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The spontaneous combustion of underground minerals causes huge property losses and ecological damage. Coal and oil shale are co associated minerals in the Fushun West Mine, and both have the ability of oxidative spontaneous combustion. To study the effect of microstructure changes on the macroscopic gas product concentration during the mineral oxidation spontaneous combustion process in the Fushun West Mine, this study used a high-temperature temperature-programmed test to obtain the change trend of gas product concentration in different oxidation stages of minerals. Using Fourier transform infrared spectroscopy (FTIR) technology, the changes in active functional groups of surface molecules during the process of mineral oxidation and spontaneous combustion were identified. Finally, using the gray correlation degree, correlation analysis between the concentration of gas products and the concentration of active functional groups in different oxidation stages was carried out. The key reactive functional groups affecting mineral spontaneous combustion were identified. The essential reason for the change in the gas product was revealed.

Coal spontaneous combustion (CSC) has a devastating effect on the environment and human health. CSC primarily involves oxygen-lean oxidation and combustion states. In this study, to investigate the evolution of the coal microstructure during CSC in an oxygen-lean atmosphere, coal for oxygen-lean combustion was obtained and combusted at characteristic temperatures by using an independently developed electric tube furnace. Through Fourier-transform infrared spectroscopy, the relative contents of the active functional group of the initial-state oxidation coal were explored, and the main active functional groups, namely aromatic hydrocarbon, the oxygen functional group, aliphatic hydrocarbon, and the hydroxyl group, with the characteristic temperature, were determined. The sensitivities of different active functional groups to oxygen concentrations were analysed. In practice, a 10 vol% oxygen concentration was the key factor, inhibiting and promoting various active functional groups and exhibiting distinct dominance at various stages. Finally, the oxygen concentrations affected the gas concentration release characteristics, thereby determining the correlation degree of the key group and the oxygen concentration, as well as verifying the oxygen concentration sensitivity of the microstructure. The groups highly correlated with CSC characteristic parameters were mainly –CH2 and –CH3. The research results suggested remarkable explanatory potential to further elucidate the evolution of microstructures in oxygen-lean CSC.

Measurements arose from strong reflections combined with occlusions significantly degrade accuracy of multi-target tracking. Few methods have addressed this problem, and thus this paper proposes a robust multi-target tracker for mixed images with occlusions. For multi-cue integration using co-inference tracking, moving object detection significantly enhances motion cue based correction in the presence of reflections. Target templates are represented by sets of color and spatiality histograms. Joint likelihoods referring to both the target motion trajectory and appearance model of the co-inference fused state are computed. Thus each optimized particle weight with the criteria of maximum joint likelihood is more reliable in the face of reflections and inter-object occlusions. State estimation is achieved with the sample-based data association probability and occlusion confidence indicator. Experimental results show that the proposed tracker outperforms the-state-of-the-art multi-target trackers on images with strong reflections and inter-object occlusions.

The ubiquitous specular reflections arose from glasses seriously degrade accuracy of previous visual trackers. Although there have been few visual tracking schemes developed for mixed images with reflections, none focuses on the issue of multi-target tracking. Thus, this paper proposes a multi-target tracking scheme on mixed images with reflections. In the framework of particle filter, the proposed scheme combines the sample-based joint probabilistic data association filter (SJPDAF) with a single target based tracker that uses co-inference and maximum likelihood for visual cue integration to improve tracking accuracy of multiple targets. The co-inference predicted states are used for measurement validation of the SJPDAF. Experimental results show that the proposed scheme works well compared with the SJPDAF tracker.