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1.
陆基增强系统(GBAS)是利用载波相位平滑伪距差分修正实现对导航辅助定位的. 其中,平滑时间常数是影响载波相位平滑伪距精度的关键参数. 本文分析研究了不同平滑时间下电离层时间梯度和空间梯度对Hatch滤波的影响. 在结合电离层时空梯度和多径效应引起的滤波总误差方差的基础上,推导出自适应的最优平滑时间常数. 分别对GBAS静态和动态两种环境下的定位误差进行实验,实验结果表明,采用本文推导出的自适应平滑时间常数降低了GBAS伪距测量误差,从而使定位精度得到增强.   相似文献   
2.
Most of previous analyses on the active earth pressure were performed in two-dimensional cases using the Mohr-Coulomb (M-C) failure function to describe the soil strength. However, all failures of retained slopes indicate a somewhat three-dimensional (3D) feature, and the M-C function is found to overestimate the tensile strength of cohesive soil. In this work, a kinematic limit analysis–based approach is developed for computing the 3D active earth pressure resulting from cohesive backfills. The concept of tensile strength cutoff is adopted to implement the reduction or elimination of tensile strength from the strength envelope. An extended 3D horn failure mechanism that is associated with the modified strength envelope is developed to characterize the collapse of retained slopes. The resultant of active earth pressure is evaluated from the work rate balance equation and expressed as an unfactored coefficient. The obtained results indicate that less support provided by the wall is required when allowing the existence of soil cohesion and 3D effects and that eliminating the tensile strength can observably increase the active earth pressure, especially for the backfill with a great level of cohesion.  相似文献   
3.
The performance of force-based and displacement-based seismic assessment methods for the life-safety limit state check of out-of-plane loaded unreinforced masonry walls is evaluated on the basis of refined numerical simulations. For this purpose, a discrete element model of a vertically spanning wall is built and validated against experimental results from static and dynamic test conditions. The model is then analysed for a large range of wall configurations. For each configuration, a static pushover analysis and a series of incremental dynamic analyses are run, the latter permitting to determine the capacity of the wall under dynamic loading. The accuracy of the assessment methods in predicting the acceleration at which the walls collapse is evaluated. It is found that the displacement-based method is more accurate, robust, and safe than the force-based method. The comparison also shows that for walls characterised by a relatively high ratio of axial load to Euler's critical load, both assessment methods lead to an overestimation of the wall capacity. As a remedy, a modification to the methods based on a recently developed mechanical model is put forward and tested. For the force-based method, it is additionally suggested to set for walls with relatively high overburden ratios the behaviour factor equal to 1. To ensure reproducibility of this study, all input and output files of the numerical simulations are made publicly available.  相似文献   
4.
新一代VIIRS/DNB(Visible Infrared Imaging Radiometer Suite Day/Night Band)夜间灯光数据因其具备更精细的时空分辨率、数据不存在饱和现象、不同年份数据可比性强等优点而迅速代替DMSP/OLS(Defense Meteorological Satellite Program's Operational Linescan System)夜间灯光数据成为新的研究热点。但由于杂散光的污染,VIIRS/DNB夏季数据数值缺失严重,致使数据在空间和时间上不连续,因此,插补缺失数据成为后续应用的前提。鉴于此,论文从插补结果异常值、与参照值对比、计算性能三个方面,系统比较了三次样条插值(样条)、三次Hermite插值(Hermite)、灰色预测模型(GM)、三次指数平滑法(指数)4种插补方法的适用性,以期为插补VIIRS/DNB夜间灯光数据提供方法选择的依据。研究结果表明:①异常值比较方面,Hermite法未出现异常值,另外3种算法仅出现少量异常值(0.02%~1.34%);②与参照值的对比方面,Hermite法与参考值接近程度最高,GM接近程度最低,样条法和指数法介于两者中间;③算法性能比较方面,4种方法都具备计算简单、容易编程的特点,但指数法的算法时长是另外3种方法的10倍以上。因此,综合多方表现,当插补月份前后两侧均有足够长的原始数据时,插补效果好、计算速度快、不会出现过冲现象的Hermite法最适宜,样条法次之;当插补月份仅单侧有足够长的数据时,适宜采用指数法(插补效果好、计算速度较慢)或GM(插补效果偏低、计算速度快)进行插补。  相似文献   
5.
Partitioning beta diversity into its two components of spatial turnover and nestedness is a more robust method for checking spatial variability in biological communities than calculating the total beta diversity alone. The relative contribution of spatial turnover and nestedness has been used to test the effects of climatic, environmental, spatial and temporal variables on community composition. In this study, we tested the effects of environmental factors and microhabitat features on total beta diversity and its spatial turnover and nestedness components using a comprehensive dataset of aquatic Heteroptera collected from four types of permanent freshwater habitats (i.e. streams, ponds, rock tanks and reservoirs) in the Western Ghats of India. We observed that communities in all four types of habitats were predominantly shaped by dissimilarity caused due to spatial turnover (>85 %). Each type of habitat showed the presence of one or more species uniquely associated with it, which might contribute to the turnover between communities. The abiotic environment (climatic factors, topological factors, soil characteristics and microhabitat features) as well as assemblage structure differed significantly between habitat types. Communities in each type of habitat were affected by different environmental factors, such as precipitation and temperature patterns for streams, altitude and rocky substrate for rock tanks, and soil characteristics and the presence of aquatic macrophytes for ponds and reservoirs. Assemblages observed in the four types of permanent habitats are thus compositionally distinct due to species replacements between local communities, which in turn are strongly influenced by environmental variables. Similar to previous studies, our results show that spatial turnover largely measures the same phenomenon as total beta diversity on a regional scale.  相似文献   
6.
《地学前缘(英文版)》2020,11(3):871-883
Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.  相似文献   
7.
When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.  相似文献   
8.
随着化肥、农膜等在农业生产中的过量投入,耕地面源污染的程度随之加重。文章选取塔里木河流域上游和田地区为研究区域,依据P-S-R框架理论,构建和田地区耕地面源污染生态风险评价指标体系,加入土壤理化数据,使用生态风险评价模型对和田地区1980 年及2016 年耕地面源污染状况进行生态风险评价,运用耕地生态风险模型、生态风险转移矩阵、Arcgis分析和田地区耕地面源污染时空分异状况。研究结论如下:和田地区1980 年耕地生态风险等级均为II级或III级,呈“中间高,两侧低”分布;2016 年耕地生态风险等级上升至IV级或V级,呈“倒W型”分布,各县耕地面源污染程度较1980 年均有较大幅度的上升,其中墨玉县和于田县在2016 年耕地生态风险等级达到最高的V级,而民丰县因自身生态环境的强脆弱性,同样需要提高关注。根据面源污染“从源头治理”的原则,应切实推进和田地区耕地生态环境保护与治理,提高政府重视程度,增强技术指导,开展试点工作,改善和田地区耕地面源污染现状。  相似文献   
9.
The Sustainable Forestry and African American Land Retention Program (SFLR) was launched in 2012 to increase adoption of sustainable forestry practices among African American landowners in the southeastern United States to prevent land loss, increase forest health, and build economic assets. One of its main goals was to build communication networks through which African American landowners could obtain and share information about forestry practices and landowner assistance programs independent of public agencies. To measure and examine the growth of these communication networks over a three-year period (2014-2017), we conducted 87 interviews with landowners (24 of whom were interviewed multiple times), SFLR personnel, and Federal and State staff members in North Carolina. We used complementary methods of data gathering and analysis, including social network analysis and qualitative analysis. Our results showed expanding communication networks will be sustained independently of the program over time, although there is still a heavy reliance on program personnel.  相似文献   
10.
GNSS和加速度计是目前动态监测超高建筑环境载荷变形的主要手段。GNSS具有无需通视、可直接获取三维位移等优点,但受精度和采样率的限制,其对微变形及高频振动信息不敏感;而加速度计具有高精度和高采样率等优点,但无法监测超高建筑低频的似静态变形。为充分发挥这两种传感器的各自优势,提出利用多速率Kalman滤波和RTS平滑方法对超高建筑GNSS和加速度计监测数据进行融合处理。试验结果表明,与单一的GNSS监测技术相比,该方法有利于削弱GNSS高频噪声的影响,提高位移数据的采样率,可有效识别超高建筑的低频和高频振动频率,提高对微变形振动的监测能力;与单一的加速度计监测技术相比,该方法可以准确监测超高建筑的低频变形信息,具有良好的工程应用价值。  相似文献   
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