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11.
运输船舶金属船体重量的神经估算 总被引:3,自引:0,他引:3
本文建立应用反向传播学习算法的多层前馈神经网络模型,对运输船舶金属船体重量进行神经估算。以散货船和油船为实例的计算结果表明,由非线性模拟神经元组成的神经网络在船体重量估算中是非常有效的。 相似文献
12.
王丹辉 《铁路工程造价管理》2013,28(4):29-32
铁路工程可行性研究是设计的第一个阶段,也是确定和控制工程投资的重要阶段。在该阶段如何依据推荐的设计方案进行投资估算,此文针对投资估算现状及存在的主要问题进行分析,通过分析与研究,提出提高可行性研究投资估算准确性的方法,以及控制工程投资的几项有效措施。 相似文献
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如今,锂离子电池已成为新能源产业和SOC的研究重点。在锂离子电池研究中,电池容量估算和计算是其中的重点研究之一。SOC直接关系到锂离子电池使用的效率和安全性,正确的SOC估算和计算方法不仅可以增加锂离子电池工作的安全性,并延长锂离子电池的使用寿命[1]。相反而言,不合适的SOC估算和计算方法不仅会加速电池的老化,而且会带来电池爆炸和燃烧的危险,危害使用者的生命和财产安全。因此,本文对各种SOC估计和计算方法进行研究,以获得更成熟和广泛使用的电池SOC估计和计算方法。 相似文献
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从瀑布式程序设计的角度,综述了苛求系统软件生命周期各阶段的可靠性方法、技术和模型,包括需求形式化建模与验证、屏蔽设计错误的多版本软件容错、函数式程序设计以及可靠度评估模型等。总结比较各自适用的开发阶段、面向的目标错误类型及优缺点。 相似文献
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在总结和分析各种工程造价估测方法的基础上,构建了基于模糊理论和造价管理系统之上的造价快速估测模型,并给出了其计算步骤和估测实例,可用于公路工程造价的快速测算。 相似文献
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Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization. 相似文献
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With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation. 相似文献
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Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future. 相似文献