基于复杂网络的机场群航线网络动态特征分析

随着航空运输和区域经济一体化的快速发展,我国已形成京津冀、长三角和珠三角三大典型机场群。文章以三大典型机场群为研究对象,构建基于滑动时间窗的航线网络拓扑结构,运用复杂网络理论分析机场群航线网络动态特征和同构性。研究结果表明,机场群航线网络具有无标度和小世界网络特征,并表现出显著的波峰和波谷动态性,并且长三角机场群的航班集中度分布明显高于其他两大机场群。文章首次从航线网络视角,运用Dice相似系数衡量了机场群内部各机场之间航线网络同构性程度。     

轨道交通与城市协同发展的空间差异性分析：以北京市为例

为识别轨道交通与城市协同发展程度的空间差异性，以及不同空间范围内城市发展水平对轨道交通客流影响的差异性，引入描述空间集聚特征的莫兰指数Moran's I 建立轨道站点周边城市发展水平评价方法，将轨道站点周边城市发展水平和站点客流空间集聚分布特征作为轨道交通与城市发展协同程度判断标准，划定若干研究区域，利用地理加权回归GWR 模型研究两者空间相关性和城市发展因素对轨道交通客流的影响. 实证研究表明：轨道站点周边城市发展水平与站点客流协同程度较高的区域，城市发展水平对站点客流影响更大；将城市发展因素对站点客流影响程度进行排序，密度高于交通便捷程度、功能多样化程度和发展紧凑度.     

融合D-S理论的锅炉人因贝叶斯网络安全性定量分析

针对船用锅炉人因安全性分析中存在的知识不确定性,采用D-S证据理论对多专家信息进行融合并建立考虑人因的贝叶斯网络,得到节点条件概率的区间表示形式.经加权平均后代入贝叶斯网络计算,与面向对象贝叶斯网络和FTA等方法的对比显示,该方法能够更加有效地融合不同专家信息,也更为符合工程实际.     

Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses

Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages.In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user.On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.     

Elman递归神经网络在结构分析中的应用

给出了Elman动态递归神经网络的网络结构和基本原理。基于Elman递归神经网络能够逼近任意非线性函数的特点,提出了一种基于Elman递归神经网络建立结构分析模型的方法。利用Elman递归神经网络对桁架进行建模,真实地反映了桁架结构的动态特性。     

活性或惰性掺和料对复合胶凝材料水化性能的影响

采用等温量热法、X射线衍射(XRD)和化学结合水量测定等方法,研究了含有不同比例的粉煤灰或磨细石英粉的复合胶凝材料在不同养护温度条件下的水化过程,观察了水化产物量和水化程度随水化龄期变化情况。在掺量相同时,矿物掺和料的种类变化不会对复合胶凝材料的初期水化过程有明显影响,其反应过程仍由其中所含硅酸盐水泥控制。活性的粉煤灰可延迟复合胶凝材料的水化反应,而惰性的石英粉则不会。热激发作用可显著促进粉煤灰的火山灰反应,但对于硅酸盐水泥的长期水化反应有抑制作用。热养护能促使水化初期生成的高含水率的CSH向低含水率的CSH变化;粉煤灰的火山灰反应生成的也是含水率较低的CSH凝胶。     

基于网格量化和区间均值的动态目标识别

动态目标识别中，当摄像机和目标之间相对距离变化时，对于同一目标的多尺度序列图像。虽然目标区域图像大小和清晰度不同，但整体结构分布具有相似性和一致性。根据这一特点，提出基于目标区域网格量化的方法。在目标区域上形成采样网格，通过相对量化提取结构特征矩阵。对于同一目标序列图像中的每一个目标区域，其结构特征矩阵基本不变。结构特征矩阵反映象素分布的自相关性质。进一步，对由干扰引起的灰度值结构分布误差，采用网格区间均值的方法加以解决。目标区域网格量化是对相邻采样点的差值进行量化匹配。区间均值是对少数相邻采样点的进一步匹配。实例表明，本文的方法运算速度快、可靠性高，达到了实用目的。     

砂性土层主动土压力计算薄层单元法

采用薄层单元法研究挡土墙后为砂土填土情况下的主动土压力计算方法，推导出了具体计算公式。建立了优化计算数学模型，并采用复合形优化算法求解。实例结果表明计算值与实测值吻合。     

轨道复合不平顺权重系数的求解方法

轨道的轨向和水平逆相位复合不平顺对列车运行的平 稳性有较大影响,在复合不平顺管理中,首先要决定加权系数。 通过理论分析提出加权系数的求解方法,并进行实例计算,初 步得出权重系数的建议值。     

Energy-aware load-balaning fault-tolerant routing scheme for ad hoc sensor networks

Most routing protocols for sensor networks try to extend network lifetime by minimizing the energy consumption, but have not taken the network reliability into account. An energy-aware, load-balancing and fault-tolerant routing scheme, termed as ELFR was propsed to adapt to the harsh environment. First a network robustness model was presented. Based on this model, the route discovery phase was designed to make the sensors to construct into a hop-leveled network which is mesh structure. A cross-layer design was adopted to measure the transmission delay so as to detect the failed nodes. The routing scheme works with acknowledge （ACK） feedback mechanism to transfer control messages to avoid producing extra control overhead messages. When nodes fail, the new healthy paths will be selected locally without rerouting. Simulation results show that our scheme is much robust, and it achieves better energy efficiency, load balancing and maintains good end-to-end delay.