• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Pattern Analysis and Applications >An automatic decision approach to coal-rock recognition in top coal caving based on MF-Score
【24h】

An automatic decision approach to coal-rock recognition in top coal caving based on MF-Score

机译：基于MF-Score的放顶煤自动识别方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Aiming at the problem of the absence of effective coal-rock recognition methods during top coal caving, we propose a new multi-class feature selection approach to detecting full coal falling/30% rock mixture falling/50% rock mixture falling/full rock falling recognition during caving. The method is based on vibration and acoustic sensors fixed under the tail beam of the hydraulic as well as signal processing techniques. Distinctive vibration and acoustic signals generate during caving that depend on the state of coal-rock mixed. Via a contribution threshold and classification accuracy P, the optimal combination of feature attributes can be automatically and sequentially decided by using the multi-class F-score (MF-Score) feature reduction approach proposed in this paper. The main idea of the MF-Score is to construct the within-class scatter matrix with a membership function based on the aggregation degree within the sample. The support vector machine classifier has been employed to test the proposed algorithm and identified the state of the coal-rock mixed. The contribution threshold is readjusted according to the calculated feature selection criterion J(k) and user requirement, and then a feedback loop is developed to keep the dynamic of the samples. Experimental results show the good generality and effectiveness of this new approach.
机译：针对顶煤放出时缺乏有效的煤岩识别方法的问题，我们提出了一种新的多类特征选择方法来检测全落煤/ 30％混合岩石落/ 50％混合岩石落/全岩石落崩塌时识别。该方法基于固定在液压尾梁下方的振动和声音传感器以及信号处理技术。放煤过程中会产生明显的振动和声音信号，具体取决于煤石混合状态。通过贡献阈值和分类精度P，可以使用本文提出的多类F分数（MF-Score）特征约简方法自动并顺序地确定特征属性的最佳组合。 MF-Score的主要思想是基于样本内的聚集度构造具有隶属函数的类内散布矩阵。支持向量机分类器已用于测试该算法，并确定了煤岩混合状态。根据计算出的特征选择准则J（k）和用户要求重新调整贡献阈值，然后建立反馈环路以保持样本的动态。实验结果表明该新方法具有良好的通用性和有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号