Abstract
High-precision extraction of particulate characteristic modes is essential for dust explosion safety measurements, such as particulate concentration and size distribution. A new solution based on information entropy multi-decision attribute reduction fuzzy rough set is proposed to analyse the particulate morphology characteristics, which effectively avoids the shortcomings of traditional technology (low accuracy, stochasticity, etc.). The proposed approach consists of three stages: membership function modelling, attribute reduction, and maximum information entropy threshold segmentation. The membership coefficient was determined with a multi-segment function by developing the fuzzy degree of the membership model for dust image pixels. The fuzzy dependence of the conditional attribute was determined to extract the fuzzy attribute reduction. Finally, the model of coal dust particulates with information entropy was improved to extract the maximum segmentation threshold, which is significant for classification. The proposed methods were evaluated over a sequence of 30 image sets. The unclassified rate evaluation reached 0.978 for particle sizes $\ge200~\mu \text{m}$ , 0.958 for [75 $\mu \text{m}$ , 200 $\mu \text{m}$ ] and 0.950 for particle sizes $ . The proposed reduction approach offered a performance improvement in terms of more important attribute implementation. The paper demonstrated that the maximum information entropy reduction model can remove the redundant attributes without compromising the precision.
Highlights
With increasing attention being paid by state and relevant experts to environmental pollution caused by substantial coal mining, coal dust recognition technology based on imagery information is attracting more extensive and in-depth studies in mines locally and abroad because of its efficient visualization and intelligent application [1], [2], Technology to extract the visual information from imagery can solve safety issues in coal mines, provide an effective and highly reliable way to adapt to adverse environments, and accurately predict coal dust explosion hazards
PERFORMANCE EVALUATION OF PARTICULATE IMAGE FEATURE 1) DATASET AND PARAMETER SETTING To verify the rationality of the information entropy multiattribute reduction model and evaluate the performance of the proposed method, 30 groups of image sets (35 pictures were taken for each group) with different sizes were utilized in this experiment
The coal dust images with particle sizes ≥ 200 μm, 75 μm ≤ particle sizes < 200 μm, and particle sizes < 75 μm are shown in Figs. 1-3 (a), respectively. the extraction results are shown in Figs. 1-3 (b), (c), and (d)
Summary
With increasing attention being paid by state and relevant experts to environmental pollution caused by substantial coal mining, coal dust recognition technology based on imagery information is attracting more extensive and in-depth studies in mines locally and abroad because of its efficient visualization and intelligent application [1], [2], Technology to extract the visual information from imagery can solve safety issues in coal mines, provide an effective and highly reliable way to adapt to adverse environments, and accurately predict coal dust explosion hazards. The dust detection mechanism is extremely complex, and there are many determinants, resulting in significant randomness of the coal dust detection. With the decrease in dust particulate dispersivity, it becomes increasingly common to find a cell at a certain resolution that contains only a few local details. The statistics indicate that data with low and median resolution are no longer applicable to model the global properties of highresolution dust images. The major work of this paper focuses on how to extract discriminative features for image classification [3]. Many studies conducted locally have focused on the development of dust particulate detection mechanisms, resulting in many varied findings.
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