ID 原文 译文
39896 选取青岛地铁4号线某标段2017年3月份到8月份的危险源及隐患数据作为原始状态，使用MATLAB R2014a仿真计算，得到不同元素的密度变化规律，将元素由危险源状态到隐患状态再到受控状态的过程显性化，展示元素的传播及控制规律，采用外界措施对特殊节点进行干预，探索最优控制效果。 Hazard source and hidden danger data of a section of Qingdao metro line 4 from march to August 2017 are selected as the original state, using by MATLAB R2014 a simulation calculation, the densities of different elements are gotten, the process of the element changed from the hazards state to the hidden state and to the controlled state is innovated, the spread and control law of the elements are displayed, the measures are adopted the special nodes are intervened, the optimal control effect is explored.
39897 在传统的联合作战中，各协作方在作战前设定好协作规则，以更好地指导战争的进行。 In traditional joint operations, all collaborators make collaboration rules in advance to guide the operations.
39898 然而，战场环境不断发生变化且通常无法预料，因此作战组织应该对变化进行实时感知并及时做出响应，以保证作战行动的有序推进。 The unknown battlefield environment varies constantly, and military organizations must make adjustments to guarantee the combat operations order.
39899 针对这一问题，建立了作战进程的上下文感知模型。 A model of operational processes based on context awareness is built.
39900 引入本体论，对上下文进行表示。 The ontology theory is introduced to present the context.
39901 在事件演算的基础上，对感知到的作战进程上下文进行分析推理，进而掌握各作战组织的能力变化及进程推进情况，为实现作战组织自同步提供决策依据。 On the basis of event calculus, the analysis is carried out to the perceived context of combat processes, and the variation of organizations ability and the situation of processes are provided to guide the self-synchronizing decision-making.
39902 由于传统的人脸识别算法效果容易受制于光照、表情、遮挡以及稀疏大噪声等外界因素的影响，如何有效提取数据特征、进一步提升算法的鲁棒性，是传统人脸识别方法发展的关键所在。 Traditional face recognition algorithms are easily affected by lighting, expressions, occlusion, and sparse noise. How to effectively extract data features for traditional face recognition algorithms is one of the most important parts.
39903 本文将多矩阵低秩分解应用在人脸特征提取中，充分利用多张人脸之间的结构相似性，探索人脸图像集的低秩子空间，进而结合低秩矩阵恢复模型来提取测试样本的低秩特征。 This paper applies the multi‑matrix low‑rank decomposition to facial feature extraction, which makes full use of the structural similarity of face datasets and explores the low‑rank subspace of the facial images collection, then combines the low‑rank matrix recovery model to extract the key features of the test sample.
39904 最后，利用主成分分析（Principalcomponent analysis，PCA）算法对所提取的特征矩阵进行进一步降维，并运用稀疏表示方法分类。 Finally, the principal component analysis（PCA） algorithm is used to reduce the dimensionality of data, and the sparse representation is utilized for classification.
39905 实验结果表明，当样本中存在一定的椒盐噪声时，本文算法在 AR、Yale 和 CMU_PIE 人脸库上均具有较好的识别精度，验证了本文算法对椒盐噪声的鲁棒性。 The results show that the algorithm in this paper has good recognition accuracy on AR, Yale and CMU_PIE face datasets when samples contain salt and pepper noise, which verifies the robustness of the algorithm to saltand pepper noise.