Joint Language Semantic and Structure Embedding for Knowledge Graph Completion

The task of completing knowledge triplets has broad downstream applications. Both structural and semantic information plays an important role in knowledge graph completion. Unlike previous approaches that rely on either the structures or semantics of the knowledge graphs, we propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information. Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models with respect to a probabilistic structured loss, where the forward pass of the language models captures semantics and the loss reconstructs structures. Our extensive experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method. We also show that our method can significantly improve the performance in a low-resource regime, thanks to the better use of semantics. The code and datasets are available at https://github.com/pkusjh/LASS.Comment: COLING 202

A Method for Identification of Transformer Inrush Current Based on Box Dimension

Magnetizing inrush current can lead to the maloperation of transformer differential protection. To overcome such an issue, a method is proposed to distinguish inrush current from inner fault current based on box dimension. According to the fundamental difference in waveform between the two, the algorithm can extract the three-phase current and calculate its box dimensions. If the box dimension value is smaller than the setting value, it is the inrush current; otherwise, it is inner fault current. Using PSACD and MATLAB, the simulation has been performed to prove the efficiency reliability of the presented algorithm in distinguishing inrush current and fault current

Location and classification of power quality disturbance based on wavelet packet and PN

A new method of location and classification of power quality disturbance based on wavelet packet and PNN was proposed according to essential characteristics of transient power quality disturbance. The disturbance signals were sampled and decomposed by using wavelet packet to extract wavelet packet reconstructed coefficient and to locate signal saltation point, then the energy of each band was calculated and normalized, energy feature vectors were constructed as input sample of PNN for network training and testing, and finally classification of different disturbance signal was achieved. Matlab simulation results show that the method can quickly and accurately locate and classify disturbance signal