Kernel-Based Semantic Relation Detection and Classification via Enriched Parse Tree Structure

This paper proposes a tree kernel method of semantic relation detection and classification （RDC） between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree kernel is presented to better capture the inherent structural information in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness and approximate matching of sub-trees. Second, an enriched parse tree structure is proposed to well derive necessary structural information, e.g., proper latent annotations, from a parse tree. Evaluation on the ACE RDC corpora shows that both the new tree kernel and the enriched parse tree structure contribute significantly to RDC and our tree kernel method much outperforms the state-of-the-art ones.

Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability.On this basis,a framework for transient fault detection and classification is created.Graph structure is generated to provide topology information to the task.Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results rapidly.Furthermore,the proposed approach is tested in various situations and its generalization ability is verified by experimental results.The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.

Long-tailed object detection of kitchen waste with class-instance balanced detector

Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual processes.However,the presence of non-degradable waste mixed in kitchen waste often follows a long-tailed distribution,making it challenging to train convolutional neural network-based object detectors,which results in the unsatisfactory detection of tailclass waste.To address this challenge,we propose a class-instance balanced detector(CIB-Det) for intelligent detection and classification of kitchen waste.CIB-Det implements two strategies for the loss function:the class-balanced strategy(CBS)and the instance-balanced strategy(IBS).The CBS focuses more on tail classes,and the IBS concentrates on hard-to-classify instances adaptively during training.Consequently,CIB-Det comprehensively and adaptively addresses the long-tailed issue.Our experiments on a real dataset of kitchen waste images support the effectiveness of CIB-Det for kitchen waste detection.