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Generic and scalable multi-layered architecture for Digital Twin implementation in industrial processes: Mineral Processing case study.

, , , , , , , and . ICAAI, page 54-61. ACM, (2023)

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Conv-LSTM for Real Time Monitoring of the Mineral Grades in the Flotation Froth., , , , , , , , and . DATA, page 89-96. SCITEPRESS, (2023)Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing., , , , , , , , and . SN Comput. Sci., 5 (4): 363 (April 2024)Fourier Transform and Machine Learning for Real-Time Monitoring of Froth Flotation in Mining Industry., , , , , , , and . CiSt, page 199-204. IEEE, (2023)Digital Transformation of the Flotation Monitoring Towards an Online Analyzer., , , , , , , , , and 4 other author(s). SADASC, volume 1677 of Communications in Computer and Information Science, page 325-338. Springer, (2022)Hybrid features extraction for the online mineral grades determination in the flotation froth using Deep Learning., , , , , , , , , and 1 other author(s). Eng. Appl. Artif. Intell., (2024)Artificial intelligence for enhanced flotation monitoring in the mining industry: A ConvLSTM-based approach., , , , , , and . Comput. Chem. Eng., (January 2024)The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines., , , , , , and . Sensors, 23 (9): 4294 (2023)Predictive maintenance in mining industry: grinding mill case study., , , , , , and . KES, volume 207 of Procedia Computer Science, page 2483-2492. Elsevier, (2022)Smart Soft Sensor for Froth flotation quality monitoring using pre-trained feature extractor and Vision Transformer ViT., , , , , , , and . SITIS, page 218-225. IEEE, (2023)Anti-Collision System for Accident Prevention in Underground Mines using Computer Vision., , , , , , and . ICAAI, page 94-101. ACM, (2022)