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.     

基于并行多通道卷积长短时记忆网络的轴承寿命预测方法

在预测轴承剩余使用寿命时，数据间的时序特性是一个可以利用的重要隐藏信息。为了更好地提取具有时序信息的特征用于预测，提出了一种基于并行多通道卷积长短时记忆网络（PMCCNN-LSTM）的剩余使用寿命预测模型。该模型主要由两部分组成：前端为并行多通道卷积网络（PMCCNN），提取信号特征，挖掘数据的时序特性，并采用逐层训练和微调的方式提升参数的收敛性；后端为长短时记忆（LSTM）网络，基于特征进行剩余使用寿命预测，并采用加权平均的方法对预测结果进行平滑处理。在一个轴承加速寿命实验的公开数据集上使用留一法验证了该模型的准确性，实验结果表明：所提模型的平均误差与最大误差分别比传统的卷积神经网络（CNN）低23.38%和15.84%，比传统的LSTM低24.14%和19.01%，比卷积长短时记忆网络（CNN-LSTM）低30.32%和23.09%。     

热处理对挤压态Mg-6Gd-5Y-1Zn合金组织与性能的影响

通过对Mg-6Gd-5Y-1Zn(质量分数,%)合金在固溶和时效处理状态下显微组织和力学性能的研究发现,α-Mg基体、沿挤压方向分布的条状18R-LPSO相、少量的Mg₂₄(GdYZn)₅ 相以及细层片状的14H-LPSO相构成了挤压态合金的组成相。挤压态合金经固溶(T4)处理后,一部分18R-LPSO相溶入基体,并且基体中的14H-LPSO相伸长同时粗化。挤压态合金经过固溶加时效(T6)处理后,大量β′相从α-Mg基体中析出。T6态合金的室温力学性能最好,其屈服强度、抗拉强度及伸长率分别为272 MPa、406 MPa和6.1%。β′相沉淀也发生在挤压态合金的直接人工时效(T5)处理过程,但相比于T6处理,14H-LPSO相和β′相在基体中的体积分数均偏低。     

面向任务口语理解研究现状综述

口语理解是对话系统重要的功能模块，语义槽填充和意图识别是面向任务口语理解的两个关键子任务。近年来，联合识别方法已经成为解决口语理解中语义槽填充和意图识别任务的主流方法，介绍两个任务由独立建模到联合建模的方法，重点介绍基于深度神经网络的语义槽填充和意图识别联合建模方法，并总结了目前存在的问题以及未来的发展趋势。     

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.     

基于深度学习的彩色以及近红外图像去马赛克

单传感器捕获的彩色-近红外（RGB-NIR）图像存在光谱干扰，从而导致重建出的标准彩色图像（RGB）图像与近红外（NIR）图像存在色彩失真以及细节信息模糊。针对这个问题提出一种基于深度学习的去马赛克方法，通过引入跳远连接与稠密连接解决了梯度消失和梯度弥散问题，使得网络更容易训练，并且提升了网络的拟合能力。首先，用浅层特征提取层提取了马赛克图像的像素相关性以及通道相关性等低级特征；然后，将得到的浅层特征图输入到连续多个的残差稠密块以提取专门针对去马赛克的高级语义特征；其次，为充分利用低级特征与高级特征，将多个残差稠密块提取到的特征进行组合；最后，通过全局跳远连接恢复最终的RGB-NIR图像。在深度学习框架Tensorflow上使用公共的图像与视觉表示组（IVRG）数据集、有植被的户外多光谱图像（OMSIV）数据集和森林（Forest）三个公开数据集进行实验。实验结果表明，所提方法优于基于多级自适应残差插值、基于卷积卷积和神经神经网络以及基于深度残差U型网络的主流的RGB-NIR图像去马赛克方法。     

Controllable 1D Patterned Assembly of Colloidal Quantum Dots on PbSO4 Nanoribbons

Control of the 1D self‐assembly pattern of colloidal quantum dots (QDs) on PbSO₄ nanoribbon (NRb) templates is achieved. The internal structure of the NRbs is investigated by X‐ray diffraction, revealing the isotropic packing of the PbSO₄ nanoclusters in the NRbs. Colloidal QDs in a chloroform/hexane mixture are adsorbed onto the region close to the edges of the NRbs and form a 1D assembly of straight single line or double lines by controlling the amount of OAm. This is the first demonstration of a densely packed 1D self‐assembly of colloidal QDs with a straight line pattern without the use of any molecular bridge or adhesive. Atomic force microscopy measurements of the NRbs show depressions in the phase profile along the width of the NRbs, corresponding to the position of the 1D QD chain. The amount of adsorbed QDs on the NRbs in solution decreases as the addition of OAm increases, suggesting that additional OAm prevents interaction between the QDs and NRbs but facilitates the uniform adsorption of the 1D assembly. The low‐dimensional self‐assembly of colloidal QDs in this study opens up the possibility for the creation of anisotropically assembled QD superstructures.     

槽波地震勘探技术在瓦斯富集区探测中的应用研究

为查清糯东煤矿11705工作面内部煤厚变化，并圈出工作面瓦斯治理高风险区域，为工作面安全高效生产提供指导，采用槽波地震透射法对11705工作面进行探测。联合槽波速度解释与能量解释两种方法，辨析透射槽波的质量，将质量差的槽波射线分别绘制在平面图上，采用射线交汇的方法划分工作面的开采风险分布区域。探测成果与实验室中解吸法的瓦斯测定成果进行了对比，结果表明，煤与瓦斯突出矿井槽波速度与瓦斯富集区存在一定相关性，在槽波高速区、波速急剧变化带或高低速相间分布区，瓦斯含量可能较高；被四周高速区所包围的低速区域瓦斯含量较高。上述结论为国内同类型矿井瓦斯富集区探测提供了借鉴。     

定向长钻孔整体水力压裂增透技术在赵固二矿的应用

针对赵固二矿煤层透气性低、钻孔有效影响半径小，实施定向长钻孔代替底板岩巷进行区域瓦斯治理期间钻孔工程量大、瓦斯抽采效果不理想的问题。结合煤层赋存特征及钻孔施工情况，采用定向长钻孔整体水力压裂增透技术，理论分析了合理坐封位置、压裂参数，完成200 m煤巷条带一次整体压裂，最大泵注压力24.3 MPa、累计注水量1 613 m³。并基于煤层全水分变化，考察确定了单个钻孔压裂影响范围达到巷道两帮30 m，有效改善了煤体储层特性，提高了煤层瓦斯抽采效率。在实现定向钻孔对预抽煤巷条带可靠控制的同时，最大程度降低了钻孔工程量、缩短了瓦斯治理周期，为实现矿区“以孔代巷”及高效安全开采提供了技术支撑。