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Energy Consumption Model of Hoisting Mechanism of Bridge and Gantry Crane Based on Bond Graph Theory
华 赵
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Graph Theory-Based Sneak Circuit Analysis and Trigger of Semi-DAB With Parasitic Parameters
IEEE transactions on power electronics, 2023Parasitic parameters and dynamic sneak paths would lead to unexpected phenomena, exerting negative impacts on the reliability and safety of semi-dual active bridge (S-DAB) dc–dc converter.
Yiting Xiao+4 more
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Graph-Theory-Based Derivation, Modeling, and Control of Power Converter Systems
IEEE Journal of Emerging and Selected Topics in Power Electronics, 2022Graph-theoretical approaches have been widely applied in many disciplines, however, their implementation in power electronics converters and systems is still in the exploring stage.
Yuzhuo Li+3 more
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Luying Zhong+4 more
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Partitioning a graph into two isomorphic pieces [PDF]
AbstractA simple graph G has the neighbour‐closed‐co‐neighbour property, or ncc property, if for all vertices x of G, the subgraph induced by the set of neighbours of x is isomorphic to the subgraph induced by the set of non‐neighbours of x. We present characterizations of graphs with the ncc property via the existence of certain perfect matchings, and
FUNK, Martin+3 more
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Transport Phenomena in Zeolites in View of Graph Theory and Pseudo-Phase Transition.
Small, 2020Transport phenomena play an essential role in catalysis. While zeolite catalysis is widely applied in industrial chemical processes, its efficiency is often limited by the transport rate in the micropores of the zeolite.
Dali Cai+3 more
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Relationship between Power Flow Transferring and Path Length using Graph Theory
2020 IEEE 1st China International Youth Conference on Electrical Engineering (CIYCEE), 2020Unexpected power changes may lead to transmission line overload and even evolve into cascading breakout. To avoid this, the key is to analyze how the power flow transfers after disturbance.
Jiawei Yu, Ziqian Yang, M. Zhan
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Graph Neural Networks: Foundation, Frontiers and Applications
Knowledge Discovery and Data Mining, 2022The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning, have become one of the fastest-
Lingfei Wu+4 more
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Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
Neural Information Processing Systems, 2023Node-level random walk has been widely used to improve Graph Neural Networks. However, there is limited attention to random walk on edge and, more generally, on $k$-simplices.
Cai Zhou, Xiyuan Wang, Muhan Zhang
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