ID 原文 译文
25975 针对视觉目标跟踪中传统搜索方法效率不高、难以求取全局最优等问题，利用量子遗传算法的全局寻优能力，提出了一种采用量子遗传算法作为搜索策略的视觉跟踪方法。 Aiming at the problem that traditional search method in visual tracking is not efficient and the global optimization is hard to be solved, as the global optimization ability of quantum genetic algorithm, we put forward a visual tracking method by using quantum genetic algorithm as the search strategy．
25976 在量子遗传算法的框架下，将像素点位置作为种群中的个体，提取颜色直方图作为特征，以相似性度量作为目标函数计算个体适应度值，找出相似度最大的像素点位置输出，最终完成跟踪。 In the framework of quantum genetic algorithm, regard thepixel positions as the individuals in the population, and extract the color histogram as characteristics. The individual fitness are calculated by taking similarity measure as the objective function. We find out the maximum similarity and output its homologous position, to finish the tracking.
25977 实验结果表明，本文方法在目标速度快、遮挡和非刚性形变等情况下具有明显优势，且算法运算量小，跟踪速度快。 The experimental results show that the algorithm has obvious advantages in fast speed, occlusionand non-rigid deformation, and the tracking speed is fast．
25978 本文研究基于音频的家庭活动识别方法，提出了一种基于加性间距胶囊神经网络识别模型，针对传统胶囊神经网络目标函数仅以输出胶囊模长作为约束的弊端，本文以几何学的视角，在胶囊神经网络结构中加入 Transi-tion 层，使用 Transition 层对胶囊单元空间关系进行变基至一维空间，再使用加性间距 Softmax 作为目标函数，以同类特征变化小，非同类特征差异大作为优化策略构建基于胶囊向量空间关系的目标函数以提高模型分类能力，最后对方法进行试验，采用音频事件对家庭活动进行分类识别。 We study the method of family activity recognition based on audio and propose a capsule neural network recognition model based on additive margin. In view of the drawbacks of the traditional capsule neural network objective functiononly with the output capsule mode length as the constraint, this paper adds a Transition layer to the capsule neural network structure from the perspective of geometry and uses the Transition layer to rebase the capsule unit spatial relationship to the one-dimensional. Then, using the additive margin Softmax as the objective function, the change of similar features is small, and the difference of non-similar features is used as the optimization strategy to construct the objective function based on the capsule vector space relationship to improve model classification ability. Finally，test this method by classified identified for audioevents for family activities.
25979 选择声学场景和事件检测与分类(Detection and Classification of A-coustic Scenes and Events，DCASE)2018 挑战任务 5 作为数据集，进行分类器构建和测试，最终平均 F1 分数达到92. 3% ，优于其他主流方法。 Selecting Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 Challenge Task5 as a dataset for classifier construction and testing, with a final average F1 score of 92. 3% , which is superior to other main-stream methods．
25980 针对灰狼优化算法(Grey Wolf Optimization，GWO)在收敛性研究上的不足，首先，通过定义灰狼群状态转移序列，建立了 GWO 算法的马尔科夫(Markov)链模型，通过分析 Markov 链的性质，证明它是有限齐次 Markov 链; The global convergence is one of the important features of an intelligent algorithm. In this paper, we take the initiative to handle the convergence of Grey Wolf Optimization (GWO) with Markov chain.
25981 其次，通过分析灰狼群状态序列最终转移状态，结合随机搜索算法的收敛准则，验证了 GWO 算法的全局收敛性; Firstly, a Markov chain model of theGWO algorithm is established through defining the state transition sequence of a gray wolf population. Analyzing the properties of the Markov chain proves that it is homogeneous finite. Secondly, based on the convergence criteria of random search algorithms, the global convergence of the GWO algorithm is verified via analyzing the final state transition sequence of the grey wolf population．
25982 最后，对典型测试函数、偏移函数及旋转函数进行仿真实验，并与多种群体智能算法进行对比分析。 Finally, simulation studies on typical testing functions, shifting functions and rotating functions are carried out comparing with a few typical swarm intelligent algorithms．
25983 实验结果表明，GWO算法具有全局收敛性强、计算耗时短和寻优精度高等优势。 The experimental results show that the GWO algorithm has excellent performance on the global convergence, the computational time and precision of optimization．
25984 电力无线接入网的安全性对于电网生产至关重要，然而由于其 IEC 60870-5-104 规约控制数据存在着高维度的特点，且无线信道质量动态变化，难以快速、有效地检测控制数据的异常。 The security of the wireless access network of electric power grids is critical for power grid productions. However, the control data anomalies are difficult to be detected in a fast and effective manner, due to the high dimension of the control protocol data in IEC 60870-5-104 protocol, as well as the dynamics on the quality of wireless channels．