ID 原文 译文
20655 针对识别左右手运动想象脑电图信号(EEG)模式精度和互信息不高的问题，该文采用基于可调Q因子小波变换(TQWT)算法来处理脑电信号。 In view of the problem of low accuracy and mutual information in left and right hand motor imagery-based Electro Encephalo Gram (EEG), a new approach based on Tunable Q-factor Wavelet Transform (TQWT)is proposed to handle with the binary-class motor imagery EEGs.
20656 首先，利用TQWT对脑电图信号进行分解； Firstly, the TQWT is utilized to decomposethe filtered EEG signal.
20657 随后，提取子频带信号的小波系数能量、自回归模型(AR)系数以及分形维数； Then, several sub-band signals are extracted and followed by calculating their energy,AutoRegressive (AR) model coefficients and fractal dimension.
20658 最后，利用线性判别分析(LDA)对提取的脑电特征进行识别。 Finally, a Linear Discriminant Analysis (LDA)classifier is used to classify these EEGs.
20659 采用BCI2003和BCI2005竞赛数据对所提出的算法进行验证，4名受试者的最高识别率分别为88.11%, 89.33%,77.13%和78.80%，最大互信息分别为0.95, 0.96, 0.43和0.45。 Two Graz datasets of BCI Competition 2003 and 2005 are employed toverify the proposed method. The maximum accuracy of classifying EEGs of four subjects is 88.11%, 89.33%,77.13% and 78.80%, respectively, and the maximum mutual information is 0.95, 0.96, 0.43 and 0.45.
20660 实验结果表明，所提算法取得了高分类精度及互信息值，验证了其有效性。 The high accuracies and mutual information demonstrate eventually the effectiveness of the proposed method.
20661 我国是个洪涝灾害多发的国家，每年7月、8月份洪涝灾害时常发生。 China is a flood disaster-prone country, where floods occur frequently every year, from July to August.
20662 因此，实现洪涝受灾区域的水体快速检测对灾害救援和评估具有重要的意义。 Therefore, rapid disaster detection and assessment of floods affected areas is of great significance.
20663 高分3号SAR卫星数据采用主动式对地观测技术，全天时、全天候成像的特点在洪涝减灾应用中具有明显的优势。 GF-3SAR satellite data has obvious advantages of all-day, all-weather imaging characteristics in flood disaster reduction applications because of its active observation technology.
20664 以湖南省洪涝灾害区域快速检测为目的，该文提出基于高分3号单极化SAR数据的洪涝区域水体快速检测方法，包括SAR预处理，顾及SAR分布特性且保边缘的马尔科夫模型洪涝水体提取，基于SAR几何构象模型的阴影虚警干扰去除等步骤，并利用人工检测结果进行相对精度评价。 For the purpose of rapid water detection in flooding area, a rapid detection method of flood area based on GF-3 single-polarized SAR data is proposed, including SAR preprocessing, flood extraction based on Markov random fields, shadow false alarm removal. Its detecting accuracy is evaluated with manual detection result.