航空动力装置任务可靠度模糊预计与分配的综合

根据Vague集的直觉性，定义一种判定Vague值真值的函数，进而给出一种确定Fuzzy隶属度的方法。将此方法应用于模糊综合评判理论，得到一种在设计初期根据专家意见，预计航空动力装置任务可靠度的模糊决策方法。根据预计值和比例组合分配法的思想，先按任务可靠度模型的等效串联模型进行任务可靠度指标的分配，然后再对等效串联模型中冗余部分的指标进行分配。     

Fuzzy模型与线性模型的比较

针对一组非线性的输入／输出数据,用两种Ｆｕｚｚｙ辨识方法进行了模型辨识,并与线性模型作了比较。结果表明,Ｆｕｚｚｙ模型比线性模型的预报精度高。尤其对于非线性系统,Ｆｕｚｚｙ模型确实有很大的潜力。     

复杂交通场景中多运动目标分割算法

针对室外复杂交通场景中多运动目标分割问题,提出了一种由变化检测和运动分割组成的算法,利用水平集算法对帧差图像进行变化检测得到运动窗口,在运动窗口范围内进行改进的k-均值聚类分割,利用运动相似性进行分割区域融合.算法避免了整个图像的分割,减少了运算量,完整的分割出运动目标.试验结果表明,算法不仅能从复杂交通场景图像序列中有效的检测和提取出运动目标并有很强的鲁棒性,而且能够解决运动目标的遮挡问题.      

Fuzzy multi-criteria decision-making approach to prioritization of space debris for removal

As private companies and government space agencies begin to seriously consider the task of active space debris removal, it is becoming increasingly more important to determine the highest priority objects to deorbit. This work sets forth an approach for prioritization of space debris through the utilization of Multi-Criteria Decision-Making methodologies and fuzzy logic, as well as both quantitative and qualitative criteria. The proposed debris prioritization approach considers various criteria including the orbit, size, mass, pairwise and total collision probabilities, and decay timeframe of each debris object. The means of assigning attributes to each assessment criterion is discussed in detail. To determine the weighting scheme for the criteria, a questionnaire was prepared and shared with experts in the field of space situational awareness. The work examines over two thousand critical debris objects selected from the existing debris catalog with respect to these criteria. The quantified attributes for each debris object are then aggregated through the fuzzy versions of the Analytic Hierarchy Process and the Technique for Order Preference by Similarity to Ideal Solution. The results of the analysis identify high-priority debris objects for removal from Earth-bound orbits.     

基于先验概率优势关系的粗糙决策分析模型

针对含有不完全偏好信息的多属性决策问题,提出了基于先验概率优势关系的粗糙决策分析模型.首先分析了扩展优势关系和限制扩展优势关系的不足,在此基础上提出先验概率优势关系的概念,并得到先验概率优势关系下的粗糙近似;然后通过对比分析,证明了新模型较上述两种优势关系模型可使分类结果更加符合实际情况和人在数据处理过程中的直观感受.最后,通过一个实例验证了新方法的可行性和有效性.     

A novel variable-lag probability hypothesis density smoother for multi-target tracking

It is understood that the forward-backward probability hypothesis density (PHD) smoothing algorithms proposed recently can significantly improve state estimation of targets. However, our analyses in this paper show that they cannot give a good cardinality (i.e., the number of targets) estimate. This is because backward smoothing ignores the effect of temporary track drop- ping caused by forward filtering and/or anomalous smoothing resulted from deaths of targets. To cope with such a problem, a novel PHD smoothing algorithm, called the variable-lag PHD smoother, in which a detection process used to identify whether the filtered cardinality varies within the smooth lag is added before backward smoothing, is developed here. The analytical results show that the proposed smoother can almost eliminate the influences of temporary track dropping and anomalous smoothing, while both the cardinality and the state estimations can significantly be improved. Simulation results on two multi-target tracking scenarios verify the effectiveness of the proposed smoother.     

火场中电梯疏散失败的FTA分析与探讨

通过对火灾导致电梯疏散失败的故障树的建立与分析,计算了导致顶事件发生的最小割集与最小径集,以及影响顶事件发生的结构重要度和割集重要度。并由此提出了火灾情况下降低电梯运行风险,提高可靠性的相关措施。为火灾情况下利用电梯进行疏散的可靠性提供了参考依据。     

侧向随动力作用下大展弦比柔性机翼的稳定性

 随动力能够诱发弹性结构发生颤振失稳。以侧向随动力和集中质量分别模拟发动机推力和外挂质量,考虑机翼垂直弯曲-扭转刚度比、集中质量大小、侧向随动力和集中质量的位置以及机翼后掠角和上反角的影响,研究了受侧向随动力作用的大展弦比柔性机翼的气动弹性稳定性。数值模拟所采用的大展弦比柔性机翼非线性气动弹性模型耦合了几何精确完全本征运动梁模型和ONERA动失速非定常气动力模型,该模型考虑了几何非线性、动失速和材料各向异性。模拟结果表明,侧向随动力对机翼颤振可以具有稳定作用,其具体表现依赖于若干变参数的影响,如：减小机翼垂直弯曲-扭转刚度比;发动机吊舱靠近翼根布置;使发动机推力作用点在法向上与机翼弹性轴靠近;单纯的集中质量避免布置在柔性机翼中部,且布置在机翼弹性轴之前或下方,这些设计或布置均有利于提高带发动机吊舱/有效载荷外挂的柔性机翼的气动弹性稳定性。     

未知测量噪声分布下的多目标跟踪算法

 粒子概率假设密度滤波(SMC-PHDF)在进行粒子更新时需要知道测量噪声的概率分布以计算似然函数,这使得SMC-PHDF依赖于测量噪声的概率模型。针对这一点不足,提出一种未知测量噪声分布下的多目标跟踪算法——基于风险评估的概率假设密度滤波(RE-PHDF)。该算法在SMC-PHDF进行概率假设密度(PHD)粒子更新时采用风险函数计算每个PHD粒子的风险值,并通过一个风险评估函数评估每个PHD粒子,然后用评估后的结果更新粒子的权值。由于粒子更新时避免了在多维测量空间中计算似然函数,算法不仅不依赖于测量噪声的概率分布,还可以节省大量计算时间。仿真结果表明：和SMC-PHDF相比,RE-PHDF在未知的复杂测量噪声环境下具有更高的鲁棒性和稳定性;同时,在两种算法跟踪精度接近的情况下,所提算法节省了50%的运行时间。     

基于SMC-CPHD的多目标跟踪算法研究

针对CPHD滤波算法在多目标跟踪中计算难处理和对于局部目标估计存在漏检的问题,提出了基于序贯蒙特卡罗方法的基数概率假设密度（SMC-CPHD）滤波算法。这种方法是将SMC和CPHD两种滤波算法的优点相结合,用一些离散的粒子去接近PHD函数,不仅解决了在滤波修正步没有闭式解的问题,而且避免了当某个目标发生漏检时,PHD权值的转移问题,在递推PHD函数的同时也递推基数分布。将此方法应用到有杂波存在复杂的多目标跟踪环境中,通过仿真实验,对CPHD滤波和SMC-CPHD滤波得出的结果进行比较,验证了本文所提出方法对多目标跟踪的可行性和精确性。