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Special topic on advanced visual measurement and intelligent detection technology 认领 引用
1
作者 XING Zhizhong 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2026年第2期I0001-I0001,共1页
Visual measurement and intelligent detection serve as core supporting technologies for modern precision instruments,intelligent sensing,and unmanned systems.With the rapid development of deep learning,multi-dimensiona... Visual measurement and intelligent detection serve as core supporting technologies for modern precision instruments,intelligent sensing,and unmanned systems.With the rapid development of deep learning,multi-dimensional signal processing,and computer vision,relevant technologies are accelerating breakthroughs toward high precision,strong robustness,multi-scenario adaptation,and end-to-end intelligence.In complex working conditions such as heterogeneous data sources,low-quality imaging,multi-scale variation,and weak feature representation,traditional methods are difficult to balance accuracy,efficiency,and generalization,while advanced visual measurement and intelligent detection technologies provide a key path for performance improvement and engineering application via multi-scale feature fusion,attention mechanism,structural en-hancement,and loss optimization. 展开更多
关键词 advanced visual measurement heterogeneous data sourceslow quality computer visionrelevant supporting technologies visual measurement intelligent detection technology unmanned systemswith deep learningmulti dimensional
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Distributed Byzantine-Resilient Learning of Multi-UAV Systems via Filter-Based Centerpoint Aggregation Rules 认领 引用 被引量：2
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作者 Yukang Cui Linzhen Cheng +1 位作者 Michael Basin Zongze Wu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期1056-1058,共3页
Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication w... Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors. 展开更多
关键词 global optimization goals multi UAV systems filter based centerpoint aggregation distributed learning optimal target trackingby stochastic gradient descent algorithm sgd distributedly optimize tracking distributed machine learningmulti uav
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