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.     

船舶摇摆运动对船载炮射击的影响

从船舶在海浪中运动模型出发,结合炮兵射击学原理,研究船舶摇摆导致火炮基座运动以及杆臂效应导致子惯导初始对准误差等因素对火炮射击的影响,主要针对船载炮射击在距离与方向的影响进行分析。并提出了采用初始装定误差补偿的方法消除船舶运动与杆臂效应对射击的影响。     

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。     

变分自编码器结合聚类算法在空战态势评估问题上的应用

针对传统态势评估方法确定权值的主观性强、处理大数据能力弱、特征提取能力不足等问题,提出基于改进变分自编码器和聚类算法的无监督空战态势评估方法。根据态势变化连续性特点,提出基于时间段的空战态势分类方法,将敌我双方态势划分为四类。在变分自编码器的基础上,提出了VAE-WRBM-MDN特征提取模型,即使用混合密度网络优化变分自编码器的特征提取能力和生成数据的相似度,使用权值不确定限制玻尔兹曼机优化网络的初始权值。将提取的特征分别输入到两种典型的聚类算法中进行聚类,并结合态势函数和实际战场情况修正聚类结果,形成正确的态势分类标准。在实验部分,分别进行了最优参数调整、关键特征提取、聚类以及修正实验。实验结果表明,模型态势分类正确率和运行时间均满足应用需求,实例评估结果与客观态势一致性强,所提方法具有实际应用价值。     

基于图像拼接技术的电子对景图制作技术?

针对当前对景图制作存在的问题,提出了一种基于图像拼接技术的电子对景图的制作技术,该技术采用SIFT算法提取海区图像的特征点,使用RANSAC算法对特征点进行精确提纯,最后使用加权平均法实现图像的平滑过渡。实验表明该技术可实现海区图像的无缝拼接,真实感强。     

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。     

五阶HWCNS在低速复杂流场中的应用

采用五阶精度显式混合加权紧致非线性格式求解雷诺平均NS方程;利用多块对接结构网格技术,对30P-30N多段翼型进行网格收敛性研究。在不考虑转捩的情况下,采用SA一方程湍流模型研究混合加权紧致非线性格式与二阶精度MUSCL格式对该翼型压力分布和典型站位速度型的影响,并与实验结果进行对比分析。采用混合加权紧致非线性格式和SA一方程湍流模型模拟梯形翼高升力构型低速复杂流场,通过对总体气动特性和压力分布的分析,探讨五阶精度显式混合加权紧致非线性格式在低速复杂外形流动中的应用能力。结果表明,对30P-30N三段翼型,采用全湍流模拟方法可以得到较好的压力分布;对梯形翼高升力构型,在附着流和边界层小分离情况下混合加权紧致非线性格式有较好的模拟能力。     

An eikonal equation based path planning method using polygon decomposition and curve evolution

Path planning is a key technique of autonomous navigation for robots, and the velocity field is an important part. Constructing velocity field in a complex workspace is still challenging. In this paper, an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the complex workspace in this paper. The artificial potential field model based on probability theory is then used to calculate the potential field of the decomposed workspace, and the velocity field is obtained by utilizing the potential field of this workspace. Path optimization is implemented by curve evolution, during which the internal force generated in the smoothing process of the initial path by a mean filter and the external force is obtained from the gradient of the workspace potential field. The parameter selection principle is deduced by analyzing the influence of several parameters on the path length and smoothness. Simulation results show that the designed polygon decomposition algorithm can effectively segment complex workspace and that the path optimization algorithm can shorten and smoothen paths.