ID 原文 译文
19565 针对新一代多普勒气象雷达的散射回波图像受非降雨等噪声回波干扰导致精细化短时气象预报准确度降低的问题，该文提出一种基于深度卷积神经网络(DCNN)的气象雷达噪声图像语义分割方法。 Considering the problem that the scattering echo image of the new generation Doppler meteorologicalradar is reduced by the noise echoes such as non-rainfall, the accuracy of the refined short-term weatherforecast is reduced. A method for semantic segmentation of meteorological radar noise image based on DeepConvolutional Neural Network(DCNN) is proposed.
19566 首先，设计一种深度卷积神经网络模型(DCNNM)，利用MJDATA数据集的训练集数据进行训练，通过前向传播过程提取特征，将图像高维全局语义信息与局部特征细节融合； Firstly, a Deep Convolutional Neural Network Model(DCNNM) is designed. The training set data of the MJDATA data set are used for training, and the feature isextracted by the forward propagation process, and the high-dimensional global semantic information of theimage is merged with the local feature details.
19567 然后，利用训练误差值反向传播迭代更新网络参数，实现模型的收敛效果最优化； Then, the network parameters are updated by using the training error value back propagation iteration to optimize the convergence effect of the model.
19568 最后，通过该模型对气象雷达图像数据进行分割处理。 Finally, themeteorological radar image data are segmented by the model.
19569 实验结果表明，该文方法对气象雷达图像的去噪效果较好，与光流法、全卷积网络(FCN)等方法相比，该文方法对气象雷达图像中真实回波和噪声回波的识别准确率高，图像的像素精度较高。 The experimental results show that the proposed method has better denoising effect on meteorological radar images, and compared with the optical flow method and the Fully Convolutional Networks (FCN), the method has high recognition accuracy for meteorological radar image real echo and noise echo, and the image pixel precision is high.
19570 涡轮式气体流量传感器在用力肺功能测试中用于记录人体呼气信号，由于旋转惯性，对于相同用力呼气容量(FVC)值，测量结果因呼出气体流量而异，且差异值通常不可接受。 Currently, the turbine air flow sensors are widely used to record the human exhalation signals inspirometry, but test results vary due to different expiratory flow for the same Forced Vital Capacity(FVC)measurements, and the differences are usually not in an acceptable range.
19571 针对该问题，该文通过在传统稳态涡轮流量计算模型的基础上引入速度惩罚项，构建一种FVC速度惩罚模型，与此同时，提出使用过幅降采样涡轮旋转周数算法，二者结合，提高了FVC测试结果的可接受性。 To address this issue, a FVC velocity penalty model is proposed by introducing speed penalty items to the traditional mathematical model of turbine.Moreover, an over-amplitude drop sampling approach is used to calculate the rotations of the turbine due to the needs for the velocity penalty model to be able to accurately obtain the number of turbine rotations.
19572 利用国际通用的标准3 L定标桶，模拟真实用力肺功能测试过程，对算法的有效性进行验证。实验结果表明：所提方法能够有效降低前述差异，在一定程度上满足美国胸科协会(ATS)和欧洲呼吸学会(ERS)所提出的用力肺功能测试可接受标准和准确度要求。 The performance of the proposed approach is evaluated by using a syringe dispenser of 3L capacity, and results demonstrate that it can reduce the differences and meet the acceptable and accuracy criteria of the AmericanThoracic Society(ATS) and the European Respiratory Society(ERS) to some extent.
19573 延长积累时间可以有效提高无源雷达的目标探测能力，但是对于高速机动目标，其速度、加速度、第二加速度等因素导致现有的检测算法在积累过程中发生距离徙动(RM)和多普勒频率徙动(DFM)，使得目标检测性能恶化。 Increasing the integration time can effectively improve the detection performance of passive radar.However, for maneuvering targets, the complex motions, such as high velocity, acceleration and jerk, causeexisting detection methods to suffer the Range Migration (RM) and Doppler Frequency Migration (DFM)during the integration time, which deteriorates the detection performance.
19574 该文针对无源雷达中变加速运动目标的长时间相参积累问题，提出一种基于相邻互相关函数(ACCF)-参数化中心频率-调频率分布(PCFCRD)-Keystone变换(KT)的相参积累算法(ACCF-PCFCRD-KT)。 This paper addresses the long timecoherent integration for a maneuvering target with high-order motion (e.g., jerk motion) in passive radarsystems. A method based on Adjacent Cross Correlation Function (ACCF), Parameterized Centroid Frequency-Chirp Rate Distribution (PCFCRD) and Keystone Transform (KT)(ACCF-PCFCRD-KT), is proposed.