ID 原文 译文
18415 该文所提出的算法不需要初始值先验条件，仿真实验表明了所提算法的有效性。 The proposed method does not require an initial priori information and simulations show the effectiveness ofthe proposed method.
18416 针对传统医学超声图像去斑方法的不足，该文提出一种自适应多曝光融合框架和前馈卷积神经网络模型图像去斑方法。 Considering the shortage of traditional medical ultrasound image despeckle methods, an adaptive multi-exposure fusion framework and feedforward convolutional neural network model image despeckle method is proposed.
18417 首先，制作超声图像训练数据集； Firstly, an ultrasound image training data set is produced.
18418 然后，提出一种自适应增强因子的多曝光融合框架，增强图像进行有效特征提取； Then, a multi-exposure fusionframework with adaptive enhancement factors is proposed to enhance the image for effective featureextraction.
18419 最后，通过网络训练去斑模型并获得去斑后的图像。实验结果表明，该文较已有的方法，能更有效地滤除医学超声图像中的斑点噪声并更多的保留图像细节。 Finally, a speckle model is trained through the network and a speckle image is obtained.Experimental results show that, compared with the existing methods, this paper can more effectively removespeckle noise in medical ultrasound images and retain more image details.
18420 极限学习机(ELM)作为一种新型神经网络，具有极快的训练速度和良好的泛化性能。 As a new type of neural network, Extreme Learning Machine (ELM) has extremely fast trainingspeed and good generalization performance.
18421 针对极限学习机在处理高维数据时计算复杂度高，内存需求巨大的问题，该文提出一种批次继承极限学习机(B-ELM)算法。 Considering the problem that the Extreme Learning Machine hashigh computational complexity and huge memory demand when dealing with high dimensional data, a Batchinheritance Extreme Learning Machine (B-ELM) algorithm is proposed.
18422 首先将数据集均分为不同批次，采用自动编码器网络对各批次数据进行降维处理； Firstly, the dataset is divided intodifferent batches, and the automatic encoder network is used to reduce the dimension of each batch.
18423 其次引入继承因子，建立相邻批次之间的关系，同时结合正则化框架构建拉格朗日优化函数，实现批次极限学习机数学建模； Secondly,the inheritance factor is introduced to establish the relationship between adjacent batches. At the same time, the Lagrange optimization function is constructed by combining the regularization framework to realize the mathematical modeling of batch ELM.
18424 最后利用MNIST, NORB和CIFAR-10数据集进行测试实验。 Finally, the MNIST, NORB and CIFAR-10 datasets are used for the test experiment.