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51.
文中以同时具有缺席型和遗漏型未知属性值的不完备目标信息系统为研究对象,根据特征关系,研究可变精度粗糙集的模型及其性质.可变精度粗糙集模型与原始的粗糙集模型不同,它是建立在集合多数包含的基础上的,因而该模型是基于特征关系的经典粗糙集模型的推广形式,而基于特征关系的经典粗糙集模型则是可变精度粗糙集模型的一种特殊表现形式.文中对新模型的主要性质作了阐述和证明,结果表明:在不完备目标信息系统中,新模型与原始的粗糙集模型相比具有更高的近似精度,可进行更为精确的度量. 相似文献
52.
船舶制造企业质量信息管理系统框架模型研究 总被引:2,自引:0,他引:2
针对船舶制造企业质量信息管理普遍存在的质量管理体系与实际生产不一致、质量信息断层和信息孤岛等问题,本文提出了一种面向产品全生命周期的质量信息管理集成方案,以质量过程控制为基础,通过规范质量信息的内容和信息传递的渠道,将大量纵横交错汇聚在产品建造过程中的既离散又相关的质量信息有机地联系起来,实现各相关部门(船东、企业内部、供应商)质量信息的透明. 相似文献
53.
中国港口信息化30年辉煌建设及其展望 总被引:3,自引:0,他引:3
回顾了中国港口信息化建设30年的主要历程,阐述了中国港口信息化领先水平的表现和用高新技术改造传统产业的成绩。介绍了中国港口信息化的典型成果——电子政务和港航企业信息化对港口生产管理的促进作用,证实了我国港口建设坚持用“高新技术改造传统产业,提高管理水平”这一发展原则的正确性。 相似文献
54.
目前公路货运站场管理模式低下,科技含量甚少,企业再发展前景渺茫,必须依靠新的高科技手段,采用信息化管理方案,以新的经营理念,打造出可持续发展的交通站场企业模式来。 相似文献
55.
针对智能船舶多传感器系统因未知海洋环境干扰和设备间干扰等因素导致的一个或数个传感器产生随机间歇性故障从而导致融合估计结果出现偏差甚至失真的问题,设计1种基于四分位滤波的容错方法,并针对该方法导致的观测时滞问题设计1种预报方法,提前预报观测值,进而抵消容错方法导致的时滞问题。此外,针对多传感器之间的互协方差难以准确估计的问题,采用CI融合估计方法进行融合估计。为验证算法的有效性和融合估计的精度,对带有间歇性故障的两传感器系统进行仿真试验,并与按矩阵、按对角阵和按标量3种分布式融合估计方法得到的结果进行对比。4种方法的均方误差系数大小对比结果显示,对于带间歇性故障的多传感器系统,设计的融合滤波不仅具有鲁棒性,而且具有较高的融合精度。 相似文献
56.
57.
In recent years, increasing attention has been drawn to the development of various applications of intelligent transportation systems (ITS), which are credited with the amelioration of traffic conditions in urban and regional environments. Advanced traveler information systems (ATIS) constitute an important element of ITS by providing potential travelers with information on the network's current performance both en-route and pre-trip. In order to tackle the complexity of such systems, derived from the difficulty of providing real-time estimations of current as well as forecasts of future traffic conditions, a series of models and algorithms have been initiated. This paper proposes the development of an integrated framework for real-time ATIS and presents its application on a large-scale network, that of Thessaloniki, Greece, concluding with a discussion on development and implementation challenges as well as on the advantages and limitations of such an effort. 相似文献
58.
As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions. 相似文献
59.
Katharina ParryMartin L. Hazelton 《Transportation Research Part B: Methodological》2012,46(1):175-188
Estimation of origin-destination (OD) matrices from link count data is a challenging problem because of the highly indeterminate relationship between the observations and the latent route flows. Conversely, estimation is straightforward if we observe the path taken by each vehicle. We consider an intermediate problem of increasing practical importance, in which link count data is supplemented by routing information for a fraction of vehicles on the network. We develop a statistical model for these combined data sources and derive some tractable normal approximations thereof. We examine likelihood-based inference for these normal models under the assumption that the probability of vehicle tracking is known. We show that the likelihood theory can be non-standard because of boundary effects, and provide conditions under which such irregular behaviour will be observed in practice. For regular cases we outline connections with existing generalised least squares methods. We then consider estimation of OD matrices under estimated and/or misspecified models for the probability of vehicle tracking. Theoretical developments are complemented by simulation experiments and an illustrative example using a section of road network from the English city of Leicester. 相似文献
60.
Dongjoo Park Laurence R. Rilett Byron J. Gajewski Clifford H. Spiegelman Changho Choi 《Transportation》2009,36(1):77-95
With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers
have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current
conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal
aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route
travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology
explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed
for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation
size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor,
(2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel
time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston,
Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It
was found that the optimal aggregation size is a function of the application and traffic condition.
相似文献
Changho ChoiEmail: |