The theory and method of wavelet packet decomposition and its energy spectrum dealing with the coal rock Interface Identification are presented in the paper. The characteristic frequency band of the coal rock signal c...The theory and method of wavelet packet decomposition and its energy spectrum dealing with the coal rock Interface Identification are presented in the paper. The characteristic frequency band of the coal rock signal could be identified by wavelet packet decomposition and its energy spectrum conveniently, at the same time, quantification analysis were performed. The result demonstrates that this method is more advantageous and of practical value than traditional Fourier analysis method.展开更多
In mining the left-over coal above the gob,stope wall rock of mining area have hard limestone.through field observation,the face-contacted block structure was found in rocks between coal seams to mine the left-over co...In mining the left-over coal above the gob,stope wall rock of mining area have hard limestone.through field observation,the face-contacted block structure was found in rocks between coal seams to mine the left-over coal above the gob.In order to probe into the movement law of rock strata and strata control measures,it is very important to identify the mobile block in face-contacted block structure of rocks between coal seams.This paper relies on the thought of block theory to establish appropriate parameter matrix and figure out its discrimination matrix in view of the fact that the block in face-contacted block structure has high intensity and stiffness,the展开更多
针对短时间主动热激励作用下煤岩介质表征差异不明显,不易快速、准确识别煤岩界面的难题,提出一种基于改进金字塔场景解析网络(pyramid scene parsing network,简称PSPnet)模型-MobileNetV2的煤岩界面快速精准识别方法。通过搭建煤岩主...针对短时间主动热激励作用下煤岩介质表征差异不明显,不易快速、准确识别煤岩界面的难题,提出一种基于改进金字塔场景解析网络(pyramid scene parsing network,简称PSPnet)模型-MobileNetV2的煤岩界面快速精准识别方法。通过搭建煤岩主动红外试验平台,采集并获取短时主动热激励作用下的煤岩界面红外热图像,构建了煤岩红外图像数据集;对传统PSPnet模型进行改进,采用轻量级网络模型MobileNetV2作为主干网络提取特征,大幅降低了网络模型所占内存和训练时间,同时将注意力机制模块(convolutional block attention module,简称CBAM)与金字塔场景解析(pyramid scene parsing,简称PSP)模块的上采样特征层和PSPnet网络模型的浅层特征层进行融合,有效提升模型对特征的细化能力。试验结果表明:基于改进的PSPnet-MobileNetV2网络模型所占内存仅为9.12 MB,较原始PSPnet模型减少了94.88%;煤和岩的交并比为96.52%和96.87%,分别提升了8.29%和7.7%;像素准确度分别为97.25%和99.15%,较原始网络模型分别提升了7.32%和1.64%;测试时间降低了53.70%。该方法为煤岩界面的快速和预先精准识别提供了一种有效技术手段。展开更多
为减小光散射法的矿井岩尘颗粒物测量质量浓度误差,仿真模拟光子在含尘空间内的随机蒙特卡洛过程,并根据煤、岩尘颗粒物在不同散射面下的捕获光子数以及不同质量浓度范围下动态时间弯曲(dynamic time warping,DTW)距离的差异进行尘源区...为减小光散射法的矿井岩尘颗粒物测量质量浓度误差,仿真模拟光子在含尘空间内的随机蒙特卡洛过程,并根据煤、岩尘颗粒物在不同散射面下的捕获光子数以及不同质量浓度范围下动态时间弯曲(dynamic time warping,DTW)距离的差异进行尘源区分,针对浓度补偿实验获取的岩尘颗粒物测量质量浓度波动较大的问题,使用移动平均和卡尔曼滤波算法进行测量质量浓度的平滑处理。研究结果表明:岩尘颗粒物捕获光子数在90°散射面下有较大差异,在3~555 mg/m^(3)范围内区分煤、岩尘的模数转换差值的DTW判断阈值为23854.06,卡尔曼滤波算法在减小相对测量误差方面比移动平均更好。1#光电传感器在194~555 mg/m^(3)范围内平均相对测量最小误差为-1.34%,2#光电传感器在3~191 mg/m^(3)范围内平均相对测量最小误差为6.06%。研究结果可为矿井粉尘光学测量装置提供数据参考。展开更多
文摘The theory and method of wavelet packet decomposition and its energy spectrum dealing with the coal rock Interface Identification are presented in the paper. The characteristic frequency band of the coal rock signal could be identified by wavelet packet decomposition and its energy spectrum conveniently, at the same time, quantification analysis were performed. The result demonstrates that this method is more advantageous and of practical value than traditional Fourier analysis method.
文摘In mining the left-over coal above the gob,stope wall rock of mining area have hard limestone.through field observation,the face-contacted block structure was found in rocks between coal seams to mine the left-over coal above the gob.In order to probe into the movement law of rock strata and strata control measures,it is very important to identify the mobile block in face-contacted block structure of rocks between coal seams.This paper relies on the thought of block theory to establish appropriate parameter matrix and figure out its discrimination matrix in view of the fact that the block in face-contacted block structure has high intensity and stiffness,the
文摘针对短时间主动热激励作用下煤岩介质表征差异不明显,不易快速、准确识别煤岩界面的难题,提出一种基于改进金字塔场景解析网络(pyramid scene parsing network,简称PSPnet)模型-MobileNetV2的煤岩界面快速精准识别方法。通过搭建煤岩主动红外试验平台,采集并获取短时主动热激励作用下的煤岩界面红外热图像,构建了煤岩红外图像数据集;对传统PSPnet模型进行改进,采用轻量级网络模型MobileNetV2作为主干网络提取特征,大幅降低了网络模型所占内存和训练时间,同时将注意力机制模块(convolutional block attention module,简称CBAM)与金字塔场景解析(pyramid scene parsing,简称PSP)模块的上采样特征层和PSPnet网络模型的浅层特征层进行融合,有效提升模型对特征的细化能力。试验结果表明:基于改进的PSPnet-MobileNetV2网络模型所占内存仅为9.12 MB,较原始PSPnet模型减少了94.88%;煤和岩的交并比为96.52%和96.87%,分别提升了8.29%和7.7%;像素准确度分别为97.25%和99.15%,较原始网络模型分别提升了7.32%和1.64%;测试时间降低了53.70%。该方法为煤岩界面的快速和预先精准识别提供了一种有效技术手段。
