ID 原文 译文
26125 该方法在 CAD 和 CAE 上分别取得了 91. 4% 和 97. 9% 的平均识别精度。 The method has achieved 91. 4% and 97. 9% average recognition accuracy on CAD and CAE respectively．
26126 传统的基于几何形态的神经元分类方法依赖于神经元空间结构特征的提取与选择，会损失大量有用的神经元分类信息。 Traditional morphology-based neuronal classification approaches largely rely on the feature extraction and selection techniques of neuronal spatial structures, a lot of useful information for neuronal classification may be lost．
26127 应用自适应投影算法将三维神经元进行转换，不需要提取神经元的几何特征，提出了一种基于深度学习网络的神经元几何形态分类方法。 Using the adaptive projection algorithm to convert the three-dimensional neuron data without feature extraction, this paper proposes a neuronal morphology classification approach based on deep learning networks．
26128 该方法将原始神经元数据进行三维体素重建，经过自适应投影过程构成二维神经元图像数据，并构建了基于双卷积门限循环神经网络的深度学习模型对神经元进行分类。 The three-dimensional voxel reconstructionis used for the original neuron data, and the two-dimensional neuron data is generated through adaptive projection process. Then, the deep learning model of double convolutional gated recurrent neural networks is established to classify neurons．
26129 将该方法应用于三种神经元分类数据集，通过与基于特征提取的神经元分类方法相比，实验结果表明该方法具有更高的分类准确率和良好的适应能力。 The proposed approach is successfully applied to three neuronal classification datasets, the experiment results show that the proposed method has higher classification accuracy and flexibility than the neuronal classification methods based on feature extraction．
26130 基于量子系统下的自由粒子模型，提出了多尺度自由粒子优化算法(Multi-scale Free Particle Optimiza-tion Algorithm，MFPOA)，并在物理模型的基础之上研究了该算法的内部机制。 Based on the free particle model of quantum system, the Multi-scale Free Particle Optimization Algorithm (MFPOA) is proposed, and the internal mechanism of the algorithm is studied on the basis of the physical model．
26131 通过类比量子系统和优化系统，将优化问题的求解过程转化成粒子在微观系统下的运动过程。 Through analogy between quantum system and optimization system, the solving process of optimization problem is transformed into the motion process of particles under the microscopic system．
26132 通过在 MATLAB 仿真平台上对自由粒子优化算法的参数设置进行了研究，并分析了与同类搜索机制的算法的区别。 The parameter setting of free particle optimization is studied on MATLAB simulation platform, and the differences between the algorithm and similar search mechanism are analyzed.
26133 最后通过实验得出，MFPOA 更适合求解单模简单函数，求解复杂多模函数需要更多的迭代次数。 Finally, experiments show that MFPOA is more suitable for solving single-mode functions, and more iterations are needed to solve complex multi-mode functions．
26134 本文研究了可生存虚拟网络多层映射问题，首先对其建立了整数线性规划模型(ILP)，然后针对较大规模问题提出一种高效的启发式算法 VNP-SVNME 对其进行求解。 This paper studies the multi-layers embedding problem of survivable virtual networks based on virtual net-work layer protection. We first establish an integer linear programming (ILP) for the SVNME problem. Then an efficient heuristic algorithm VNP-SVNME is proposed to solve the large-scale problem．