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1: Abburu et al. (2020). COGNITWIN – Hybrid and Cognitive Digital Twins for the Process Industry 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)    https://ieeexplore.ieee.org/document/9198403
2: Abburu et al. (2020) Cognitive Digital Twins for the Process Industry. International Conference on Advanced Cognitive Technologies and Applications, 2020    https://publica.fraunhofer.de/entities/publication/62ccdf53-6cb5-4d07-bd6c-635c9ad910f9/details
3: Albayrak & Srivastava (2021) Tmat-SynDat: A synthetic Data Generator. DigiPro breakfast seminar.    https://www.youtube.com/watch?v=y6AZ2rL4Beg
4: Albayrak & Unal (2021) Digitalization of a Steel Pipe Production Factory: STEEL4.0- A Family of Products Developed on Routes from Industry 3.0 to Industry 4.0. UDCS    https://udcs21.karabuk.edu.tr/wp-content/uploads/2021/04/UDCS21_Proceeding-Book.pdf
5: Albayrak & Unal (2021) Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery. Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry    https://doi.org/10.1007/978-3-030-69367-1_11
6: Berre, Arne. SINTEF    https://www.sintef.no/en/all-employees/employee/arne.j.berre/ available for guest lectures on digital twins
7: Berre, A. (2022) Cognitive digital twins for Europe's manufacturers. Projects - Our future transformed, 43, pp. 42-44.    https://insightm.co.uk/wp-content/uploads/2022/08/Projects_43_Achema22_Digital_V2_low_res.pdf
8: Berre, A. (2023) COGNITWIN toolbox portal demonstratos Part 1.    https://www.youtube.com/watch?v=-QI6c6_sVJw
9: Berre, A. (2023) COGNITWIN toolbox portal demonstrator Part 2.    https://www.youtube.com/watch?v=F6eLHRM4DVg
10: Breakfast Webinar - Tmat-SynDat: A Synthetic Data Generator    https://www.youtube.com/watch?v=y6AZ2rL4Beg
11: COGNITWIN (2020) Newsletter 1.    https://www.sintef.no/projectweb/cognitwin/dissemination-materials/
12: COGNITWIN (2021) Newsletter 2.    https://www.sintef.no/projectweb/cognitwin/dissemination-materials/
13: COGNITWIN (2022) Newsletter 3.    https://www.sintef.no/projectweb/cognitwin/dissemination-materials/
14: COGNITWIN (2022) Newsletter 4.    https://www.sintef.no/projectweb/cognitwin/dissemination-materials/
15: COGNITWIN (2023) Newsletter 5.    https://www.sintef.no/globalassets/projectweb/cognitwin/cognitwin-newsletter-5_2023.pdf
16: D1.1: A report on existing level of digitalisation and describing challenges for Non-ferrous pilots, incl. identification of novel sensors    https://www.sintef.no/globalassets/project/cognitwin/public-reports/d1.1.pdf
17: D2.1: A report on existing level of digitalisation and describing challenges for Steel pilots, incl. identification of novel sensors    https://www.sintef.no/globalassets/project/cognitwin/public-reports/d2.1.pdf
18: D3.1: A report on existing level of digitalization, describing challenges for Engineering pilot, incl. identification of novel sensors and collected information for cognitive modelling    https://www.sintef.no/globalassets/project/cognitwin/public-reports/d3.1.pdf
19: D4.2 (2021) Initial platform, sensor and data interoperability toolbox    https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5d9f712fc&appId=PPGMS Deliverable 4.2
20: D5.2 (2021) Initial Hybrid AI and cogitive twin toolbox    https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5d9fb743a&appId=PPGMS Deliverable 5.2
21: Faltings et al. (2022) Impact on Inference Model Performance for ML Tasks Using Real-Life Training Data and Synthetic Training Data from GANs. Information, 13(1)    https://doi.org/10.3390/info13010009
22: Fouling management for combustion-thermal power plant (DigiPro Breakfast Webinar - 7 June 2022)    https://www.youtube.com/watch?v=3h1Lgmz9aD0
23: Fraunhofer (2023) Introduction to FA3ST service.    https://www.youtube.com/watch?v=F-1HTd-MKPU
24: Ikonen & Selek (2020) Calibration of physical models with process data using FIR filtering. ANZCC 2020    https://ieeexplore.ieee.org/document/9318340 OA at jultika.oulu.fi
25: Ikonen (2021) Physics-driven model tuning tool    https://cognitwin.github.io/toolbox http://cc.oulu.fi/~iko/COGNITWIN/
26: Ikonen (2022) State estimation tools (EnKF, UKF)    https://cognitwin.github.io/toolbox http://cc.oulu.fi/~iko/COGNITWIN/
27: Ikonen et al. (2021) Fusing Physical Process Models with Measurement Data Using FIR Calibration. Journal of Control Engineering and Applied Informatics    http://www.ceai.srait.ro/index.php?journal=ceai&page=article&op=view&path%5B%5D=7173
28: Ikonen et al. (2022) On-line estimation of circulating fluidized bed boiler fuel composition. 2022 UKACC 13th International Conference on Control (CONTROL)    https://ieeexplore.ieee.org/document/9781460 OA at jultika.oulu.fi
29: Iveland (2021) Hybrid tvilling. AMNYTT nro 7, pp. 82-89.    https://magazine.amnytt.no/amnytt-amnyttno-7-2021/0833196001639059560?page=82 OA at jultika.oulu.fi
30: Iveland (2022) Digitalisering gir bedre produksjonsprosess hos Elkm Bremanger, Joomag, nro 1, pp 24-29.    https://viewer.joomag.com/min-drift-vedlikehold-1-2022/0654136001646327559/p24?short& OA
31: IMS (2021) Estimation of power plant fuel characteristics.    https://www.youtube.com/watch?v=fgZBryVu_7g
32: IMS (2022) Fouling monitoring demonstration.    https://www.youtube.com/watch?v=BnaNwiNQI2s
33: IMS (2023) Fouling management tool.    https://www.youtube.com/channel/UCgHunz1V68YGOxaqVkkyN1A release in 2023
34: IMS (2023) Quick guide to toolbox tools.    https://www.youtube.com/channel/UCgHunz1V68YGOxaqVkkyN1A release in 2023
35: Jacoby & Usländer (2020) Digital Twin and Internet of Things—Current Standards Landscape. Applied Sciences, 10/18, 2020    https://doi.org/10.3390/app10186519
36: Jacoby (2021) Industrie 4.0-compliant and Data-Sovereign Digital Twins.    https://www.youtube.com/watch?v=sIOF6dnrgqo
37: Jacoby (2022) FA³ST - Fraunhofer Advanced Asset Administration Shell Tools (for Digital Twins)    https://github.com/FraunhoferIOSB/FAAAST-Service
38: Jacoby et al. (2021) An Approach for Realizing Hybrid Digital Twins Using Asset Administration Shells and Apache StreamPipes. Information, 12/6, 2021    https://doi.org/10.3390/info12060217
39: Jawahery (2021) Hybrid Digital Twin – Gas Treatment Centre at Aluminium Production Plant.    https://www.youtube.com/watch?v=ipNHulZVHnM
40: Johansen & Lovfall (2021) Physics-based model for ladle refractory erosion    https://www.youtube.com/watch?v=RUf4UKQgyJA
41: Johansen, S. T., P. Unal, Ö. Albayrak, E. Ikonen, K. J. Linnestad, S. Jawahery, A. K. Srivastava, B. T. Løvfall (2023) Hybrid and Cognitive Digital Twins for the Process Industry. Open Engineering    https://www.degruyter.com/journal/key/eng/html to appear
42: Johansen et al (2023) Pragmatism in industrial modelling, applied to "ladle lifetime in the steel industry" submitted to JSAIMM special edition
43: Johansen et al. (2023) 1 Pragmatical physics-based model for ladle lifetime prediction submitted to JSAIMM special edition
44: Linnestad, K.J., K. Hildal, L.K. Jakobsson, S. Eidnes, V. Tjessem, E.L. Bjørnstad, S.O. Wasbø (2022): “A hybrid digital twin for optimal Si-production”, Silicon for the chemical and solar industry, Trondheim, 14-16.June 2022    https://dx.doi.org/10.2139/ssrn.4121131
45: Liukkonen et al. (2022) Hybrid Modelling Approach to Optimize Fouling Management in a Circulating Fluidized Bed Boiler. Fluidized bed conversion conference 2022 (FBC24), 8–11 May 2022, Gothenburg.
46: Neuvonen (2022) Outlier detection tool    https://cognitwin.github.io/toolbox
47: Neuvonen & Aho (2022) Subspace identification tools    https://cognitwin.github.io/toolbox
48: Neuvonen et al. (2021) Estimating fuel characteristics from simulated circulating fluidized bed furnace data. ICSC    https://ieeexplore.ieee.org/document/9666596 OA at jultika.oulu.fi
49: Neuvonen et al. (2022) Heat exchanger fouling estimation for combustion–thermal power plants including load level dynamics. IEEE Systems, Man, and Cybernetics, Prague    https://ieeexplore.ieee.org/document/9945541 OA at jultika.oulu.fi
50: Schorr et al. (2021) Neuroscope: An Explainable AI Toolbox for Semantic Segmentation and Image Classification of Convolutional Neural Nets. Applied Sciences    https://doi.org/10.3390/app11052199
51: Scortex (2021) FPGA based inference at 100FPS on High resolution color images.    https://www.youtube.com/watch?v=ZDyZwtBZnMM
52: Selek (2022) Heat exchanger physical model.    https://cognitwin.github.io/toolbox
53: Stojanovic & Bader (2020) Smart Service Management - Design Guidelines and Best Practices, 2020    https://link.springer.com/chapter/10.1007/978-3-030-58182-4_12
54: Stojanovic et al. (2020) Methodology and Tools for Digital Twin Management—The FA3ST Approach. IoT, 2, 2021    https://doi.org/10.3390/iot2040036
55: Unal et al. (2021) Data-Driven Artificial Intelligence and Predictive Analytics for the Maintenance of Industrial Machinery with Hybrid and Cognitive Digital Twins. Technologies and Applications for Big Data Value Ch 14    https://link.springer.com/content/pdf/10.1007/978-3-030-78307-5.pdf#page=314
56: Unal et al. (2021) A Comparison of State-of-Art Machine Learning Algorithms on Fault Indication and Remaining Useful Life Determination by Telemetry Data. 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)    http://dx.doi.org/10.1109/ficloud49777.2021.00019
57: Waszak et al. (2022) Let the Asset Decide. Int Conf Software Architecture (ICSA 2022).    https://ieeexplore.ieee.org/document/9779654
58: Yallic, F., Ö. Albayrak and P. Unal (2022) Asset administration shell generation and usage for digital twins: A case study for non-destructive testing. International Workshop on Emerging Trends and Case-Studies in Industry 4.0 and Intelligent Manufacturing.    http://dx.doi.org/10.5220/0011561400003329
59: Yoruc et al. (2021) A Workflow for Synthetic Data Generation and Predictive Maintenance for Vibration Data . Information, 12 (10), 2021    https://doi.org/10.3390/info12100386

59 items in dbase.