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.     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Structure,mechanical and thermal properties of Y-doped CrAlN coatings

CrAlYN coatings with different Y contents (0, 5 and 12 at.%) were deposited by cathodic arc evaporation to investigate the influence of Y-addition on the structure, mechanical and thermal properties of CrAlN coatings by using X-ray diffraction, scanning electron microscopy, differential scanning calorimetry, thermal gravimetric analysis and nanoindentation. The structural transformation of single phase cubic Cr_0.42Al_0.58N and Cr_0.39Al_0.56Y_0.05N coatings to cubic–wurtzite mixed Cr_0.32Al_0.56Y_0.12N coating leads to a drop in hardness from (30.2±0.7) GPa of Cr_0.42Al_0.58N and (32.0±1.0) GPa of Cr_0.39Al_0.56Y_0.05N to (25.2±0.7) GPa of Cr_0.32Al_0.56Y_0.12N. The incorporation of 5 at.% Y retards the thermal decomposition of CrAlN, verified by the postponed precipitation of w-AlN and N-loss upon annealing. Correspondingly, Cr_0.39Al_0.56Y_0.05N coating consistently exhibits the highest hardness value during thermal annealing. Nevertheless, alloying with Y exerts an adverse effect on the oxidation resistance of CrAlN.     

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。     

Microstructural,hydraulic conductivity and geochemical changes of drying mature fine oil sand tailings in column experiments

This study offers new insights into two-lift deposition of mature fine tailings under atmospheric drying. The interaction of newly added lift and former lift(s) was evaluated using column experiments in terms of volumetric water content, electrical conductivity (EC), hydraulic conductivity, geochemistry and microstructure. Water content and EC followed the same trend and decreasing of water content appears to be responsible for significant reduction in EC. Evaporation on top of the column reduced the water content to almost zero. The obtained results support the coupling between the hydraulic and chemical processes that should be considered by active operators.     

双层毛细芯对环路热管传热性能的实验分析

环路热管作为一种高效的相变传热装置,其性能与位于蒸发器和储液槽之间的毛细芯结构密切相关。为了更深入研究双层毛细芯对环路热管传热性能的影响,利用不同颗粒直径铜粉制备双层毛细芯,在毛细芯总厚度为5 mm的条件下,通过调整大粒径和小粒径层的相对厚度来改变毛细芯厚度比,对平板型蒸发器环路热管启动和变工况运行进行实验测试。实验结果表明:在同一工况下,不同厚度比的双层毛细芯启动特性存在显著差异,启动过程中出现小粒径层蒸发效率低引起的温度过冲和环路热管中气液两相流变化导致的温度振荡;同时存在一个较优的双铜层毛细芯厚度比,大粒径(180～280μm)铜层厚度为3 mm可提高蒸发效率,小粒径(56～71μm)铜层厚度为2 mm可提供足够毛细抽吸力保证环路热管稳定运行。搭载该厚度毛细芯的环路热管不仅启动速度快(125 s),而且总热阻和蒸发器壁面温度均最低,最大加热功率达到120 W(21.10 W/cm~2),对应热阻为0.17 K/W。     

Fabrication of highly sensitive MnO2/F-MWCNT/Ta hybrid nanocomposite sensor with different MnO2 overlayer thickness for H2O2 detection

In this work, we report the development of MnO₂/F-MWCNT/Ta hybrid nanocomposite sensor with different MnO₂ overlayer thickness for the detection of H₂O₂ in real samples. A novel two-step process using e-beam evaporation and spray pyrolysis deposition was adopted for the synthesis of hybrid MnO₂/F-MWCNT/Ta electrodes. SE morphology revealed smaller-sized, compact grains of MnO₂ infiltrated on the outermost walls of MWCNTs. Raman analysis confirmed the existence of carbon nanotubes with abundant structural defects of MnO₂ in the composite. The cyclic voltammetry results displayed a high peak current and narrowed over potential towards the reduction of H₂O₂. The sensor displayed a fast response (<5?s), wide linear range (2–1510?μM) and a low limit of detection (0.04?μM) with significant anti-interfering properties, promising for the development of highly sensitive and reproducible biosensors. The three dimensional nanocomposite sensor also exhibited good recovery (> 98%), thus providing a favourable tool for analysis of H₂O₂ in milk samples.