Real-time object tracking via least squares transformation in spatial and Fourier domains for unmanned aerial vehicles

This paper addresses the problem of real-time object tracking for unmanned aerial vehicles. We consider the task of object tracking as a classification problem. Training a good classifier always needs a huge number of samples, which is always time-consuming and not suitable for realtime applications. In this paper, we transform the large-scale least-squares problem in the spatial domain to a series of small-scale least-squares problems with constraints in the Fourier domain using the correlation filter technique. Then, this problem is efficiently solved by two stages. In the first stage, a fast method based on recursive least squares is used to solve the correlation filter problem without constraints in the Fourier domain. In the second stage, a weight matrix is constructed to prune the solution attained in the first stage to approach the constraints in the spatial domain. Then, the pruned classifier is used for tracking. To evaluate proposed tracker's performance, comprehensive experiments are conducted on challenging aerial sequences in the UAV123 dataset. Experimental results demonstrate that proposed approach achieves a state-ofthe-art tracking performance in aerial sequences and operates at a mean speed of beyond 40 frames/s. For further analysis of proposed tracker's robustness, extensive experiments are also performed on recent benchmarks OTB50, OTB100, and VOT2016.     

基于Kriging模型梯度解析解的改进一次二阶矩方法

改进一次二阶矩（AFOSM）法是一种基于功能函数梯度的结构可靠性分析方法，鉴于其对隐式函数的梯度较难求解，提出了一种基于Kriging模型梯度解析解的AFOSM方法，利用Kriging代理模型的解析表达式推导得到功能函数对输入变量的梯度解析解，为AFOSM中设计点的确定提供高精度的梯度信息。通过Kriging与AFOSM的结合，很好地解决了基于有限元模型的隐式情况下梯度计算量相当大、可靠性分析难的问题。数值与工程算例验证了所提Kriging梯度解析解的较高精确性，同时验证了所提基于Kriging解析解的AFOSM结构可靠性分析方法的正确性与较高精度。     

基于图像识别的机械振动信号特征提取与寿命预测方法研究

滚动轴承作为许多机械设备的关键组件,被广泛应用于机械制造、航空航天等领域,其健康状态直接影响了相应设备的剩余寿命,因此在设备故障预测与健康管理(Prognostics and Health Management, PHM)领域,滚动轴承寿命预测具有很高的研究价值。目前基于数据驱动的轴承寿命预测方法主要利用特征提取并构造健康因子(Health Indicator, HI),然而在这一过程中特征的选择与融合依然依赖于专家先验知识,并且健康因子也很难从复杂的时序数据中进行提取。因此,提出了一种新型的数据驱动寿命预测算法,在特征提取方面,通过连续小波变换(Continuous Wavelet Transform,CWT)将传感器振动信号转换为时频谱图,再通过深度残差网络(Deep residual network, ResNet)结合时空卷积网络(Temporal Convolutional Network, TCN)将时频谱图中的时域频域特征构造成为健康因子,最后完成剩余寿命预测。本研究在PRONOSTIA数据集上与现有的数据驱动算法进行了对比,证明了该算法可以更准确地完成剩余寿命预测。     

基于Hilbert-Huang变换的航天器发射段力学环境分析

文章基于Hilbert-Huang变换,对某载人航天器发射过程中整流罩分离期间测量的力学环境参数进行处理分析,利用HHT的基函数自适应性特点,得到振动时间历程信号的时频域Hilbert谱,准确识别出信号主频率及对应时间范围。此应用案例可为地面力学试验条件制定以及运载接口条件确定提供参考。     

Hybrid integration method for highly maneuvering radar target detection based on a Markov motion model

To detect highly maneuvering radar targets in low signal-to-noise ratio conditions, a hybrid long-time integration method is proposed, which combines Radon-Fourier Transform (RFT), Dynamic Programming (DP), and Binary Integration (BI), named RFT-DP-BI. A Markov model with unified range-velocity quantification is formulated to describe the maneuvering target’s motion. Based on this model, long-time hybrid integration is performed. Firstly, the whole integration time is divided into multiple time segments and coherent integration is performed in each segment via RFT. Secondly, non-coherent integration is performed in all segments via DP. Thirdly, 2/4 binary integration is performed to further improve the detection performance. Finally, the detection results are exported together with target range and velocity trajectories. The proposed method can perform the long-time integration of highly maneuvering targets with arbitrary forms of motion. Additionally, it has a low computational cost that is linear to the integration time. Both simulated and real radar data demonstrate that it offers good detection and estimation performances.     

Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases

Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features, as well as its easy burying in the complex, non-stationary structural vibrations and strong background noises. In this paper, a method based on the flexible analytical wavelet transform (FAWT) possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings. During the route of the proposed method, the proper FAWT bases are constructed via genetic optimization algorithm (GA) based on maximizing the spectral correlated kurtosis (SCK) which is firstly presented and proved to be efficient and effective in indicating interested fault mode. Via using the customized FAWT bases for each interested fault mode, the original vibration measurements are decomposed into fine frequency subbands, and the sensitive subband which enhances the signal-to-noise ratio (SNR) is selected to exhibit the fault signature on its envelope spectrum. The proposed method is tested via simulated signals, and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace, inner-race and roller defects. The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.     

Numerical investigation on flow nonuniformity-induced hysteresis in scramjet isolator

Numerical simulation and theoretical analysis were conducted to study the hysteresis inside scramjet isolator during the reciprocating process of back pressure variation. It is revealed that only a regular reflection is theoretically possible for two leading shocks when the inflow Mach number is greater than 2.0, and no hysteresis can occur in the transition between shock reflection types. Nevertheless, wall suction, gas injection, and background waves cause non-uniformity of the incoming flow and would make hysteresis possible. Besides the classical hysteresis in the transition between shock reflection, new kinds of hysteresis were found in both the deflection angle of separated boundary layer and the location of the shock train. Moreover, the occurrence of hysteresis in the deflection angle of the separated boundary layer is accompanied with the shock reflection hysteresis. In the case with background waves or gas injection, hysteresis in the starting position of leading shock was observed too. As back pressure decreases, the leading shock does not follow the same path as that as the back pressure increases, and it is anchored at the location where the background shock or the injection interacts with the leading shock. It is inferred that, if two strong adverse pressure gradient regions move towards and interact with each other, hysteresis will take place when they start to separate.     

300M钢激光熔覆耐磨防腐自润滑涂层温度场数值模拟研究

为了提高飞机起落架减震支柱300M超高强钢的抗磨性能,突破由于温度梯度过大诱发的激光熔覆耐磨防腐自润滑涂层裂纹等技术瓶颈,基于ANSYS软件的生死单元法编制热循环程序,考虑自润滑相和耐磨相材料热物性参数随温度的变化、相变潜热、激光熔覆过程中与外界的换热、激光熔覆功率、激光熔覆扫描速率等因素对激光熔覆过程温度场、熔池、温度梯度的影响,建立300M超高强钢激光熔覆耐磨防腐自润滑涂层温度仿真模型。结果表明:基体的熔化需要激光、熔化的粉末等综合作用使传导到该区域的有效能量达到熔化的临界值,熔化高度增加率随激光功率的增加先降低后增加,熔化高度减少率随激光扫描速率的增加先变小后变大;由于激光熔覆的不同区域温度、冷却速率差异等综合因素影响,熔覆层熔池的纵截面为勺状熔池;伴随激光功率的增加,由于熔覆层不同区域对能量输入产生的温度响应速率差异导致Z方向温度梯度值增加,且最大冷却速率增加;伴随激光扫描速率的增加,激光输入能量降低,降低了高温区域温度及激光的快速局部加热等综合影响,Z方向温度梯度值降低。通过调控激光参数,在保持熔覆层结合强度的条件下,基体熔凝区能被控制到最小,并能够降低温度梯度。     

基于卷积稀疏表示的图像融合方法

图像融合是图像处理领域中比较重要的一门技术,传统的图像融合方法会降低图像融合质量。针对稀疏表示在图像融合中存在一定的缺陷,提出了一种基于卷积稀疏表示的图像融合方法。首先,对高频子带系数进行合理有效处理,利用相似度分析和视觉显著性进行融合。然后,将低频子带系数整体融合改进为使用Butworth低通滤波对低频子带进行分解,得到低频近似子带和强边缘子带。最后,再用改进的脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)对强边缘子带进行融合。实验结果表明,与其它传统的图像融合方法相比,信息熵(Information Entropy,IE)提高了将近3%,标准差(Standard Deviation,SD)提高了将近9%,空间频率(Space Frequency,SF)提升了将近30%,互信息(Mutual Information,MI)提升了将近25%。同时,时间效率也有了一定程度地提升。     

结冰对民用飞机飞行性能的影响分析

在获取民用飞机适航合格审定证明前,必须通过飞行试验,并在试验中演示验证民用飞机能在CCAR25部附录C确定的连续和间断的最大结冰状态下安全运行。基于此背景,计算并得到某型机两种构型在45min待机冰型下的飞行性能,与无冰状态下对应构型的飞行性能进行对比分析。结果表明,结冰对飞机的飞行性能具有较大的影响。