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
While it is very reasonable to use a multigraph consisting of multiple edges between vertices to represent various relationships, the multigraph has not drawn much attention in research. To visualize such a multigraph...While it is very reasonable to use a multigraph consisting of multiple edges between vertices to represent various relationships, the multigraph has not drawn much attention in research. To visualize such a multigraph, a clear layout representing a global structure is of great importance, and interactive visual analysis which allows the multiple edges to be adjusted in appropriate ways for detailed presentation is also essential. A novel interactive two-phase approach to visualizing and exploring multigraph is proposed. The approach consists of two phases： the first phase improves the previous popular works on force-directed methods to produce a brief drawing for the aggregation graph of the input multigraph, while the second phase proposes two interactive strategies, the magnifier model and the thematic-oriented subgraph model. The former highlights the internal details of an aggregation edge which is selected interactively by user, and draws the details in a magnifying view by cubic Bezier curves; the latter highlights only the thematic subgraph consisting of the selected multiple edges that the user concerns. The efficiency of the proposed approach is demonstrated with a real-world multigraph dataset and how it is used effectively is discussed for various potential applications.展开更多
Deep space networks,satellite networks,ad hoc networks,and the Internet can be modeled as DTNs(Delay Tolerant Networks).As a fundamental problem,the maximum flow problem is of vital importance for routing and service ...Deep space networks,satellite networks,ad hoc networks,and the Internet can be modeled as DTNs(Delay Tolerant Networks).As a fundamental problem,the maximum flow problem is of vital importance for routing and service scheduling in networks.However,there exists no permanent end-to-end path since the topology and the characteristics of links are time-variant,resulting in a crucial maximum flow problem in DTNs.In this paper,we focus on the single-source-single-sink maximum flow problem of buffer-limited DTNs,followed by a valid algorithm to solve it.First,the BTAG(Buffer-limited Time Aggregated Graph)is constructed for modeling the buffer-limited DTN.Then,on the basis of BTAG,the two-way cache transfer series and the relevant transfer rules are designed,and thus a BTAG-based maximum flow algorithm is proposed to solve the maximum flow problem in buffer-limited DTNs.Finally,a numerical example is given to demonstrate the effectiveness of the proposed algorithm.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.
基金supported by the National Natural Science Fundation of China(61103081)
文摘While it is very reasonable to use a multigraph consisting of multiple edges between vertices to represent various relationships, the multigraph has not drawn much attention in research. To visualize such a multigraph, a clear layout representing a global structure is of great importance, and interactive visual analysis which allows the multiple edges to be adjusted in appropriate ways for detailed presentation is also essential. A novel interactive two-phase approach to visualizing and exploring multigraph is proposed. The approach consists of two phases： the first phase improves the previous popular works on force-directed methods to produce a brief drawing for the aggregation graph of the input multigraph, while the second phase proposes two interactive strategies, the magnifier model and the thematic-oriented subgraph model. The former highlights the internal details of an aggregation edge which is selected interactively by user, and draws the details in a magnifying view by cubic Bezier curves; the latter highlights only the thematic subgraph consisting of the selected multiple edges that the user concerns. The efficiency of the proposed approach is demonstrated with a real-world multigraph dataset and how it is used effectively is discussed for various potential applications.
基金supported by the National Science Foundation(Nos.91338115,61231008)National S&T Major Project(No.2015ZX03002006)+2 种基金the Fundamental Research Funds for the Central Universities(Nos.WRYB142208,JB140117)Shanghai Aerospace Science and Technology Innovation Fund(No.201454)the 111 Project(No.B08038).
文摘Deep space networks,satellite networks,ad hoc networks,and the Internet can be modeled as DTNs(Delay Tolerant Networks).As a fundamental problem,the maximum flow problem is of vital importance for routing and service scheduling in networks.However,there exists no permanent end-to-end path since the topology and the characteristics of links are time-variant,resulting in a crucial maximum flow problem in DTNs.In this paper,we focus on the single-source-single-sink maximum flow problem of buffer-limited DTNs,followed by a valid algorithm to solve it.First,the BTAG(Buffer-limited Time Aggregated Graph)is constructed for modeling the buffer-limited DTN.Then,on the basis of BTAG,the two-way cache transfer series and the relevant transfer rules are designed,and thus a BTAG-based maximum flow algorithm is proposed to solve the maximum flow problem in buffer-limited DTNs.Finally,a numerical example is given to demonstrate the effectiveness of the proposed algorithm.
