基于模糊理论的复合材料超声检测缺陷类型识别

本文以复合材料常见缺陷分层、孔隙、疏松的超声波检测缺陷信号为研究对象,选取缺陷波的时域及频域特征,考虑到各特征量之间的不可避免地存在耦合关系,而且对缺陷区分的重要性、敏感度也有很大的差别,采用加权多面体隶属度概念构造隶属函数,依据最大隶属原则进行模式识别,从而区分复合材料缺陷类型。     

基于特征点互信息预配准的医学图像配准技术

基于最大互信息的医学图像配准算法近几年来成为医学图像处理领域的热点。由局部极值导致的寻优困难是困扰该算法的核心问题,混合优化算法成功地解决了互信息函数的寻优问题,但延长了配准时间。文中研究了互信息函数峰值周围的局部极值特征,提出安全区域的概念。利用特征点互信息理论,并结合多灰度级和多分辨率策略,提出一种基于混合优化算法寻优和特征点互信息预配准的改进型算法。经过模拟数据和实际数据配准实验证明,该算法在保证了配准精度的同时,提高了配准的速度,稳健性更强,具有临床推广价值。     

An unsupervised pattern recognition methodology based on factor analysis and a genetic-DBSCAN algorithm to infer operational conditions from strain measurements in structural applications

Structural Health Monitoring (SHM) suggests the use of machine learning algorithms with the aim of understanding specific behaviors in a structural system. This work introduces a pattern recognition methodology for operational condition clustering in a structure sample using the well known Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The methodology was validated using a data set from an experiment with 32 Fiber Bragg Gratings bonded to an aluminum beam placed in cantilever and submitted to cyclic bending loads under 13 different operational conditions (pitch angles). Further, the computational cost and precision of the machine learning pipeline called FA + GA-DBSCAN (which employs a combination of machine learning techniques including factor analysis for dimensionality reduction and a genetic algorithm for the automatic selection of initial parameters of DBSCAN) was measured. The obtained results have shown a good performance, detecting 12 of 13 operational conditions, with an overall precision over 90%.     

基于AI+神经网络的数字化故障电弧识别方案研究

线路故障电弧的特征随着电力电子技术的提高更趋复杂，传统的故障电弧识别方式已无法应对新型用电系统。对比传统故障电弧识别方案，研究故障电弧产生类型和新型检测技术，提出基于谐波因素、总谐波畸变率、电流零休时间、电流变化速率、电流周期性等典型时域、频域特征的AI+神经网络的数字化故障电弧识别方案。该方案运用回归算法向量机对特征图进行分类处理，通过正向传输运算、反向传输运算和迭代回归运算三个步骤执行卷积神经网络，频繁迭代回归以获得最佳特征识别图。本文以调光器的故障电弧识别为例，对故障识别方案进行验证，结果表明采用此方案可实现更高效、更精准、更稳定地识别故障电弧。     

经验模态分解火箭发动机多界面粘结检测特征信号提取技术

针对固体火箭发动机在存放期间粘结界面易出现脱粘的情况,采用超声纵波对其一二界面粘结质量进行检测,得到的二界面脱粘缺陷回波信号通常混叠了 一界面多次反射透射的信号,无法与一界面粘结信号区分.为了区分不同界面回波信号,选用EMD(经验模态分解)对回波信号分解,并根据相关性系数得到信号主导作用模态分量,提取其检测信号幅度特征...     

碘工质霍尔推力器内部过程数值模拟

随着霍尔推力器的大力发展，碘工质霍尔推力器越来越受到研究人员的重视。深入了解碘工质霍尔推力器放电室内部过程，为优化推力器性能和拓展空间应用提供依据。建立了二维PIC/DSMC/MCC混合方法模型，结合鞘层和二次电子发射模型，根据碘工质特性，加入解离-电离过程，在定壁温条件下，针对200 W碘工质霍尔推力器放电室内部过程开展了数值模拟，考察其放电室内等离子体的多场耦合特性以及与壁面的相互作用过程。研究其放电通道内部的等离子体行为，分析放电室内的等离子体参数，获取其离子数密度、离子轴向运动速度、电子温度等特征参数，将模拟结果和氙工质进行比较。结果表明:相较于氙工质，碘工质霍尔推力器存在解离区，宽度约为2 mm，位于近阳极区之后、电离区之前。     

多视角多尺度陨石坑特征检测识别方法

针对序列图像间视角尺度变化大带来的特征无法检测和识别效率低等问题，提出了多视角多尺度陨石坑特征检测识别方法。基于明暗区域信息和形态学处理实现陨石坑图像特征边缘粗提取，利用快速傅里叶变换计算模板与图像特征的匹配度，从而实现图像特征所在区域粗定位。在此基础上，通过引入惯性测量信息计算图像间重叠面积，更新模板形状，并预测搜索区域，解决了序列图像间视角尺度变换及信息冗余带来的特征无法检测和识别效率低的困难，最终在单一陨石坑区域内实现其精细检测。     

A clustering based method to complete frame of discernment

When the existing information does not contain all categories, the Generalized Evidence Theory (GET) can deal with information fusion. However, the question of how to determine the number of categories through GET is still intriguing. To address this question, a modified k-means clustering, named centers initialized clustering is proposed, filling the gap of identification and complement of the frame of discernment. Based on this clustering method, the number of categories is determined. The initialized centers selected by center density keep the cluster results constant, enhancing the stability of clustering results. Besides, constructing Generalized basic Probability Assignment (GBPA) modules in a conservative way improves the reliability of the results. The mass of empty set in combined GBPAs is the indicator of the number of categories. Experiments on real and artificial data sets are conducted to show the effectiveness.     

复杂结构件工序模型自动构建技术研究

工序模型动态集成了航天产品及其零部件的设计、工艺、制造等信息，工序模型构建是航天制造领域三维工艺设计的关键内容，可为控制航天器制造工艺状态、提高加工质量提供使能工具。首先，介绍了正向和逆向两类建模方法的技术原理与特点，将主要建模方法分为特征法、工艺信息法两类;其次，深入分析了切削体造型等不同技术方法的原理与特点;最后，总结了该技术的关键问题并展望其发展方向。本文可为今后航天结构件工序模型自动构建技术的研究提供理论指导与支持。     

Unintentional modulation evaluation in time domain and frequency domain

With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devices. However, electromagnetic signals have an unavoidable device-specific characteristic unintentionally generated by a transmitter, appearing in the form of an Un Intentional Modulation(UIM), namely Radio Frequency Fingerprint(RFF). RFFs can be used to uniquely identify an emitter ...