脉冲放电等离子体控制火箭发动机高频燃烧不稳定性机理

为研究等离子体对火箭发动机高频燃烧不稳定性的影响,提出了一种基于脉冲激励准直流放电等离子体的控制方案,采用数值仿真方法研究了脉冲放电等离子体对燃烧室流场平均参数及动态特征的影响规律。结果表明:脉冲激励下燃烧室平均温度和压力都较定常激励下有所降低,对整个燃烧室的影响可以忽略。与定常激励相似,等离子体可以在一段时间内抑制高频压力振荡,而且在特定控制参数下其对不稳定燃烧的抑制效果优于定常激励方式;从功率谱密度分析可知脉冲激励下燃烧室压力振荡特征频率由燃烧室固有声学频率和脉冲激励频率两者共同决定,提高激励频率则特征频率幅值有所降低。脉冲激励方式与定常激励一样不改变燃烧室压力-释热耦合特征,但是通过降低释热率能够改变压力振荡幅值,进而实现对高频不稳定燃烧的抑制。在所研究工况中,激励频率为50 kHz、占空比为20%的脉冲控制参数下等离子体的抑制效果最佳。      

在强干扰和中分辨率条件下用氮分子第一负系（O，O）带测量转动温度

本文介绍了在强干扰和中分辨率条件下，用氮分子第一负系（Ｏ，Ｏ）带测量转动温度的相对子带积分强度方法。     

自适应时间平滑的演化谱聚类

传统的聚类算法一般只适用于静态数据的处理,而真实世界的数据往往数据量大且变化多,静态的聚类算法不能为动态数据提供其演化规律的分析学习。演化数据的聚类,一方面要正确反映每一时刻数据的合理簇划分,另一方面又要使动态的聚类结果在演化过程中尽可能平滑。本文提出了一种自适应时间平滑的演化聚类框架,该模型考虑到当前时刻数据与历史时刻数据的未知关联,通过限定时间回溯的范围,自适应地寻找与当前快照最相关的历史快照,并通过有机融合基于Itakura-Saito距离的静态相似度和基于时间序列的动态相似度,计算各个时间片快照上的相似度矩阵。本文进一步提出了两种自适应时间平滑的演化谱聚类算法,从不同的角度定义时间代价,得到不同的演化聚类结果。在真实数据集上的实验表明这两种算法能够有效地利用历史数据,在聚类结果上准确性更高,时间平滑性也更好。     

Dynamic airspace configuration method based on a weighted graph model

This paper proposes a new method for dynamic airspace configuration based on a weighted graph model. The method begins with the construction of an undirected graph for the given airspace, where the vertices represent those key points such as airports, waypoints, and the edges represent those air routes. Those vertices are used as the sites of Voronoi diagram, which divides the airspace into units called as cells. Then, aircraft counts of both each cell and of each air-route are computed. Thus, by assigning both the vertices and the edges with those aircraft counts, a weighted graph model comes into being. Accordingly the airspace configuration problem is described as a weighted graph partitioning problem. Then, the problem is solved by a graph partitioning algorithm, which is a mixture of general weighted graph cuts algorithm, an optimal dynamic load balancing algorithm and a heuristic algorithm. After the cuts algorithm partitions the model into sub-graphs, the load balancing algorithm together with the heuristic algorithm transfers aircraft counts to balance workload among sub-graphs. Lastly, airspace configuration is completed by determining the sector boundaries. The simulation result shows that the designed sectors satisfy not only workload balancing condition, but also the constraints such as convexity, connectivity, as well as minimum distance constraint.     

被动型氢原子钟原子跃迁谱线测试与分析

被动型氢原子钟的原子跃迁谱线特性决定了其稳定度指标.为使其稳定度指标达到最优,通过设计数字式测试电路对氢原子钟物理部分进行探测,获得了不同参数配置下的原子跃迁谱线,并通过数据处理分析其信噪比、增益和线宽等特性,提出了一种参数优化方案,为原子钟闭环参数设置提供了依据.     

Guided wave propagation analysis in stiffened panel using time-domain spectral finite element method

Stiffened panels have been widely utilized in fuselages and wings as critical load-bearing components. These structures are prone to be damaged under long-term and extreme loads, and their health monitoring has been a common concern. The guided wave-based monitoring method is regarded as an efficient approach to detect the damage in stiffened plates because of its wide monitoring range and high sensitivity to micro-damage. Efficient simulation of wave propagation can theoretically demonstrate the detection mechanism of the method. In this study, a Time-Domain Spectral Finite Element Method (TD-SFEM) is adopted to study the wavefield in stiffened plates, where continuous Absorbing Layers with Increasing Damping (ALID) strategy is proposed to circumvent the disturbance of reflected waves on boundaries. After the convergence analysis, the developed TD-SFEM with ALID is validated by the finite element method first. Then, wave scattering and the influence of the stiffener are investigated in detail by comparing the results with the non-stiffened structure. Finally, the effects of the parameters of the stiffener, such as the height and width, on wave propagation are studied, respectively. The results illustrate that the proposed TD-SFEM with ALID is an efficient approach to study the wave propagation in the stiffened plate and can reveal the mechanism of influence of the stiffener. It is found that the height of the stiffener changes the interference of wavefield in the plate, while the effects of the width are mainly in wave scattering and mode conversion.     

Pose matters: Pose guided graph attention network for person re-identification

Person re-Identification (reID), aiming at retrieving a person across different cameras, has been playing a more and more important role in the construction of smart city and social security. For deep-learning-based reID methods, it has been proved that using local feature together with global feature could help to give robust representation for person retrieval. Human pose information can provide the locations of human skeleton to effectively guide the network to pay more attention to these key areas, and can also help to reduce the noise distractions from background or occlusions. Based on human pose, a Pose Guided Graph Attention (PGGA) network is proposed in this paper, which is a multi-branch architecture consisting of one branch for global feature and two branches for local key-point features. A graph attention convolution layer is carefully designed to re-assign the contribution weight of each extracted local feature by modeling the similarity relations. The experimental results demonstrate the effectiveness of our approach on discriminative feature learning. Our model achieves the state-of-the-art performance on several mainstream evaluation datasets. A plenty of ablation studies and different kinds of comparison experiments are conducted to prove the effectiveness of this work, including the tests on occluded datasets and cross-domain datasets. Moreover, we further design supplementary tests in practical scenario to indicate the advantage of our work in real-word applications.     

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.     

飞秒光学频率梳在计量领域的应用

飞秒光学频率梳是超快激光光学领域的重要研究方向，通过将锁模激光器重复频率（f_r）及载波包络相移频率（f_o）锁定至微波频率基准，在时域上呈现出飞秒量级的周期性超短脉冲序列，在频域上呈现出一系列离散等间隔排列的纵模分量，广泛应用于时间频率传递，空间尺度测量和精密光谱测量等诸多计量科学研究领域。根据飞秒光频梳的基本原理，着重介绍了其在计量领域的应用研究，分析了在计量领域的重要作用及研究的重要意义。     

一种针对多光谱遥感图像的桥梁快速识别算法

提出了一种针对多光谱图像中桥梁的识别算法。首先,根据水体和背景地物在不同光谱波段的亮度差异,计算多光谱图像的水体指数得到水体增强图,搜索其具有明显双峰的直方图得到最优阈值,实现河流的完整提取；其次,利用桥梁的存在会导致局部水体的光谱异常,沿河流中间线进行潜在桥梁区域的快速提取；再进一步利用桥梁长度以及与河流的空间关系进行鉴别,有效剔除虚警。利用 SPOT4遥感影像进行实验,结果表明本文算法运算量小,对于多个桥梁的识别具有很好的实用性。