An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: the case of Iran

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car ownership function and its determinants, ANN and FLR (including eight well-known FLR) approaches are applied to the collected data. Next, the preferred ANN is selected based on sensitivity analysis results for the test data while the preferred FLR is identified with regard to ANOVA and MAPE results. The results obtained from the performance comparison demonstrate the considerable superiority of the preferred ANN over the preferred FLR regarding the nonlinear and complex nature of the car ownership function in Iran. This is the first study that presents an ANN-FLR approach for car ownership forecasting capable of handling complexity and non-linearity, uncertainty, pre-processing, and post-processing.     

基于ANSYS的少片变截面钢板弹簧可靠性分析

在ANSYS软件中建立某少片变截面铜板弹簧的有限元模型,通过分析结果与实验结果的对比,验证了模型的正确性。考虑多种不确定性因素的影响,应用ANSYS中提供的概率分析系统,采用蒙特卡罗法模拟抽样,对该钢板弹簧进行概率有限元可靠性分析。     

间接测量结果置信概率的计算

首先假设随机误差服从正态分布,系统误差服从均匀分布,然后根据概率知识计算出两种复合误差所服从的分布,进而计算出对应不同置信区间的置信概率．     

认知结构方法在交通信息融合中的应用

认知结构不确定推理框架采用4值(最值、置信度、信任值、似真值)表达不确定认知情况,并采用认知结构运算来进行不确定信息处理。该方法可以用来处理交通不确定信息的表达、融合和决策,进一步还可以对交通仿真中面临的多种不确定信息进行有效处理和应用。文中探讨了采用认知结构进行交通信息融合的方法,并对认知结构方法进行了深入研究,引入了2个公设,将认知结构的运算由定义转化为定理。     

考虑行程时间不确定性的服务设施时空可达性度量

在我国拥挤的多模式交通网络中,出行者的行程时间具有较大的不确定性.行程时间不确定性是影响人们出行行为选择的重要因素,对于某些重要活动(如就医等),人们对行程时间的可靠度有较高要求.本文以南京市玄武区综合医院的时空可达性为例,采用引力模型和行程时间预算模型深入探索多模式交通网络中服务设施的时空可达性,提出了考虑行程时间不确定性的时空可达性度量方法,针对不同交通模式分析了出行者的风险规避行为,为城市多模式交通规划和土地利用布局提供了有效依据及评价指标.研究表明,对于不同的交通模式,在不同可靠性下服务设施的时空可达性具有显著差异.     

基于D-S证据理论的舰船技术状态评估方法

在简要叙述船舶技术状态评估现状的基础上,提出了基于D-S证据理论的舰船技术状态评估方法。介绍了船舶技术状态评估的重要性和发展现状,以及可处理由不知道所引起的不确定性的D-S证据理论和基于D-S证据理论的决策方法;通过实例详细阐述如何用D-S证据理论进行舰船技术状态评估。实例证明,通过应用D-S证据理论,提高了舰船技术状态评估的准确性和可靠性,降低了评估的不确定性。     

基于蒙特卡洛法的实船功率性能试验不确定度分析

针对实船功率性能测量与分析涉及因素多,难以通过传统GUM法对修正得到的理想状态功率与航速结果进行不确定度评价的问题,提出基于蒙特卡洛法的实船功率性能不确定度分析方法,分析实船功率性能试验中的主要不确定度源,依据ISO15016数据处理方法建立测量模型,以大连海事大学教学实习船"育鲲"轮为对象分析实船功率性能试验与不确定...     

光学平行装置精度测量及数据处理

通过对光学平行装置精度测量及数据的分析处理,确定了均方根、合成不确定度和扩展不确定度,为光学平行装置测量精度的校准提供了依据。     

Integrated imputation of activity-travel diaries incorporating the measurement of uncertainty

Procedures to transform GPS tracks into activity-travel diaries have been increasingly addressed due to their potential benefit to replace traditional methods used in travel surveys. Existing approaches for data annotation however are not sufficiently accurate, which normally involves a prompted recall survey for data validation. Imputation algorithms for transportation mode detection seem to be largely dependent on speed-related features, which may blur the quality of classification results, especially with transportation modes having similar speeds. Therefore, in this paper we propose an enhanced integrated imputation approach by incorporating the critical indicators related to trip patterns, reflecting the effects of uncertain travel environments, including bus stops and speed percentiles. A two-step procedure which embeds a segmentation model and a transportation mode inference model is designed and examined based on purified prompted recall data collected in a large-scale travel survey. Results show the superior performance of the proposed approach, where the overall accuracy at trip level reaches 93.2% and 88.1% for training and surveyed data, respectively.     

Improving the robustness of an MPC-based obstacle avoidance algorithm to parametric uncertainty using worst-case scenarios

Previous work by the authors focused on obstacle avoidance in large, high-speed autonomous ground vehicles within unknown and unstructured environments. This work resulted in a nonlinear model predictive control based algorithm that simultaneously optimises both the speed and steering commands. The algorithm can exploit the dynamic limits of the vehicle to navigate it to a target position as quickly as possible without compromising safety. In the algorithm, a model of the vehicle is used explicitly to predict and optimise future actions, but in practice the model parameter values are not known exactly. Thus, in this paper, the robustness of the algorithm to parametric uncertainty is evaluated. It is first demonstrated that using nominal parameter values in the algorithm leads to safety issues in 24% of the evaluated scenarios with the considered parametric uncertainty distributions. To improve the algorithm's robustness, a novel double-worst-case formulation is developed that simultaneously accounts for the robust satisfaction of the two safety requirements of high-speed obstacle avoidance: collision-free and no-wheel-lift-off. Results from simulations with stratified random scenarios and worst-case scenarios show that the double-worst-case formulation renders the algorithm robust to all uncertainty realisations tested. The trade-off between robustness and the task completion performance is also quantified.