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Artificial Neural Networks Industrial and Control Engineering Applications Part 4

Tham khảo tài liệu 'artificial neural networks industrial and control engineering applications part 4', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 94 Artificial Neural Networks - Industrial and Control Engineering Applications composition elements were known based on the type of mineral. Powder-based samples are used to train validate and test the composition retrieval algorithm while the natural rocks and minerals are used only to test the mineral identification capability. Beam Splitter Joule-meter Pulse delay generator Polarizer Lens Ầ 2 Plate Sample Spectrometer Fig. 1. Experimental configuration of a LIBS system. Concentration fraction Std name Rock71306 SiO2 0.0062 Al2O3 0.001 MgO 0.218 CaO 0.3002 Na2O 0.0003 K2O 0.00038 TiO2 0.00015 Fe2O3 0.0021 MnO 0.00108 AndesiteJA1 0.00157 0.6397 0.1522 0.0157 0.057 0.0384 0.0077 0.0085 0.0707 I ---AndesiteJA1 I hi 270 280 290 300 310 320 330 340 350 Wavelength nm Fig. 2. Examples of LIBS spectra for materials with different composition. Let us consider few examples of raw LIBS spectra. Spectral signatures of a carbonate rock Rock 71306 and an andesite JA1 are shown in Fig. 2. Due to large difference in compositions of these two materials their discrimination can be easily arranged. Here a monitoring of intensities of several key atomic lines Si Al Ca Ti and Fe in this case can be employed. Therefore identification or classification of types of minerals with a strong difference in composition can be easily achieved using simple logic algorithms. In this case we rather care about the presence of specific spectral lines than the exact measurement of their intensity and correspondence to elemental concentration. Artificial Neural Networks for Material Identification Mineralogy and Analytical Geochemistry Based on Laser-Induced Breakdown Spectroscopy 95 The situation however can be much more complex when one deals with identification of materials with high degree of similarity or with retrieval of compositional data quantitative analysis . Such an example is presented in Fig. 3. Here the strategy for these two applications may diverge. Such that for material .