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通过室内土壤水平一维入渗试验,测定了不同入渗水头作用下不同入渗时间对应的累计入渗量和湿润锋长度,分析了累计入渗量与湿润锋长度、累计入渗量与入渗时间平方根的关系,探讨了采用不同入渗水头作用下的水平入渗试验测定古尔班通古特沙漠南缘丘间地风沙土渗透系数的适用性。研究结果表明:累计入渗量与湿润锋长度呈显著的线性关系;累计入渗量与入渗时间平方根呈显著的线性关系,Philip公式较好地反映了不同入渗水头作用下的水平入渗特点;采用水平土柱入渗试验测定古尔班通古特沙漠南缘丘间地风沙土渗透系数是可行的。研究结果可为干旱区风沙土水力学研究提供参数,为推求风沙土水动力学参数的方法选择提供理论参考。 相似文献
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EAST ASIA SUMMER MONSOON ONSET DATE CALCULATED FROM OBSERVED, REANALYZED AND COMBINED DAILY RAINFALL
In this paper, the East Asia summer monsoon onset date lines in East China are calculated by the definition similar to the traditional one, with the ECMWF reanalyzed 850 hPa daily wind and observed, reana-lyzed and combined daily rainfall during 1980~1993. To make the onset date line as close as possible to the previous work, the earliest onset date limits have to be applied for the regions with different latitude and the daily mean datasets have to be smoothed by space before calculation, therefore their space-resolution is reduced to about 3 longitude ×1°latitude. The results show that the multiyear mean summer monsoon onset date lines are quite similar to each other. Compared with the one from the reanalysis, the 14-year average onset date line form combination is obviously improved in the southern Sichuan Basin and the correlation between observed and combined onset date is also slightly higher over the Huaihe valley and Northeast China. Since daily rainfall combination also improved the long term daily mean and standard deviation through the pentad CMAP, if no better daily dataset is available, such a kind of daily rainfall combination can be used to get reasonable result in the Indian monsoon region without sufficient observatories or over the North Pacific without any ground observation at all in future study. 相似文献
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对流层延迟是影响全球卫星导航系统(GNSS)测量精度的重要因素. 针对现有对流层延迟模型稳定性差,精度较低等问题,在无实测气象参数条件下,提出一种基于Keras平台的长短期记忆神经网络(LSTM)的对流层延迟预测模型. 选取全球均匀分布的8个测站,使用其2016年第90-131年积日共42 天的整点对流层延迟数据预测其第132-136年积日的整点数据. 以国际GNSS服务(IGS)中心提供的对流层产品为真值,分析比较LSTM模型和反向传播(BP)神经网络模型的预测效果. 研究表明,LSTM模型预测结果的均方根误差基本达到mm级,其平均绝对误差和平均绝对百分比误差均比BP模型低,LSTM模型在精度和稳定性上较BP模型均有明显提高;LSTM模型在中高纬区域的均方根误差(RMSE)均值达到7.82 mm,中高纬地区更适合使用该模型. 相似文献
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针对室内外场景结合的导航应用服务需求的发展以及现有室内外场景感知方法的识别稳定性较低、准确率不高问题,本文提出一种基于泛在信号融合的室内外场景鲁棒感知算法,利用室内外场景中融合的泛在信号降低单一信号识别误差;同时为提高传统AdaBoost算法对不平衡数据集的分类精度,采用概率神经网络(PNN)作为训练的弱分类器,并引入熵权法,对迭代产生的弱分类器的权重进行修正来提高强分类器的分类准确率.现实场景下的实验表明,本文算法在采用室内外环境中的WiFi信号、全球卫星导航系统(GNSS)可用星数、光照强度这三种融合的泛在信号进行室内外场景感知时性能最佳,对于不同角度方向下的室内外场景切换,可以在误报率仅为1.7%的情况下,达到98%的识别准确率,验证了本文所提算法的准确性和鲁棒性. 相似文献
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针对在未知环境中行人轨迹跟踪问题,提出一种基于行人航位推算的改进航迹跟踪算法,有效实现了在磁场干扰环境下行人轨迹的高精确跟踪.在步数估计中,利用双重阈值二值化算法对加速度数据进行分析,进而获得行人的运动步数;在航向估计中,利用自适应融合算法来获取航向.该融合过程首先根据姿态更新微分方程,将角速度与加速度融合获取一次航向... 相似文献
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对GPS定位中的SA误差作了系统辨识。表明:SA误差可用时变AR(p)模型表述,AR(p)的时变参数可由RLS算法结合F判据建立的AR(p)模型辨识机获得。用该方法获得的模型对SA误差进行预报。在剔除野值后精度在10m之内。还提出了一种将以AR(p)模型表述的SA误差加入GPSKALMAN滤波器的方法,可得到一种具有SA预报能力的GPSKALMAN滤波器。 相似文献
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COUPLED PHYSICAL-ECOLOGICAL MODELLING IN THE CENTRAL PART OF JIAOZHOU BAY Ⅱ.COUPLED WITH AN ECOLOGICAL MODEL 总被引:2,自引:0,他引:2
Sharpies‘ 1-D physical rrozlel maploying tide-wind driven turbulence closure and surface heating-cooling physics, was coupled with an eculogical rnodet with 9-biochemical components: phytoplankton, zooplankton, shellfish, autotmphic and heterotrophic bacterioplankton, dissolved organic carbon (DOC), suspended detritus and sinking particles to simulate the armual evolution of ecosystem in thecentral part of Jiaozhou Bay. The coupled modeling results showed that the phytoplankton shading effectcould reduce seawater temperamre by 2℃, so that photosynthesis efficiency should be less than 8% ; that the loss of phytoplankton by zooplankton grazing in winter tended to be compensated by phytoplankton advection and diffusion from the otrtside of the Bay; that the incidem irradiance intensity could be the mostimportant factor for phytoplankton grcr, wth rate; and that it was the bacterial secondary prnduction that maintained the maximum zooplankton biomass in winter usually observed in the 1990s, indicating that themicrobial food loop was extremely important for ecosystem study of Jiaozhou Bay. 相似文献