基于改进混合高斯模型的运动目标检测算法

在运动目标停滞的情况下,传统的混合高斯模型会将运动目标误判为背景,导致目标漏检。为此,提出一种基于改进混合高斯模型的目标检测算法。引入背景学习参数,结合前一帧的检测结果自适应地更新背景,从而提取完整的运动目标。利用像素的八连通区域信息抑制噪声,提高算法在复杂环境中的稳定性。实验结果表明,与传统检测方法相比,该算法能够在复杂环境中准确地检测出短暂停滞的运动目标。     

机载音频管理组件自动测试系统的设计与实现

音频管理组件是民航飞机话音通信系统的传输中枢,交联复杂,测试项目繁多,且主要机型的相关组件标准各异,造成目前该类组件的测试效率低下,人为差错率高,测试成本高昂;通过对各机型相关组件自动测试需求的分析,建立测试模型,整合测试资源,完成了基于PXI总线的多机型音频管理组件综合测试系统的设计;测试系统采用模块化结构,结合自主设计的多种测试仿真组件,通过统一的测试执行管理平台控制测试资源完成自动测试;实践证明,该系统工作稳定,完全满足多种机载音频管理组件的测试需求,并显著提高了的测试的效率和准确率。     

TCAS视频存储器板卡故障诊断系统研究

TCAS是保障飞机飞行安全的重要机载电子设备,然而国内对TCAS系统板卡只能进行板卡级的测试,不能进行元件级的维修和测试。以TCAS系统中的视频存储器板为例,根据板卡电路特点及适航要求提出一种改进后的基于故障树分析的元件级故障诊断新方法,该方法通过对待测板卡提供外围激励信号、编写程控控制命令、故障树分析和编写输出结果优化程序等对待测板卡进行故障检测与定位,该系统通过调试与运行证明效果良好,并由实验数据得出故障检测精度大大提高,为国内自主研发元件级自动检测设备和机载电子板卡的深度维修提供了可靠的理论依据。     

并行多信道软件无线电接收机仿真

讨论了软件无线电接收机的基本理论:信道划分和解调算法。在此基础上研究了下变频技术和低通滤波技术在并行多信道接收机中的应用,提出了采用软件无线电中的FM和AM解调方式解调信号,并完成了软件无线电接受机的系统仿真。     

航空发动机燃-滑油热交换器焊缝裂纹原因分析

某型航空发动机燃-滑油热交换器出现焊缝裂纹,导致漏油事故,补焊后短时间内又出现裂纹,严重危及飞行安全.通过对燃-滑油热交换器动力学、材料、制造工艺、工况等方面进行深入分析,指出燃-滑油热交换器焊缝裂纹的产生,是采用“一定三活”的安装方式所形成的“潜在故障区”、发动机高速运转时产生的振动应力集中、油液流动和温度场变化产生...     

基于干扰补偿趋近律的离散滑模多周期重复控制

针对离散时间系统的多周期干扰抑制问题,提出一种离散滑模多周期重复控制器设计方法.利用非线性幂次函数设计新型离散趋近律,将周期差分等效干扰以加权形式嵌入到趋近律中,构造带干扰补偿作用的离散趋近律,并据此设计离散滑模多周期重复控制器.为了进行控制器参数整定和表征闭环系统的收敛性能,推导准滑模域边界层的表达式.所提出控制方法既能够消除多周期干扰信号,也能够减小准滑模域边界层.数值仿真验证了所提出控制方法的有效性.     

变工况下航空逆变器健康评估方法研究

工况的变化会引起功率变换器电路健康表征参数随之变化,导致无法判断健康表征参数是因电路性能的退化还是因工况的变化引起的。针对该关键问题,以航空逆变器为研究对象,首先采用多评价指标优选模型优选出相关敏感的健康表征参数;然后基于极限学习机建立工况-无故障情况下健康表征参数映射模型;最后基于当前健康表征参数与映射模型输出的健康表征参数之间的相对变化量构建考虑工况条件的电路健康指标,实现不同工况下航空逆变器的健康评估。实验结果表明,该评估方法可以有效减小工况变化对健康指标的影响。在变工况情况下,相比于直接基于欧氏距离构建健康指标的评估方法,平均绝对误差（Mean Absolute Error,MAE）和均方根误差（Root-Mean-Square Error,RMSE）分别降低了64.4%、66.8%。     

Type-2 fuzzy modeling for desulphurization of steel process

This paper presents a new type-2 fuzzy logic system model for desulphurization process of a real steel industry in Canada. The type-2 fuzzy logic system permits us to model rule uncertainties where every membership value of an element has a second order membership value of its own. In this paper, we propose an indirect method to create second order membership grades that are amplitudes of type-2 secondary membership functions, where the primary memberships are extracted by implementation of fuzzy clustering approach. In this research, Gaussian Mixture Model (GMM) is used for the creation of second order membership grades. Furthermore, a reduction scheme is implemented which results in type-1 membership grades. In turn, this leads to a reduction of the complexity of the system. Two methods are used for the estimation of the membership functions: indirect and direct methods. In the indirect method, the system uses an interpolation scheme for the estimation of the most appropriate membership functions. In the direct method, the system is tuned by an inference algorithm for the optimization of the main parametric system. In this case, the parameters are: Schweizer and Sklar t-norm and s-norm, combination of FATI and FITA inference approaches, and Yager defuzzification. Finally, the system model is applied to the desulphurization process of a real steel industry in Canada. It is shown that the proposed type-2 fuzzy logic system is superior in comparison to multiple regression and type-1 fuzzy logic systems in terms of robustness, and error reduction.     

Experimental determination of elastic properties of impact damage in carbon fibre/epoxy laminates

This paper describes an investigation of in-plane elastic properties of impact damaged regions in composite laminates. Quasi-isotropic carbon fibre/epoxy laminates were impacted and the impact damage examined by ultrasonic C-scanning, optical microscopy and thermal deplying. After impact damage observations, specimens were cut from the laminates and tested in tension and compression. The elastic modulus of the impact damage was, in both tension and compression, mainly controlled by the amount of fibre breakage. Interestingly, layers with broken fibres could sustain some load in compression, which led to higher elastic modulus in compression than in tension. The effect of delaminations on the elastic modulus was quite small in both tension and compression. The through-the-thickness variation of in-plane stiffness was studied by successively removing plies. The variation in stiffness was negligible, probably as a result of the very uniform distribution of delaminations and fibre breakage through the thickness of the laminates.     

Damage detection in aluminum and composite elements using neural networks for Lamb waves signal processing

One of the important factors in the structural health monitoring systems is the amount of data that need to be analysed in real time. This study investigated the use of artificially deteriorated signals of Lamb waves in training the novelty detection (ND) system for the early damage detection. In this system Auto-associative Neural Networks were trained using principal components calculated on the basis of experimentally measured signals. The specimens studied relate to two different materials commonly used in the aerospace industry, i.e. aluminium and glass fibre reinforced polymer. Lamb waves measured in these specimens are a good example that the ND algorithm works correctly in case of simple as well as complex signals. Furthermore, it was found that the designed ND system remained sensitive and robust even when it used raw signals with a relatively low sampling rate, on a fairly narrow time window and even noised signals.