线性高速开关阀的设计及验证

受限于成本原因，电控液压制动系统中多使用高速开关阀，但在液压阀开关控制中电磁阀的敲击噪声、液压冲击噪声和压力波动造成制动控制品质和精度劣化，因此通过脉冲宽度调制控制实现高速开关阀线性化调控性能是此类高速开关阀的设计关键。高速开关阀动态运动特性受瞬态液动力、非线性电磁力与机械惯性、弹簧力综合作用，电磁阀动力学特性决定其线性调控工作范围窄，需要系统设计电磁阀系列结构力学参数，才能实现电磁阀阀口一定开度范围内的多种非线性力的线性化变化。为此，建立高速开关阀与液压控制单元的动力学模型与联合仿真模型，通过仿真与试验验证，分析出阀座锥角、节流孔径、气隙大小等结构参数对电磁阀线性特性的影响。从而设计出合理的电磁阀结构参数，并应用于一款液压控制回路中，实现线性工作范围的拓宽，满足汽车制动安全控制的要求。

Design and Verification of Linear High-speed On-off Valve

With the rapid development of artificial intelligence, the application of deep learning in the field of unmanned driving is gradually mature, but the traffic sign detection task still has a lot of room for improvement as a difficult problem. Traffic sign detection under urban road has the characteristics of complex environment, many small targets, many kinds of targets and unbalanced quantity. Aiming at these problems, an improved scheme based on Libra R-CNN is proposed. The target detection network Libra R-CNN is proposed based on balance, which can better deal with the problem of many kinds of targets and unbalanced quantity. In the anchor extraction stage of Libra R-CNN network, GA-RPN is used to generate the anchor. It can produce more accurate and diversified samples during the training period, thus reduce the background influence and deal with the problem that small targets difficult to locate, and improve the detection accuracy. The effectiveness of the method is verified by experiments. The experiment was conducted on MS COCO 2017 and traffic sign dataset. The mAP of the improved Libra R-CNN increased by more than 2.7 percentage points. The experimental results show that the performance of the improved network is significantly improved compared with the original target detection network.