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- W3099505087 abstract "Unsupervised anomaly detection (AD) is a major topic in the field of Cyber-Physical Production Systems (CPPSs). A closely related concern is dimensionality reduction (DR) which is: 1) often used as a preprocessing step in an AD solution, 2) a sort of AD, if a measure of observation conformity to the learned data manifold is provided. We argue that the two aspects can be complementary in a CPPS anomaly detection solution. In this work, we focus on the nonlinear autoencoder (AE) as a DR/AD approach. The contribution of this work is: 1) we examine the suitability of AE reconstruction error as an AD decision criterion in CPPS data. 2) we analyze its relation to a potential second-phase AD approach in the AE latent space 3) we evaluate the performance of the approach on three real-world datasets. Moreover, the approach outperforms state-of-the-art techniques, alongside a relatively simple and straightforward application." @default.
- W3099505087 created "2020-11-23" @default.
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- W3099505087 date "2019-02-01" @default.
- W3099505087 modified "2023-10-11" @default.
- W3099505087 title "Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder" @default.
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- W3099505087 doi "https://doi.org/10.1109/icit.2019.8755116" @default.
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