Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the se...Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.展开更多
The problem of logical node(LN)importance quantification in an IEC 61850 based substation automation system(SAS)is investigated in this paper.First,a weighted and directed static complex network model is established b...The problem of logical node(LN)importance quantification in an IEC 61850 based substation automation system(SAS)is investigated in this paper.First,a weighted and directed static complex network model is established by analyzing the characteristics of SAS,according to IEC 61850.Then,we propose a method,which combines topology value and information adjunction value by introducing a first-order linear feedback controller to quantify the value of LNs.On this basis,some definitions for equivalent network conversion are proposed to greatly reduce the complexity of the original network topology.Also,the absolute value and relative value are introduced to quantify LN importance from the perspective of the node’s necessity and influence,respectively.Finally,simulation results of the case study demonstrate that the proposed method is effective and provides a broader and clearer perspective for viewing the logical node importance for IEC61850 based SAS.展开更多
基金This work was supported by the National Key R&D Program of China under Grant No.20201710200.
文摘Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.
基金This work was supported in part by the National Natural Science Foundation of China(U1866209)。
文摘The problem of logical node(LN)importance quantification in an IEC 61850 based substation automation system(SAS)is investigated in this paper.First,a weighted and directed static complex network model is established by analyzing the characteristics of SAS,according to IEC 61850.Then,we propose a method,which combines topology value and information adjunction value by introducing a first-order linear feedback controller to quantify the value of LNs.On this basis,some definitions for equivalent network conversion are proposed to greatly reduce the complexity of the original network topology.Also,the absolute value and relative value are introduced to quantify LN importance from the perspective of the node’s necessity and influence,respectively.Finally,simulation results of the case study demonstrate that the proposed method is effective and provides a broader and clearer perspective for viewing the logical node importance for IEC61850 based SAS.
