薄煤层中垂直裂缝地震响应特征模拟分析

通过定义一个简单的薄煤层模型,在不断增加煤层中垂直裂缝数量的条件下,利用基于分裂节点的有限元数值方法模拟了含有不同条数裂隙煤层的地震反射波场;通过与不含裂缝、各向同性薄煤层反射的多波波场对比分析发现:裂缝的存在会对煤层的反射产生影响,但裂缝密度低时影响微弱,裂缝密度增加到一定程度后会出现类似调谐作用的反射能量干涉加强;裂缝密度进一步升高,对煤层反射的影响主要反映在高频成分的增加,对振幅影响较弱,说明煤层的反射强度主要受薄层的干涉作用影响较大;从理论地震波场分析的角度证明了薄煤层含裂缝的不可忽视性及反演的可能性。     

不同煤层掘进工作面瞬变电磁法探测应用研究

煤矿井下瞬变电磁法成为煤矿井下超前探测工作的重要手段，在矿井水害预测预报上发挥着很大的作用。随着矿井上组煤层资源的逐渐枯竭，煤矿开采资源逐渐延伸向下组煤层。同一井田范围内同煤层及其顶底板的岩性特征一般相差不大，瞬变电磁法在同煤层施工过程中接收到的物理背景场较为接近；而不同煤层及其顶底板岩性特征通常会有较大的差别，瞬变电磁法在不同煤层施工过程中会接收到差别较大的物理背景场。当矿井存在多煤层开采时，通过大量数据研究瞬变电磁法在不同煤层巷道探测中的响应特征规律，分析并掌握不同煤层条件对瞬变电磁法探测结果的影响，能够提高瞬变电磁法在多煤层探测成果的准确性。     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

机翼极限环颤振的CFD/CSD全隐式紧耦合方法

针对轴承振动实时状态监控的需要，从符号动力学角度提出了符号聚合近似与Lempel-ziv复杂度（SAX-LZC）融合的振动监控参数。首先，以Logistic映射和Duffing方程为对象，从理论角度验证了SAX-LZC对动力学结构表征的准确性，并验证了该指标的抗噪能力和计算效率；其次，将SAX-LZC指标与信息熵、样本熵、多分段Lempel-ziv复杂度等动力学参数性能进行了综合对比；最后，从实验角度对轴承早期微弱异常进行了监测，并对轴承典型故障进行了特征提取。理论研究结果表明，SAX-LZC具有动力学结构表征准确、抗噪能力好、计算高效简洁等优点，克服了常规动力学参数工程应用能力弱的问题。实验研究结果表明，SAX-LZC对早期微弱异常有准确的监测，对不同种类故障具有较好的区分度，弥补了时域和频域对轴承微弱故障表征能力不足的缺陷，是一种轴承振动实时状态监控与故障特征提取的有效参数。     

Microstructure−fracture toughness relationships and toughening mechanism of TC21 titanium alloy with lamellar microstructure

The independent influence of microstructural features on fracture toughness of TC21 alloy with lamellar microstructure was investigated. Triple heat treatments were designed to obtain lamellar microstructures with different parameters, which were characterized by OM and SEM. The size and content of α plates were mainly determined by cooling rate from single β phase field and solution temperature in two-phase field; while the precipitation behavior of secondary α platelets was dominantly controlled by aging temperature in two-phase field. The content and thickness of α plates and the thickness of secondary α platelets were important microstructural features influencing the fracture toughness. Both increasing the content of α plates and thickening α plates (or secondary α platelets) could enhance the fracture toughness of TC21 alloy. Based on energy consumption by the plastic zone of crack tip in α plates, a toughening mechanism for titanium alloys was proposed.     

Propagation and termination of an internal crack in continuously cast austenitic stainless steel analyzed by ∑ values and the Schmid factor

The formation mechanism of an internal crack was clarified from the viewpoint of the crystallography and thermal expansion. An inverse pole figure map obtained by EBSD pattern showed that the crack propagated along the grain boundaries having high ∑ values within the columnar zone. After the crack initiation, these positions were considered to undergo cracking followed by propagation toward the equiaxed side. Near the termination position, the grains ahead of crack propagation had a Schmid factor higher than 0.45 consuming elastic strain energy. Thermal expansion measurements showed that the grain with (0 0 1) orientation had the largest expansion while that with (0 1 1) the smallest. The grain boundaries neighboring the combination of (0 0 1) and (0 1 1) grains had the largest thermal stress. Therefore, thermal stress contributed to the initiation of cracking. It was thus proposed to enlarge the equiaxed zone to prevent cracking by discontinuing the crack propagation.     

基于Faster R-CNN的颜色导向火焰检测

基于深度特征的目标检测方法Faster R-CNN在火焰检测任务上存在检测效率低的问题,因此提出了基于颜色引导的抛锚策略。该策略设计火焰颜色模型来限制锚的生成,即利用火焰颜色约束锚的生成区域,从而减少了初始锚的数量,提升了计算效率。为了进一步提高网络的计算效率,将区域生成网络中的卷积层替换成掩膜卷积。为了验证所提方法的检测效果,采用BoWFire和Corsician数据集进行验证。实验结果表明,该方法实际检测速度相较于原Faster R-CNN提高了10.1%,BoWFire上该方法的火焰检测F值为0.87,Corsician上该方法的准确度可达99.33%。所提方法可以提高火焰检测的效率,并能够准确检测图像中的火焰。     

小尺度交通信号灯的检测与状态识别

交通信号灯识别包括检测和状态识别，在智能交通系统中发挥重要作用。基于YOLOv3算法提出了一种交通信号灯检测与状态识别模型。针对交通信号灯相较于交通场景中其他目标具有尺度小的特性进行了算法的设计：降低骨干网络的下采样倍率以增加小尺度目标的特征描述能力；通过增大特征图的尺度来改进多尺度特征融合；引入广义交并比作为检测任务的损失函数来改进目标边界框的回归效果。同时，根据交通信号灯本身的特性，使用颜色和形状约束的方法对信号灯进行状态识别和类别验证。最后在公开的Bosch交通信号灯数据集上和实际的城区道路进行了实验验证。实验结果表明，所提出的算法能够提升交通信号灯识别的精度和召回率，识别准确率可以达到90%左右。     

Human action recognition toward massive-scale sport sceneries based on deep multi-model feature fusion

In sport sceneries, automatically recognizing human actions is a useful technique that can be popularly applied in may domains, such as human body tracking and athlete behavior analysis Most state-of-the-art deep architectures have achieved competitive performance in recognizing human action. However, it is still a challenging task due to the unavoidable occlusion, camera angle changes, and varied human posture. In this paper, we propose a novel deep multimodal feature fusion algorithm for human action recognition. The key technique is a multi-model feature fusion scheme. More specifically, we fuse visual feature, skeleton posture, probability maps and audio signal into a hybrid feature, which is utilized to represent human action. Then these feature channels are optimally combined using a deep model, wherein the weights of multiple feature channels can be predicted intelligently. Finally, the optimally fused feature are fed into a multi-class SVM for conducting human action recognition. Extensive comparative results and parameter analysis have shown the effectiveness of our proposed method.