面向机载探测系统大视场光学成像的需求,开展了大视场光学成像系统光学设计、自由曲面光学元件超精密加工、形位误差同步检测以及系统集成与成像实验研究。首先,采用视场扩展法进行大视场自由曲面离轴反射光学系统的设计;其次,进行铝合...面向机载探测系统大视场光学成像的需求,开展了大视场光学成像系统光学设计、自由曲面光学元件超精密加工、形位误差同步检测以及系统集成与成像实验研究。首先,采用视场扩展法进行大视场自由曲面离轴反射光学系统的设计;其次,进行铝合金自由曲面反射镜纳米精度加工和高频抑制工艺探索,并实现了基于计算全息元件的自由曲面形位高精度检测;最后,进行了光学系统的装调集成与成像实验。结果表明,系统的视场角为30°×5°,全视场光学传递函数值大于0.7,接近衍射极限,最大像元均方根半径为2.075μm,自由曲面光学元件的面形精度均方根(Root Mean Square,RMS)值优于20 nm,位置精度优于1μm,装配集成后能够满足大视场高分辨的场景使用要求,同时具备稳定可靠和快响制造等特点。展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
文摘面向机载探测系统大视场光学成像的需求,开展了大视场光学成像系统光学设计、自由曲面光学元件超精密加工、形位误差同步检测以及系统集成与成像实验研究。首先,采用视场扩展法进行大视场自由曲面离轴反射光学系统的设计;其次,进行铝合金自由曲面反射镜纳米精度加工和高频抑制工艺探索,并实现了基于计算全息元件的自由曲面形位高精度检测;最后,进行了光学系统的装调集成与成像实验。结果表明,系统的视场角为30°×5°,全视场光学传递函数值大于0.7,接近衍射极限,最大像元均方根半径为2.075μm,自由曲面光学元件的面形精度均方根(Root Mean Square,RMS)值优于20 nm,位置精度优于1μm,装配集成后能够满足大视场高分辨的场景使用要求,同时具备稳定可靠和快响制造等特点。
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
