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Cinzia Giannetti
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2020 – today
- 2024
- [c9]Robert Gibbs, Cinzia Giannetti, Thomas Baynes, Cameron Pleydell-Pearce:
Leveraging Machine Learning Insights at the Hot Strip Rolling Mill, Tata Steel, Port Talbot. I2MTC 2024: 1-6 - [d1]James Flynn
, Cinzia Giannetti
A Novel LSTM Pipeline to Detect Anomalies in Manufacturing Production (Datasets). IEEE DataPort, 2024 - 2023
- [j11]Samuel Latham
A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill. SN Comput. Sci. 4(5): 628 (2023) - [c8]Kayal Lakshmanan, Aurash Karimi, Alex Carr, Philippe Wauters, Michael Auinger, Cameron Pleydell-Pearce, Cinzia Giannetti:
A Hybrid Modelling Approach Based on Deep Learning for the Prediction of the Silicon Content in the Blast Furnace. KES 2023: 2204-2213 - 2022
- [j10]Cinzia Giannetti
Towards scalable and reusable predictive models for cyber twins in manufacturing systems. J. Intell. Manuf. 33(2): 441-455 (2022) - [j9]Qiushi Cao
KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0. Robotics Comput. Integr. Manuf. 74: 102281 (2022) - [j8]Christian Andrew Griffiths
Comparison of a Bat and Genetic Algorithm Generated Sequence Against Lead Through Programming When Assembling a PCB Using a Six-Axis Robot With Multiple Motions and Speeds. IEEE Trans. Ind. Informatics 18(2): 1102-1110 (2022) - [c7]Samuel Latham, Cinzia Giannetti:
Root Cause Classification of Temperature-related Failure Modes in a Hot Strip Mill. IN4PL 2022: 36-45 - [c6]Kayal Lakshmanan, Eugenio Borghini, Arnold Beckmann, Cameron Pleydell-Pearce, Cinzia Giannetti:
Data modelling and Remaining Useful Life estimation of rolls in a steel making cold rolling process. KES 2022: 1057-1066 - 2021
- [c5]Callum O'Donovan, Cinzia Giannetti, Grazia Todeschini:
A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders. ICAART (2) 2021: 373-380 - [c4]James Flynn
Making Green Transport a Reality: A Classification Based Data Analysis Method to Identify Properties Suitable for Electric Vehicle Charging Point Installation. IGARSS 2021: 6229-6232 - 2020
- [j7]Aniekan Essien
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders. IEEE Trans. Ind. Informatics 16(9): 6069-6078 (2020)
2010 – 2019
- 2019
- [j6]Chunxu Li
Segmentation and generalisation for writing skills transfer from humans to robots. Cogn. Comput. Syst. 1(1): 20-25 (2019) - [c3]Aniekan Essien
A Scalable Deep Convolutional LSTM Neural Network for Large-Scale Urban Traffic Flow Prediction using Recurrence Plots. AFRICON 2019: 1-7 - [c2]Aniekan Essien
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders. INISTA 2019: 1-6 - 2017
- [c1]Cinzia Giannetti:
A framework for improving process robustness with quantification of uncertainties in Industry 4.0. INISTA 2017: 189-194 - 2016
- [j5]Rajesh S. Ransing
A quality correlation algorithm for tolerance synthesis in manufacturing operations. Comput. Ind. Eng. 93: 1-11 (2016) - [j4]Cinzia Giannetti, Rajesh S. Ransing:
Risk based uncertainty quantification to improve robustness of manufacturing operations. Comput. Ind. Eng. 101: 70-80 (2016) - 2015
- [j3]Cinzia Giannetti, Meghana R. Ransing, Rajesh S. Ransing, David C. Bould, David T. Gethin, Johann Sienz:
Organisational Knowledge Management for Defect Reduction and Sustainable Development in Foundries. Int. J. Knowl. Syst. Sci. 6(3): 18-37 (2015) - 2014
- [j2]Cinzia Giannetti, Rajesh S. Ransing
A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data. Comput. Ind. Eng. 72: 217-229 (2014) - 2013
- [j1]Rajesh S. Ransing
A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects. Comput. Ind. 64(5): 514-523 (2013)
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last updated on 2024-08-28 22:30 CEST by the dblp team
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