神经网络集成的分布式入侵检测方法

分布式入侵检测系统需具有分布式检测功能及部件增量更新能力.文中提出了一种基于神经网络集成的分布式入侵检测方法,采用单个Agent检测与多个Agent协同检测的两级集成算法实现分布式入侵检测;在发现新的入侵时,Agent上的神经网络集成采用基于资源分配网的增量学习算法进行更新.实验结果表明,该算法能有效检测各种攻击,并且具有对未知攻击的增量学习能力.     

轻质碳-酚醛防热材料缺陷类型及影响分析

文章研究了一种新型的烧蚀防热材料——纤维增强纳米多孔轻质耐烧蚀材料的缺陷类型及形成机理。通过对材料的成型工艺、力热性能以及微观结构形貌状态的研究,确定了材料成型过程中可能形成的缺陷类型及其对产品力热性能的影响,并给出了材料的检测方法及合格判据。所得结果可为其他大尺寸整体成型的纳米增强防热材料产品提供检测依据。     

空间物理过程时空协同探测的覆盖分析方法研究

针对空间物理过程时空协同探测任务的定量化探测覆盖分析需求, 在采用时空相关函数方法描述和分析空间物理时空过程的基础上, 提出一种基于θ-t_R覆盖图的探测覆盖分析方法. 通过分析不同设计方案在一定累计时间周期内的θ-t_R覆盖图, 根据其覆盖范围的不同选择所需要的设计方案. 采用该方法对两条共轨道面椭圆轨道的时空协同探测过程进行了定量的覆盖分析, 验证了该方法的有效性.     

电离层中小尺度扰动的多频多普勒快速探测与分析

电离层反射回波的高频多普勒频移测量是研究电离层扰动的重要方法之一.利用基于单频信号相位变化率测量的实时探测方法,获取连续高精度多频多普勒频高图,以实现中小尺度电离层扰动的快速探测研究.该方法被应用于敏捷式高性能电离层无线电多频探测系统样机平台.经验证在该平台上获取的多普勒频移分辨率可达0.039Hz,频高图探测周期最短小于1min.基于这种快速探测方法和平台在武汉观测站进行了较长时间的观测和数据采集,获得高精度多普勒频移并反演出电子浓度等值面法向运动速度,得到电离层反射寻常波的多普勒频高图和垂直扰动速度等信息,进而推演出电离层扰动随时间和空间的实时变化特征.对这些时域信号进行频谱分析,初步结果显示这些扰动主要是由极区活动激发的中国中部地区冬季出现率较高的中尺度TID.此外,对三种常用的电离层扰动反演分析方法进行了对比研究,结果显示电离层扰动的变化趋势基本一致,说明观测数据和研究方法可靠稳定,为多频多点电离层扰动的传播特性观测研究提供了基础.     

磁层顶通量传输事件的经验重构

在地球磁层顶附近观测到的通量传输事件（Flux Transfer Event,FTE）一般被认为是瞬态局域磁重联的产物,是太阳风质量、动量和能量进入地球内磁层的重要通道.重构FTE的磁场结构可促进对其形成、演化过程及其与周围等离子体环境相互作用的理解.Grad-Shafranov重构法和磁通量绳拟合法等传统磁场重构方法适用于满足特定物理条件的磁场结构.基于平面线性插值原理,设计了一种不受具体物理条件限定的二维FTE磁场结构重构法.模型测试以及对THEMIS和Cluster卫星簇分别观测到的两个FTE的实际应用表明,在合适的多卫星位形条件下,该方法能快速有效重构出FTE的磁场空间分布,有助于推测FTE的磁场线位形,理解卫星测量数据的时间变化,以及分析等离子体物理量相对于FTE的磁场空间分布特征.     

A new skeleton based flying bird detection method for low-altitude air traffic management

In low-altitude air traffic management, non-cooperation targets are the greatest threat to security of low-flying aircraft. Among various aviation fatalities, flying bird is the main factor with the highest risk and directs economic losses amounted to nearly 10 billion US dollars each year. Therefore, Flying Bird Detection (FBD) has attracted considerable attention in low-altitude air traffic management. In this paper, we propose a skeleton based FBD method via describing bird motion information with a set of key poses. To overcome the variability of birds, the skeleton feature is selected as a relatively fixed and common characteristic for the pose appearance of flying bird. Based on the geometric topology among some key parts of bird body, a set of key poses can be described by some extracted skeleton features, which are used to represent the bird motion information. Aimed at robustly handling with the pose variations, multiple pose-specific classifiers are individually trained to learn the representative poses of the flying bird. At the detection stage, the flying bird skeleton features are combined with extracted key-pose sets to perform the flying bird classification task from each image. Afterwards, the key-frame pose-change set and the consistency of the classification results from sequent images are employed to validate the final detection results. Experiments on flying bird datasets demonstrate the effectiveness and efficiency of the proposed method.     

基于动态RCS的隐身飞机探测概率研究

动态RCS是影响战斗机突防概率的重要因素。围绕动态RCS这一关键要素,建立了空空动态RCS求解模型,得到了典型机动下战斗机的动态RCS变化规律,并分析了基于动态RCS的典型机动下的雷达探测概率。基于仿真的静态RCS数据库,耦合飞机抖动模型,研究了拉升机动、空间机动以及空空对抗下某型隐身飞机的动态RCS。通过仿真发现:战斗机的机腹、垂尾及尾喷管部位RCS较大,从而导致了较高的探测概率;对于拉升机动,保持较大的航路捷径更有利于隐蔽突防。     

Improving EGT sensing data anomaly detection of aircraft auxiliary power unit

The reliability of the on-wing aircraft Auxiliary Power Unit (APU) decides the cost and the comfort of flight to a large degree. The most important function of APU is to help start main engines by providing compressed air. Especially on the condition of sudden shutdown in the air, APU can offer additional thrust for landing. Therefore, its condition monitoring has drawn much attention from the academic and industrial field. Among the on-wing sensing data which can reflect its condition, Exhaust Gas Temperature (EGT) is one of the most important parameters. To ensure the reliability of EGT, one kind of data-driven anomaly detection framework for EGT sensing data is proposed based on the Gaussian Process Regression and Kernel Principal Component Analysis. The situations of one-dimensional and two-dimensional input data for EGT anomaly detection are considered, respectively. The cross-validation experiments are carried out by utilizing the real condition data of APU, which are provided by China Southern Airlines Company Limited Shenyang Maintenance Base. The anomalous stuck condition of EGT sensing data is also detected. Experimental results show that the proposed EGT sensing data anomaly detection method can achieve better performance of false positive ratio, false negative ratio and accuracy.     

一种直升机协同搜潜的阵型最优化方法

分析了两种常用检查反潜阵型的各项参数特点，基于指标化评估搜潜阵型对于任务的合理性，提出了一种选取最优搜潜阵型及参数的方法，并通过示例验证了该方法的可行性。     

Automatic lineament extraction in a heavily vegetated region using Landsat Enhanced Thematic Mapper (ETM+) imagery

Lineament extraction from satellite remotely sensed data has been one of the widely used applications of remote sensing in geology. In fact, recent advances in digital image processing allow such lineament extraction to be accomplished in semi-automatic to fully automatic approaches. However, satellite remotely sensed data acquired in heavily vegetated regions such as tropical rainforest, are vulnerable to higher inherent noise levels attributed to the resultant effects of scattering by clouds and adjacency effects of highly inhomogeneous vegetation cover within the pixel dimension. In this study, we examined the effects of noise levels to lineament extraction using a fully automatic approach, consisting of a combination of edge-line detection algorithms. Ancillary information from a digitized topographic map and image classification was used to discriminate between cultural and natural lineaments from the extracted lineaments. Adapting the combination of edge detection and a line-linking algorithm, we have found the optimal parameters for automatic lineament extraction of such complex areas using Enhanced Thematic Mapper (ETM+) data. A noise level of 30% is the maximum threshold before artifacts are generated. It is therefore concluded that the combination of edge-based and line-linking digital image processing operations with the priori local optimal parameters is crucial in lineament feature extraction in heavily vegetated regions.