基于线路实测数据统计推断的应力谱分布类型比较

在分析研究轨道车辆结构实测动应力谱分布的基础上,提出用威布尔分布和对数正态分布的组合分布来描述动应力分布.利用实测数据分别拟合出威布尔、对数正态、组合分布三个分布函数,利用拟合结果及实测应力谱进行应力谱统计推断,得到各自的推断应力谱并与实测谱比较,证明了组合分布对实测应力谱分布描述的优越性.同时利用各分布函数估计得到实测中未出现的可能最大值,然后扩展出包含可能最大值的扩展应力谱,在损伤累计理论的基础上,证明了威布尔分布对分布估计的保守性,以及组合分布对应力谱分布估计的有效性.     

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.     

三大统计学派推断思想的比较研究——以贝伦斯—费歇尔问题为例

本文介绍了贝伦斯-费歇尔问题的历史及三大统计学派在此问题上的主要解决方法,从一个侧面反映了奈曼置信推断、贝叶斯辩证推断与费歇尔信念推断思想的差异。     

A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data

There are many systems to evaluate driving style based on smartphone sensors without enough awareness from the context. To cover this gap, we propose a new system namely CADSE system to consider the effects of traffic levels and car types on driving evaluation. CADSE system includes three subsystems to calibrate smartphone, to classify the maneuvers, and to evaluate driving styles. For each maneuver, the smartphone sensors data are gathered in three successive time intervals referred as pre-maneuver, in-maneuver, and post-maneuver times. Then, we extract some important mathematical and experimental features from these data. Afterwards, we propose an ensemble learning method on these features to classify the maneuvers. This ensemble method includes decision tree, support vector machine, multi-layer perceptron, and k-nearest neighbors. Finally, we develop a rule-based fuzzy inference system to integrate the outputs of these algorithms and to recognize dangerous and safe maneuvers. CADSE saves this result in driver’s profile to consider more for dangerous driving recognition. The experimental results show that accuracy, precision, recall, and F-measure of CADSE system are greater than 94%, 92%, 92%, and 93%, respectively that prove the system efficiency.     

基于ANFIS的特高压输电线路故障分类识别方法

提出了一种基于自适应神经模糊推理系统(ANFIS)的特高压输电线路故障分类识别方法,以分类识别10种常见的输电线路故障.该方法以故障后1个工频周期内故障电流分量的标准差和四分位距作为故障分类识别的特征量.分析了噪声和谐波对这2个特征量的影响;建立了基于ANFIS的故障分类识别模型.大量仿真试验表明:提出的故障分类识别方法能快速、准确地识别各类故障,并且不易受故障初始角、故障位置和过渡电阻的影响,对噪声、谐波、电流互感器传变特性及采样频率有良好的适应性,分类识别正确率能达到99.5%.     

单船动态风险评估模糊推理系统的设计与模拟

对在航船舶进行动态的风险评估,分析影响船舶安全航行的风险因素,构建了船舶风险评估模型,定义了各项风险因素的隶属函数,并参考专家经验建立模糊推理规则.按照建立的模型,在MATLAB平台上运用模糊推理系统构建风险评估系统,并应用该系统对在航船舶进行定量风险评估.实例证明,该系统能够合理地评估在航船舶的整体风险水平,为驾驶员决策提供参考,具有应用潜力.     

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.     

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.     

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.