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

, , , , , , , , и . KDIR, стр. 429-436. SCITEPRESS, (2023)

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Abdelmalek Nasraoui

Abdelmalek Nigro

 

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Smart Soft Sensor for Froth flotation quality monitoring using pre-trained feature extractor and Vision Transformer ViT., , , , , , , и . SITIS, стр. 218-225. IEEE, (2023)Fourier Transform and Machine Learning for Real-Time Monitoring of Froth Flotation in Mining Industry., , , , , , , и . CiSt, стр. 199-204. IEEE, (2023)Advancing Flotation Process Optimization Through Real-Time Machine Vision Monitoring: A Convolutional Neural Network Approach., , , , , , , , и . KDIR, стр. 429-436. SCITEPRESS, (2023)Conv-LSTM for Real Time Monitoring of the Mineral Grades in the Flotation Froth., , , , , , , , и . DATA, стр. 89-96. SCITEPRESS, (2023)Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing., , , , , , , , и . SN Comput. Sci., 5 (4): 363 (апреля 2024)Digital Transformation of the Flotation Monitoring Towards an Online Analyzer., , , , , , , , , и 4 other автор(ы). SADASC, том 1677 из Communications in Computer and Information Science, стр. 325-338. Springer, (2022)Hybrid features extraction for the online mineral grades determination in the flotation froth using Deep Learning., , , , , , , , , и 1 other автор(ы). Eng. Appl. Artif. Intell., (2024)