloudViT comprises two innovative modules:Feature Extraction(E_Module)and Downsampling(D_Module).These modules are able to significantly reduce the number of model parameters and computational complexity while maintaining translation invariance and enhancing contextual comprehension.Overall,the CloudViT includes 0.93×10^(6)parameters,which decreases more than ten times compared to the SOTA(State-of-the-Art)model CloudNet.Comprehensive evaluations conducted on the HBMCD and SWIMCAT datasets showcase the outstanding performance of CloudViT.It achieves classification accuracies of 98.45%and 100%,respectively.Moreover,the efficiency and scalability of CloudViT make it an ideal candidate for deployment inmobile cloud observation systems,enabling real-time cloud image classification.The proposed hybrid architecture of CloudViT offers a promising approach for advancing ground-based cloud image classification.It holds significant potential for both optimizing performance and facilitating practical deployment scenarios.展开更多
Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation...Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation technologies are mostly based on target magnitudes for simulations,making it difficult to meet image simulation requirements for different signal-to-noise ratio(SNR)needs.Therefore,design of a simulation method that generates target image sequences with various SNRs based on the optical detection system parameters will be important for faint space target detection research.Addressing the SNR calculation issue in optical observation systems,this paper proposes a ground-based detection image SNR calculation method using the optical system parameters.This method calculates the SNR of an observed image precisely using radiative transfer theory,the optical system parameters,and the observation environment parameters.An SNR-based target sequence image simulation method for ground-based detection scenarios is proposed.This method calculates the imaging SNR using the optical system parameters and establishes a model for conversion between the target’s apparent magnitude and image grayscale values,thereby enabling generation of target sequence simulation images with corresponding SNRs for different system parameters.Experiments show that the SNR obtained using this calculation method has an average calculation error of<1 dB when compared with the theoretical SNR of the actual optical system.Additionally,the simulation images generated by the imaging simulation method show high consistency with real images,which meets the requirements of faint space target detection algorithm research and provides reliable data support for development of related technologies.展开更多
The advent of artificial intelligence(AI)has propelled augmented reality(AR)display technology to a pivotal juncture,positioning it as a contender for the next generation of mobile intelligent terminals.However,the pu...The advent of artificial intelligence(AI)has propelled augmented reality(AR)display technology to a pivotal juncture,positioning it as a contender for the next generation of mobile intelligent terminals.However,the pursuit of advanced AR displays,particularly those capable of delivering immersive 3D experiences,is significantly hindered by the performance limitations of current hardware and the complexity of system integration.In this study,we present an innovative multi-focal plane AR display system that integrates a non-orthogonal polarization-multiplexing metasurface,freeform optical elements,and an OLED display screen.All optical elements are integrated into a single solid-state architecture,based on a joint optimization design approach of ray tracing and diffraction theory.The multi-focal plane AR visual effect is realized by the compact and multiplexing metasurface,which performs distinct phase functions across diverse polarization channels.Meanwhile,freeform surfaces offer ample design flexibility for the collaborative optimization of multi-focal plane imaging and the see-through systems.Followed by a mechanical design and prototype assembly,we demonstrate the system's capabilities in real-time and multi-focal plane display.The digital images at all virtual image distances seamlessly integrate with the real environment,fully exhibiting the system's high parallelism and real-time interactivity.With the innovative design concept and joint design method,we believe that our work will spur more innovative and compact intelligent solutions for AR displays and inject new vitality into hybrid optical systems.展开更多
Surgical navigation has evolved significantly through advances in augmented reality,virtual reality,and mixed reality,improving precision and safety across many clinical applications,including neurosurgery,maxillofaci...Surgical navigation has evolved significantly through advances in augmented reality,virtual reality,and mixed reality,improving precision and safety across many clinical applications,including neurosurgery,maxillofacial,spinal,and arthroplasty procedures.By integrating preoperative imaging with real-time intraoperative data,these systems provide dynamic guidance,reduce radiation exposure,and minimize tissue damage.Key challenges persist,including intraoperative registration accuracy,flexible tissue deformation,respiratory compensation,and real-time imaging quality.Emerging solutions include artificial intelligence-driven segmentation,deformation-field modeling,and hybrid registration techniques.Future developments will include lightweight,portable systems,improved non-rigid registration algorithms,and greater clinical adoption.Despite advances in rigid-tissue applications,soft-tissue navigation requires additional innovation to address motion variability and registration reliability,ultimately advancing minimally invasive surgery and precision medicine.展开更多
Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the re...Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the recognition and interpretation of hand gestures captured in video data.However,sign language datasets remain relatively limited compared to those of other languages,which hinders the training and performance of deep learning models.Additionally,the distinct word order of sign language,unlike that of spoken language,requires context-aware and natural sentence generation.To address these challenges,this study applies data augmentation techniques to build a Korean Sign Language dataset and train recognition models.Recognized words are then reconstructed into complete sentences.The sign recognition process uses OpenCV and MediaPipe to extract hand landmarks from sign language videos and analyzes hand position,orientation,and motion.The extracted features are converted into time-series data and fed into a Long Short-Term Memory(LSTM)model.The proposed recognition framework achieved an accuracy of up to 81.25%,while the sentence generation achieved an accuracy of up to 95%.The proposed approach is expected to be applicable not only to Korean Sign Language but also to other low-resource sign languages for recognition and translation tasks.展开更多
Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects...Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects on silicon carbide(SiC)wafers has become essential.This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage.Thecomplex machining textures on wafer surfaces hinder conventional machine vision models,often leading to misjudgment.To address this,deep learning algorithms were applied for defect classification.Because defects are rare and imbalanced across categories,data augmentation was performed using aWasserstein generative adversarial network with gradient penalty(WGAN-GP),along with conventionalmethods.An improved YOLOv8-seg instance segmentationmodel was then trained and tested on datasets with different augmentation strategies.Experimental results showed that,when trained withWGAN-GP–generated data,YOLOv8-seg achieved mean average precision values of 87.0%(bounding box)and 86.6%(segmentation mask).Compared with the traditional WGAN-GP,the proposed model reduced Frechet inception distance by 32.2%and multiscale structural similarity index by 29.8%,generating more realistic and diverse defect images.The proposed framework effectively improves defect detection accuracy under limited data conditions and shows strong potential for industrial applications.展开更多
IoT devices are highly vulnerable to cyberattacks due to their widespread,distributed nature and limited security features.Intrusion detection can counter these threats,but class imbalance between normal and abnormal ...IoT devices are highly vulnerable to cyberattacks due to their widespread,distributed nature and limited security features.Intrusion detection can counter these threats,but class imbalance between normal and abnormal traffic often degrades model performance.We propose a novel multi-generator adversarial data augmentation method that blends the strengths of TMG-GAN(Tabular Multi-Generator Generative Adversarial Network)and R3GAN(Re-GAN).Our approach uses multiple class-specific generators to create diverse,high-quality synthetic samples,improving training stability and minority-class detection.A dual-branch discriminator-classifier enhances authenticity and class prediction,while feature similarity and decoupling techniques ensure clear class separation.Experiments on TON-IoT and Edge-IIoTset datasets show our method outperforms existing techniques like hybrid sampling,SNGAN(Spectral Normalization GAN),and TMG-GAN,achieving higher detection accuracy and better minority-class recall for imbalanced IoT intrusion detection.展开更多
To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detecti...To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detection framework integrating image quality evaluation and text-to-image data augmentation.First,a HyperNetwork-based image quality assessment module is introduced to filter low-quality inspection images in terms of clarity and structural integrity,resulting in a high-quality training dataset.Second,a text-to-image diffusion model is utilized for sample augmentation.By designing text prompts that describe various bolt defect types under diverse lighting and viewing conditions,the model automatically generates realistic synthetic samples.The generated images are further filtered using a combination of quality and perceptual similarity metrics to ensure consistency with the real data distribution.Building upon the sparse RCNN baseline,a dynamic label assignment mechanism and a random decision path detection head are incorporated to enhance bounding box matching and prediction accuracy.Experimental results demonstrate that the proposed method significantly improves detection accuracy(mAP@0.5) over the original sparse RCNN while maintaining low computational cost,enabling more efficient and intelligent inspection of transmission line components.展开更多
Ground-based microwave radiometers profilers(MWRPs)have been used in numerical weather prediction(NWP)systems and show different impacts on forecasts.Currently,there are around hundreds of ground-based MWPRs used in w...Ground-based microwave radiometers profilers(MWRPs)have been used in numerical weather prediction(NWP)systems and show different impacts on forecasts.Currently,there are around hundreds of ground-based MWPRs used in weather stations over China;however,the application of MWPRs in NWP systems is rather limited.In this work,two MWRP retrieved profiles were assimilated into the Weather Research and Forecasting(WRF)model for a rainstorm event that occurred in Beijing,China.The quality of temperature and humidity profiles retrieved from the MWRP was evaluated against radiosonde observations and showed the reliability of the two MWRP products.Then,comparisons between the measurements of ground-based rain gauges and the corresponding forecasted precipitation in different periods of the rainstorm were investigated.The results showed that assimilating the two MWRPs affected the distribution and intensity of rainfall,especially in the early stage of the rainstorm.With the development of the rainstorm,adding MWRP data showed only a slight influence on the precipitation during the stable and mature period of the rainstorm,since the two MWRP observations were too limited to affect the large area of heavy rainfall.展开更多
A long-term （9 years） gravity change in Chinese mainland is obtained on the basis of observation of the ground-based national gravity network. The result shows several features that may be related to sore, large-sca...A long-term （9 years） gravity change in Chinese mainland is obtained on the basis of observation of the ground-based national gravity network. The result shows several features that may be related to sore, large-scale groundwater pumping in North China, glacier-water flow and storage in Tianshan region, and pre seismic gravity changes of the 2008 MsS. 0 Wenchuan earthquake, which are spatially similar to co-seismi, changes but reversed in sign. These features are also shown in the result of the satellite-based GRACE obser vation, after a height effect is corrected with GPS data.展开更多
The joint European Space Agency and Chinese Academy of Sciences Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission will explore global dynamics of the magnetosphere under varying solar wind and interplane...The joint European Space Agency and Chinese Academy of Sciences Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission will explore global dynamics of the magnetosphere under varying solar wind and interplanetary magnetic field conditions,and simultaneously monitor the auroral response of the Northern Hemisphere ionosphere.Combining these large-scale responses with medium and fine-scale measurements at a variety of cadences by additional ground-based and space-based instruments will enable a much greater scientific impact beyond the original goals of the SMILE mission.Here,we describe current community efforts to prepare for SMILE,and the benefits and context various experiments that have explicitly expressed support for SMILE can offer.A dedicated group of international scientists representing many different experiment types and geographical locations,the Ground-based and Additional Science Working Group,is facilitating these efforts.Preparations include constructing an online SMILE Data Fusion Facility,the discussion of particular or special modes for experiments such as coherent and incoherent scatter radar,and the consideration of particular observing strategies and spacecraft conjunctions.We anticipate growing interest and community engagement with the SMILE mission,and we welcome novel ideas and insights from the solar-terrestrial community.展开更多
Ground-based synthetic aperture radar(GB-SAR) has been successfully applied to the ground deformation monitoring.However, due to the short length of the GB-SAR platform, the scope of observation is largely limited. Th...Ground-based synthetic aperture radar(GB-SAR) has been successfully applied to the ground deformation monitoring.However, due to the short length of the GB-SAR platform, the scope of observation is largely limited. The practical applications drive us to make improvements on the conventional linear rail GB-SAR system in order to achieve larger field imaging. First, a turntable is utilized to support the rotational movement of the radar.Next, a series of high-squint scanning is performed with multiple squint angles. Further, the high squint modulation phase of the echo data is eliminated. Then, a new multi-angle imaging method is performed in the wave number domain to expand the field of view. Simulation and real experiments verify the effectiveness of this method.展开更多
Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural networ...Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 were inputted into the Mono RTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234 GHz to 58.8 GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model's effect by comparing its output with the real measured data and the microwave radiometer's own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1 K and 2.0 K; the water vapor density's RMS error is between 0.2 g/m^3 and 1.93 g/m3; and the relative humidity's RMS error is between 2.5% and 18.6%.展开更多
The lunar surface is a typical vacuum environment,and its harsh heat rejection conditions bring great challenges to the thermal control technology of the exploration mission.In addition to the radiator,the sublimator ...The lunar surface is a typical vacuum environment,and its harsh heat rejection conditions bring great challenges to the thermal control technology of the exploration mission.In addition to the radiator,the sublimator is recommended as one of the promising options for heat rejection.The sublimator makes use of water to freeze and sublimate in a porous medium,rejecting heat to the vacuum environment.The complex heat and mass transfer process involves many physical phenomena such as the freezing and sublimation phase change of water in the porous medium and the movement of the phase-change interface.In this paper,the visualized ground-based experimental approaches of space sublimation cooling were presented to reveal the moving law of threephase point and the growth phenomenon of ice-peak and icicle in microchannels under vacuum conditions.The visualized experiments and results prove that the freezing ice is divided into the porous ice-peak and the transparent icicle.As the sublimation progresses,the phase-change interface moves downward steadily,the length of the ice-peak increases,but the icicle decreases.The visualized experiments of space sublimation cooling in the capillary have guiding significance to reveal the sublimation cooling mechanism of water in the sublimator for lunar exploration missions.展开更多
Measurements of carbon dioxide(CO_(2)),methane(CH_(4)),and carbon monoxide(CO)are of great importance in the Qinghai-Tibetan region,as it is the highest and largest plateau in the world affecting global weather and cl...Measurements of carbon dioxide(CO_(2)),methane(CH_(4)),and carbon monoxide(CO)are of great importance in the Qinghai-Tibetan region,as it is the highest and largest plateau in the world affecting global weather and climate systems.In this study,for the first time,we present CO_(2),CH_(4),and CO column measurements carried out by a Bruker EM27/SUN Fourier-transform infrared spectrometer(FTIR)at Golmud(36.42°E,94.91°N,2808 m)in August 2021.The mean and standard deviation of the column-average dry-air mixing ratio of CO_(2),CH_(4),and CO(XCO_(2),XCH_(4),and XCO)are 409.3±0.4 ppm,1905.5±19.4 ppb,and 103.1±7.7 ppb,respectively.The differences between the FTIR co-located TROPOMI/S5P satellite measurements at Golmud are 0.68±0.64%(13.1±12.2 ppb)for XCH_(4)and 9.81±3.48%(–10.7±3.8 ppb)for XCO,which are within their retrieval uncertainties.High correlations for both XCH_(4)and XCO are observed between the FTIR and S5P satellite measurements.Using the FLEXPART model and satellite measurements,we find that enhanced CH_(4)and CO columns in Golmud are affected by anthropogenic emissions transported from North India.This study provides an insight into the variations of the CO_(2),CH_(4),and CO columns in the Qinghai-Tibetan Plateau.展开更多
Performance test of a high precise accelerometer or an inertial sensor on the ground is inevitably limited by the seismic noise. A torsion pendulum has been used to investigate the performances of an electrostatic acc...Performance test of a high precise accelerometer or an inertial sensor on the ground is inevitably limited by the seismic noise. A torsion pendulum has been used to investigate the performances of an electrostatic accelerometer, where the test mass is suspended by a fiber to compensate for its weight, and this scheme demonstrates an advantage, compared with the high-voltage levitation scheme, in which the effect of the seismic noise can be suppressed for a few orders of magnitude in low frequencies. In this work, the capacitive electrode cage is proposed to be suspended by another pendulum, and theoretical analysis shows that the effects of the seismic noise can be further suppressed for more than one order by suspending the electrode cage.展开更多
With the development of adaptive optics and post restore processing techniques,large aperture ground-based telescopes can obtain high-resolution images(HRIs)of targets.The pose of the space target can be estimated fro...With the development of adaptive optics and post restore processing techniques,large aperture ground-based telescopes can obtain high-resolution images(HRIs)of targets.The pose of the space target can be estimated from HRIs by several methods.As the target features obtained from the image are unstable,it is difficult to use existing methods for pose estimation.In this paper a method based on real-time target model matching to estimate the pose of space targets is proposed.First,the physicallyconstrained iterative deconvolution algorithm is used to obtain HRIs of the space target.Second,according to the 3D model,the ephemeris data,the observation time of the target,and the optical parameters of the telescope,the simulated observation image of the target in orbit is rendered by a scene simulation program.Finally,the target model searches through yaw,pitch,and roll until the correlation between the simulated observation image and the actual observation image shows an optimal match.The simulation results show that the proposed pose estimation method can converge to the local optimal value with an estimation error of about 1.6349°.展开更多
基金supported by the Innovation and Development Special Project of the China Meteorological Administration(Grant No.CXFZ2024J058)the Guangdong Province Basic and Applied Basic Research Foundation Meteorological Joint Fund Project(Grant No.2024A1515510036)+1 种基金the National Key R&D Program of China(Grant No.2022YFC3004101)the Technical Innovation Team Project of Guangzhou Meteorological Satellite Ground Station(Grant No.CXTD202401).
文摘FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.
基金funded by the National Key Research and Development Program of China(Grant No.2023YFB3907500)the National Natural Science Foundation(Grant No.42330602)the“Fengyun Satellite Remote Sensing Product Validation and Verification”Youth Innovation Team of the China Meteorological Administration(Grant No.CMA2023QN12)。
文摘The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.
基金funded by Innovation and Development Special Project of China Meteorological Administration(CXFZ2022J038,CXFZ2024J035)Sichuan Science and Technology Program(No.2023YFQ0072)+1 种基金Key Laboratory of Smart Earth(No.KF2023YB03-07)Automatic Software Generation and Intelligent Service Key Laboratory of Sichuan Province(CUIT-SAG202210).
文摘Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning based model,for the types identification.However,traditional approaches such as convolutional neural networks(CNNs)encounter difficulties in capturing global contextual information.In addition,they are computationally expensive,which restricts their usability in resource-limited environments.To tackle these issues,we present the Cloud Vision Transformer(CloudViT),a lightweight model that integrates CNNs with Transformers.The integration enables an effective balance between local and global feature extraction.To be specific,CloudViT comprises two innovative modules:Feature Extraction(E_Module)and Downsampling(D_Module).These modules are able to significantly reduce the number of model parameters and computational complexity while maintaining translation invariance and enhancing contextual comprehension.Overall,the CloudViT includes 0.93×10^(6)parameters,which decreases more than ten times compared to the SOTA(State-of-the-Art)model CloudNet.Comprehensive evaluations conducted on the HBMCD and SWIMCAT datasets showcase the outstanding performance of CloudViT.It achieves classification accuracies of 98.45%and 100%,respectively.Moreover,the efficiency and scalability of CloudViT make it an ideal candidate for deployment inmobile cloud observation systems,enabling real-time cloud image classification.The proposed hybrid architecture of CloudViT offers a promising approach for advancing ground-based cloud image classification.It holds significant potential for both optimizing performance and facilitating practical deployment scenarios.
基金supported by Open Fund of National Key Laboratory of Deep Space Exploration(NKDSEL2024014)by Civil Aerospace Pre-research Project of State Administration of Science,Technology and Industry for National Defence,PRC(D040103).
文摘Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation technologies are mostly based on target magnitudes for simulations,making it difficult to meet image simulation requirements for different signal-to-noise ratio(SNR)needs.Therefore,design of a simulation method that generates target image sequences with various SNRs based on the optical detection system parameters will be important for faint space target detection research.Addressing the SNR calculation issue in optical observation systems,this paper proposes a ground-based detection image SNR calculation method using the optical system parameters.This method calculates the SNR of an observed image precisely using radiative transfer theory,the optical system parameters,and the observation environment parameters.An SNR-based target sequence image simulation method for ground-based detection scenarios is proposed.This method calculates the imaging SNR using the optical system parameters and establishes a model for conversion between the target’s apparent magnitude and image grayscale values,thereby enabling generation of target sequence simulation images with corresponding SNRs for different system parameters.Experiments show that the SNR obtained using this calculation method has an average calculation error of<1 dB when compared with the theoretical SNR of the actual optical system.Additionally,the simulation images generated by the imaging simulation method show high consistency with real images,which meets the requirements of faint space target detection algorithm research and provides reliable data support for development of related technologies.
基金funding provided by National Natural Science Foundation of China(U21A20140)National Key Research and Development Program of China(2021YFA1401200)+2 种基金Beijing Natural Science Foundation(JQ24028)Beijing Nova Program(20240484557)Synergetic Extreme Condition User Facility(SECUF).
文摘The advent of artificial intelligence(AI)has propelled augmented reality(AR)display technology to a pivotal juncture,positioning it as a contender for the next generation of mobile intelligent terminals.However,the pursuit of advanced AR displays,particularly those capable of delivering immersive 3D experiences,is significantly hindered by the performance limitations of current hardware and the complexity of system integration.In this study,we present an innovative multi-focal plane AR display system that integrates a non-orthogonal polarization-multiplexing metasurface,freeform optical elements,and an OLED display screen.All optical elements are integrated into a single solid-state architecture,based on a joint optimization design approach of ray tracing and diffraction theory.The multi-focal plane AR visual effect is realized by the compact and multiplexing metasurface,which performs distinct phase functions across diverse polarization channels.Meanwhile,freeform surfaces offer ample design flexibility for the collaborative optimization of multi-focal plane imaging and the see-through systems.Followed by a mechanical design and prototype assembly,we demonstrate the system's capabilities in real-time and multi-focal plane display.The digital images at all virtual image distances seamlessly integrate with the real environment,fully exhibiting the system's high parallelism and real-time interactivity.With the innovative design concept and joint design method,we believe that our work will spur more innovative and compact intelligent solutions for AR displays and inject new vitality into hybrid optical systems.
基金Supported by the National Natural Science Foundation of China(NSFC)under Grants 62025104,62422102,62331005,62301034,and U22A2052the Beijing Natural Science Foundation-Daxing Innovation Joint Fund(L256040).
文摘Surgical navigation has evolved significantly through advances in augmented reality,virtual reality,and mixed reality,improving precision and safety across many clinical applications,including neurosurgery,maxillofacial,spinal,and arthroplasty procedures.By integrating preoperative imaging with real-time intraoperative data,these systems provide dynamic guidance,reduce radiation exposure,and minimize tissue damage.Key challenges persist,including intraoperative registration accuracy,flexible tissue deformation,respiratory compensation,and real-time imaging quality.Emerging solutions include artificial intelligence-driven segmentation,deformation-field modeling,and hybrid registration techniques.Future developments will include lightweight,portable systems,improved non-rigid registration algorithms,and greater clinical adoption.Despite advances in rigid-tissue applications,soft-tissue navigation requires additional innovation to address motion variability and registration reliability,ultimately advancing minimally invasive surgery and precision medicine.
基金supported by the Institute of Information&Communications Technoljogy Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program grant funded by the Korea government(MSIT)(IITP-2026-RS-2022-00156334,50%)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1C1C2011105,50%).
文摘Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the recognition and interpretation of hand gestures captured in video data.However,sign language datasets remain relatively limited compared to those of other languages,which hinders the training and performance of deep learning models.Additionally,the distinct word order of sign language,unlike that of spoken language,requires context-aware and natural sentence generation.To address these challenges,this study applies data augmentation techniques to build a Korean Sign Language dataset and train recognition models.Recognized words are then reconstructed into complete sentences.The sign recognition process uses OpenCV and MediaPipe to extract hand landmarks from sign language videos and analyzes hand position,orientation,and motion.The extracted features are converted into time-series data and fed into a Long Short-Term Memory(LSTM)model.The proposed recognition framework achieved an accuracy of up to 81.25%,while the sentence generation achieved an accuracy of up to 95%.The proposed approach is expected to be applicable not only to Korean Sign Language but also to other low-resource sign languages for recognition and translation tasks.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
基金funded by the National Science and Technology Council(NSTC),Taiwan,grant number NSTC 114-2218-E-167-001.
文摘Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects on silicon carbide(SiC)wafers has become essential.This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage.Thecomplex machining textures on wafer surfaces hinder conventional machine vision models,often leading to misjudgment.To address this,deep learning algorithms were applied for defect classification.Because defects are rare and imbalanced across categories,data augmentation was performed using aWasserstein generative adversarial network with gradient penalty(WGAN-GP),along with conventionalmethods.An improved YOLOv8-seg instance segmentationmodel was then trained and tested on datasets with different augmentation strategies.Experimental results showed that,when trained withWGAN-GP–generated data,YOLOv8-seg achieved mean average precision values of 87.0%(bounding box)and 86.6%(segmentation mask).Compared with the traditional WGAN-GP,the proposed model reduced Frechet inception distance by 32.2%and multiscale structural similarity index by 29.8%,generating more realistic and diverse defect images.The proposed framework effectively improves defect detection accuracy under limited data conditions and shows strong potential for industrial applications.
基金Supported by the Key R&D Projects in Hubei Province(2025BAB018,2022BAA041)and Wuhan University Comprehensive Undergraduate Education Quality Reform Project。
文摘IoT devices are highly vulnerable to cyberattacks due to their widespread,distributed nature and limited security features.Intrusion detection can counter these threats,but class imbalance between normal and abnormal traffic often degrades model performance.We propose a novel multi-generator adversarial data augmentation method that blends the strengths of TMG-GAN(Tabular Multi-Generator Generative Adversarial Network)and R3GAN(Re-GAN).Our approach uses multiple class-specific generators to create diverse,high-quality synthetic samples,improving training stability and minority-class detection.A dual-branch discriminator-classifier enhances authenticity and class prediction,while feature similarity and decoupling techniques ensure clear class separation.Experiments on TON-IoT and Edge-IIoTset datasets show our method outperforms existing techniques like hybrid sampling,SNGAN(Spectral Normalization GAN),and TMG-GAN,achieving higher detection accuracy and better minority-class recall for imbalanced IoT intrusion detection.
基金Supported by the Science and Technology Project from State Grid Corporation of China (No.5700-202490330A-2-1-ZX)。
文摘To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detection framework integrating image quality evaluation and text-to-image data augmentation.First,a HyperNetwork-based image quality assessment module is introduced to filter low-quality inspection images in terms of clarity and structural integrity,resulting in a high-quality training dataset.Second,a text-to-image diffusion model is utilized for sample augmentation.By designing text prompts that describe various bolt defect types under diverse lighting and viewing conditions,the model automatically generates realistic synthetic samples.The generated images are further filtered using a combination of quality and perceptual similarity metrics to ensure consistency with the real data distribution.Building upon the sparse RCNN baseline,a dynamic label assignment mechanism and a random decision path detection head are incorporated to enhance bounding box matching and prediction accuracy.Experimental results demonstrate that the proposed method significantly improves detection accuracy(mAP@0.5) over the original sparse RCNN while maintaining low computational cost,enabling more efficient and intelligent inspection of transmission line components.
基金This work was supported by the National Key R&D Program of China[grant number 2017YFC1501700]the National Natural Science Foundation of China[grant number 41575033].
文摘Ground-based microwave radiometers profilers(MWRPs)have been used in numerical weather prediction(NWP)systems and show different impacts on forecasts.Currently,there are around hundreds of ground-based MWPRs used in weather stations over China;however,the application of MWPRs in NWP systems is rather limited.In this work,two MWRP retrieved profiles were assimilated into the Weather Research and Forecasting(WRF)model for a rainstorm event that occurred in Beijing,China.The quality of temperature and humidity profiles retrieved from the MWRP was evaluated against radiosonde observations and showed the reliability of the two MWRP products.Then,comparisons between the measurements of ground-based rain gauges and the corresponding forecasted precipitation in different periods of the rainstorm were investigated.The results showed that assimilating the two MWRPs affected the distribution and intensity of rainfall,especially in the early stage of the rainstorm.With the development of the rainstorm,adding MWRP data showed only a slight influence on the precipitation during the stable and mature period of the rainstorm,since the two MWRP observations were too limited to affect the large area of heavy rainfall.
基金supported by the National Natural Science Foundation of China (41004030)
文摘A long-term （9 years） gravity change in Chinese mainland is obtained on the basis of observation of the ground-based national gravity network. The result shows several features that may be related to sore, large-scale groundwater pumping in North China, glacier-water flow and storage in Tianshan region, and pre seismic gravity changes of the 2008 MsS. 0 Wenchuan earthquake, which are spatially similar to co-seismi, changes but reversed in sign. These features are also shown in the result of the satellite-based GRACE obser vation, after a height effect is corrected with GPS data.
基金supported by Royal Society grant DHFR1211068funded by UKSA+14 种基金STFCSTFC grant ST/M001083/1funded by STFC grant ST/W00089X/1supported by NERC grant NE/W003309/1(E3d)funded by NERC grant NE/V000748/1support from NERC grants NE/V015133/1,NE/R016038/1(BAS magnetometers),and grants NE/R01700X/1 and NE/R015848/1(EISCAT)supported by NERC grant NE/T000937/1NSFC grants 42174208 and 41821003supported by the Research Council of Norway grant 223252PRODEX arrangement 4000123238 from the European Space Agencysupport of the AUTUMN East-West magnetometer network by the Canadian Space Agencysupported by NASA’s Heliophysics U.S.Participating Investigator Programsupport from grant NSF AGS 2027210supported by grant Dnr:2020-00106 from the Swedish National Space Agencysupported by the German Research Foundation(DFG)under number KR 4375/2-1 within SPP"Dynamic Earth"。
文摘The joint European Space Agency and Chinese Academy of Sciences Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission will explore global dynamics of the magnetosphere under varying solar wind and interplanetary magnetic field conditions,and simultaneously monitor the auroral response of the Northern Hemisphere ionosphere.Combining these large-scale responses with medium and fine-scale measurements at a variety of cadences by additional ground-based and space-based instruments will enable a much greater scientific impact beyond the original goals of the SMILE mission.Here,we describe current community efforts to prepare for SMILE,and the benefits and context various experiments that have explicitly expressed support for SMILE can offer.A dedicated group of international scientists representing many different experiment types and geographical locations,the Ground-based and Additional Science Working Group,is facilitating these efforts.Preparations include constructing an online SMILE Data Fusion Facility,the discussion of particular or special modes for experiments such as coherent and incoherent scatter radar,and the consideration of particular observing strategies and spacecraft conjunctions.We anticipate growing interest and community engagement with the SMILE mission,and we welcome novel ideas and insights from the solar-terrestrial community.
基金supported by the National Natural Science Foundation of China(61801007)the Beijing Natural Science Foundation(4194075)。
文摘Ground-based synthetic aperture radar(GB-SAR) has been successfully applied to the ground deformation monitoring.However, due to the short length of the GB-SAR platform, the scope of observation is largely limited. The practical applications drive us to make improvements on the conventional linear rail GB-SAR system in order to achieve larger field imaging. First, a turntable is utilized to support the rotational movement of the radar.Next, a series of high-squint scanning is performed with multiple squint angles. Further, the high squint modulation phase of the echo data is eliminated. Then, a new multi-angle imaging method is performed in the wave number domain to expand the field of view. Simulation and real experiments verify the effectiveness of this method.
基金National Key Research and Development Program of China(2017YFC1501704,2016YFA0600703)Projects of International Cooperation and Exchanges NSFC(NSFC-RCUK_STFC)(61661136005)+2 种基金Major State Basic Research Development Program of China(973 Program)(2013CB430101)Six Talent Peaks Project in Jiangsu Province(2015-JY-013)Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,National Satellite Meteorological Center,China Meteorological Administration
文摘Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 were inputted into the Mono RTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234 GHz to 58.8 GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model's effect by comparing its output with the real measured data and the microwave radiometer's own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1 K and 2.0 K; the water vapor density's RMS error is between 0.2 g/m^3 and 1.93 g/m3; and the relative humidity's RMS error is between 2.5% and 18.6%.
基金primarily funded by the cooperative project offered by Beijing Key Laboratory of Space Thermal Control Technologyfunded by China Postdoctoral Science Foundation(No.2020 M671618)。
文摘The lunar surface is a typical vacuum environment,and its harsh heat rejection conditions bring great challenges to the thermal control technology of the exploration mission.In addition to the radiator,the sublimator is recommended as one of the promising options for heat rejection.The sublimator makes use of water to freeze and sublimate in a porous medium,rejecting heat to the vacuum environment.The complex heat and mass transfer process involves many physical phenomena such as the freezing and sublimation phase change of water in the porous medium and the movement of the phase-change interface.In this paper,the visualized ground-based experimental approaches of space sublimation cooling were presented to reveal the moving law of threephase point and the growth phenomenon of ice-peak and icicle in microchannels under vacuum conditions.The visualized experiments and results prove that the freezing ice is divided into the porous ice-peak and the transparent icicle.As the sublimation progresses,the phase-change interface moves downward steadily,the length of the ice-peak increases,but the icicle decreases.The visualized experiments of space sublimation cooling in the capillary have guiding significance to reveal the sublimation cooling mechanism of water in the sublimator for lunar exploration missions.
基金supported by the National Natural Science Foundation of China(Grant No.42205140,41975035)the National Key Research and Development Program of China(2021YFB3901000).
文摘Measurements of carbon dioxide(CO_(2)),methane(CH_(4)),and carbon monoxide(CO)are of great importance in the Qinghai-Tibetan region,as it is the highest and largest plateau in the world affecting global weather and climate systems.In this study,for the first time,we present CO_(2),CH_(4),and CO column measurements carried out by a Bruker EM27/SUN Fourier-transform infrared spectrometer(FTIR)at Golmud(36.42°E,94.91°N,2808 m)in August 2021.The mean and standard deviation of the column-average dry-air mixing ratio of CO_(2),CH_(4),and CO(XCO_(2),XCH_(4),and XCO)are 409.3±0.4 ppm,1905.5±19.4 ppb,and 103.1±7.7 ppb,respectively.The differences between the FTIR co-located TROPOMI/S5P satellite measurements at Golmud are 0.68±0.64%(13.1±12.2 ppb)for XCH_(4)and 9.81±3.48%(–10.7±3.8 ppb)for XCO,which are within their retrieval uncertainties.High correlations for both XCH_(4)and XCO are observed between the FTIR and S5P satellite measurements.Using the FLEXPART model and satellite measurements,we find that enhanced CH_(4)and CO columns in Golmud are affected by anthropogenic emissions transported from North India.This study provides an insight into the variations of the CO_(2),CH_(4),and CO columns in the Qinghai-Tibetan Plateau.
基金Supported by the National Natural Science Foundation of China under Grant No 11235004
文摘Performance test of a high precise accelerometer or an inertial sensor on the ground is inevitably limited by the seismic noise. A torsion pendulum has been used to investigate the performances of an electrostatic accelerometer, where the test mass is suspended by a fiber to compensate for its weight, and this scheme demonstrates an advantage, compared with the high-voltage levitation scheme, in which the effect of the seismic noise can be suppressed for a few orders of magnitude in low frequencies. In this work, the capacitive electrode cage is proposed to be suspended by another pendulum, and theoretical analysis shows that the effects of the seismic noise can be further suppressed for more than one order by suspending the electrode cage.
文摘With the development of adaptive optics and post restore processing techniques,large aperture ground-based telescopes can obtain high-resolution images(HRIs)of targets.The pose of the space target can be estimated from HRIs by several methods.As the target features obtained from the image are unstable,it is difficult to use existing methods for pose estimation.In this paper a method based on real-time target model matching to estimate the pose of space targets is proposed.First,the physicallyconstrained iterative deconvolution algorithm is used to obtain HRIs of the space target.Second,according to the 3D model,the ephemeris data,the observation time of the target,and the optical parameters of the telescope,the simulated observation image of the target in orbit is rendered by a scene simulation program.Finally,the target model searches through yaw,pitch,and roll until the correlation between the simulated observation image and the actual observation image shows an optimal match.The simulation results show that the proposed pose estimation method can converge to the local optimal value with an estimation error of about 1.6349°.
