513 research outputs found
Optimized Hierarchical Power Oscillations Control for Distributed Generation Under Unbalanced Conditions
Control structures have critical influences on converter-interfaced
distributed generations (DG) under unbalanced conditions. Most of previous
works focus on suppressing active power oscillations and ripples of DC bus
voltage. In this paper, the relationship between amplitudes of the active power
oscillations and the reactive power oscillations are firstly deduced and the
hierarchical control of DG is proposed to reduce power oscillations. The
hierarchical control consists of primary and secondary levels. Current
references are generated in primary control level and the active power
oscillations can be suppressed by a dual current controller. Secondary control
reduces the active power and reactive power oscillations simultaneously by
optimal model aiming for minimum amplitudes of oscillations. Simulation results
show that the proposed secondary control with less injecting negative-sequence
current than traditional control methods can effectively limit both active
power and reactive power oscillations.Comment: Accepted by Applied Energ
Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI
Recently, regularization model-driven deep learning (DL) has gained
significant attention due to its ability to leverage the potent
representational capabilities of DL while retaining the theoretical guarantees
of regularization models. However, most of these methods are tailored for
supervised learning scenarios that necessitate fully sampled labels, which can
pose challenges in practical MRI applications. To tackle this challenge, we
propose a self-supervised DL approach for accelerated MRI that is theoretically
guaranteed and does not rely on fully sampled labels. Specifically, we achieve
neural network structure regularization by exploiting the inherent structural
low-rankness of the -space data. Simultaneously, we constrain the network
structure to resemble a nonexpansive mapping, ensuring the network's
convergence to a fixed point. Thanks to this well-defined network structure,
this fixed point can completely reconstruct the missing -space data based on
matrix completion theory, even in situations where full-sampled labels are
unavailable. Experiments validate the effectiveness of our proposed method and
demonstrate its superiority over existing self-supervised approaches and
traditional regularization methods, achieving performance comparable to that of
supervised learning methods in certain scenarios
Towards Balanced Alignment: Modal-Enhanced Semantic Modeling for Video Moment Retrieval
Video Moment Retrieval (VMR) aims to retrieve temporal segments in untrimmed
videos corresponding to a given language query by constructing cross-modal
alignment strategies. However, these existing strategies are often sub-optimal
since they ignore the modality imbalance problem, \textit{i.e.}, the semantic
richness inherent in videos far exceeds that of a given limited-length
sentence. Therefore, in pursuit of better alignment, a natural idea is
enhancing the video modality to filter out query-irrelevant semantics, and
enhancing the text modality to capture more segment-relevant knowledge. In this
paper, we introduce Modal-Enhanced Semantic Modeling (MESM), a novel framework
for more balanced alignment through enhancing features at two levels. First, we
enhance the video modality at the frame-word level through word reconstruction.
This strategy emphasizes the portions associated with query words in
frame-level features while suppressing irrelevant parts. Therefore, the
enhanced video contains less redundant semantics and is more balanced with the
textual modality. Second, we enhance the textual modality at the
segment-sentence level by learning complementary knowledge from context
sentences and ground-truth segments. With the knowledge added to the query, the
textual modality thus maintains more meaningful semantics and is more balanced
with the video modality. By implementing two levels of MESM, the semantic
information from both modalities is more balanced to align, thereby bridging
the modality gap. Experiments on three widely used benchmarks, including the
out-of-distribution settings, show that the proposed framework achieves a new
start-of-the-art performance with notable generalization ability (e.g., 4.42%
and 7.69% average gains of [email protected] on Charades-STA and Charades-CG). The code
will be available at https://github.com/lntzm/MESM.Comment: Accepted to AAAI 202
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