Horizon control, maintaining the alignment of the shearer's exploitation gradient with the coal seam gradient, is a key technique in longwall mining automation. To identify the coal seam gradient, a geological mod...Horizon control, maintaining the alignment of the shearer's exploitation gradient with the coal seam gradient, is a key technique in longwall mining automation. To identify the coal seam gradient, a geological model of the coal seam was constructed using in-seam seismic surveying technology. By synthesizing the control resolution of the range arm and the geometric characteristics of the coal seam, a gradient identification method based on piecewise linear representation(PLR) is proposed. To achieve the maximum exploitation rate within the shearer's capacity, the control resolution of the range arm is selected as the threshold parameter of PLR. The control resolution significantly influenced the number of line segments and the fitting error. With the decrease of the control resolution from 0.01 to 0.02 m, the number of line segments decreased from 65 to 15, which was beneficial to horizon control. However, the average fitting error increased from 0.055 to 0.14 m, which would induce a decrease in the exploitation rate. To avoid significant deviation between the cutting range and the coal seam, the control resolution of the range arm must be lower than 0.02 m. In a field test, the automated horizon control of the longwall face was realized by coal seam gradient identification.展开更多
为解决因排查效率低、数据更新不及时等因素导致低压配电网户变关系连接形式与实际不符的问题,提出一种基于角度分段线性近似(anglepiecewiselinearrepresentation,APLR)和改进密度峰值聚类(improved clustering by fast search find of...为解决因排查效率低、数据更新不及时等因素导致低压配电网户变关系连接形式与实际不符的问题,提出一种基于角度分段线性近似(anglepiecewiselinearrepresentation,APLR)和改进密度峰值聚类(improved clustering by fast search find of density peaks,ICFSFDP)相结合的户变关系识别方法。首先,根据电压曲线中相邻线段的角度变化量提取曲线的转折点,利用APLR对曲线进行自适应降维重构;随后,使用ICFSFDP算法对降维数据组展开聚类分析,在决策图中由拟合函数与坐标轴围成面积的最小值得到最优类簇数目,进而得到聚类和非聚类中心用户;最后,使用动态时间弯曲(dynamic time warping,DTW)距离计算聚类和非聚类中心用户之间的距离相似度,进而得到户变关系。将所提方法应用于模拟和真实数据中,均可证实所提方法的有效性。算例分析结果表明:该方法能够对时间间隔不同、不等维的序列进行分析,且不需要人为设定聚类算法的参数,户变关系识别准确率高。展开更多
时间序列的近似表示和相似度量是时间序列数据挖掘的重要任务之一,是进行相似匹配的关键。该文针对现有的各种基于分段线性表示(Piecewise Linear Representation,PLR)相似度量方法存在的序列长度依赖和多分辨率条件下的潜在识别误差等...时间序列的近似表示和相似度量是时间序列数据挖掘的重要任务之一,是进行相似匹配的关键。该文针对现有的各种基于分段线性表示(Piecewise Linear Representation,PLR)相似度量方法存在的序列长度依赖和多分辨率条件下的潜在识别误差等缺点,提出了一种序列分段线性弧度表示和基于弧度距离的相似度量方法,实现了序列的快速在线分割和相似度计算。该方法简洁直观,利用分段弧度对分段趋势进行细粒度划分来保留序列主要形态特征,有效地提高了度量结果的准确性和多分辨率条件下的稳定性。该方法具有序列分割算法独立性特点,可用于时间序列的相似查询、模式匹配、分类和聚类。展开更多
基金supported in part by the Funds of the National Natural Science Foundation of China (Nos. 51874279 and U1610251)the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘Horizon control, maintaining the alignment of the shearer's exploitation gradient with the coal seam gradient, is a key technique in longwall mining automation. To identify the coal seam gradient, a geological model of the coal seam was constructed using in-seam seismic surveying technology. By synthesizing the control resolution of the range arm and the geometric characteristics of the coal seam, a gradient identification method based on piecewise linear representation(PLR) is proposed. To achieve the maximum exploitation rate within the shearer's capacity, the control resolution of the range arm is selected as the threshold parameter of PLR. The control resolution significantly influenced the number of line segments and the fitting error. With the decrease of the control resolution from 0.01 to 0.02 m, the number of line segments decreased from 65 to 15, which was beneficial to horizon control. However, the average fitting error increased from 0.055 to 0.14 m, which would induce a decrease in the exploitation rate. To avoid significant deviation between the cutting range and the coal seam, the control resolution of the range arm must be lower than 0.02 m. In a field test, the automated horizon control of the longwall face was realized by coal seam gradient identification.
文摘为解决因排查效率低、数据更新不及时等因素导致低压配电网户变关系连接形式与实际不符的问题,提出一种基于角度分段线性近似(anglepiecewiselinearrepresentation,APLR)和改进密度峰值聚类(improved clustering by fast search find of density peaks,ICFSFDP)相结合的户变关系识别方法。首先,根据电压曲线中相邻线段的角度变化量提取曲线的转折点,利用APLR对曲线进行自适应降维重构;随后,使用ICFSFDP算法对降维数据组展开聚类分析,在决策图中由拟合函数与坐标轴围成面积的最小值得到最优类簇数目,进而得到聚类和非聚类中心用户;最后,使用动态时间弯曲(dynamic time warping,DTW)距离计算聚类和非聚类中心用户之间的距离相似度,进而得到户变关系。将所提方法应用于模拟和真实数据中,均可证实所提方法的有效性。算例分析结果表明:该方法能够对时间间隔不同、不等维的序列进行分析,且不需要人为设定聚类算法的参数,户变关系识别准确率高。
文摘时间序列的近似表示和相似度量是时间序列数据挖掘的重要任务之一,是进行相似匹配的关键。该文针对现有的各种基于分段线性表示(Piecewise Linear Representation,PLR)相似度量方法存在的序列长度依赖和多分辨率条件下的潜在识别误差等缺点,提出了一种序列分段线性弧度表示和基于弧度距离的相似度量方法,实现了序列的快速在线分割和相似度计算。该方法简洁直观,利用分段弧度对分段趋势进行细粒度划分来保留序列主要形态特征,有效地提高了度量结果的准确性和多分辨率条件下的稳定性。该方法具有序列分割算法独立性特点,可用于时间序列的相似查询、模式匹配、分类和聚类。
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.50474041)西北工业大学基础研究基金(theRe-search Foundation of Northwestern Polytechnical University under Grant No.W018101)
