ID 原文 译文
853 最后，利用具有不平衡特性的无损检测图像进行实验，结果表明本文算法具有更高的分割准确率和更好的视觉效果。 Lastly, the non-de-structive testing (NDT)images with the characters of unequal cluster sizes are used for experiments, the results show that the proposed algorithm has better segmentation accuracy and visual effects.
854 块效应和未知且时变的噪声强度会降低时域流信号动态稀疏重构的性能， Performance of dynamic sparse recovery for streaming signals in time domain will degrade for the exist-ence of blocking artifacts and unknown time-varying noise intensity.
855 为解决该问题，本文基于重叠正交变换和稀疏贝叶斯学习框架，提出一种对时域流信号进行动态压缩感知的鲁棒稀疏贝叶斯学习重构算法。 To solve the above problems, a robust sparse Bayesian learning algorithm for dynamic compressive sensing of streaming signals in time domain is proposed based on the framework of lapped orthogonal transform and sparse Bayesian learning.
856 该算法在消除块效应的同时，能够处理噪声强度未知且时变情形下的动态稀疏重构问题，相比现有的流信号稀疏贝叶斯学习算法具有更强的抗噪鲁棒性。 In addition to eliminating the blocking artifacts, the proposed algorithm handles dynamic sparse Bayesian learning problems effectively under conditions of unknown time-varying noise intensity, which has better robustness against existing sparse Bayesian learning algorithms for streaming signals.
857 尽管现有的时域流信号压缩感知的有效算法并不多，但实验表明，本文算法的重构信误比和重构成功率均明显高于现有的基于稀疏贝叶斯学习的流信号重构算法和基于 L1-同伦的流信号重构算法， Though there are not many existing effective algorithms for compressed sensing of streaming signals, experiments show that the proposed algorithm has obviously larger reconstruction signal-to-noise ratio and higher success rates for reconstruction than existing re-covery algorithms for streaming signals based on sparse Bayesian learning or L1-homotopy;
858 且达到相同的重构成功率所需的观测数目少于另两种算法，计算量和运行效率则与稀疏贝叶斯学习算法相近。 also, the measurement number required for particular success rates is obviously less than that of the other two algorithms, the computation cost and running time is approximately the same with the existing sparse Bayesian learning algorithm.
859 大量研究表明，microRNA(miRNA)在人类复杂疾病研究中发挥着重要作用。识别 miRNA 与疾病之间的关系对于提高复杂疾病的治疗水平具有重要意义。 Numerous studies have shown that microRNA (miRNA)plays important role in the study of human com-plex diseases. Identifying the association between miRNAs and diseases is important for improving the therapeutic level ofcomplex diseases.
860 然而，传统实验方式常受限于小规模和高成本，因此迫切需要计算模拟的方式快速有效地预测 miRNA-疾病间的潜在关系。 However, traditional experimental is often limited to small-scale and high-cost, so computational simula-tion is urgently needed to quickly and effectively predict the potential miRNAs-disease associations.
861 本文通过结合深度学习的堆叠自动编码器算法与旋转森林分类器对 miRNA-疾病间关系进行预测。 In this study, a new method is proposed to predict the miRNA-disease association by combining deep learning stacked automatic encoder algo-rithm with rotation forest classifier.
862 该方法能够有效抽取出融合了疾病语义相似性、miRNA 功能相似性和 miRNA序列信息的高级特征并对其进行准确分类。 This method can effectively extract high-level features that combine disease semanticsimilarity, miNRA functional similarity and miRNA sequence information, and accurately classify them.