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    Temporal Relational Reasoning in Videos

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    Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets - Something-Something, Jester, and Charades - which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.Comment: camera-ready version for ECCV'1

    Control of DFIG based wind generation systems under unbalanced network supply

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    This paper develops a dynamic model and control scheme for DFIG systems to improve the performance and stability under unbalanced grid conditions. A dynamic DFIG model containing the positive and negative sequence components is presented using stator voltage orientation. The proposed model accurately illustrates the active power, reactive power and torque oscillations, and provides a basis for DFIG control system design during unbalanced network supply. Various control targets such as eliminating the oscillations of the torque, active/reactive power are discussed and the required rotor negative sequence current for fulfilling different control targets are described. Performance of a DFIG-based wind turbine under unbalanced condition using the proposed control method is evaluated by simulation studies using Matlab/Simulink. The proposed control scheme significantly attenuates the DFIG torque or active power oscillations during network unbalance whereas significant torque/power oscillations exist with the conventional control schemes