Adaptive N-Step Technique for Real-Time Anomaly Detection in Smart Manufacturing-Reference-Cited by-同舟云学术

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Adaptive N-Step Technique for Real-Time Anomaly Detection in Smart Manufacturing

Published:2022-05-24 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS)
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Author:

Shetve D.¹,Raju I.¹,Prasad R. Vara¹,Trestian R.¹,Nguyen H.¹,Venkataraman H.¹

Affiliation:

1. Next Generation Communication Group,IIIT Sri City,AP,India

Funder

UKIERI

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9816848/9816852/09816882.pdf?arnumber=9816882

Reference23 articles.

1. Cluster Analysis for Anomaly Detection in Accounting Data: An Audit Approach

2. CATS: Cluster-Aided Two-Step Approach for Anomaly Detection in Smart Manufacturing

3. Anomaly detection in temperature data using DBSCAN algorithm

4. Comparative evaluation of region query strategies for DBSCAN clustering

5. Clustering and Unsupervised Anomaly Detection with l2 Normalized Deep Auto-Encoder Representations;aytekin;2018 International Joint Conference on Neural Networks (IJCNN),0

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1. DTMA: Visual Object Inspection and Mechanism for Digital Twin with Robotic Arm;2024-01-31

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