Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems.While graph neural networks show promise for this task,existing methods often neglect the long-tailed distribution ...Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems.While graph neural networks show promise for this task,existing methods often neglect the long-tailed distribution inherent in real-world grid fault data and fail to provide reliability estimates for their decisions.To address these dual challenges,we propose a novel multi-expert collaboration uncertainty-aware power fault recognition framework with cross-view graph learning.Its core innovations are two synergistic modules:(1)The infographics aggregation module tackles the long-tail problem by learning robust graph-level representations.It employs an information-driven optimization loss within a contrastive graph architecture,explicitly preserving global invariance and local structural information across diverse(including rare)fault states.This ensures balanced representation learning for both the head and tail classes.(2)The multi-expert reliable decision module addresses prediction uncertainty.It trains individual expert classifiers using the Dirichlet distribution to explicitly model the credibility(uncertainty)of each expert’s decision.Crucially,a complementary collaboration rule based on evidence theory dynamically integrates these experts.This rule generates active weights for expert participation,prioritizing more certain experts and fusing their evidence to produce a final decision with a quantifiable reliability estimate.Collaboratively,these modules enable reliable diagnosis under data imbalance:The Infographics Module provides discriminative representations for all fault types,especially tail classes,while the Multi-Expert Module leverages these representations to make decisions with explicit uncertainty quantification.This synergy significantly improves both the accuracy and the reliability of fault recognition,particularly for rare or ambiguous grid conditions.Ultimately,extensive experiment evaluations on the four datasets reveal that the proposed method outperforms the state-of-the-art methods in the fault diagnosis of smart grids,in terms of accuracy,precision,f score and recall.展开更多
We develop a new geometric approach to deal with qubit information systems using colored graph theory. More precisely, we present a one to one correspondence between graph theory, and qubit systems, which may be explo...We develop a new geometric approach to deal with qubit information systems using colored graph theory. More precisely, we present a one to one correspondence between graph theory, and qubit systems, which may be explored to attack qubit information problems using torie geometry considered as a powerful tool to understand modern physics including string theory. Concretely, we examine in some details the cases of one, two, and three qubits, and we find that they are associated with CP1, CP1×CP1 and CP1×CP1× CP1 toric varieties respectively. Using a geometric procedure referred to as a colored toric geometry, we show that the qubit physics can be converted into a scenario handling toric data of such manifolds by help of hypercube graph theory. Operations on toric information can produce universal quantum gates.展开更多
Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can signi...Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can significantly improve the performance of GNNs,however,injecting high-level structure and distance into GNNs is an intuitive but untouched idea.This work sheds light on this issue and proposes a scheme to enhance graph attention networks(GATs)by encoding distance and hop-wise structure statistics.Firstly,the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node.Secondly,the derived structure information,distance information,and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors.Thirdly,the derived embedding vectors are fed into GATs,such as GAT and adaptive graph diffusion network(AGDN)to get the soft labels.Fourthly,the soft labels are fed into correct and smooth(C&S)to conduct label propagation and get final predictions.Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks(DHSEGATs)achieve a competitive result.展开更多
Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual inform...Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual information to identify the causal structure of multivariate time series causal graphical models.A three-step procedure is developed to learn the contemporaneous and the lagged causal relationships of time series causal graphs.Contrary to conventional constraint-based algorithm, the proposed algorithm does not involve any special kinds of distribution and is nonparametric.These properties are especially appealing for inference of time series causal graphs when the prior knowledge about the data model is not available.Simulations and case analysis demonstrate the effectiveness of the method.展开更多
Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mo...Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.展开更多
基金supported by the Development Department Science and Technology Project(52992624000X).
文摘Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems.While graph neural networks show promise for this task,existing methods often neglect the long-tailed distribution inherent in real-world grid fault data and fail to provide reliability estimates for their decisions.To address these dual challenges,we propose a novel multi-expert collaboration uncertainty-aware power fault recognition framework with cross-view graph learning.Its core innovations are two synergistic modules:(1)The infographics aggregation module tackles the long-tail problem by learning robust graph-level representations.It employs an information-driven optimization loss within a contrastive graph architecture,explicitly preserving global invariance and local structural information across diverse(including rare)fault states.This ensures balanced representation learning for both the head and tail classes.(2)The multi-expert reliable decision module addresses prediction uncertainty.It trains individual expert classifiers using the Dirichlet distribution to explicitly model the credibility(uncertainty)of each expert’s decision.Crucially,a complementary collaboration rule based on evidence theory dynamically integrates these experts.This rule generates active weights for expert participation,prioritizing more certain experts and fusing their evidence to produce a final decision with a quantifiable reliability estimate.Collaboratively,these modules enable reliable diagnosis under data imbalance:The Infographics Module provides discriminative representations for all fault types,especially tail classes,while the Multi-Expert Module leverages these representations to make decisions with explicit uncertainty quantification.This synergy significantly improves both the accuracy and the reliability of fault recognition,particularly for rare or ambiguous grid conditions.Ultimately,extensive experiment evaluations on the four datasets reveal that the proposed method outperforms the state-of-the-art methods in the fault diagnosis of smart grids,in terms of accuracy,precision,f score and recall.
文摘We develop a new geometric approach to deal with qubit information systems using colored graph theory. More precisely, we present a one to one correspondence between graph theory, and qubit systems, which may be explored to attack qubit information problems using torie geometry considered as a powerful tool to understand modern physics including string theory. Concretely, we examine in some details the cases of one, two, and three qubits, and we find that they are associated with CP1, CP1×CP1 and CP1×CP1× CP1 toric varieties respectively. Using a geometric procedure referred to as a colored toric geometry, we show that the qubit physics can be converted into a scenario handling toric data of such manifolds by help of hypercube graph theory. Operations on toric information can produce universal quantum gates.
文摘Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can significantly improve the performance of GNNs,however,injecting high-level structure and distance into GNNs is an intuitive but untouched idea.This work sheds light on this issue and proposes a scheme to enhance graph attention networks(GATs)by encoding distance and hop-wise structure statistics.Firstly,the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node.Secondly,the derived structure information,distance information,and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors.Thirdly,the derived embedding vectors are fed into GATs,such as GAT and adaptive graph diffusion network(AGDN)to get the soft labels.Fourthly,the soft labels are fed into correct and smooth(C&S)to conduct label propagation and get final predictions.Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks(DHSEGATs)achieve a competitive result.
基金supported by the National Natural Science Foundation of China under Grant Nos.60972150, 10926197,61201323
文摘Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual information to identify the causal structure of multivariate time series causal graphical models.A three-step procedure is developed to learn the contemporaneous and the lagged causal relationships of time series causal graphs.Contrary to conventional constraint-based algorithm, the proposed algorithm does not involve any special kinds of distribution and is nonparametric.These properties are especially appealing for inference of time series causal graphs when the prior knowledge about the data model is not available.Simulations and case analysis demonstrate the effectiveness of the method.
基金supported by the National Basic Research Program of China(973 Program) through grant 2012CB316004the Doctoral Program of Higher Education(SRFDP)+1 种基金Research Grants Council Earmarked Research Grants(RGC ERG) Joint Research Scheme through Specialized Research Fund 20133402140001National Natural Science Foundation of China through grant 61379003
文摘Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.
