ID 原文 译文
41416 该传感器使用陶瓷基片作为内层结构，以304不锈钢管作为保护外层，采用飞秒激光刻写的纯石英光纤光栅作为测温敏感元件。 It uses a ceramic substrate as the inner structure, a 304 stainless steel tube as the outer protection, and a pure silica fiber grating written by a femtosecond laser as the temperature sensing element.
41417 搭建了恒温油浴光纤传感测试系统，采用循环升降温的方法对其进行了温度特性研究， An optical fiber sensing test system of constant temperature oil bath was built, and the temperature characteristics of the sensor were studied by the method of cycling temperature rise and fall.
41418 结果表明该传感器测温性能良好，升降温实验的线性相关系数均为0.999 9， Test results show that the sensor presents good temperature measurement performance, and the linear correlation coefficient of the temperature rise and fall experiment is 0.999 9.
41419 在长期的循环实验中波长重复性为±2pm，迟滞误差小于5pm， In the long-term cycle experiment, the wavelength repeatability reaches±2 pm, and the hysteresis error is less than 5 pm.
41420 有较高的可靠性，可用于电机内部的温度监测。 It has high reliability and can be used for temperature monitoring inside the motor.
41421 单幅图像去雾技术虽然已经取得较大的进展，但是算法较为复杂，运行时间较长。 Single image dehazing technology has made great progress, but the algorithm is complicated and the running time is long.
41422 为了实现视频实时去雾，以硬件实现为目的，对暗通道先验算法进行改进，降低其时间复杂度。 For the purpose of hardware implementation, the dark channel priori algorithm is improved to reduce its time complexity and realize real-time dehazing.
41423 提出了一种暗通道图优化方法，保留了图像的边缘信息，消除了光晕效应，省去了透射率细化的复杂操作; A dark channel map optimization method is proposed, which retains the edge information of the image, and eliminates the halo effect and the complicated operation of transmittance refinement.
41424 提出了适应于硬件实现的大气光值估计和调节及透射率补偿方法，解决了视频帧间闪烁及天空等明亮区域的色彩失真问题。 The method for estimating and adjusting atmospheric light, and a transmittance compensation method suitable for hardware implementation are proposed, which solves the problem of flicker between video frames and color distortion in bright areas such as the sky.
41425 基于现场可编程门阵列(FPGA)对所提出算法进行了硬件实现。结果表明，该算法可以实时处理帧速为60f/s、分辨率为1 920×1 080的视频图像，相比传统去雾算法速度更快，去雾质量更高。 The hardware implementation of the proposed algorithm is based on FPGA, and the results show that it can process video images with the resolution of 1 920×1 080 at the frame rate of 60 f/s in real time, which is much better than that of the traditional dehazing algorithm.