Research on Pulsar Time Steered Atomic Time Algorithm Based on DPLL

In today’s society,there is a wide demand for high-precision and high-stability time service in the fields of electric power,communication,transportation and finance.At present,the time standard in various countries is mainly based on atomic clocks,but the frequency drift of atomic clocks will affect the long-term stability performance.Compared with atomic clocks,millisecond pulsars have better long-term stability and can complement with the excellent short-term stability of atomic clocks.In order to improve the long-term stability of the atomic timescale,and then improve the timing accuracy,this paper proposes an algorithm for steering the atomic clock ensemble(ACE)by ensemble pulsar time(EPT)based on digital phase locked loop(DPLL).First,the ACE and EPT are generated by the ALGOS algorithm,then the ACE is steered by EPT based on DPLL to calibrate the long-term frequency drift of the atomic clock,so that the generated steered atomic time follows both the short-term stability characteristics of ACE and the long-term stability characteristics of EPT,and finally,the steered atomic time is used to calibrate the local cesium clock.The experimental results show that the long-term stability of atomic time after steering is improved by 2 orders of magnitude compared with that before steering,and the daily drift of a local cesium clock after calibration is less than 9.47 ns in 3 yr,3 orders of magnitude higher than that before calibration on accuracy.

DLF-YOLOF:an improved YOLOF-based surface defect detection for steel plate

Surface defects can affect the quality of steel plate.Many methods based on computer vision are currently applied to surface defect detection of steel plate.However,their real-time performance and object detection of small defect are still unsatisfactory.An improved object detection network based on You Only Look One-level Feature(YOLOF)is proposed to show excellent performance in surface defect detection of steel plate,called DLF-YOLOF.First,the anchor-free detector is used to reduce the network hyperparameters.Secondly,deformable convolution network and local spatial attention module are introduced into the feature extraction network to increase the contextual information in the feature maps.Also,the soft non-maximum suppression is used to improve detection accuracy significantly.Finally,data augmentation is performed for small defect objects during training to improve detection accuracy.Experiments show the average precision and average precision for small objects are 42.7%and 33.5%at a detection speed of 62 frames per second on a single GPU,respectively.This shows that DLF-YOLOF has excellent performance to meet the needs of industrial real-time detection.