现代谱估计在飞行器仿真模型验证中的应用

仿真模型的正确与否决定着仿真的精度和置信度，而仿真模型验证是保证仿真模型正确的有效手段。现代谱估计方法对窗口以外的行为进行合理假设，克服了古典谱估计方法的缺点，相对于古典谱估计方法有优势。重点介绍了最大熵谱估计方法的基本原理以及在飞行器仿真模型验证中的应用，并举例予以说明。     

几种无语音检测噪音估计方法的比较研究

噪音谱的估计是谱相减方法中关键的一环。传统的噪声谱的估计是通过对输入语音作语音检测，区分出纯噪声段，根据噪声段的频谱估计出噪声谱。该方法的准确性局限于语音检测算法的性能，在信噪比较低时，性能下降很快。近年来人们提出了多种不用语音检测的噪声估计方法，这些方法不区分语音和非语音段，在每一帧都进行噪声谱的更新。评估了几种无语音检测的噪音估计方法，比较了它们用于谱相减时在语音识别中的性能，提出了一种新的基于能量聚类的无语音检测噪音估计方法，通过实验验证了它的优良性能。     

一种基于非线性频谱分析的故障预报方法及其应用

论文改进和完善了一种广义频率响应函数(GFRF)的辨识算法,并依此提出了一种基于非线性频谱分析的故障预报方法。最后利用该方法成功地对某型直升机并联电动舵机进行了故障预报。工程应用实例证明了该预报方法的有效性。另外,该方法计算量小,实时性强,适合于在线故障预报,为早期故障预报的研究提供了一种新思路。     

AR估计滤波误差能量最小二乘递推法

利用滤波误差能量给出了一个新的最小二乘快速递推算法。数值试验表明，该算法计算稳定性好，对噪声不敏感，能快速递推到高阶模型，并将该算法同Ｍａｒｐｌｅ算法等作了比较。     

An efficient and robust integration technique for applied random vibration analysis

The mode-based finite element formulation of the equations of motion is usually adopted for linear random vibration analysis (RVA). In general, the RVA of large systems requires a large number of numerical integrations which is very time-consuming for a reasonable level of desired accuracy. Moreover, conventional numerical integration methods may fail to converge when the integrands are highly oscillatory due to slow propagation velocities. In this paper, a robust general-purpose RVA integration technique which can overcome these drawbacks is presented. Multi-point base and nodal excitations including wave passage effect and frequency-independent spatial correlation can be taken into account in the analysis. The proposed technique is based on the closed-form solutions for polynomial-type power spectral density functions and has been verified to be efficient and accurate for many engineering problems. This paper describes the implementation details, presents two examples taken from engineering applications and demonstrates the dramatic time-saving in the computation compared to numerical integration solutions. Response statistics, such as standard deviation of structural responses, are computed and displayed over the entire structures for these examples.     

地物波谱的自组织神经网络分类

地物反射波谱是遥感技术中的核心，由于测量环境，条件等差异使得实际测量值的分类与期望之间存在很大差异，带来应用困难。本应用自组织神经网络对测量样本值进行非监督分类，得到由测量数据值本身特征而产生的类别，与期望类别作比较，分析差异原因，最后为测量值提出科学的应用方法。     

Stochastic inverse heat conduction using a spectral approach

An adjoint‐based functional optimization technique in conjunction with the spectral stochastic finite element method is proposed for the solution of an inverse heat conduction problem in the presence of uncertainties in material data, process conditions and measurement noise. The ill‐posed stochastic inverse problem is restated as a conditionally well‐posed L₂ optimization problem. The gradient of the objective function is obtained in a distributional sense by defining an appropriate stochastic adjoint field. The L₂ optimization problem is solved using a conjugate‐gradient approach. Accuracy and effectiveness of the proposed approach is appraised with the solution of several stochastic inverse heat conduction problems. Copyright © 2004 John Wiley & Sons, Ltd.     

Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAI_e) (r²=0.55, p<0.001) and with the lidar-derived estimate of tree occurrence (r²=0.79, p<0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response (r²=0.30, r=−0.55 and r²=0.41, r=−0.64, respectively; p<0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction (r²=0.99, p<0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures (r²=0.77, p<0.001) than at 4 m resolution (r²=0.58, p<0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data (r²=0.66, p<0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem.     

系数矩阵含参数分裂形式的SOR迭代法收敛性分析

给出预条件方后线性方程组的系数矩阵的一类含参数的分裂形式,使系数矩阵的分裂更加一般化,同时讨论在该形式下的SOR迭代法的收敛性,并与一般的预条件方法进行比较分析,说明这种方法收敛性更好,最后找到参数的最优选取.     

Simulation of X-ray pulsar signals with multiphysical properties

In view of the few and discontinuous X-ray pulsar data, based on the physical properties of the X-ray pulsar real data from the Rossi X-ray Timing Explorer (RXTE) spacecraft, this paper adopts the fast iterative method and the 10 μs time transformation model, simulates the X-ray pulsar signals which have the periodic property, spectral property, and the background noise property as well as a variety of delay effects. We design and implement simulation software which can simulate the X-ray pulsar signals with multi-physical properties. The results of comparing the real data and simulation data in the same situation show that the simulation noises and the actual noises have the same distribution, that the period stability of the simulated X-ray pulsar data agrees with the real data, with the error of only 0.43％, in different energy levels, that the simulated data's integrated profile and the real data's standard profile are highly correlated with the Pearson correlation coefficient of 0.99, and that under the 10μs time transformation model, the time conversion accuracy of the simulation data can reach 10^-7s, the error between the simulation and the real data being just only 10^-7s, which meets the time transformation accuracy. Therefore, the proposed method can realize the high-fidelity simulations and generate the continuous pulsar signal with many physical properties.