用于射频滤波器的LN单晶薄膜XBAR的性能研究

基于铌酸锂(LN)薄膜的横向激发体声波谐振器(XBAR)能够兼具大机电耦合系数(K²)和高谐振频率(f)特性，有望满足5G应用的频段要求。然而，常规LN薄膜单层XBAR结构的温度稳定性较差，频率温度系数(TCF)较低。该文提出一种具有SiO₂温度补偿层的SiO₂/LN双层结构XBAR，并建立了精确分析层状结构XBAR的有限元模型。理论分析表明，该双层结构XBAR上激励的主模式是一阶反对称(A1)兰姆波。通过合理优化结构参数配置，能够获得高谐振频率(f~4.75 GHz)和大机电耦合系数(K²~8%)，同时其温度稳定性也得到显著改善(TCF~-36.1×10^-6/℃)，相较于单层XBAR结构提高了近70×10^-6/℃，这为研制温补型高频、大带宽声学滤波器提供了理论基础。     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

广义正交码索引调制系统的性能分析

摘 要：为了提高码索引调制（code index modulation，CIM）系统的传输效率，提出了一种具有更低复杂度的单输入单输出（single input single output，SISO）的广义正交码索引调制（generalized orthogonal code index modulation，GQCIM）系统。CIM 系统使用扩频码和星座符号传输信息，但只能激活两个扩频码索引和一个调制符号。而 GQCIM 系统以一种新颖的方式克服了只激活一个调制符号的限制，同时充分利用了调制符号的正交性，增加扩频码索引以传输更多的额外信息位，提高了系统的传输效率。此外，分析了GQCIM系统的理论性能，推导了误码率性能的上界。通过蒙特卡罗仿真验证了GQCIM系统的性能，对比发现GQCIM系统的理论和仿真性能一致。而且在相同的传输效率下，结果显示GQCIM系统的性能优于同样具有正交性的调制系统，如广义码索引调制（generalized code index modulation，GCIM）系统、CIM系统、码索引调制-正交空间调制（code index modulation aided quadrature spatial modulation，CIM-QSM）系统、码索引调制-正交空间调制（code index modulation aided spatial modulation，CIM-SM）系统、脉冲索引调制（pulse index modulation，PIM）系统。     

基于推理注意力机制的二阶段网络图像修复

现有的图像修复方法在处理大面积缺失或高度纹理化的图像时，通常会产生扭曲的结构或与周围区域不一致的模糊纹理，无法重建合理的图像结构。为此，提出了一种基于推理注意力机制的二阶段网络图像修复方法。首先通过边缘生成网络生成合理的幻觉边缘信息，然后在图像补全网络完成图像的重建工作。为了进一步生成视觉效果更逼真的图像，提高图像修复的精确度，在图像补全网络采用推理注意力机制，有效控制了生成特征的不一致性，从而生成更有效的信息。所提方法在多个数据集上进行了实验验证，结果表明该图像修复方法的结构相似性指数达到了88.9%，峰值信噪比达到了25.56 dB，与现有的图像修复方法相比，该方法具有更高的图像修复精确度，生成的图像更逼真。     

Ensemble relation network with multi-level measure

Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shotlearning image classification methods only use top-level features and adopt a single measure. In that way, the localfeatures of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure (ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space tocompare the similarity between the samples in the local features, and finally integrates the similarity scores from thefeature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details andglobal information of different grains. Experimental results on different fine-grained datasets show that the proposedmethod achieves good classification performance and also proves its rationality.     

Opportunistic Routing Protocol Based EPO–BES in MANET for Optimal Path Selection

Wireless Personal Communications - Despite its various benefits, the Mobile Ad-hoc Network (MANET) has a number of obstacles due to its mobility, unstable topology, energy efficiency, and other...     

Effect of interface types on the heat-resistance of Cansas-II SiCf /SiC composites

The heat-resistance of the Cansas-II SiC/CVI-SiC mini-composites with a PyC and BN interface was studied in detail. The interfacial shear strength of the SiC/PyC/SiC mini-composites decreased from 15 MPa to 3 MPa after the heat treatment at 1500 °C for 50 h, while that of the SiC/BN/SiC mini-composites decreased from 248 MPa to 1 MPa, which could be mainly attributed to the improvement of the crystallization degree of the interface and the decomposition of the matrix. Aside from the above reasons, the larger declined fraction of the interfacial shear strength of the SiC/BN/SiC mini-composites might also be related to the gaps in the BN interface induced by the volatilization of B₂O₃·SiO₂ phase, leading to a significant larger declined fraction of the tensile strength of the SiC/BN/SiC mini-composites due to the obvious expansion of the critical flaws on the fiber surface. Therefore, compared with the CVI BN interface, the CVI PyC interface has better heat-resistance at high temperatures up to 1500 °C due to the fewer impurities in PyC.