Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decode...Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.展开更多
当前虚拟电厂(virtual power plant,VPP)建设方兴未艾。虚拟电厂参与电网调度需要综合考虑虚拟电厂运营商的经济性要求与电网的安全性要求,但在目前的电力系统信息安全要求下,市场化虚拟电厂运营商不掌握电网拓扑和实时潮流信息,随着虚...当前虚拟电厂(virtual power plant,VPP)建设方兴未艾。虚拟电厂参与电网调度需要综合考虑虚拟电厂运营商的经济性要求与电网的安全性要求,但在目前的电力系统信息安全要求下,市场化虚拟电厂运营商不掌握电网拓扑和实时潮流信息,随着虚拟电厂聚合资源的增多,可能带来严重的电网安全隐患。基于我国电力调度体系现状,提出了虚拟机组的定义,并从虚拟机组分区聚合构建方式、虚拟电厂参与分层调控方式、虚拟电厂参与电力市场方式等方面进行阐述,提出了一套分区聚合、分层调度的市场化虚拟电厂调控架构。算例表明,所提出的调控架构能够支撑虚拟电厂运营商在不掌握拓扑信息的情况下安全且经济地参与电网调度和市场交易。展开更多
基金support for this work was supported by Key Lab of Intelligent and Green Flexographic Printing under Grant ZBKT202301.
摘要Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.
摘要当前虚拟电厂(virtual power plant,VPP)建设方兴未艾。虚拟电厂参与电网调度需要综合考虑虚拟电厂运营商的经济性要求与电网的安全性要求,但在目前的电力系统信息安全要求下,市场化虚拟电厂运营商不掌握电网拓扑和实时潮流信息,随着虚拟电厂聚合资源的增多,可能带来严重的电网安全隐患。基于我国电力调度体系现状,提出了虚拟机组的定义,并从虚拟机组分区聚合构建方式、虚拟电厂参与分层调控方式、虚拟电厂参与电力市场方式等方面进行阐述,提出了一套分区聚合、分层调度的市场化虚拟电厂调控架构。算例表明,所提出的调控架构能够支撑虚拟电厂运营商在不掌握拓扑信息的情况下安全且经济地参与电网调度和市场交易。