| ID | 原文 | 译文 |
| 11014 | 最后通过对一个实际软件系统的可靠性分析,验证了代数方法的适用性与有效性。 | Finally through the analysis of the reliability of a practical software system, to verify the applicability and effectiveness of algebra method. |
| 11015 | 通过大脑对外界环境感知的神经结构与认知功能的相关研究,构建仿脑的媒体神经认知计算(multimedia neural cognitive computing,MNCC)模型。 | Through the brain to the outside environment perception neural structures and cognitive function of related research, build media in imitation of a brain neural cognitive computing (multimedia neural cognitive computing, MNCC) model. |
| 11016 | 该模型模拟了感官的信息感知、新皮层功能柱的认知功能、丘脑的注意控制结构、海马体的记忆存储和边缘系统的情绪控制环路等大脑基本的神经结构和认知功能。 | The model simulates the function of sensory information perception, the neocortex column of cognitive function, the attention of the thalamus control structure, the hippocampus, memory storage and the mood of the limbic system control loop of the brain such as basic neural structures and cognitive function. |
| 11017 | 在此基础上,构建基于MNCC的高分辨率遥感图像场景分类算法。 | On this basis, build the high resolution remote sensing image scene classification algorithm based on MNCC. |
| 11018 | 首先,图像经仿射变换后切分为若干图块,通过深度神经网络提取图块的稀疏激活特征,采用概率主题模型获取图块初始场景类别,并利用图块分类错误信息反馈控制场景显著区特征的提取; | First, the image segmentation after affine transformation for some pieces, through deep sparse activation characteristics of neural network to extract the map, using probability model theme get figure of initial scene category, and use map block classification error feedback control scene area feature extraction; |
| 11019 | 其次,根据图块的上下文获取场景语义的时空特征,并在此基础上进行图块分类和场景预分类; | Secondly, according to the figure of the context for scenario semantic characteristics of space and time, and on this basis to classification of figure block classification and scene; |
| 11020 | 最后,用场景预分类误差构造奖惩函数,控制和选择深度神经网络中场景区分度较大的稀疏激活特征,并通过增量式强化集成学习,获得最后的场景分类。 | Finally, using scene recognition error structure is rewards and punishment function, control and choice for larger scenario in the neural network to distinguish the depth of sparse activation characteristics, and through the incremental strengthen integration study, obtain the final scene classification. |
| 11021 | 在两个标准的高分辨率遥感图像数据集上的实验结果表明,MNCC算法具备较好场景分类结果。 | In two standard of high resolution remote sensing image data set on the experimental results show that the algorithm of MNCC have a good scene classification results. |
| 11022 | 针对基于单一颜色特征的粒子滤波跟踪算法易受光照变化、部分遮挡及相似干扰物的影响,而利用多特征融合的粒子滤波方法存在各特征权值、跟踪模板及窗口大小自适应选取问题,提出了一种基于模糊测度的多特征融合鲁棒粒子滤波跟踪算法。 | Particle filter tracking algorithm based on single color features are susceptible to illumination changes, partial sheltering and similarity, the effect of distractors, and the use of multiple features fusion particle filter method has the feature weights, tracking template and adaptive window size selection problem, this paper proposes a more robust feature fusion based on fuzzy measure particle filter tracking algorithm. |
| 11023 | 采用颜色及边缘方向直方图来描述目标量测模型,通过分别计算这两类特征在候选目标与参考目标之间的Bhattacharyya距离来确定其各自特征的模糊测度,通过查取模糊规则表来自适应地确定两类特征的权重; | Color and edge direction histogram is used to describe the target measurement model, through the two types of features are calculated respectively in the candidate Bhattacharyya distance between the target and reference target to determine the characteristics of each fuzzy measure, fuzzy rules by pick up table from adapt to determine the weights of the two kinds of characteristics; |