Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks

Anomaly detection is a significant task in sensors’ signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors’ applications. Deep learning algorithms are effective tools for anomaly detection due to their capability to address imbalanced data...

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書誌詳細

主要な著者:	Fatemeh Esmaeili, Erica Cassie, Hong Phan T. Nguyen, Natalie O. V. Plank, Charles P. Unsworth, Alan Wang
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2023-03-01
シリーズ:	Bioengineering
主題:	time series anomaly detection outlier detection semi-supervised learning signal processing autoencoder (AE) vanilla autoencoder
オンライン･アクセス:	https://www.mdpi.com/2306-5354/10/4/405
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