scholarly journals Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection

2019 ◽  
Author(s):  
Jia-Xing Zhong ◽  
Nannan Li ◽  
Weijie Kong ◽  
Shan Liu ◽  
Thomas H. Li ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Plug And Play ◽  
Label Noise ◽  
Play Action

Hyperspectral Anomaly Detection via Deep Plug-and-Play Denoising CNN Regularization

2021 ◽  
pp. 1-16
Author(s):  
Xiyou Fu ◽  
Sen Jia ◽  
Lina Zhuang ◽  
Meng Xu ◽  
Jun Zhou ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Plug And Play

scholarly journals MILAD: Robust Anomaly Detection for Electric Vehicles with Label Noise

2021 ◽  
Vol 2132 (1) ◽  
pp. 012047
Author(s):  
Yu Ye ◽  
Bailin Feng ◽  
Wujun Tao
Keyword(s):  
Anomaly Detection ◽  
Electric Vehicles ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Series Data ◽  
Battery System ◽  
Label Noise ◽  
Safety Hazard ◽  
Detection Algorithms ◽  
Potential Safety

Abstract One of the bottlenecks restricting the development of electric vehicle industry is the safety problem. Although numerous of anomaly detection algorithms for electric vehicles have been proposed, most of them may perform poorly due to the complexity and unpredictability of real scenes. We consider that there may be a certain degree of potential safety hazard in the battery system of electric vehicles before, during and after the process of faults in the real scenes, that is, label noise. In order to solve this problem, we propose a Multi-Instance Learning based Anomaly Detection (MILAD) framework, to perform anomaly detection for electric vehicles with label noise problem. Extensive cross validation experiments fully verify that the framework can effectively detect the existence of abnormal conditions in the presence of label noise in multivariate time series data.

Anomaly detection in ad-hoc networks based on deep learning model: A plug and play device

2019 ◽  
Vol 84 ◽  
pp. 82-89 ◽  
Author(s):  
Fang Feng ◽  
Xin Liu ◽  
Binbin Yong ◽  
Rui Zhou ◽  
Qingguo Zhou
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Learning Model ◽  
Plug And Play ◽  
Hoc Networks ◽  
Deep Learning Model

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

Proposal of the Anomaly Detection Method of Live-alone by LOF using Multiple Sensors

2015 ◽  
Vol 135 (12) ◽  
pp. 749-755
Author(s):  
Taiyo Matsumura ◽  
Ippei Kamihira ◽  
Katsuma Ito ◽  
Takashi Ono
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Multiple Sensors
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