基于多源时间频域特征融合的煤炭通道识别研究
Yao Zhang1, Yang Yang1, Qingliang Zeng1
1College of Mechanical and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
ACS omega
|July 24, 2023
概括
这项研究开发了一种使用多源时间频域特征融合 (MS-TFDF-F) 的煤识别模型. 该模型达到99%的准确性,为煤矿开采提供了显著的环境和经济效益.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 环境科学 环境科学
- 信号处理 信号处理
背景情况:
- 煤炭需求的增加导致大量的废弃物,对环境,经济和社会造成负担.
- 准确地识别煤接口对于高层煤洞穴中有效的废物管理至关重要.
研究的目的:
- 开发和验证一个采用多源时间频域特征融合 (MS-TFDF-F) 的煤识别模型.
- 分析传感器数量对识别精度的影响,并评估模型的好处.
主要方法:
- 分析煤共生及其对顶部煤洞穴的有害影响.
- 从模拟的煤混合物 (0-100%的含量) 收集MS信号,并提取时间频域特征 (TFDF).
- 开发MS-TFDF-F模型,对特征选择融合方法进行比较,并调查传感器数量的变化.
主要成果:
- 时间频域特征选择融合方法 (TFDFS-FM) 的准确性更高.
- 在采用AdaBoost算法时,MS-TFDF-F模型在合来自六个传感器的信号时实现了99%的识别准确度.
- 定性分析证实了显著的经济,社会,环境和资源效益.
结论:
- 开发的MS-TFDF-F模型提供了一个非常准确的方法来识别煤炭.
- 这种方法通过减少损失和固体废物排放来支持平衡的采矿业务.
- 该模式为中国的环境,经济,资源和社会带来了巨大的好处.
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