Geometric distortion(GD)critically constrains the precision of *** well-established methods to correct GD requires calibration observations,which can only be obtained using a special dithering strategy during the obse...
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Geometric distortion(GD)critically constrains the precision of *** well-established methods to correct GD requires calibration observations,which can only be obtained using a special dithering strategy during the observation ***,this special observation mode is not often used,especially for historical observations before those GD correction methods were *** a result,some telescopes have no GD calibration observations for a long period,making it impossible to accurately determine the GD *** limits the value of the telescope observations in certain astrometric scenarios,such as using historical observations of moving targets in the solar system to improve their *** investigated a method for handling GD that does not rely on the calibration *** this advantage,it can be used to solve the GD models of telescopes which were intractable in the *** method was implemented in Python and released on *** was then applied to solve GD in the observations taken with the 1 m and 2.4 m telescopes at Yunnan *** resulting GD models were compared with those obtained using well-established methods to demonstrate the ***,the method was applied in the reduction of observations for two targets,the moon of Jupiter(Himalia)and binary GSC 2038-0293,to show its *** GD correction,the astrometric results for both targets show ***,the mean residual between the observed and computed position(O-C)for binary GSC 2038-0293 decreased from 36 to 5 mas.
Binarized ReLU activations are considered as a metric space equipped with the Hamming distance. While for two-layer ReLU networks with random Gaussian weights it can be shown theoretically that local metric properties...
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We introduce a novel position offset label prediction subtask to the encoder-decoder architecture for grammatical error correction (GEC) task. To keep the meaning of the input sentence unchanged, only a few words shou...
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Facial expression recognition (FER) technology has numerous applications in various fields such as health, entertainment and gaming, transportation, advertising and marketing, education, and many more. The recognition...
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Understanding documents is central to many real-world tasks but remains a challenging topic. Unfortunately, there is no well-established consensus on how to comprehensively evaluate document understanding abilities, w...
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This study proposes an image-based three-dimensional(3D)vector reconstruction of industrial parts that can gener-ate non-uniform rational B-splines(NURBS)surfaces with high fidelity and *** contributions of this study...
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This study proposes an image-based three-dimensional(3D)vector reconstruction of industrial parts that can gener-ate non-uniform rational B-splines(NURBS)surfaces with high fidelity and *** contributions of this study include three parts:first,a dataset of two-dimensional images is constructed for typical industrial parts,including hex-agonal head bolts,cylindrical gears,shoulder rings,hexagonal nuts,and cylindrical roller bearings;second,a deep learning algorithm is developed for parameter extraction of 3D industrial parts,which can determine the final 3D parameters and pose information of the reconstructed model using two new nets,CAD-ClassNet and CAD-ReconNet;and finally,a 3D vector shape reconstruction of mechanical parts is presented to generate NURBS from the obtained shape *** final reconstructed models show that the proposed approach is highly accurate,efficient,and practical.
Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal **...
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Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal *** at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is ***,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample *** the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original *** algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.
作者：
Zhang, XindiLi, BohanCai, ShaoweiInstitute of Software
Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing China
Satisfiability Modulo Theory (SMT) generalizes the propositional satisfiability problem (SAT) by extending support for various first-order background theories. In this paper, we focus on the SMT problems in Non-Linear...
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In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature *** key observation is that to recover the underlying structures as well as surface details,given partial input...
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In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature *** key observation is that to recover the underlying structures as well as surface details,given partial input,a fundamental component is a good feature representation that can capture both global structure and local geometric *** accordingly first propose FSNet,a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local *** then integrate FSNet into a coarse-to-fine pipeline for point cloud ***,a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point ***,a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate *** efficiently exploit local structures and enhance point distribution uniformity,we propose IFNet,a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point *** have conducted qualitative and quantitative experiments on ShapeNet,MVP,and KITTI datasets,which demonstrate that our method outperforms stateof-the-art point cloud completion approaches.
K-Means algorithm is one of the most common clustering algorithms widely applied in various data analysis applications. Yinyang K-Means algorithm is a popular enhanced K-Means algorithm that avoids most unnecessary ca...
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