Print Email Facebook Twitter Automation in sensing and raw material characterization - A conceptual framework Title Automation in sensing and raw material characterization - A conceptual framework Author Desta, F.S. (TU Delft Resource Engineering) Buxton, M.W.N. (TU Delft Resource Engineering) Contributor Balaguer, Carlos (editor) Asama, Hajime (editor) Kragic, Danica (editor) Lynch, Kevin (editor) Date 2018 Abstract The use of sensor technologies for material characterization is rapidly growing and innovative advancement is observed. However, the use of sensor combinations for a raw material characterization in mining is very limited and automation of the material identification process using a combined sensor signal is not defined. Potential sensor technologies for raw material characterization were evaluated based on the applicability and technological maturity. To ensure a rapid implementation of the Real-time mining (RTM) project concept, mature technologies such as Red Green Blue (RGB) imaging, Visible Near Infrared (VNIR) hyperspectral imaging, Short Wave Infrared (SWIR) hyperspectral imaging, Fourier-Transform Infrared Spectroscopy (FTIR), Laser Induced Breakdown Spectroscopy (LIBS) and Raman were selected. Each selected technology was assessed for automation in sensing and applicability (for characterization of the test case materials). Based on the results the sensor data were further considered for data fusion. The proposed sensor combinations approach encompasses three levels of data fusion: low-level, mid-level and high-level. The data of the different sensors are fused together in order to acquire a wide range of mineral properties within each lithotype and an improved classification and predictive models. The preferred level of data fusion and preferred sensor data combinations will be used to develop a multi-variate statistical interpretation rule which relates combination of sensors signals with raw material properties. Thus a tool which integrates the combined sensor signal with materials properties will be developed and used to automate the material characterization process. Subject automationdata fusionmaterial characterizationpolymetallic sulphidessensors data To reference this document use: http://resolver.tudelft.nl/uuid:e88024b1-a323-4554-b5f6-944787a0dd35 DOI https://doi.org/10.1109/IROS.2018.8593774 Publisher IEEE, Piscataway, NJ, USA ISBN 978-1-5386-8095-7 Source 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018: Madrid, Spain, 1-5 October 2018 Event 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, 2018-10-01 → 2018-10-05, Madrid, Spain Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type conference paper Rights © 2018 F.S. Desta, M.W.N. Buxton Files PDF Feven_Desta_Manuscript.pdf 446.16 KB Close viewer /islandora/object/uuid:e88024b1-a323-4554-b5f6-944787a0dd35/datastream/OBJ/view