Improving freight axle load is the most effective method to improve railway freight capability; based on the imported technologies of railway freight bogie, the 27 t axle load side-frame cross-bracing bogie and sub-fr...Improving freight axle load is the most effective method to improve railway freight capability; based on the imported technologies of railway freight bogie, the 27 t axle load side-frame cross-bracing bogie and sub-frame radial bogie are developed in China. In order to analyze and compare dynamic interactions of the two newly developed heavy-haul freight bogies, we establish a vehi- cle-track coupling dynamic model and use numerical calculation methods for computer simulation. The dynamic performances of the two bogies are simulated separately at various conditions. The results show that at the dipped joint and straight line running conditions, the wheel-rail dynamic interactions of both bogies are basically the same, but at the curve negotiation condition, the wear and the lateral force of the side-frame cross-bracing bogie are much higher than that of the sub-frame radial bogie, and the advantages become more obvious when the curve radius is smaller. The results also indicate that the sub- frame radial bogie has better low-wheel-rail interaction characteristics.展开更多
The side information quality has an immense effect on the compression efficiency of the distributed video coding (DVC) sys- tem. This article, based on the hierarchical motion estimation (HME), proposes a new side inf...The side information quality has an immense effect on the compression efficiency of the distributed video coding (DVC) sys- tem. This article, based on the hierarchical motion estimation (HME), proposes a new side information generation algorithm which is integrated into DVC system. First, forward motion estimation (FME) and bidirectional motion estimation (BME) on the basis of variable block size HME algorithm are used to acquire relatively accurate motion vectors. Second, a motion vector filter (MVF) is i...展开更多
针对列车运行故障图像动态检测系统(Trouble of moving Freight car Detection System,TFDS)中挡键丢失故障,提出一种基于形状上下文的列车挡键丢失图像识别算法。取正常挡键区域图像作为模板,到待测TFDS图像中遍历,采用形状上下文描述...针对列车运行故障图像动态检测系统(Trouble of moving Freight car Detection System,TFDS)中挡键丢失故障,提出一种基于形状上下文的列车挡键丢失图像识别算法。取正常挡键区域图像作为模板,到待测TFDS图像中遍历,采用形状上下文描述图像的形状特征,加权形状上下文距离与弯曲能量以定义形状距离作为图像匹配的相似度指标,最后根据模板图像是否遍历出与其相似的区域图像作为挡键丢失的判断依据。采用Matlab编程,通过截取大量测试图像实验发现,所定义的形状距离阈值取0.16,对测试图像中有无挡键能很好地区分。采用形状上下文描述,自定义形状距离作为图像匹配的相似度指标具有很高的可靠性,该算法为TFDS图像故障识别提供了一种新的思路。展开更多
基金supported by the National Natural Science Foundation of China (No. 50975238)
文摘Improving freight axle load is the most effective method to improve railway freight capability; based on the imported technologies of railway freight bogie, the 27 t axle load side-frame cross-bracing bogie and sub-frame radial bogie are developed in China. In order to analyze and compare dynamic interactions of the two newly developed heavy-haul freight bogies, we establish a vehi- cle-track coupling dynamic model and use numerical calculation methods for computer simulation. The dynamic performances of the two bogies are simulated separately at various conditions. The results show that at the dipped joint and straight line running conditions, the wheel-rail dynamic interactions of both bogies are basically the same, but at the curve negotiation condition, the wear and the lateral force of the side-frame cross-bracing bogie are much higher than that of the sub-frame radial bogie, and the advantages become more obvious when the curve radius is smaller. The results also indicate that the sub- frame radial bogie has better low-wheel-rail interaction characteristics.
基金National Natural Science Foundation of China (60702012)
文摘The side information quality has an immense effect on the compression efficiency of the distributed video coding (DVC) sys- tem. This article, based on the hierarchical motion estimation (HME), proposes a new side information generation algorithm which is integrated into DVC system. First, forward motion estimation (FME) and bidirectional motion estimation (BME) on the basis of variable block size HME algorithm are used to acquire relatively accurate motion vectors. Second, a motion vector filter (MVF) is i...
文摘针对列车运行故障图像动态检测系统(Trouble of moving Freight car Detection System,TFDS)中挡键丢失故障,提出一种基于形状上下文的列车挡键丢失图像识别算法。取正常挡键区域图像作为模板,到待测TFDS图像中遍历,采用形状上下文描述图像的形状特征,加权形状上下文距离与弯曲能量以定义形状距离作为图像匹配的相似度指标,最后根据模板图像是否遍历出与其相似的区域图像作为挡键丢失的判断依据。采用Matlab编程,通过截取大量测试图像实验发现,所定义的形状距离阈值取0.16,对测试图像中有无挡键能很好地区分。采用形状上下文描述,自定义形状距离作为图像匹配的相似度指标具有很高的可靠性,该算法为TFDS图像故障识别提供了一种新的思路。
