面向实时人群动力学分析的深度基本图网络

当前行人疏散实验中基本图计算方法通常是通过对每个行人进行跟踪实现的.但这种跟踪方法难以实现实时人群动力学分析.针对这一问题,提出了深度基本图网络.实验提出的网络框架由两个模块组成,即多尺度递归卷积神经网络(MSR-Net)和光流模块,分别对行人密度和行人速度进行估计.具体来讲,MSR-Net学习了输入图像与行人密度图之...     

走滑断层作用下X80管道设计应变规律和神经网络预测

走滑断层是埋地管道常见的地质灾害威胁,断层作用下管道会发生较大的拉压应变而失效。为得到X80管道的设计应变,基于有限元方法建立了走滑断层作用下管道的应变响应数值计算模型,模型使用壳单元模拟管道,非线性弹簧单元模拟土壤约束,采用西二线实际工程的管道应变影响参数范围,计算了管道的设计应变;为预测管道的设计应变值,基于以上参数化分析得到的4 817组设计应变结果,采用人工神经网络建立了管道设计应变预测模型。结果表明:该神经网络模型预测结果的最大相对误差小于10%,预测准确性良好,且该方法具有较高的计算效率,可以为断层作用下埋地管道的应变设计与评估提供参考。     

基于ARIMA和BP神经网络组合模型的我国碳排放强度预测

预测我国碳排放强度的长期变动趋势, 对国家进行宏观经济管理和节能减排工作具有重要的参考价值。运用深入分析自回归移动平均模型和神经网络的特性,并在此基础上建立ARIMA模型和BP神经网络组合模型,将碳排放强度的时间序列的数据结构分解为线性和非线性残差部分,对我国碳排放强度的变化趋势进行了综合分析与预测。结果显示:今后10 a我国碳排放强度总体是逐步下降的,但到2020年我国碳排放强度仅比2005年下降34%,比我国政府提出碳排放强度下降40%~45%的目标还有一定的差距。因此,要在2020年实现我国碳排放强度目标,必须要调整宏观经济政策,采取各种政策措施以实现目标     

西安f7地裂缝发展变化的预测研究

论文首先分析和总结了影响西安地裂缝发展变化的主要人为因素。随后,利用三维非稳定渗流分析方法研究了人类活动对地下水位的影响;根据西安市新增固定资产投资的变化,预测了地面荷载的变化;以时间、地下水位和地面荷载为基本变量,建立了预测f 7地裂缝活动趋势的BP神经网络模型;最后,借助MATLAB语言进行编程,利用训练稳定的网络模型,预测了f 7地裂缝的活动趋势以及年平均的垂直位移沉降量,为进一步研究西安地铁2号线安全运行和防护提供参考依据。     

大坝下游定点区域溶解氧饱和度预测

大坝通过坝身泄流时,巨大的洪水卷吸空气中的大量气体直泻到下游河床,造成下游河道特别是近坝区域气体含量过饱和,给下游水生生物带来不利影响,其中鱼类气泡病就是水体气体过饱和造成的典型病症。为探明大坝泄流对坝下河道溶解氧饱和度的影响,以三峡 葛洲坝区域坝下近坝区定点区域的溶解氧实测资料为基础,建立溶解氧饱和度的BP网络预测模型,对大坝不同运行工况进行下游溶解氧饱和度的预测仿真计算,并将仿真预测值与实测及数值模拟结果进行比较分析。结果得出BP网络模型能很好地逼近坝下近坝区指定区域的溶解氧饱和度,可根据大坝运行工况对下游水体溶解氧饱和度进行快速预测,为有效控制坝下水体溶解氧饱和度提供借鉴     

The relationships between air pollutants, meteorological parameters and concentration of airborne fungal spores

Fungal spores are an important component of bioaerosol and also considered to act as indicator of the level of atmospheric bio-pollution. Therefore, better understanding of these phenomena demands a detailed survey of airborne particles.The objective of this study was to examine the dependence of two the most important allergenic taxa of airborne fungi - Alternaria and Cladosporium - on meteorological parameters and air pollutant concentrations during three consecutive years (2006-2008). This study is also an attempt to create artificial neural network (ANN) forecasting models useful in the prediction of aeroallergen abundance.There were statistically significant relationships between spore concentration and environmental parameters as well as pollutants, confirmed by the Spearman’s correlation rank analysis and high performance of the ANN models obtained. The concentrations of Cladosporium and Alternaria spores can be predicted with quite good accuracy from meteorological conditions and air pollution recorded three days earlier.     

初始pH值对磷酸盐还原除磷的影响研究

以超高盐（盐度7％,以NaCI计）高磷榨菜废水为研究对象,考察了初始pH值对磷酸盐还原进程的影响。实验结果表明,初始pH值对磷酸盐还原除磷效能影响显著。初始pH为8时,磷酸盐还原除磷率达到最高,平均值为65．45％。同时,初始pH值还会影响污泥中活性磷的形成以及基体对磷化氢的吸附。此外,偏碱性有利于磷形态转化,且BD-P（主要是一些可溶性的、还原性强的、带有Fe-P化合物的集合）含量的高低调控着生物膜内间隙水中溶解态可反应性磷（DRP）和可还原水溶态磷（RSP）含量,最终决定着磷酸盐还原进程。随着初始pH值的升高,污泥对磷化氢的吸附能力降低导致污泥中结合态磷化氢（MBP）含量不断减少。     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

BP神经网络在台风路径预报中的应用

利用前馈型BP神经网络模型,对发生于中国沿海的热带气旋的移动路径进行了预报应用研究.根据中国<台风年鉴>发布的每个台风过程记录,对预报试验的台风个例分别选取了经度、纬度、中心气压和最大风速等81个因子,由多元回归选取了其中相关性好的因子,进行网络的学习训练,在获取前24h间隔6h的4次台风信息的基础上,用来预报了台风未来24h,48h和72h的短期路径变化.将该方法预报结果与CLIPER模式预报结果进行了比较,结果表明,BP神经网络模式的预报精度比CLIPER模式的高.     

人工神经网络方法在拟建小区域环境质量评价中的应用

人工神经网络的评价方法用于小区域环境质量评价中,根据本地区特点因地制宜地选择环境质量参数,代入模型中进行环境质量评价及预测,对用于环境质量评价的BP人工神经网络模型进行了改进,即对网络模型的训练样本进行了扩充,从而提高了模型的抗干扰能力和准确性.将改进了的BP人工神经网络模型应用于四川省资阳市沱江二桥拟建项目小区域的大气、地表水环境质量评价中, 对该市小区域大气、地表水环境质量状况进行评价,评价结果表明,BP人工神经网络模型用于环境质量评价是可行的,且评价结论客观,评价模型普遍适用.