密集阵高功率微波空间功率合成

对基于矩形阵列的高功率微波二维密集阵阵列合成进行了研究。仿真分析了均匀矩形栅格阵列的远场方向图,结果表明采用密集阵可以实现高效的、具有确定主波束的空间功率合成。并分析了阵元间距及阵元初相位对阵列空间功率合成的影响,结果表明:阵元间距越小,栅瓣越少,主波束宽度越宽,具有确定主波束的临界距离越小;当目标高度超过阵临界距离时,阵元初相位相差越小合成效率越高,阵列初相位分布范围超过/2时,阵列得不到确定的主波束,进行阵列设计时应充分考虑阵元间距及初相位对阵列合成的影响。     

椭圆强非局域空间光孤子

对傍轴椭圆高斯光束在具有椭圆对称响应特性的强非局域非线性介质中的演化规律进行研究，得到了光束各参量演化的精确解析解，分析了单向空间光孤子和强非局域椭圆空间光孤子的形成条件，发现了椭圆光孤子的相移与介质响应函数的椭圆率有关. 关键词： 椭圆对称强非局域响应介质 椭圆强非局域空间光孤子 相移     

生长温度对磁控溅射ZnO薄膜的结晶特性和光学性能的影响

采用反应射频磁控溅射方法，在Si (100) 基片上制备了具有高c轴择优取向的ZnO薄膜.利用 原子力显微镜、透射电子显微镜、X射线衍射分析、拉曼光谱等表征技术，研究了沉积温度 对ZnO薄膜的表面形貌、晶粒尺度、应力状态等结晶性能的影响；通过沉积温度对透射光谱 和光致荧光光谱的影响，探讨了ZnO薄膜的结晶特性与光学性能之间的关系.研究结果显示， 在室温至500℃的范围内，ZnO薄膜的晶粒尺寸随沉积温度的增加而增加，在沉积温度为500 ℃时达到最大；当沉积温度为750℃时，ZnO薄膜的晶粒尺度有所减小；在室温至750℃的范 围内，薄膜中ZnO晶粒与Si基体之间均存在着相对固定的外延关系；在沉积温度低于500℃时 ，制备的ZnO薄膜处于压应变状态，而750℃时沉积的薄膜表现为张应变状态.沉积温度的不 同导致ZnO薄膜的折射率、消光系数、光学禁带宽度以及光致荧光特性的变化，沉积温度对 紫外光致荧光特性起着决定性的作用.此外，探讨了影响薄膜近紫外光致荧光发射的可能因 素. 关键词： ZnO薄膜 表面形貌 微观结构 光学常数     

非完整系统Chetaev动力学和vakonomic动力学的等价条件

就一般非完整约束系统，从约束方程满足的变分恒等式出发，利用增广位形流形上的向量场定义三类非自由变分，即非完整变分：vakonomic变分、Hlder变分、Suslov变分，并讨论它们之间的关系以及它们成为自由变分的充要条件.利用非完整变分以及相应的积分变分原理建立两类动力学方程：vakonomic方程和Routh方程或Chaplygin方程.通过vakonomic方程分别与Routh方程和Chaplygin方程比较，得到它们具有共同解的两类充分必要条件.这些条件并不是约束的可积性条件. 关键词： 非完整约束 非完整变分 Chetaev条件 vakonomic动力学     

预测器-减法器-恢复器构成的滤波器去除刺激伪迹的研究

为了去除混在瞬态诱发耳声发射(TEOAE)信号中的刺激伪迹,同时保留瞬态诱发耳声发射信号的有效信息,本文首次提出将预测器-减法器-恢复器构成的滤波器,用于去除受试者耳中的刺激伪迹,来准确提取TEOAE信号。此方法与导出的非线性响应方法相比,不但能准确有效地去除刺激伪迹,而且能保留TEOAE信号的有效成分。是一种非常有前景的应用于临床的方法。     

Ge_x Si_(1-x)/Si超晶格中纵声学声子的喇曼散射强度研究

本文用光弹理论,在全面考虑了超晶格中两种材料的声速,质量密度和光弹常数存在差别的基础上,计算了Ge_xSi_(1-x)/Si超晶格中折迭纵声学声子的喇曼散射强度,在高达50cm~(-1)的频率范围内,理论值和实验符合得很好。     

Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.     

FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion

Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects’ features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.     

Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

A misalignment fault is a kind of potential fault in double-fed wind turbines. The reasonable and effective fault prediction models are used to predict its development trend before serious faults occur, which can take measures to repair in advance and reduce human and material losses. In this paper, the Least Squares Support Vector Machine optimized by the Improved Artificial Fish Swarm Algorithm is used to predict the misalignment index of the experiment platform. The mixed features of time domain, frequency domain, and time-frequency domain indexes of vibration or stator current signals are the inputs of the Least Squares Support Vector Machine. The kurtosis of the same signals is the output of the model, and the 3σ principle of the normal distribution is adopted to set the warning line of misalignment fault. Compared with other optimization algorithms, the experimental results show that the proposed prediction model can predict the development trend of the misalignment index with the least prediction error.