深度学习分类模型的应用,用于区域评估屋顶压力支随着时间的推移而产生的演变效应,在煤矿面部
Hao-Jie Li1, Xiang Fu1,2,3, Yi-Fan Qin1
1College of Mining Engineering, Taiyuan University of Technology, Taiyuan, 030000, China.
Heliyon
|June 6, 2024
概括
本研究引入了一个深度学习模型,通过分析动态压力变化来评估采矿中的液压支质量. 优化的LeNet-5网络实现了85.25%的准确性,改善了智能采矿操作的动态支持质量评估.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 地质技术工程 地质技术工程
背景情况:
- 液压支腿压力对于评估采矿工作面部支质量至关重要.
- 目前的方法专注于静态压力分析,忽视动态压力变化.
- 动态压力变化显著影响整体支的有效性和安全性.
研究的目的:
- 开发一种深度学习模型,用动态压力数据来评估液压支质量.
- 描述工作面区域内支持质量的动态演变.
- 为了使运营商能够对液压支持系统进行有针对性的调整.
主要方法:
- 实时液压支压力数据被收集并预处理成时空子矩阵样本.
- 一个优化的LeNet-5卷积神经网络被开发和训练在预处理的数据.
- 模型性能根据分类准确性,F1分数和回忆进行评估,并与其他网络进行比较.
主要成果:
- 优化的LeNet-5网络在液压支质量方面实现了85.25%的分类准确度.
- 该模型在F1得分和回忆方面表现优异,与其他评估网络相比.
- 网络呈现出更快的融合和加速培训速度,表明了效率.
结论:
- 深度学习,特别是优化的LeNet-5网络,有效地评估液压支的时空支质量.
- 该模型提供了一种新的方法来评估动态支持质量,超越了传统的静态方法.
- 这项技术通过实现对液压支状态的精确实时调整来增强智能采矿操作.
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