ID 原文 译文
50257 并且所提算法也在保证系统性能的同时，实现了多播组间良好的公平性。 and the proposed algorithm is also to ensure that the performance of the system at the same time, to achieve a good fairness between the multicast group.
50258 为了识别复杂产品关键质量特性(critical-to-quality characteristics，CTQs)，提出基于遗传模拟退火算法(genetic simulated annealing algorithm，GSA)的特征选择算法。 In order to identify the complex product key quality characteristics (critical - to - quality characteristics, CTQs), based on the genetic simulated annealing algorithm (based simulated annealing algorithm, GSA) feature selection algorithm.
50259 所提算法将遗传算法(genetic algorithm，GA)与模拟退火算法(simulated annealing algorithm，SA)结合，兼有不错局部搜索与全局搜索能力。 Proposed algorithm to the genetic algorithm (based algorithm, GA) and simulated annealing algorithm (simulated annealing algorithm, SA), have good global search and local search ability.
50260 提出一种综合适应度函数应用于所提算法，以同时优化CTQ集分类性能和所选质量特性数。 In this paper, a comprehensive fitness function used in the proposed algorithm, to simultaneously optimize classification performance and quality of the selected features of CTQ sets.
50261 算例结果表明，所提算法能有效过滤无关、冗余质量特性，识别关键质量特性; Numerical example results show that the proposed algorithm can effectively filter irrelevant and redundant features, quality identification of key quality characteristics;
50262 与Memetic算法和信息增益(information gain，IG)算法相比，所提算法在识别更少关键质量特性的同时，得到更高预测精度。 And Memetic algorithm and information gain (information gain, IG) algorithm, the proposed algorithm in identifying key quality characteristics of less at the same time, get a higher forecasting precision.
50263 针对经典的奇异值分解(singular value decomposition，SVD)在图像处理中的不足，提出了一种6通道多尺度奇异值分解(multi-scale SVD，MSVD)的构造方法，并将其应用于多聚焦图像融合中。 For classic singular value decomposition (singular value decomposition, SVD) in image processing, this paper proposes a six channel multi-scale singular value decomposition (multi - scale SVD, MSVD) constructor, and applied to more focus on image fusion.
50264 首先，在经典SVD的基础上，利用矩阵分块的方法，给出了一种6通道MSVD的构造方法。 First of all, on the basis of the classical SVD, by using the method of matrix block, presents a six channel MSVD constructor.
50265 其次，对参加融合的多聚焦图像进行6通道MSVD分解，得到高层低频和各层5个方向的高频，对分解的低频子图像采用取平均、高频子图像采用区域能量取大的融合规则进行融合，并进行MSVD逆变换得到融合结果图像。 Secondly, for the fusion of more focused image 6 channel MSVD decomposition, with senior and five in the direction of high frequency and low frequency on the decomposition of low frequency images with average, high-frequency sub image using the fusion rules of the integration of regional energy, and MSVD inverse transformation get the fusion result image.
50266 最后，对融合结果图像进行主观分析和客观评价。 Finally, the result of the fusion image analysis of subjective and objective evaluation.