ArticleACS omega2023
Research on Coal Gangue Recognition Based on Multi-source Time-Frequency Domain Feature Fusion.
Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The over-exploitation of resources caused by the increasing coal demand has resulted in a sharp increase in solid waste emissions mainly gangue, which has made the burden on the environment, economy, resources, and society of our country heavier. In order to achieve a balance between energy consumption and solid waste emission in the process of top coal caving, this study carried out coal gangue recognition research based on multi-source time-frequency domain feature fusion (MS-TFDF-F). First, the process of coal gangue symbiosis and the harm of gangue in top coal caving are analyzed, and the fundamental method of comprehensive treatment of gangue is put forward, which is the accurate recognition of the coal gangue interface. Second, by building a top coal caving simulation test bed, the MS signals generated in the caving process of the coal gangue mixture with a gangue content of 0-100% are collected and the TFDFs are extracted. Third, the MS-TFDF-F-based coal gangue recognition model is established. Then, the recognition effect of the two TFDF-F sample sets was compared, and the results show that the time-frequency domain feature selection fusion method (TFDFS-FM) has higher accuracy. On this basis, this paper studies the variation law of the number of sensors on the coal gangue recognition accuracy of MS information fusion. Finally, the economic, social, environmental, and resource benefits of the model are qualitatively described. The final results show that the MS-TFDF-F-based coal gangue recognition model has the strongest recognition ability when fusing six sensor signals, and the recognition accuracy reaches 99% under the AdaBoost algorithm. The establishment of this model brings huge benefits to China's environment, economy, resources, and society, and it is helpful to realize the balance between loss reduction mining and solid waste emission reduction in the process of top coal caving.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.