Decomposition and replacement: Spatial knowledge distillation for monocular depth estimation

Knowledge distillation has become a key technique for making smart and light-weight networks through model compression and transfer learning. Unlike previous methods that applied knowledge distillation to the classification task, we propose to exploit the decomposition-and-replacement based distillation scheme for depth estimation from a single RGB color image. To do this, Laplacian pyramid-based knowledge distillation is firstly presented in this paper. The key idea of the proposed method is to transfer the rich knowledge of the scene depth, which is well encoded through the teacher network, to the student network in a structured way by decomposing it into the global context and local details. This is fairly desirable for the student network to restore the depth layout more accurately with limited resources. Moreover, we also propose a new guidance concept for knowledge distillation, so-called ReplaceBlock, which replaces blocks randomly selected in the decoded feature of the student network with those of the teacher network. Our ReplaceBlock gives a smoothing effect in learning the feature distribution of the teacher network by considering the spatial contiguity in the feature space. This process is also helpful to clearly restore the depth layout without the significant computational cost. Based on various experimental results on benchmark datasets, the effectiveness of our distillation scheme for monocular depth estimation is demonstrated in details. The code and model are publicly available at : https://github.com/tjqansthd/Lap_Rep_KD_Depth.     

Establishment of a color tolerance for yarn-dyed fabrics from different color-depth yarns

Yarn-dyed fabric is often woven from warp and weft yarns in the same color depth to ensure a uniform color appearance. The difference in color depth between warp and weft tends to result in the uneven color of the yarn-dyed fabric. This article aims to establish a color tolerance for yarn-dyed fabric that can be woven with a qualified color appearance but from the warp and weft yarns in different color depths. A total of 27 yarn-dyed fabric samples in three color series (red, yellow, and blue) were evaluated by using the yarn-dyed fabric from warp and weft yarns in the same color depth of 2% (on weight of fabric, owf) as the standard. Visual assessment and instrumental measurement of color were carried out to establish the color tolerance ellipse that was defined as CMC (Color Measurement Committee) color differences (2:1) of no more than 1.00. It was found that the color strengths (K/S) and color differences (ΔE_CMC(2:1)) of these fabric samples for each color series had linear relationships with the color depths of warp and weft yarns. The color tolerance ellipses indicated that, even though the warp and weft yarns had an apparent color difference, they could be woven in fabrics with relatively uniform color appearance and meet the requirements for yarn-dyed fabric. This work provided valuable insight into the production of qualified yarn-dyed fabrics from unqualified dyed yarns.     

考虑煤层倾角和钻孔倾角的钻屑量理论研究

为准确预测与预防深部开采煤与瓦斯突出,建立了考虑煤层倾角与钻孔倾角的新钻屑量理论计算公式,并分析了煤层倾角和钻孔倾角对钻屑量的影响。以平煤股份有限公司八矿为研究对象,将钻屑量理论计算值与该矿己15-21030风巷现场钻屑量实测值进行对比验证,得出在煤层倾角一定时,钻屑量随钻孔倾角增大呈幂指数递减关系,减小的比率呈“V”形变化趋势,原钻屑量理论值与实测值平均误差为5.5%,在考虑煤层倾角与钻孔倾角情况下的新钻屑量理论值与实测值平均误差为3.9%。     

基于卷积神经网络的单目深度估计

针对利用深度学习方法对街道图像进行深度估计，提出采用语义分割的方法解决深度图出现边界模糊等问题；估计深度通过左右视角图生成视差图进行无监督的训练。在网络模型中添加语义分割层，采取多个空洞卷积并行的结构增加感受野，同时减少了图像下采样的次数，降低了由于下采样带来的信息损失，使得的结果更加准确。这也是在深度估计中首次与空洞卷积相结合增加准确率。通过对KITTI街道数据集进行训练，与现有结果相比，除了增加检测准确性，降低错误率之外，使得效果图中的物体更加清晰，并且在效果图中还保留了一些原模型中被忽视掉的细节信息，将原始图像更加完整的表现出来。     

大斜度井段低沉没度工况排采泵阀动力学特性

目前对于煤系地层天然气井所配套有杆泵泵阀动力特性的研究,主要是移植和借鉴常规油气井抽油泵泵阀的分析方法,多针对油气井开采较高的沉没度,并没有考虑低沉没度和大斜度工况下的泵阀动力学和水力摩阻等参数的影响,也没有揭示水平井泵阀顺利开启所需要的具体条件。为此,综合考虑低沉没度和大斜度等因素耦合的影响,推导造斜段泵阀随井液运动微分方程组,建立有杆泵井液流经泵阀阀隙水力摩阻数学模型,基于数值模拟的方法研究了水平井泵阀动力学、水力摩阻与临界沉没度。研究结果表明:①低沉没度和大斜度耦合作用下,增大冲程和冲次会提高造斜段泵阀阀球的升程、速度和加速度,缩短加速度趋向平缓所用的时间,并且水平井泵阀开启瞬间阀球会出现短暂的周期性波动;②受弹簧力与阀球重力的双重作用,水平井有杆泵的临界沉没度明显低于直井工况且固定阀球伴随弹簧快速复位,这有利于顺利开启水平井的游动阀球和固定阀球并提高泵效;③增大冲程、冲次和泵径会使水平井有杆泵井液流经泵阀水力摩阻及其临界沉没度变大,并且增大冲程更有利于提高低流速井液入泵流速,但同时也会显著地提高临界沉没度。结论认为,该研究成果对于保障煤系地层天然气井连续稳定排采和提高有杆泵的可靠性都具有重要的参考意义。     

水槽中闸门瞬时溃决时上下游水深及流量特性试验研究

鉴于研究水库溃坝时溃坝波传播过程及流量变化规律对大坝安全管理具有十分重要的意义,特做了30组工况下的平底无阻力矩形水槽瞬时溃坝模型试验。结果表明,瞬时溃坝后下游无水时的溃坝波呈反"S"型向下游演进,下游有水时会有相对稳定的激波区产生;渐进流区水力特性仅受初始上游水深影响,而激波区水力特性除受上游水深影响外,还受初始下游水深比的影响,随着下游水深的增大,所涌起的激波区范围越广水深越大,但溃坝流量越小。研究结果为大坝或水闸设计及其安全管理提供了参考依据。     

井下安全阀力学性能分析

井下安全阀对安全性和可靠性要求较高。以渤海油田常用油管携带式井下安全阀为分析对象,对其工作压力、地面控制压力、全开启和全关闭压力、最大下入深度和自平衡能力等关键力学性能进行分析。井下安全阀的工作压力值与地面液控压力值相等,其选取依据为井口压力; 全开启和全关闭液控压力值可通过压力曲线法得到; 最大下入深度取决于弹簧安装时的预紧力; 自平衡能力与活塞面积和自平衡孔面积的比值有关,比值越大,自平衡能力越强。     

准噶尔盆地南缘四棵树地区中浅层砾岩层识别刻画

准噶尔盆地南缘中、下组合构造普遍位于中浅层砾岩层之下,砾岩层具有横向展布范围广且不规则、纵横向速度变化大等特点,严重制约中、下组合目标的精细落实。因此,搞清砾岩层结构及分布特征,对下伏构造形态和高点位置的准确落实尤为重要。结合微测井、钻井、测井资料以及地震剖面对准噶尔盆地南缘四棵树地区中浅层砾岩层结构特征进行分析,对低速和高速砾岩层的顶底界面进行识别刻画,并分区、分段建立了砾岩层时深关系曲线,用于指导深度域下组合构造成图和叠前深度偏移中浅层速度模型的建立。实例应用证明,南缘四棵树地区中浅层砾岩层识别刻画对准确落实构造目标,提高地震剖面成像品质至关重要,同样也可以在准噶尔盆地南缘其他类似地区进行推广应用。     

Study on estimating electric potential in space between parallel electrodes

We have been studying on estimating distribution of permittivity between measurement electrodes using capacitance and electric potential. Two arc electrodes were separated by long distance and there electrodes were surrounded by additional electrodes respectively. In past research work, we carried out numerical electric analysis for calculating the capacitance and electric potential using Finite Element Method (FEM) and compared with experimental and numerical results. The capacitance values were almost agreed with experimental and numerical results. However, the electric potential values were different between experimental and numerical results in conventional studies. In this paper, we proposed an equivalent circuit including the stray capacity and measurement method for capacitance, the electric potential in space between long distance electrodes was estimated.     

DCNN深度特征与交替方向乘子的相关滤波跟踪

针对采用相关滤波的判别式目标跟踪遇到的瓶颈问题：由于目标快速移动引起边界效应，使得相关滤波器在学习与更新过程中可能会引入错误，最终错误的累积将导致跟踪失败。在采集深度学习特征与样本相似性度量的基础上，提出一种引入交替方向乘子方法的改进相关滤波目标跟踪算法，选择DCNN深度特征有效地表征待跟踪目标的初始状态，通过在线分类过程中样本相似性比对与半监督学习，辅助解决相关滤波器在学习过程中存在的自学习问题。所提目标跟踪算法特别适合训练样本为持续获得的、同时存储空间较小的机器学习过程，提高目标在快速运动与部分遮挡等复杂情况下的跟踪成功率，针对VOT2016标准测试视频的实验表明：当目标面临快速运动时，对比CN、SAMF、STC算法，所提DA-CFT跟踪算法将跟踪成功率分别由60.4%～73.4%、67.2%～82.9%、80.9%～88.1%提升至85.6%～91.0%。