ID 原文 译文
53037 然后，利用云模型在解决随机性和不确定性问题方面的优势，构建综合云评估模型，将云重心评判法引入到EDG启动状态评估中。 Then, taking the advantages of the cloud model in solving the problems with randomness and uncertainty, a comprehensive cloud evaluation model is constructed, and the cloud barycenter evaluation method is introduced into the EDG start-up state evaluation.
53038 最后，以实际核电现场多次测试数据为分析案例，利用本文提出的特征提取方法和综合云评估模型对测量状态进行评估，验证了所提方法的有效性，为EDG启动状态评估提供了有效途径。 Finally, the proposed method is verified by using the actual nuclear power field test cases, indcating that it is an effective method for EDG start-up state evaluation.
53039 针对滚动轴承工作环境中含有强烈的环境噪声，其振动信号具有非平稳、非线性特征以及提取特征困难等问题，本文提出一种基于集合经验模态分解(EEMD)的滚动轴承智能故障诊断方法。 Aiming at the problems that the rolling bearing's working environment contains strong environmental noise, its vibration signal has non-stationary, non-linear features and difficulty in extracting features, an intelligent fault diagnosis of rolling bearings based on ensemble empirical mode decomposition(EEMD) is proposed.
53040 首先通过卷积神经网络(C N N)提取振动信号关键特征，并将提取到的特征向量输入到支持向量机(SVM)中进行故障识别与分类。 First, the key features of the vibration signal are extracted through a convolutional neural network(CNN), and the extracted feature vectors are input to support vector machine(SVM) for fault identification and classification.
53041 为了提高诊断性能，本文利用集合经验模态分解方法对原始振动信号进行分解，将复杂的振动信号分解为仅包含单一成分的本征模态分量(IMF)， In order to improve the diagnostic performance, the set of empirical modal methods are used to decompose the original vibration signal, the complex vibration signal is decomposed into intrinsic mode function(IMF) containing only a single component for feature extraction,
53042 然后利用一维卷积神经网络对IMF进行特征提取，最后采用SVM进行分类。 and then the one-dimensional convolution neural network is used to perform feature extraction on the IMF, and SVM is finally used for classification.
53043 结果表明，所提出的方法相比于现有方法收敛速度更快，诊断正确率可高达99%，突出了该方法的优越性。 The experiment results show that the proposed method converges faster than the existing methods, and the diagnostic accuracy rate can be as high as 99%, highlighting the superiority of the method.
53044 为满足车联网中海量数据的采集、传输以及对这些数据的快速处理的需求，可采用移动边缘计算(MEC)技术。 In order to meet the needs for the collection and transmission of massive data in the Internet of vehicles and the fast processing of these data, mobile edge computing(MEC) technology can be used.
53045 本文考虑移动边缘计算中基站连接方式和物理资源的特点， This paper considers the characteristics of base station connection methods and physical resources in mobile edge computing,
53046 对边缘服务器的部署问题进行了分析， analyzes the deployment of edge servers,