Learning User Intention in Networked Mobile Robot Control

Teleoperated networked robot often has unpredictable behaviors due to uncertain time delay from data transmission over Internet. The robot cannot accomplish the desired actions of the remote operator in time, which severely impairs reliability and efficiency of the robot system. This paper investigated a novel approach, learning user intention, to compensate the uncertain time delay with the autonomy of a mobile robot. The user intention to control and operate the robot was modeled and incrementally inferred based on Bayesian techniques so that the desired actions could be recognized and completed by the robot autonomously. Thus the networked robot is able to fulfill the task assigned without frequent interaction with the user, which decreases data transmission and improves the efficiency of the whole system. Experimental results show the validity and feasibility of the proposed method.     

基于模糊推理的交通拥堵判别方法

文章从交通量、平均速度、天气条件、前一时段的拥堵状况等与交通拥堵有直接关系的模糊条件入手,根据确定的模糊集合和模糊规则,提出了一种用于交通拥堵判别的模糊推理算法,并利用GPS采集数据对算法进行了验证分析。结果表明,该模糊推理算法能准确判别道路的交通拥堵情况。     

A Bayesian approach for modeling origin-destination matrices

The majority of origin destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel census studies, in contrast, covers the entire population of a specific study area of interest. In such cases where reliable historical data exist, statistical methodology may serve as a flexible alternative to traditional travel demand models by incorporating estimation of trip-generation, trip-attraction and trip-distribution in one model. In this research, a statistical Bayesian approach on OD matrix estimation is presented, where modeling of OD flows derived from census data, is related only to a set of general explanatory variables. A Poisson and a negative binomial model are formulated in detail, while emphasis is placed on the hierarchical Poisson-gamma structure of the latter. Problems related to the absence of closed-form expressions are bypassed with the use of a Markov Chain Monte Carlo method known as the Metropolis-Hastings algorithm. The methodology is tested on a realistic application area concerning the Belgian region of Flanders on the level of municipalities. Model comparison indicates that negative binomial likelihood is a more suitable distributional assumption than Poisson likelihood, due to the great degree of overdispersion present in OD flows. Finally, several predictive goodness-of-fit tests on the negative binomial model suggest a good overall fit to the data. In general, Bayesian methodology reduces the overall uncertainty of the estimates by delivering posterior distributions for the parameters of scientific interest as well as predictive distributions for future OD flows.     

一种新的区间值模糊集推理方法

在两向量相似度的基础上, 定义了两个区间值模糊集在一点处相似度, 然后在此基础上提出了一种新的模糊推理方法--区间值模糊集相似度推理方法.并且对该种方法的还原性进行了讨论,给出了满足还原算法的条件.     

变流器传导干扰分析与磁环抑制作用研究

文章重点介绍了变流器系统传导干扰的产生和传播机理;结合仿真手段分析了磁环抑制共模干扰原理,以及磁环在功率电路中使用的设计要点;最后对磁环抑制变流器传导干扰进行仿真分析;从而总结相关参数,可为变流器EMC设计提供指导。     

大功率光伏并网逆变器电磁兼容的研究

介绍了大功率光伏并网逆变器的主电路拓扑,分析其电磁兼容问题,并提出了相应解决办法。实验结果表明,所提出的电磁兼容解决措施有效,所设计的逆变器电磁兼容性能良好、运行稳定。     

SUPPRESSING CONDUCTED EMI FROM A DC/DC HIGH POWER CONVERTER BASED ON MIXED-MODE FILTER

Conducted electromagnetic interference （EMI） from a 7. 5kVA DC/DC high power converter is investigated to agree with EN class A. Here in some passive methods of suppressing conducted EM Noise, such as mixedmode （MM） EMI filters, snubbing circuits and other means, are used. Based on measurement, the sources of noise are detected with the characteristics analyzed in detail. The MM EMI filters is valuable means with which low-frequency part and some of the high frequency part of conducted EM Noise can be efficiently reduced. How to lay out the MM filters on both sides of the converter is outlined in detail. In addition, multiple grounding and RDC snubbing circuits are employed to improve the performance in high frequency. The experimental results confirm the methods adopted.     

An integrated solution for lane level irregular driving detection on highways

Global Navigation Satellite Systems (GNSS) has been widely used in the provision of Intelligent Transportation System (ITS) services. Current meter level system availability can fulfill the road level applications, such as route guide, fleet management and traffic control. However, meter level of system performance is not sufficient for the advanced safety applications. These lane level safety applications requires centimeter/decimeter positioning accuracy, with high integrity, continuity and availability include lane control, collision avoidance and intelligent speed assistance, etc. Detecting lane level irregular driving behavior is the basic requirement for these safety related ITS applications. The two major issues involved in the lane level irregular driving identification are accessing to high accuracy positioning and vehicle dynamic parameters and extraction of erratic driving behaviour from this and other related information. This paper proposes an integrated solution for the lane level irregular driving detection. Access to high accuracy positioning is enabled by GNSS and Inertial Navigation System (INS) integration using filtering with precise vehicle motion models and lane information. The detection of different types of irregular driving behaviour is based on the application of a Fuzzy Inference System (FIS). The evaluation of the designed integrated systems in the field test shows that 0.5 m accuracy positioning source is required for lane level irregular driving detection algorithm and the designed system can detect irregular driving styles.     

An integrated Bayesian approach for passenger flow assignment in metro networks

This paper proposes an integrated Bayesian statistical inference framework to characterize passenger flow assignment model in a complex metro network. In doing so, we combine network cost attribute estimation and passenger route choice modeling using Bayesian inference. We build the posterior density by taking the likelihood of observing passenger travel times provided by smart card data and our prior knowledge about the studied metro network. Given the high-dimensional nature of parameters in this framework, we apply the variable-at-a-time Metropolis sampling algorithm to estimate the mean and Bayesian confidence interval for each parameter in turn. As a numerical example, this integrated approach is applied on the metro network in Singapore. Our result shows that link travel time exhibits a considerable coefficient of variation about 0.17, suggesting that travel time reliability is of high importance to metro operation. The estimation of route choice parameters conforms with previous survey-based studies, showing that the disutility of transfer time is about twice of that of in-vehicle travel time in Singapore metro system.     

120km／h重载货车摇枕载荷谱最大载荷的统计推断研究

高速铁路货车在实际运行中最大载荷虽然极少出现，但会引起零部件较大的损伤，对其疲劳强度有较大影响。由于实际测试样本有限，最大载荷不一定能实测得到。为了使编制出的载荷谱能够全面反映摇枕的载荷分布特征，对实测的载荷谱进行统计分析，确定可能出现的最大值。采用线性相关系数为目标函数的优化算法，对实测摇枕载荷的理论分布进行了统计拟合，然后利用概率计算的方法求出了相应的最大载荷。