ID 原文 译文
20355 八阵图算法(ESF)是一种具有广义Feistel结构的轻量级分组密码算法，可用在物联网环境下保护射频识别(RFID)标签等资源受限的环境中，目前对该算法的安全性研究主要为不可能差分分析。 Eight-Sided Fortress (ESF) is a lightweight block cipher with a generalized Feistel structure, whichcan be used in resource-constrained environments such as protecting Radio Frequency IDentification (RFID)tags in the internet of things. At present, the research on the security of ESF mainly adopts the impossibledifferential cryptanalysis.
20356 该文通过深入研究S盒的特点并结合ESF密钥扩展算法的性质，研究了ESF抵抗相关密钥不可能差分攻击的能力。 The ability of ESF to resist the related-key impossible differential cryptanalysis isstudied based on the characteristics of its S-boxes and key schedule.
20357 通过构造11轮相关密钥不可能差分区分器，在此基础上前后各扩展2轮，成功攻击15轮ESF算法。 By constructing an 11-round related-keyimpossible differential distinguisher, an attack on 15-round ESF is proposed by adding 2-round at the top and2-round at the bottom.
20358 该攻击的时间复杂度为240.5次15轮加密，数据复杂度为261.5个选择明文，恢复密钥比特数为40 bit。 This attack has a time complexity of 240.5 15-round encryptions and a data complexityof 261.5 chosen plaintexts with 40 recovered key-bit.
20359 与现有结果相比，攻击轮数提高的情况下，时间复杂度降低，数据复杂度也较为理想。 Compared with published results, the time complexity isdecreased and the data complexity is ideal with the number of attack rounds increased.
20360 现有的多目标进化聚类算法应用于图像分割时，往往是在图像像素层面上进行聚类，运行时间过长，而且忽略了图像区域信息使得图像分割效果不太理想。 When multi-objective evolutionary clustering algorithms are applied to image segmentation, theimage pixels are always utilized to be clustered. It results in a long running time. In addition, due to notconsidering the image region information, the image segmentation effect is not ideal.
20361 为了提高多目标进化聚类算法的分割效果和时间效率，该文将图像区域信息与部分监督信息引入多目标进化聚类，提出图像区域信息驱动的多目标进化半监督模糊聚类图像分割算法。 In order to improve thesegmentation effect and time efficiency of the multi-objective evolutionary clustering algorithm, the imageregion information and some supervised information are introduced into multi-objective evolutionary clustering.Then a multi-objective evolutionary semi-supervised fuzzy clustering image segmentation algorithm driven byimage region information is presented.
20362 该算法首先利用超像素策略获得图像的区域信息，然后结合部分监督信息，设计融合区域信息和监督信息的适应度函数，接着通过多目标进化策略对多个适应度函数进行优化得到最优解集。 First, the region information of the image is obtained through the super-pixel strategy. Second, two novel fitness functions are designed by introducing the supervised information andregion information. Third, the multi-objective evolutionary strategy is used to optimize these two fitnessfunctions to obtain an optimal solution set.
20363 最后构造融合区域信息与监督信息的最优解评价指标，实现从最优解集中选取一个最优解。 Finally, an optimal solution evaluation index with region information and supervision information is constructed and utilized to select an optimal solution from the optimal solution set.
20364 实验结果表明：与已有多目标进化聚类算法相比，该算法不但分割效果有所提升，而且运行效率得以提高。 Experimental results show the proposed algorithm outperforms comparison methods insegmentation performance and running efficiency.