ID	原文	译文
1783	实验结果表明 EGolden-SWOA 具有更好的寻优精度和稳定性。	and the experimental results show that EGolden-SWOA has a better performance in convergence rate and stability.
1784	进一步对 EGolden-SWOA 进行求解大规模问题的实验，实验结果表明 EGolden-SWOA 可以有效解决大规模优化问题。	The high dimensional function test shows that EGolden-SWOA perform well in solving large scale optimization problem.
1785	最后将 EGolden-SWOA 应用于压力容器和蝶形弹簧设计优化问题，结果表明 EGolden-SWOA 在工程优化方面的性能优于 RCSA(Rough Crow Search Algorithm)、CPSO(Co-evolutionary Particle Swarm Optimization)等改进算法，可以有效运用于实际工程优化问题。	Finally, EGolden-SWOA is applied to the optimi-zation design of the pressure vessel and tension/compression spring, the result shows that its performance in project optimi-zation is better than RCSA and CPSO, it can be effectively applied to project optimization.
1786	针对乳腺 MR 图像组织复杂、灰度不均匀、难分割的特点，本文提出双树复小波(DTCWT)变换结合密度聚类的图像分割方法。	Breast MR image segmentation is difficult because of complex organization and intensity inhomogeneity. This paper proposes a segmentation method based on dual-tree complex wavelet transform and density clustering.
1787	首先利用复小波域双变量模型结合各向异性扩散函数对图像进行去噪处理;	Firstly, the image is denoised by using complex wavelet domain bivariate model combined with an isotropic diffusion function;
1788	进而通过简单线性迭代聚类(SLIC)算法将图像划分成一定数量的超像素区域，根据事先设置的阈值搜索每个超像素的近邻，从而降低基于 K 近邻的密度峰值快速搜索聚类(KNN-DPC)算法寻找每个样本近邻的时间;	Then sim-ple linear iterative clustering (SLIC)algorithm is used to obtain the neighbors of each superpixel, thereby reducing the time of searching for the nearest neighbor of each sample in KNN-DPC algorithm.
1789	最终，引入超像素区域的近邻信息度量样本密度，采用 KNN-DPC 算法的分配策略自适应聚类。	Finally, nearest neighbor sample density infor-mation of superpixel region is introduced, and distribution strategies from KNN-DPC algorithm are used for adaptive cluste-ring.
1790	仿真和临床数据分割结果表明，所提算法能有效的实现乳腺 MR 图像的分割。	The segmentation results of simulation and clinical data show that the proposed algorithm can segment breast MR ima-ges effectively.
1791	数据中心网络中同时存在截止时间流(deadline flow)和非截止时间流(non-deadline flow)，为降低非截止时间流的平均完成时间(Average Flow Complete Time，AFCT)同时维持低截止时间错失率(Deadline Miss Rate，DMR)，本文提出了一种基于松弛时间与累计发送量的混合流调度机制(Slack Time and Accumulation based Mix-flowScheduling，STAM)。	Applications deployed in data center networks generate a mix of flows with and without deadlines. To re-duce the average flow complete time (AFCT)while maintain a low deadline miss rate (DMR), a slack time and accumula-tion-based mix-flow scheduling mechanism (STAM)is proposed in this paper.
1792	首先通过引入松弛时间的概念，衡量截止时间流对非截止时间流在传输时延上的宽容度;	Firstly, the delay tolerance of deadline flowson non-deadline flows is measured with the introduction of Slack Time.