基于区域约束的机场场面目标跟踪

现有的可靠性鉴定试验方案在一定的风险约束下需要较大的试验工作量,而大型复杂装备受经费、试验条件等因素的限制难以进行大量的试验。针对这一问题,以整机故障率为验证指标,提出利用分系统试验数据制定整机可靠性鉴定试验方案的新方法。该方法首先利用分系统试验数据计算整机故障率置信上限,然后利用两点分位数方法确定整机故障率的先验分布参数,最后依据贝叶斯最大后验风险准则制定了新的可靠性鉴定试验方案。实例对比分析表明,与传统试验方案相比,新方案将明显节约试验时间,减少试验工作量。     

网构软件选择的可信性评估算法

网构软件可以加快软件开发的速度和降低成本。然而,随着网构软件数量的增多,如何选择合适的构件成为一个具有挑战性的问题。现有的研究工作,有的只给出了可信性评估模型,没有具体的实现方案;有的只是考虑了的可靠性一个可信属性。提出基于贝叶斯网络的可信性评估模型：首先对构件进行结构分析,根据其模块划分和结构层次等建立相应的可信性评估体系;其次,采用自底向上的计算方法确定各个软件实体的可信性评估值;最后,利用客观数据对评估指标进行修正,得到统一的可信性评估结果。实验结果表明：算法可以客观、明确的对网构软件进行评估,能够科学、有效的指导网构软件的选择。     

A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes

Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance.     

Enforcement avoidance behavior near automated speed enforcement areas in Korean expressways

Automated speed enforcement system (ASES) has been deployed as a safety countermeasure on Korean roadways to reduce speeding-related traffic crashes; information on ASES locations is mandated to be open to the public. However, because drivers are alerted about enforcement via on-board navigation systems and roadside signs, they can avoid enforcement by momentarily reducing their speeds near ASES locations. This enforcement avoidance behavior (EAB) can induce sudden changes in speed near the enforcement locations and thereby increase risk of crash occurrence.     

Model for microbiologically influenced corrosion potential assessment for the oil and gas industry

Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. Microbiologically influenced corrosion (MIC) is inherently more complex to predict, detect and measure because, for instance, the presence of biofilm and/or bacterial products is not sufficient to indicate active microbiological corrosion. The major challenge for current MIC models is to correlate factors that influence corrosion (i.e. chemical, physical, biological and molecular variables) with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.

List of acronyms: APB: acid producing bacteria; Aw: water activity; BN: Bayesian network; MIC: microbiologically influenced corrosion; MMMs: molecular microbiological methods; NRB: nitrate-reducing bacteria; OOBN: object-oriented Bayesian network; PWRI: produced water re-injection; SPs: screening parameters; SRB: sulphate-reducing bacteria; SRPs: sulphate-reducing prokaryotes; TDSs: total dissolved solids     

基于相似理论和贝叶斯理论的航材失效率确定

为提高舰载航空装备航材备件保障能力,结合某舰载航空装备供应保障需求,基于相似理论和贝叶斯理论进行航材备件失效率研究。基于相似理论初步确定航材备件的初始失效率,在实际使用数据的基础上,运用贝叶斯理论对初始失效率修正得到后验失效率,最后结合实例进行了理论应用分析。     

基于深度学习的上海城市街景与景观 美学公众认知研究

随着中国城镇化转型与城市更新的开展,服务城市大众美学认知与文化精神的城市风貌与景观塑造成为当前的迫切需求。以上海典型街景为例,结合问卷调查、深度学习与贝叶斯统计方法,构建具大范围评估应用潜力的公众审美感知模型,进行公众景观偏好研究。发现公众对于街景的不同场景审美感知差异较大;景观美学感知概念模型中关键景观要素为土地、建筑、树、墙;想象度、自然度、对比度和审美干扰度是影响公众对景观美学认知的重要指标。研究结果可进一步推广到大规模公众景观认知快速评估,为城市设计提供本土公众偏好美学理论的信息支持。     

A Multiplicative Model for Gauge R & R Studies

We introduce a multiplicative measurement error model and analyze a gauge R & R study with the new model using data from a sample exchange program. Some aspects of designing a gauge R & R study are considered. Also, we analyze data from a factorial experiment where the measurement error arises from the new model using a simultaneous analysis of experimental and gauge R & R study data. WinBUGS code for these analyses is provided. Copyright © 2014 John Wiley & Sons, Ltd.     

2021年上半年建筑业发展统计分析

在统计数据基础上,从建筑业增加值、建筑安装工程投资和建筑业总产值、从业人数和企业数量、劳动生产率、合同总额和新签合同额、房屋建筑施工和竣工面积、对外承包工程等方面对2021年上半年建筑业总体情况进行了分析。从建筑业总产值、新签合同额、跨省完成建筑业总产值、从业人员与劳动生产率等方面,对2021年上半年各地区建筑业的基本情况进行了分析。     

Bridge condition modelling and prediction using dynamic Bayesian belief networks

The development of a condition-based deterioration modelling methodology at bridge group level using Bayesian belief network (BBN) is presented in this paper. BBN is an efficient tool to handle complex interdependencies within elements of engineering systems, by means of conditional probabilities specified on a fixed model structure. The advantages and limitations of the BBN for such applications are reviewed by analysing a sample group of masonry bridges on the UK railway infrastructure network. The proposed methodology is then extended to develop a time dependent deterioration model using a dynamic Bayesian network. The condition of elements within the selected sample of bridges and a set of conditional probabilities for static and time dependent variables, based on inspection experience, are used as input to the models to yield, in probabilistic terms, overall condition-based deterioration profiles for bridge groups. Sensitivity towards various input parameters, as well as underlying assumptions, on the point-in-time performance and the deterioration profile of the group are investigated. Together with results from ‘what if’ scenarios, the potential of the developed methodology is demonstrated in relation to the specification of structural health monitoring requirements and the prioritisation of maintenance intervention activities.