ID 原文 译文
40726 首先在原始的U-Net基础上改进，添加循环残差卷积模块并融合注意力门机制，在增强特征提取性能的同时将注意力放在目标结节区域，通过抑制无关的特征响应获得较高的检测精度; Firstly, recurrent residual convolution module and fusion attention gate mechanism were added in U-Net.The improved method can enhance the feature extraction performance, focus on the target nodule area and suppress irrelevant feature responses to obtain higher detection accuracy.
40727 其次改进损失函数解决肺结节图像数据不均衡问题，获得较高的检测敏感度; Secondly, improved loss function can solve the problem of unbalanced image data of lung nodules to obtain higher detection sensitivity.
40728 最后通过三维卷积神经网络(3D CNN)分类候选结节，降低检测的假阳性。 Finally, the candidate nodules are classified by a three-dimensional convolutional neural network(3 D CNN) to reduce false positive.
40729 在两个数据集上进行实验验证，结果表明本文提出的算法提升了检测速度和敏感度，取得了比现有算法更好的性能，具有较好的泛化能力。 Experimental verification on two datasets shows that the algorithm proposed in this paper improves the detection speed and sensitivity, achieves better performance than existing algorithms, and has better generalization capabilities.
40730 针对运动想象脑电信号(EEG)的非线性、非平稳性特点，提出了一种结合小波包变换(WPT)和串并行卷积神经网络(SPCNN)的脑电信号分类方法。 Aiming at the non-linear and non-stationary characteristics of motor imagery ElectroEncephaloGram(EEG) signals, a novel EEG signal classification method combining wavelet packet transform(WPT) and serial-parallel convolutional neural network(SPCNN) is proposed.
40731 在小波包变换过程中，对脑电信号进行时频分解，选取与运动想象密切相关的频率段进行重构， In the process of wavelet packet transform, the EEG signal is decomposed in time and frequency, and the frequency band closely related to motor imagination is selected for reconstruction.
40732 重构后的脑电信号保留了有效的时频信息。 The reconstructed EEG signal retains effective time-frequency information.
40733 考虑到脑电信号不同通道之间以及通道内的特征，构建了SPCNN网络模型自动提取有效的特征并进行分类。 Then, considering the features between and within the different channels of the EEG signal, the SPCNN network model is constructed to automatically extract the effective features and classify them.
40734 利用公开的竞赛数据集BCI competition IV 2b进行验证，结果表明:该方法能自适应的提取到有效特征，平均分类准确率达到了84.77%，比卷积神经网络提高了6.49%， Use the public competition data set BCI competition IV 2 b to verify, the results show that the method can adaptively extract effective features, and the average classification accuracy reaches 84.77%, which is 6.49% higher than the convolutional neural network.
40735 为脑机接口系统的研究提供了一种分类方法。 It provides a classification method for the research of brain-computer interface systems.