ID	原文	译文
2173	互信息分析方法是基于信息论提出的一种描述两信号间信息交互情况的算法，其在脑电信号领域的有效性已得到了充分证实。	The mutual information analysis is a method based on information theory to describe the information inter-action between two signals.
2174	针对当前测谎方法中脑电信号特征提取困难以及大脑整体认知功能分析在脑认知科学研究中越来越被重视的情况，本文首次将互信息分析方法应用到脑电测谎领域中，使用互信息量化大脑各节点之间的相关性，对计算结果进行统计分析，	In view of the difficulty in extracting features of EEG signals in the current lie detection method and the circumstance that the analysis of the overall cognitive function of the brain were increasingly important in brain cog-nitive science research, this paper applied the mutual information analysis method to the field of EEG lie detection for the first time and quantified the correlation between the brain nodes and perform statistical analysis on the calculation results.
2175	选取出在两类人群中具有显著性差异的电极对的互信息作为分类特征，进行模式识别，得到了 99.67% 的准确率。	The mutual information of the electrode pairs with significant differences in the two groups were selected as the classification features, on which the pattern recognition was performed, resulting in the accuracy rate of 99. 67% .
2176	这一结果表明，互信息分析方法是一种有效的脑功能连接分析方法，为基于脑电信号连接分析的测谎研究提供了一种新的途径。	This result proves that the mutual information analysis is an effective brain functional connection analysis method, which provides a new way for lie detection research based on EEG signal connection analysis.
2177	另外，对说谎与诚实两类受试者的大脑功能网络的分析结果表明:	In addition, the brain function network of both lying and honest subjects was also analyzed. The results show that
2178	处于说谎状态时，大脑的额叶、顶叶、颞叶及枕叶之间协同实现谎言功能，并在躯体行为所对应的脑区与其他脑区的连接上也表现出相对诚实组的显著性差异，以上结果均有助于进一步揭示谎言的神经活动机制。	when lying, the frontal, parietal, temporal, and occipital regions of the brain cooperate to achieve the lie function, and in the connection between the brain regions corresponding to the physical behavior and other brain regions, significant differences between the two groups was also shown. These above results will help us fur-ther reveal the neural activity mechanism of the lie.
2179	通过大数据交易过程模型优化，实现对大数据交易过程的精确建模，对于构建稳定、鲁棒和精确的交易平台至关重要。	Through the optimization of big data transaction process model, the accurate modeling of big data transac-tion process is realized, which is significant for building a stable, robust and accurate transaction platform.
2180	然而，大数据交易流程随时间而变化，传统的静态模型优化方法无法反映现实流程模型的时态变化特征。	However, the bigdata transaction process changes over time, and traditional static model optimization methods cannot reflect the characteristics of time-varying changes in real-world process models.
2181	为此，本文提出一种基于概念漂移的大数据交易模型优化方法，在概念漂移点检测和定位的基础上，设计大数据交易日志分割算法，演算日志精准分割点，构建具有时变特性的大数据交易分段模型，实现基于日志分割的模型优化。	For this reason, this paper proposes an optimization approach of big data transaction model. Based on the detection and location of concept drift points, the approach designs a big data transac-tion log segmentation algorithm and calculates log precise segmentation points to build a large data transaction time-varying segmented model and to realize model optimization.
2182	该方法在天元大数据交易平台的应用实践表明，优化模型在拟合度和精确度方面均优于静态模型，对大数据交易演化过程的适配性更强。	The proposed approach has got used in Tianyuan Big Data Transaction Platform, which shows that the optimization model has an advantage over the static model in fitness, precision and adaptationto the big data transaction process.