Proceedings ArticleDOI
Knowledge-Graph Based Multi-Target Deep-Learning Models for Train Anomaly Detection
Zhiliang Qin,Chen Cen,Wang Jie,Teo Sin Gee,Vijay Ramaseshan Chandrasekhar,Zhongbo Peng,Zeng Zeng +6 more
TLDR
By establishing a knowledge graph of the normally operating systems, a progressive approach to use a multi-target network to segment each component of the considered system sequentially by decoupling the segmentation and the classification task is proposed.Abstract:
The state-of-art image segmentation algorithms can be applied to accurately localize objects by using deep convolutional neural networks (CNN). In this paper, we consider the anomaly detection problem encountered in a train wheel system. We propose a progressive approach to use a multi-target network to segment each component of the considered system sequentially by decoupling the segmentation and the classification task. Moreover, we use the knowledge graph approach to establish a semantic consistency matrix by quantifying the spatial relationship between various components. We show that by establishing a knowledge graph of the normally operating systems, we are able to identify a faulty component effectively.read more
Citations
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Book ChapterDOI
A Tutorial and Survey on Fault Knowledge Graph
XiuQing Wang,ShunKun Yang +1 more
TL;DR: This article will provide an overview of the fault KG and summarize the key techniques for building a KG to guide the construction of the KG in the fault domain, and introduces some of the open source tools that can be used to build a KGs process, enabling researchers and practitioners to quickly get started in this field.
Journal ArticleDOI
Knowledge Integration in Smart Factories
TL;DR: In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data and can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements.
Journal ArticleDOI
Latency-aware service migration with decision theory for Internet of Vehicles in mobile edge computing
Journal ArticleDOI
Hierarchical Semantic Graph Reasoning for Train Component Detection
TL;DR: Cheng et al. as discussed by the authors proposed hierarchical graphical reasoning (HGR), which utilizes the hierarchical structures of trains for train component detection, which contains multiple graphical reasoning branches, each of which is utilized to conduct graphical reasoning for one cluster of train components based on their sizes.
Proceedings ArticleDOI
A Fault Knowledge Graph Creation Method and Application based on Fault Tree Analysis and Failure Mode, Effects and Criticality Analysis
Lixiang Wang,Jun Yu Cai +1 more
TL;DR: In this paper , a Fault Knowledge Graph (FKG) based on the knowledge graph (KG) in the field of faults is proposed for fault diagnosis, which can well analyze the relationship between various faults, achieve prediction, and promote development.
References
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Journal ArticleDOI
Knowledge graph refinement: A survey of approaches and evaluation methods
TL;DR: A survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.
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TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
TL;DR: This paper demonstrates how the U-Net type architecture can be improved by the use of the pre-trained encoder and compares three weight initialization schemes: LeCun uniform, the encoder with weights from VGG11 and full network trained on the Carvana dataset.
Book ChapterDOI
Large-Scale Object Classification Using Label Relation Graphs
Jia Deng,Jia Deng,Nan Ding,Yangqing Jia,Andrea Frome,Kevin Murphy,Samy Bengio,Yuan Li,Hartmut Neven,Hartwig Adam +9 more
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Deep Multitask Learning for Railway Track Inspection
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Deep convolutional neural networks for detection of rail surface defects
TL;DR: This paper proposes a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects, and compares the results of different network architectures characterized by different sizes and activation functions.