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Advancing Flotation Process Optimization Through Real-Time Machine Vision Monitoring: A Convolutional Neural Network Approach.

, , , , , , , , and . KDIR, page 429-436. SCITEPRESS, (2023)

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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)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)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)Smart Soft Sensor for Froth flotation quality monitoring using pre-trained feature extractor and Vision Transformer ViT., , , , , , , and . SITIS, page 218-225. IEEE, (2023)Predictive maintenance in mining industry: grinding mill case study., , , , , , and . KES, volume 207 of Procedia Computer Science, page 2483-2492. Elsevier, (2022)Data-Driven and Model-Driven Approaches in Predictive Modelling for Operational Efficiency: Mining Industry Use Case., , , , , , , and . MEDI, volume 14396 of Lecture Notes in Computer Science, page 116-127. Springer, (2023)Anti-Collision System for Accident Prevention in Underground Mines using Computer Vision., , , , , , and . ICAAI, page 94-101. ACM, (2022)