Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

无缝钢管局部径向模具塑性成形过程的三维有限元数值模拟

采用大变形弹塑性有限元理论,用DYNA3D(研究版)有限元软件,对无缝钢管局部径向模具塑性成形过程中当凸模的凹槽尺寸与凸模的圆弧半径发生变化时进行了数值模拟研究,得出了对成形钢管表面质量控制具有重要参考价值的结论,解决了成形钢管表面的压痕问题。     

Specific features of the growth of Scots pine vegetative organs in the forest-steppe zone of Cisbaikalia

The dependence of the annual increment of vegetative organs on air temperature and humidity and the amount of precipitation per phenophase, as well as the period of their growth in Scots pine, were studied in the forest-steppe zone of Cisbaikalia. The multiple effect of variation in environmental conditions on the annual increment of organs increased when precipitation in September of the previous year was taken into account. These factors proved to have a stronger effect on variation in the annual increment of needles (R ² = 0.59, p = 0.00005) than that in the annual ring width (R ² = 0.38, p = 0.0002).     

星形大分子液晶在气相色谱分析中的应用研究

将首次合成的偶氮苯硅氧烷类星形液晶大分子化合物用于毛细管柱气相色谱固定液 ,在其相转变温度附近具有良好的选择性 ,成功地分离了二甲酚异构体。     

叠氮增塑剂/硝化棉共混物的分子动力学模拟

目的 揭示叠氮增塑剂与硝化棉的相互作用机理,探索叠氮类增塑剂结构与性能间的关系,筛选能量性能和稳定性均较好的叠氮化合物。方法 建立硝化棉(NC)、1,3–二(叠氮乙酰氧基)–2–甲基–2–硝基丙烷(DAMNP)、1,3–双(叠氮乙酰氧基)–2–乙基–2–硝基丙烷(ENPEA)和1,8–二叠氮基–3,6–氧杂辛烷(AZTEGDN)纯物质模型以及NC/DAMNP、NC/ENPEA、NC/AZTEGDN共混物体系的微观分子模型,利用分子动力学模拟(MD)方法,对叠氮增塑剂/硝化棉的微观性质进行预测。分析共混体系的溶度参数、MSD值、结合能、径向分布函数、力学性能和玻璃化转变温度等性能。结果 DAMNP、ENPEA、AZTEGDN与NC之间的溶度参数差值较小。ENPEA与NC的结合能最大,DAMNP次之,AZTEGDN最小。3种增塑剂与NC的相互作用主要为vdW相互作用。加入增塑剂分子可以改善NC的运动能力,由于AZTEGDN的分子结构体积小,改善效果明显。径向分布函数分析结果进一步证明,NC与增塑剂分子间存在较强的氢键作用。结论 添加叠氮增塑剂可以改善NC的运动能力、力学性能,降低NC的玻璃化转变温度,起到了良好的增塑效果。     

2003年夏秋季大气环流异常对西北地区降水的影响

根据2003年6～10月西北地区118站逐日降水资料,对该地区、该时段的大气环流及其对该地区降水的影响进行了分析研究.     

煤层透气性系数的优化和简化计算方法

煤层透气性系数是用以考察保护层开采效果和设计瓦斯抽采系统的重要参数之一,钻孔径向流量法是求取该系数最常用的算法。利用理论推导和图形分析方法,探讨流量准数、时间准数、煤层透气性系数与参数A,B间的关系。在此基础上,针对科研工作和现场管理的实际情况,提出求解透气性系数的2种新算法及其算法流程,并对时间准数-参数A,B函数关系中的间断点问题进行讨论和分析,解决此区间内求解透气性系数相应计算公式等问题。理论和实例分析表明:算法1在不更改最终计算公式和结果的前提下,省去了繁琐的试算过程,既保证了可靠性也优化了计算流程;算法2以稍减精度的代价对计算流程进行进一步的简化,其结果仍有较高精度。     

瞬态径向热流法测定松散煤体变导热系数

松散煤体的导热系数是指在单位梯度作用下,松散煤体单位时间内通过单位面积的热量,表征了松散煤体导热性能的强弱,是研究煤自燃的重要参数。目前,使用最广泛的热线法,测试结果易受电阻升温变化影响。针对当前测试方法的不足,本文在二维径向导热模型的基础上,设计了瞬态径向热流法测定松散煤体变导热系数测试装置,并建立了相应的导热系数解算模型。通过测定不同煤样随温度变化的变导热系数。结果表明,随着温度的升高,松散煤体的导热系数不断上升,两者之间基本成线性关系,测试结果符合松散煤体的导热特性。     

RBF神经网络可靠度分析方法在边坡稳定性研究中的应用

针对边坡稳定性可靠度分析,当状态函数无法显式表达且传统计算方法求解复杂问题困难时,提出一种基于ABAQUS和粒子群优化径向基函数神经网络的可靠度分析方法。基于ABAQUS的强度折减方法计算所选随机变量对应的安全系数,利用径向基函数神经网络的数据拟合功能,建立模型并映射出安全系数和随机变量之间的关系,构造响应面功能函数;利用蒙特卡罗生成的大量随机样本代入功能函数得到相应的安全系数,进而计算边坡的失稳概率和可靠度指标来反映边坡稳定性。研究结果表明:相对于传统方法,本文方法计算效率更高、误差更小,适合实际工程应用。     

Heavy metal （Pb,Zn） uptake and chemical changes in rhizosphere soils of four wetland plants with different radial oxygen loss

Lead and Zn uptake and chemical changes in rhizosphere soils of four emergent-rooted wetland plants; Aneilema bracteatum, Cyperus alternifolius, Ludwigia hyssopifolia and Veronica serpyllifolia were investigated by two experiments: (1) rhizobag filled with “clean” or metal-contaminated soil for analysis of Pb and Zn in plants and rhizosphere soils; and (2) applied deoxygenated solution for analyzing their rates of radial oxygen loss (ROL). The results showed that the wetland plants with di erent ROL rates had significant e ects on the mobility and chemical forms of Pb and Zn in rhizosphere under flooded conditions. These e ects were varied with di erent metal elements and metal concentrations in the soils. Lead mobility in rhizosphere of the four plants both in the “clean” and contaminated soils was decreased, while Zn mobility was increased in the rhizosphere of the “clean” soil, but decreased in the contaminated soil. Among the four plants, V. serpyllifolia, with the highest ROL, formed the highest degree of Fe plaque on the root surface, immobilized more Zn in Fe plaque, and has the highest e ects on the changes of Zn form (EXC-Zn) in rhizosphere under both “clean” and contaminated soil conditions. These results suggested that ROL of wetland plants could play an important role in Fe plaque formation and mobility and chemical changes of metals in rhizosphere soil under flood conditions.