Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data

Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.     

Connected variable speed limits control and car-following control with vehicle-infrastructure communication to resolve stop-and-go waves

The vision of intelligent vehicles traveling in road networks has prompted numerous concepts to control future traffic flow, one of which is the in-vehicle actuation of traffic control commands. The key of this concept is using intelligent vehicles as actuators for traffic control systems. Under this concept, we design and test a control system that connects a traffic controller with in-vehicle controllers via vehicle-to-infrastructure communication. The link-level traffic controller regulates traffic speeds through variable speed limits (VSL) gantries to resolve stop-and-go waves, while intelligent vehicles control accelerations through vehicle propulsion and brake systems to optimize their local situations. It is assumed that each intelligent vehicle receives VSL commands from the traffic controller and uses them as variable parameters for the local vehicle controller. Feasibility and effectiveness of the connected control paradigm are tested with simulation on a two-lane freeway stretch with intelligent vehicles randomly distributed among human-driven vehicles. Simulation shows that the connected VSL and vehicle control system improves traffic efficiency and sustainability; that is, total time spent in the network and average fuel consumption rate are reduced compared to (uncontrolled and controlled) scenarios with 100% human drivers and to uncontrolled scenarios with the same intelligent vehicle penetration rates.     

牵引网馈线自适应保护原理及配置方案研究

首先讨论了影响牵引网馈线保护原理及配置的因素,然后分析了牵引网馈线自适应保护的原理,其中重点探讨对距离保护传统阻抗多边形进行改进的可行性,并利用现场运行数据进行了仿真验证,最后提出了牵引网馈线自适应保护的配置方案．     

基于自适应算法的单矢量传感器抗相干干扰

将矢量传感器组合指向性vs的零点对准已知的相干干扰方向,获得"干净"的目标信号,然后结合自适应算法抵消矢量传感器原始数据中各通道的相干干扰,达到抗相干干扰的目的.文中阐述了方法的原理,进行了多种情况下的仿真,并给出实验结果.仿真和实验结果表明该方法能有效抑制方位已知的相干干扰.     

仿人形机器人运动的自适应控制

用Danevit-Hartenberg坐标变换法推导了仿人机器人的动力学与运动学方程,建立了仿人机器人的广义雅可比矩阵.设计了自适应控制器,以保证该仿人机器人系统的渐近稳定.给出了该机器人在做高难度舞蹈动作时各关节的运动轨迹以及仿真截图.结果验证了该方法的可行性和有效性.     

基于决策树技术的建筑企业信用评价研究

在决策树理论的指导下,通过信息增益的应用和公式的构造获取属性重要程度评价值,结合决策树的C5．0算法得到建筑企业的信用评价模型。经过对该模型进行测试和评价,取得了较好的预测效果。     

知识转移与科层制解构

本文从知识转移角度探讨了组织环境的变化,剖析了科层制组织在知识一决策配置方式上所存在的不适应性,并据此简要预测了未来的组织发展趋势。     

通有决策系统稳定性与可靠性分析

从定量分析的角度,提出了通有决策系统的系统稳定性与决策可靠性概念,并给出一套简易的决策系统稳定性度量和系统决策可靠性的判定方法。该度量和方法可以作为一个通有系统进行决策的前提条件和前期考察依据。     

基于自适应可变权重的铁路继电器机电参数融合方法研究

铁路继电器性能优劣直接影响铁路系统的正常运行.为分析铁路继电器长期动作造成的性能退化,研究一种融合继电器机械参数和电参数的方法来综合反映继电器性能状态.利用熵权法分析继电器性能参数所含退化信息量,客观确定参数基础权重.根据性能参数退化特征,引入考虑关键参数优化差异的劣化参数凸显因子和异常趋势惩罚因子,对基础权重进行优化...     

项目决策数学模型研究及算法实现

运用层次分析理论和模糊综合评价理论对某项目决策的3种拟建方案进行了科学合理的分析,探讨了其数学模型的建立,编写算法程序验证了模型的有效性和正确性,取得了较为客观和稳定的结果,为科学决策提供了数学理论基础.