机电产品安全风险评价模型研究

目前，我国的机电产品在其全寿命周期中基本上不做安全风险评价，一个重要的原因是缺乏有效的风险评价模型。为此，本文提出了一种适合于我国机电产品在设计阶段进行风险评价的通用模型。     

火星探测器气动外形/弹道一体化多目标优化

针对火星探测器概念设计阶段的需求,提出了融合气动外形、弹道和开伞条件的一体化多目标优化设计方法。首先建立了火星探测器进入段三自由度弹道运动方程,基于修正牛顿理论推导了适用于具有较大半锥角球锥外形的气动参数估算模型,采用Sutton-Graves公式计算了驻点热流密度。以开伞高度、总吸热量和容积率为目标函数建立了火星探测器气动外形/弹道一体化多目标优化模型,采用基于分解的多目标进化算法(MOEA/D)进行求解计算并与参考设计进行了对比。数值结果表明:多目标优化方法提供多个三目标均优于参考设计的Pareto最优解,为火星探测器的概念设计提供了一定的参考依据。     

中国出口贸易多样性的度量及政策建议

首先对出口贸易多样性的度量方法进行了梳理和评述,同时利用BACI在1996~2010年间的贸易细分数据,从出口产品多样性和出口地区多样性的角度度量了中国出口贸易多样性及其变化趋势。实证测度结果表明：虽然中国出口产品种类有略微减少的趋势,但中国出口产品多样性水平却在不断提高;中国出口贸易伙伴的数量仍然在增加,但中国出口地区多样性在2000年以后呈现＂U型＂的变化趋势,且自2008年开始中国出口地区依赖的风险在上升。因此,进一步提升中国出口产品多样性和中国出口地区多样性,是未来中国深化对外经济开放的一个重要政策取向。     

多变量气动设计问题分层协同优化

 通过验证实例分析,气动设计中精细化优化模型对设计结果收敛精度的提高有很大帮助,但同时也带来优化算法搜索困难的问题,并且由于不同类型设计变量之间的相互耦合干扰使优化难以收敛到全局最优解。于是提出基于响应均值灵敏度的概念对大规模的设计变量进行重要性分组的策略,依据设计变量分组情况应用系统分解思想对多变量设计问题进行分层协同优化来降低系统的复杂度,这既保证了精细化设计的要求,又缓解了优化算法对大规模问题搜索困难的问题。与传统气动优化方法相比,基于系统分解的分层协同优化算例有较大寻优效率和性能提升,证明了该方法的可行性。     

Novel passive localization algorithm based on double side matrix-restricted total least squares

In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares (DSMRTLS) is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms.     

Monitoring of rice cropping intensity in the upper Mekong Delta,Vietnam using time-series MODIS data

Information on rice growing areas is important for policymakers to devise agricultural plans. This research explores the monitoring of rice cropping intensity in the upper Mekong Delta, Vietnam (from 2001 to 2007) using time-series MODIS NDVI 250-m data. Data processing includes three steps: (1) noise is filtered from the time-series NDVI data using empirical mode decomposition (EMD); (2) endmembers are extracted from the filtered time-series data and trained in a linear mixture model (LMM) for classification of rice cropping systems; and (3) classification results are verified by comparing them with the ground-truth and statistical data. The results indicate that EMD is a good filter for noise removal from the time-series data. The classification results confirm the validity of LMM, giving an overall accuracy of 90.1% and a Kappa coefficient of 0.7. The lowest producer and user accuracies were associated with single crop rain-fed rice class due to the mixed pixel problems. A strong yearly correlation at the district level was revealed in the MODIS-derived areas (R² ? 0.9). Investigation of interannual changes in rice cropping intensity from 2001 to 2007 showed a remarkable conversion from double to triple crop irrigated rice from 2001 to 2003, especially in the Thoai Son and Phu Tan districts. A big conversion from triple crop rice back to double crop rice cultivation was also observed in Phu Tan from 2005 to 2006. These changes were verified by visual interpretation of Landsat images and examination of NDVI profiles.     

Space object material identification method of hyperspectral imaging based on Tucker decomposition

Space object material identification method based on Tucker decomposition is proposed and demonstrated experimentally. Space target generally has a low spatial resolution because of the limitation in detection distance. Hyperspectral imaging (HSI) technology can capture the image from visible light to shortwave infrared continuously while providing the spatial and spectral information of the target, thereby introducing a new space target detection and identification approach. However, the data obtained by the HSI system often contain mixed pixels, thereby causing difficulties in target material identification. In this work, a material identification method of hyperspectral data based on Tucker decomposition is proposed by combining mixed spectral theory with tensor decomposition. The feasibility of the method is verified by using satellite model hyperspectral data with different spatial resolutions compared with the endmember obtained from the non-negative matrix decomposition (NMF), independent component analysis (ICA), tensor singular value decomposition (t-SVD). The average CORR of NMF, ICA, t-SVD and the proposed method is 0.2694, 0.5818, 0.6397 and 0.937, correspondingly. Therefore, the proposed method has demonstrated a more remarkable performance in terms of material identification, the analysis results of the material abundance distribution that used the proposed method.     

基于希尔伯特-黄变换的雷达数据误差修正处理技术

由于雷达跟踪测量数据总存在误差,而目前采用的误差处理算法修正效果并不理想,为了有效估计数据中的未知误差,提高数据处理的精度,提出了一种基于希尔伯特-黄算法的雷达误差残差诊断方法,利用得到的内蕴模态函数拟合出误差残差的特征,准确分离出设备的误差信息。通过对某雷达的实测数据分析证明该方法较其他方法对解决该类问题更为有效,且在事后高精度数据处理中具有较好的实用性。     

基于正交分解法的非平稳随机振动响应计算

为高效地获得非平稳随机振动响应的高精度结果,对过滤白噪声非平稳激励信号用精确积分法获得了 K L 分解的精确向量,并对其进行了分析。单自由度和多自由度仿真结果表明, 用较少量的K\|L向量即可满足精度的要求,尤其对峰值的把握较准确。在二自由度的非线性杜芬剪切系统中利用基于能量的等价线性化方法与正交展开方法进行迭代计算并与蒙特卡洛方法进行比较,仿真结果验证了方法的精确性。     

基于Pauli基分解的极化校准算法

极化校准是现代雷达精确获取目标极化散射特性的前提和基础。为解决某全极化雷达的校准问题,提出一种基于Pauli基分解的极化校准算法,给出了算法的正交条件和相关推导,可选择任意满足正交条件的三个目标做为定标体。对校准算法的误差来源和误差对校准结果的影响分别进行了理论分析和仿真分析。给出了不同条件下该校准算法和其它校准算法的仿真结果,以及某全极化雷达校准试验数据分析结果。结果表明：基于Pauli基分解的极化校准算法能有效消除天线的变极化效应,可更准确地校准目标的极化散射矩阵,并可应用于大型地基极化雷达、极化合成孔径雷达以及极化相控阵雷达等的极化校准。