介入移动汇聚节点的无线传感器网络高效数据收集方法

引入移动汇聚节点解决无线传感器网络高效数据收集问题.网络中固定汇聚节点与移动汇聚节点共存,全部传感器节点都拥有维护到固定汇聚节点的路由,移动汇聚节点进入网络后定期向其附近小范围内的传感器节点扩散自己的声明信息,传感器节点向距自己跳数最小的汇聚节点发送或转发数据包.移动汇聚节点和距离其一跳的传感器节点之间通过有效的应答机制来保证数据的可靠传输.通过仿真结果显示引入移动汇聚节点的数据收集在节省能耗方面明显优于传统网络.在延长网络生存时间的同时,可以获得较高的数据传输成功率和较短的数据传输延迟.     

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针对目前广泛应用于城市轨道交通信号系统中,基于IEEE802.11系列标准的两种WLAN传输方式——空间自由波和波导管. 简要分析两种传输方式的工程和技术特点. 深入分析WLAN射频覆盖和通信机制. 结合北京轨道交通亦庄、昌平线组合传输方法的应用实例,提出空间自由波和波导管进行组合的组合传输无线覆盖方式,并简要介绍车地无线通信系统的系统结构和工作方式,无线AP的性能特点和分布原则. 计算WLAN自由空间损耗、信号接收强度、链路系统裕量. 提出WLAN技术在信号系统车地通信中应用的前景.     

铁路路网运输能力可靠性研究

铁路路网运输能力可靠性是指在规定的条件(路网结构、固定设备、移动设备及一定人力与运输组织水平)下和规定的时间内,考虑日常随机因素以及不可抗力因素等的影响,铁路路网所能提供的各种服务能满足OD需求波动的能力.本文在分析了国内外路网能力可靠性研究和铁路运输的特点上,提出了铁路路网能力可靠性的概念,并构建了相关路网能力的计算...     

无标度网下传播拥塞量与时间关系的研究

文章以复杂网络知识为基础,通过对无标度网络及度分布平均场的介绍,利用网络中度与集群系数的联系,研究因特网信息包传播过程中的一般拥塞模型,并解析无标度网络中的拥塞量与时间的关系。这样就能够预测拥塞量,以便及时有效执行相关措施     

Asynchronous n-step Q-learning adaptive traffic signal control

Ensuring transportation systems are efficient is a priority for modern society. Intersection traffic signal control can be modeled as a sequential decision-making problem. To learn how to make the best decisions, we apply reinforcement learning techniques with function approximation to train an adaptive traffic signal controller. We use the asynchronous n-step Q-learning algorithm with a two hidden layer artificial neural network as our reinforcement learning agent. A dynamic, stochastic rush hour simulation is developed to test the agent’s performance. Compared against traditional loop detector actuated and linear Q-learning traffic signal control methods, our reinforcement learning model develops a superior control policy, reducing mean total delay by up 40% without compromising throughput. However, we find our proposed model slightly increases delay for left turning vehicles compared to the actuated controller, as a consequence of the reward function, highlighting the need for an appropriate reward function which truly develops the desired policy.     

Social networks and joint/solo activity–travel behavior

The social dimension of activity–travel behavior has recently received much research attention. This paper aims to make a contribution to this growing literature by investigating individuals’ engagements in joint activities and activity companion choices. Using activity–travel diary data collected in Hong Kong in 2010, this study examines the impact of social network attributes on the decisions between solo and joint activities, and for joint activities, the choices of companions. Chi-square difference tests are used to assess the importance of social network variables in explaining joint activity behavior. We find that the inclusion of social network attributes significantly improves the goodness-of-fit of the model with only socioeconomic variables. Specifically, individuals receiving emotional support and social companionship from family members/relatives are found to more likely undertake joint activities with their family members/relatives; the size of personal social networks is found to be a significant determinant of companion choices for joint activities; and activity companions are found to be significant determinants of travel companions. The findings of this study improve the understanding about activity–travel, especially joint activity–travel decisions.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

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。