Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.
针对传统YOLOv3算法中存在检测框定位不精确的问题,提出了一种改进的YOLOv3算法用来重新估计检测框位置,提高智能汽车在雾霾交通环境下的定位精度。首先运用图像去雾算法对采集到的图片进行预处理,然后构造定位置信度替代分类置信度作为参考项来选择估计检测框位置,并改进非极大值抑制(NMS)算法,引入软化非极大值抑制(soft-NMS),最后使用加权平均的方式来更新坐标位置,以达到提高定位精度的目的。实验结果表明,先经过单尺度retinex去雾算法处理图片,再通过改进的YOLOv3算法进行车辆检测,与使用原始的YOLOv3算法进行检测相比平均精度均值mAP(mean average precision)提高了0.44%,在满足检测实时性的同时,能够检测到更多的目标,对检测车辆的定位也更加精确。
