A Deep Convolutional Autoencoder-Based Approach for Anomaly Detection With Industrial, Non-Images, 2-Dimensional Data: A Semiconductor Manufacturing Case Study-Reference-Cited by-同舟云学术

A Deep Convolutional Autoencoder-Based Approach for Anomaly Detection With Industrial, Non-Images, 2-Dimensional Data: A Semiconductor Manufacturing Case Study

Published:2022-07 Issue:3 Volume:19 Page:1477-1490
ISSN:1545-5955
Container-title:IEEE Transactions on Automation Science and Engineering
language:
Short-container-title:IEEE Trans. Automat. Sci. Eng.

Author:

Maggipinto Marco¹,Beghi Alessandro²^ORCID,Susto Gian Antonio²^ORCID

Affiliation:

1. Department of Information Engineering, University of Padua, Padua, Italy

2. Department of Information Engineering and the Human-Inspired Technology Research Center, University of Padua, Padua, Italy

Funder

Italian Minister for Education (MIUR) under the initiative “Departments of Excellence”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/8856/9814439/09684242.pdf?arnumber=9684242

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4. Discovering cluster-based local outliers

5. Isolation Forest

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