航空公司安全信息管理水平评价方法研究

分析了目前我国航空公司在安全信息管理和安全信息管理水平综合评价方面存在的问题.从航空公司安全信息管理的实际工作出发,结合安全管理体系(SMS)中对于安全信息管理的要求,建立了航空公司安全信息管理水平评价指标体系,并针对每个评价指标建立了4个等级的评价标准.基于主成分分析方法建立了航空公司安全信息管理水平综合评价模型.按照建立的指标体系和评价标准对5家航空公司进行了安全信息管理水平评分,并以此数据为基础,利用建立的综合评价模型对5家公司的安全信息管理水平进行了综合评价和排序,针对评价结果对各航空公司提高安全信息管理水平提出了具体的建议措施.     

Adaptive co-management and network learning in the Room for the River programme

Adaptive co-management and learning are paramount for integrated flood risk management. Relevant literature focuses on adaptation at the level of physical and societal systems. The level of projects and programmes is largely overlooked, but they comprise interventions that adapt our physical systems and they provide opportunities for learning to contribute to transitions of societal systems. This paper aims to increase understanding on how learning takes place and can be stimulated within a programme. The mixed-method case study of Room for the River, a €2.3 billion programme for flood risk management, shows that a programme can be organised using various governance arrangements to stimulate learning and be a means for adaptive co-management to deliver upon environmental objectives.     

基于人工神经网络建模的PTC功率智能检测技术

PTC是家用空调器中主要器件之一,具有耗电量大、非线性的特征。目前PTC常用的功率检测方法是通过"专用电量计量芯片结合电压、电流采样电路"来实现,存在成本高和可靠性低问题。提出一种基于人工神经网络的PTC功率纯软件计算方法,无需增加任何硬件成本。通过实验验证,精度达±3%,满足产品化需求,成熟可靠。     

对应分析在CFIT事故致因研究中的应用

为揭示可控飞行撞地(CFIT)事故致因规律,根据Reason-SHEL模型得出的10个CFIT事故致因因子,分析从飞行安全基金会数据库中整理出的2006—2011年间40起CFIT事故。在此基础上建立CFIT事故年份-成因列联表,再利用SPSS软件对表格进行对应分析。将事故成因和年份同时反映在二维散点图中,实现数据可视化。最后对图形进行向量、圆心接近点以及区域化等3项分析。根据结果得出,CFIT事故的主成因是人因操作和外界环境;随着年份增加,飞行员状态(药物/疲劳、经验不足)对造成CFIT事故的重要性越来越明显;不同年份有不同的重要致因因子。     

危险品运输网络优化设计研究综述

为分析当今反恐新形势下的危险品运输网络优化设计问题的研究现状,系统总结国内外关于一般场景和恐怖袭击威胁2种情况下危险品运输网络优化设计研究的主要模型和方法,梳理两者现有的研究内容,并讨论其共性及恐怖袭击威胁情景下的研究的新特点。结果发现:目前关于解决危险品运输网络优化设计问题模型的鲁棒性研究较为缺乏,尤其是模型在更为复杂和不确定性更强的恐怖袭击情景中适用性不强;危险品运输的鲁棒优化模型即使在突发事件条件下,也可以使决策者能够作出相对满意的决策;鲁棒性危险品运输网络能够规避风险扰动,可用来防止在恐怖袭击中因危险品车辆爆炸等造成更大的危害。     

贮罐类重大危险源三维风险分级模型研究

为了实现重大危险源分级监管,基于风险管理理论,建立贮罐类重大危险源定性三维分级模型和风险定量分级模型。提出风险评价敏感性因素,选取可能性影响因素、严重性影响因素、敏感性影响因素3类风险评价指标。使用层次分析法(AHP)计算贮罐风险分级指标权重。根据风险可接受准则,将贮罐类重大危险源风险等级划分为4级,实现基于三维风险模型的贮罐类重大危险源快速分级。结果表明:用贮罐类重大危险源三维风险分级模型,通过简单数学模型计算贮罐风险值,能为企业提供风险分级标准,有助于实现政府对贮罐类重大危险源分级监管。     

地铁运营突发事件应急响应模式的马尔科夫链分析

为研究城市轨道交通网络化运营线路的风险传导规律和耦合关系,构建基于随机Petri网的同构马尔科夫链模型。通过模型分析突发事件应急响应模式中线路之间的相互影响,以及各线路启动突发事件应急响应模式对整个系统稳态的影响。结果表明,用该模型可从数学上研究城市轨道交通运营线路之间的传导规律和耦合关系,找出影响整个应急指挥系统效率的关键因素,最终提高地铁应对突发事件的能力。     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.     

Calibrating a Basin‐Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration

Remotely sensed vegetation indices correspond to canopy vigor and cover and have been successfully used to estimate groundwater evapotranspiration (ET_g) over large spatial and temporal scales. However, these data do not provide information on depth to groundwater (dtgw) necessary for groundwater models (GWM) to calculate ET_g. An iterative approach is provided that calibrates GWM to ET_g derived from Landsat estimates of the Enhanced Vegetation Index (EVI). The approach is applied to different vegetation groups in Mason Valley, Nevada over an 11‐year time span. An uncertainty analysis is done to estimate the resulting mean and 90% confidence intervals in ET_g to dtgw relationships to quantify errors associated with plant physiologic complexity, species variability, and parameter smoothing to the 100 m GWM‐grid, temporal variability in soil moisture and nonuniqueness in the solution. Additionally, a first‐order second moment analysis shows ET_g to dtgw relationships are almost exclusively sensitive to estimated land surface, or maximum, ET_g despite relatively large uncertainty in extinction depths and hydraulic conductivity. The EVI method of estimating ET_g appears to bias ET_g during years with exceptionally wet spring/summer conditions. Excluding these years improves model performance significantly but highlights the need to develop a methodology that accounts not only on quantity but timing of annual precipitation on phreatophyte greenness.