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Lupu, Radu [Author]; Lupu, Iulia [Author]; Stamule, Tanase [Author]; Roman, Mihai [Author]

Entropy as leading indicator for extreme systemic risk events

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https://ipe.ro/rjef/rjef4_2022/rjef4_2022p58-73.pdf

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2022

Published in: Romanian journal of economic forecasting ; 25(2022), 4, Seite 58-73
Assafo, Maryam [Author]; Langendorfer, Peter [Author]

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions - [published Version]

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New York, NY : IEEE, 2021

Published in: IEEE access : practical research, open solutions 9 (2021)
Kiefer, Daniel [Author]; Wezel, Stefan [Author]; Böttcher, Alexander [Author]; Grimm, Florian [Author]; Straub, Tim [Author]; Bitsch, Günter [Author]; van Dinther, Clemens [Author]

Anomaly detection in hobbing tool images: using an unsupervised deep learning approach in manufacturing industry

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Amsterdam : Elsevier, 2024
Denkena, B. [Author]; Dittrich, M.-A [Author]; Noske, H. [Author]; Stoppel, D. [Author]; Lange, D. [Author]

Data-based ensemble approach for semi-supervised anomaly detection in machine tool condition monitoring

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https://nbn-resolving.org/urn:nbn:de:101:1-2023120701311450652417

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Hannover: Gottfried Wilhelm Leibniz Universität Hannover, 2021 ; Hannover: Technische Informationsbibliothek (TIB), 2021

Published in: CIRP Journal of Manufacturing Science and Technology ; 35 (2021), S. 795-802
Denkena, B. [Author]; Dittrich, M.-A. [Author]; Noske, H. [Author]; Stoppel, D. [Author]; Lange, D. [Author]

Data-based ensemble approach for semi-supervised anomaly detection in machine tool condition monitoring - [published Version]

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Amsterdam [u.a.] : Elsevier, 2021

Published in: CIRP Journal of Manufacturing Science and Technology 35 (2021) ; CIRP Journal of Manufacturing Science and Technology
Ströbel, Robin [Author]; Bott, Alexander [Author]; Wortmann, Andreas [Author]; Fleischer, Jürgen [Author]

Monitoring of tool and component wear for self-adaptive Digital Twins : a multi-stage approach through anomaly detection and wear cycle analysis

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Basel, Switzerland: MDPI, 2023

Published in: Machines ; 11 (2023), 1032
Asefi, Sajjad; Mitrovic, Mile; Ćetenović, Dragan; Levi, Victor; Gryazina, Elena; Terzija, Vladimir

Anomaly Detection, Classification and Identification Tool (ADCIT)

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Elsevier BV, 2023

Published in: Software Impacts
Bhuyan, Monowar H.; Bhattacharyya, D. K.; Kalita, J. K.

Network Anomaly Detection: Methods, Systems and Tools

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Institute of Electrical and Electronics Engineers (IEEE), 2014

Published in: IEEE Communications Surveys & Tutorials
Fan, Shu-Kai S.; Tsai, Du-Ming; Jen, Chih-Hung; Hsu, Chia-Yu; He, Fei; Juan, Li-Ting

Data Visualization of Anomaly Detection in Semiconductor Processing Tools

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Institute of Electrical and Electronics Engineers (IEEE), 2022

Published in: IEEE Transactions on Semiconductor Manufacturing
Puranik, Tejas; Gharbi, Aroua; Bagdatli, Burak; Fischer, Olivia Pinon; Mavris, Dimitri N.

Benchmarking Deep Neural Network Architectures for Machining Tool Anomaly Detection

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ASTM International, 2020

Published in: Smart and Sustainable Manufacturing Systems
Laura Victoria Vigoya Morales; Manuel López-Vizcaíno; Diego Fernández Iglesias; Víctor Manuel Carneiro Díaz

Anomaly Detection in IoT: Methods, Techniques and Tools

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2019

Published in: Proceedings
Lin, Chia-Yu; Weng, Chih-Ping; Wang, Li-Chun; Shuai, Hong-Han; Tseng, Wen-Peng

Edge-Based RNN Anomaly Detection Platform in Machine Tools

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Informa UK Limited, 2019

Published in: Smart Science
Herranz, Gorka; Antolínez, Alfonso; Escartín, Javier; Arregi, Amaia; Gerrikagoitia, Jon Kepa

Machine Tools Anomaly Detection Through Nearly Real-Time Data Analysis

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MDPI AG, 2019

Published in: Journal of Manufacturing and Materials Processing
Christodoulou, Vyron; Bi, Yaxin; Wilkie, George

A tool for Swarm satellite data analysis and anomaly detection

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Public Library of Science (PLoS), 2019

Published in: PLOS ONE
Guo, Yuejun; Xu, Qing; Li, Peng; Sbert, Mateu; Yang, Yu

Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools

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MDPI AG, 2017

Published in: Entropy
Shi, Chengming; Luo, Bo; Li, Hongqi; Li, Bin; Mao, Xinyong; Peng, Fangyu

Anomaly Detection via Unsupervised Learning for Tool Breakage Monitoring

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EJournal Publishing, 2016

Published in: International Journal of Machine Learning and Computing
Kiefer, Daniel; Wezel, Stefan; Böttcher, Alexander; Grimm, Florian; Straub, Tim; Bitsch, Günter; Van Dinther, Clemens

Anomaly Detection in Hobbing Tool Images: Using An Unsupervised Deep Learning Approach in Manufacturing Industry

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Elsevier BV, 2024

Published in: Procedia Computer Science
He, Zhangyue; Tang, Yanni; Zhao, Kaiqi; Liu, Jiamou; Chen, Wu

Dependable Software Engineering. Theories, Tools, and Applications: Graph-Based Log Anomaly Detection via Adversarial Training

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Springer Nature Singapore, 2024

Published in: Dependable Software Engineering. Theories, Tools, and Applications
Reeber, Tim; Henninger, Jens; Weingarz, Niklas; Simon, Peter M.; Berndt, Maximilian; Glatt, Moritz; Kirsch, Benjamin; Eisseler, Rocco; Aurich, Jan C.; Möhring, Hans - Christian

Tool condition monitoring in drilling processes using anomaly detection approaches based on control internal data

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Elsevier BV, 2024

Published in: Procedia CIRP
Xu, Zhicheng; Yip, Lenny Wai Sze; To, Suet

Condition monitoring of three-axis ultra-precision milling machine tool for anomaly detection

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Elsevier BV, 2023

Published in: Procedia CIRP

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