Sparse spatiotemporal feature learning for pipeline anomaly detection

机译：稀疏的时空特征学习管道异常检测

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

Spatiotemporal systems are often difficult to represent in the presence of noise and exhibit higher modelling complexity. This scenario is found in infrastructure applications such as continuous pipeline monitoring, where it is of interest to pinpoint abn

机译：时尚系统通常难以存在噪音并且表现出更高的建模复杂性。此方案在诸如连续管道监控之类的基础架构应用中找到，在那里对PinPoint ABN感兴趣

著录项

来源
《IEEE International Conference on Cognitive Informatics and Cognitive Computing》|2019年|1 v.|共7页
会议地点
作者
King Ma; Henry Leung;
展开▼
作者单位

展开▼
会议组织
原文格式 PDF
正文语种
中图分类计算技术、计算机技术;
关键词
spatiotemporal dynamic modelling; pipeline monitoring; acoustics; anomaly detection; feature learning;

机译：时尚动态建模;管道监测;声学;异常检测;特征学习;

相似文献

外文文献
中文文献
专利

1. Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal Event Detection in Large Videos [J] . Wenqing Chu, Hongyang Xue, Chengwei Yao, Multimedia, IEEE Transactions on . 2019,第1期

机译：大型视频异常事件检测的稀疏编码引导时空特征学习
2. Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model [J] . Touati Redha, Mignotte Max, Dahmane Mohamed Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of . 2020,第期

机译：异构图像中无监督变化检测的异常特征学习：深稀疏的残余模型
3. Sparse kernel learning-based feature selection for anomaly detection [J] . Peng Zhimin, Gurram Prudhvi, Kwon Heesung, Aerospace and Electronic Systems, IEEE Transactions on . 2015,第3期

机译：基于稀疏核学习的特征选择用于异常检测
4. Sparse spatiotemporal feature learning for pipeline anomaly detection [C] . King Ma, Henry Leung IEEE International Conference on Cognitive Informatics and Cognitive Computing . 2019

机译：稀疏时空特征学习用于管道异常检测
5. Anomaly Detection and Classification of Sparse Gamma-Ray Spectra Using Machine Learning Algorithms for Depleted Uranium Remediation [D] . Finney, Austin Matthew. 2020

机译：使用机器学习算法对耗尽铀修复机器学习算法的异常检测和分类
6. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction [O] . Kevin Faust, Quin Xie, Dominick Han, 2018

机译：使用非线性特征空间降维可视化组织病理学深度学习分类和异常检测
7. Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model [O] . Redha Touati, Max Mignotte, Mohamed Dahmane 2020

机译：异构图像中无监督变化检测的异常特征学习：深稀疏的残余模型
8. Modeling Spatiotemporal Contextual Dynamics with Sparse-Coded Transfer Learning. [R] . Fu, Y. 2012

机译：用稀疏编码传递学习建模时空语境动力学。

Sparse spatiotemporal feature learning for pipeline anomaly detection

摘要

著录项

相似文献

相关主题

期刊订阅