ID 原文 译文
17875 该算法采用相位锁定值(PLV)构建了同步性脑网络，分析多导联脑电信号之间的同步性和相关性，并生成2阶张量序列作为训练集，运用支持张量机(STM)模型实现正负情绪的二分类。 The algorithm uses Phase Locking Value (PLV) to construct a synchronous brain network, in order to analyze the synchronization and correlation between multi-channel EEG signals, and generate a second-order tensor sequence as a training set. The Support Tensor Machine (STM) model can distinguish a two-category of positive and negative emotions.
17876 该文基于DEAP脑电情绪数据库，详细分析了同步性脑网络张量序列的选取方法，最佳张量序列窗口的大小和位置，解决了传统情绪分类算法特征冗余的问题，提高了模型训练速度。 Based on the DEAP EEG emotion database, this paper analyzes the selection method of synchronic brain network tensor sequence, the research on the size and position of the optimal tensor sequence window solves the problem of traditional emotion classification algorithm which always exists feature redundancy, and improves the model training speed.
17877 仿真实验表明，基于支持张量机的同步性脑网络分类方法的情绪准确率优于支持向量机、C4.5决策树、人工神经网络、K近邻等以向量为特征的情绪分类模型。 The results show that the accuracy of the emotional classification method based on SBN-STM is better than support vector machine, C4.5 decision tree, artificial neural network, and K-nearest neighbor which using vectors as input feature.
17878 针对地震后高层建筑物结构损伤监测问题，该文提出一种基于方向码匹配(OCM)和边缘增强匹配(EEM)算法的微小位移测量算法。 A micro-displacement measurement algorithm is proposed based on the Orientation Code Matching(OCM) and Edge Enhanced Matching (EEM) algorithms for monitoring the structural damage of tall buildingsafter earthquake.
17879 该算法先将原始图像梯度信息与像素强度融合，增强图像信息；采用相位相关法进行匹配运算，匹配速度比归一化互相关法提升了96.1%； The algorithm first fuses the gradient information of the original image with the pixel intensity to enhance the image information; Then the phase correlation method is used to perform the matching operation, the matching speed is 96.1% higher than the normalized cross-correlation method;
17880 最后使用亚像素插值法，使测量结果达到亚像素精度。 Finally, the sub-pixel interpolation method is used to make the measurement achieve sub-pixel accuracy.
17881 实验结果表明，该文算法避免了OCM和EEM算法量化过程中图像梯度信息的损失，大大提高了模板匹配精度，匹配速度比OCM提升了43.3%，比EEM提升了19.6%。 Experimental results show that the proposed algorithm avoids the loss of image gradient information during the quantization ofOCM and EEM algorithms, greatly improves the template matching accuracy, and the matching speed is 43.3%higher than OCM and 19.6% higher than EEM.
17882 近年来，基于运动矢量的视频隐写引起了信息隐藏领域研究者的广泛关注。 In recent years, research on motion vector-based video steganography has attracted considerable attention from researchers in the field of information hiding.
17883 许多视频隐写方法通过合理地对运动矢量定义加性嵌入失真函数获得了良好的性能，然而这些方法忽略了载体元素之间的相互嵌入影响。 Many video steganographic methods incorporatingmotion vector-based additive embedding distortion functions have achieved good performance. However, themutual embedding impact between cover elements in video steganography is neglected in these additiveembedding distortion functions.
17884 该文提出的利用非加性嵌入失真的视频隐写方法为运动矢量设计了可以反映相互嵌入影响的联合嵌入失真，并通过分解联合失真实现修改概率的转换，从而动态、合理地在运动矢量的水平分量和垂直分量分配秘密消息。 In this paper, joint distortion which reflects the mutual embedding impact formotion vectors is designed. By decomposing joint embedding distortion, modification probability transformationcan be achieved and embedding payloads can be dynamically and reasonably allocated in horizontalcomponents and vertical components of motion vectors. Therefore, the video steganography method using non-additive embedding distortion is proposed.