基于改进核极限学习机和集成学习理论的目标机动轨迹预测

为了提高目标轨迹预测的精度以及预测模型的泛化能力,提出基于改进蝙蝠算法优化的核极限学习机(Kernel Extreme Learning Machine,KELM)和集成学习理论目标机动轨迹预测模型。构建KELM模型,并采用改进的蝙蝠算法对KELM的参数进行优化;以优化后的KELM神经网络为弱预测器,结合集成学习算法生成强预测器,通过训练不断优化强预测的结构和参数,得到一种基于集成学习理论的目标机动轨迹预测模型;基于不同规模的样本,将所得预测模型与逆传播神经网络、支持向量机和极限学习机等模型进行对比分析。仿真结果表明:所提目标机动轨迹预测模型具有较好的预测精度和泛化能力。     

Recent progress on transition metal oxides and carbon-supported transition metal oxides as catalysts for thermal decomposition of ammonium perchlorate

As a main oxidizer in solid composite propellants, ammonium perchlorate (AP) plays an important role because its thermal decomposition behavior has a direct influence on the characteristic of solid com-posite propellants. To improve the performance of solid composite propellant, it is necessary to take measures to modify the thermal decomposition behavior of AP. In recent years, transition metal oxides and carbon-supported transition metal oxides have drawn considerable attention due to their extraor-dinary catalytic activity. In this review, we highlight strategies to enhance the thermal decomposition of AP by tuning morphology, varying the types of metal ion, and coupling with carbon analogue. The enhanced catalytic performance can be ascribed to synergistic effect, increased surface area, more exposed active sites, and accelerated electron transportation and so on. The mechanism of AP decom-position mixed with catalyst has also been briefly summarized. Finally, a conclusive outlook and possible research directions are suggested to address challenges such as lacking practical application in actual formulation of solid composite propellant and batch manufacturing.     

A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy

Feature extraction is an important part of signal processing, which is significant for signal detection, classification, and recognition. The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields. Reverse dispersion entropy (RDE) proposed by us recently, as a nonlinear dynamic analysis method, has the advantages of fast computing speed and strong anti-noise ability, which is more suitable for measuring the complexity of signal than traditional permutation entropy (PE) and dispersion entropy (DE). Empirical wavelet transform (EWT), based on the theory of wavelet analysis, can decompose a complex non-stationary signal into a number of empirical wavelet functions (EWFs) with compact support set spectrum, which has better decomposition performance than empirical mode decomposition (EMD) and its improved algorithms. Considering the advantages of RDE and EWT, on the one hand, we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy; on the other hand, we use RDE as the features of EWFs to improve the signal separability and stability. Finally, we propose a novel signal feature extraction technology based on EWT and RDE in this paper. Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals. Moreover, it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.     

轴向输出TE10模式的紧凑型相对论磁控管的粒子模拟

研究了一种轴向输出TE10模式的紧凑型相对论磁控管,该器件采用6谐振腔结构并工作在π模式上,通过4腔轴向输出的结构设计使得谐振腔结构与矩形输出波导之间形成了较好的过渡,实现了矩形TE10模式的微波输出。与衍射输出相对论磁控管相比,这种结构设计不仅能减小磁控管的轴向和径向的尺寸,使得系统更加紧凑化,而且能在输出波导中获得更加纯净的低阶微波模式。利用粒子模拟软件,初步分析和优化了器件的工作性能。模拟结果表明:当电压为500k V,磁场为0.5T时,该器件的工作频率为2.58GHz,输出功率为1.0GW,功率转换效率达到25.0%。     

近距格斗规避方式研究

空中格斗过程中,当歼击机处于有利位置时,飞行员应操纵歼击机进行机动飞行以便能采用一定的攻击方式攻击目标飞机,当飞行员判断出空战态势对其不利时,往往采用一定的规避机动方法躲避敌机的跟踪和攻击。文中重点阐述了歼击机在空对空格斗状态下常用的几种规避机动方式:逃逸法、保持航向正切法和终端消极偏差法,给出了其定义,分析了其主要的运动特征,并对规避路线运动学方程及边界等运动特性进行了一定的研究。     

装备维护系统效能评估人素系数的建模与分析

传统的系统效能评估模型中,一般不考虑人的因素。然而,在国内外航空装备的维护过程中,由于人为失误引起的系统故障却占了相当大的比重。提出对人机系统效能评估的改进模型,并建立了装备维护系统效能评估中的人素系数模型,通过对人的可用性、可信性及固有能力的分析,得出了人素系数的确定方法。此系数的确定使航空装备维护管理部门能够更合理地进行装备维护系统的效能评估,有利于更好地组织配置人力资源。     

基于Petri网的防空群决策信息传输能力分析

决策信息传输能力的大小,直接决定着防空作战行动的作战效能,进而决定了防空作战的成败。针对防空群决策信息传输问题,用Petri网建模,从时效性和可靠性两个方面,对防空群的决策信息传输能力进行了分析,得到了不同指挥方式下的信息传输能力的度量。对防空群通信能力的分析和指挥体制的优化有一定的借鉴和指导意义。     

基于探索性分析法仿真的建模方法研究

针对探索性分析法的主要特点,探讨建立适合探索性分析法仿真的模型问题.提出了通过分解和校准建立多分辨率模型的方法,该方法包括对低分辨率模型的校准、对高分辨率模型的分解以及输入参数的取值方法,很好地解决了模型的实用以及模型的有效性问题,为探索性分析法仿真的应用提供了具体的方法指导.     

基于VMD和SVM的舰船辐射噪声特征提取及分类识别

针对复杂海洋背景下舰船声频辐射噪声特征提取困难的问题,提出一种基于变分模态分解、中心频率、复杂度特征和支持向量机的舰船辐射噪声特征提取及分类识别方法。对四类舰船辐射噪声信号使用变分模态方法分解,得到一定数量的固有模态函数。通过比较提取能量最大的固有模态函数中心频率和排列熵作为特征参数,并利用支持向量机方法对四类舰船信号样本进行分类识别。实验结果表明,该方法可以实现对舰船辐射噪声的特征提取,与已有方法对比,该方法具有较高的识别率。     

基于不同能量值特征优选的水声目标识别

采用Bartlet平均周期图、小波变换和经验模态分解方法分别提取信号的频段能量值特征、IMF能量值特征,重点对IMF能量值特征进行特征优选。通过设计BP神经网络分类器,对实测的四类舰船目标的辐射噪声信号进行测试,取得了较好的识别效果。