内插算法中流动站和基准站相对位置对定位精度的影响

GPS网络RTK技术是20世纪90年代新兴的测量技术,其中内插算法的应用最为广泛。决定内插算法计算结果精度的关键因素就是流动站的定位精度。而影响流动站最终定位精度的因素有很多种,如基准站的布设,基准站点点位坐标的误差等。本文主要讨论了其中的一个重要影响因素——基准站和流动站的相对位置。通过讨论,将给出不同情况下的定位精度,并最终给出了最佳精度的相对位置。     

相对点位精度计算公式的探讨

相对点位精度计算公式的探讨施一民（上海同济大学２０００９２）工程控制同往往是以限定的相对点位误差来作出其精度保证。如何进行客观、合理的相对点位精度评定，的确是有其理论和实际意义的。文献’‘’对此进行了探讨，并指出，用通常采用的相对误差椭圆计算公式衡论...     

变形监测网平差基准的选择及点位稳定性分析

通过对变形监测网平差基准的选择及参考点位稳定性分析,提出了在绝对网中采用固定基准平差,采用平均间隙法进行点位稳定性分析;在相对网中采用稳健迭代权法找到平差基准,采用单点位移分量法进行点位稳定性分析。通过实践监测数据表明:采用上述方法进行监测数据处理,能够得到最符合真值的监测点位变形量。     

CPⅢ点的点位误差对全站仪自由设站影响的探讨

CPⅢ网是高速铁路客运专线上布板、安装轨道的基准控制网,该网的精度较高,CPⅢ点的点位精度也很高,但其误差也会对后续测量产生影响。文章采用仿真计算的方法就全站仪在CPⅢ网中自由设站是否需要考虑CPⅢ点的点位误差的影响进行探讨,得到应该加以考虑的结论。     

GPS监测网中系统误差与形变信息的分离方法研究

本文根据应变分析的理论 ,在GPS监测网中 ,考虑因星历误差等因素的影响 ,使其各期观测的坐标框架不同 ,在观测后期基线相对于前期 (以前期为参考基准 )存在系统误差影响。为消除这种误差的影响 ,在后期的平差计算时 ,引入了 9个应变参数。这样 ,在后期的平差计算中 ,同时解得坐标变化量及 9个应变参数 (后期相对于前期的系统误差 ) ,坐标变化量就是后期点位相对于前期点位的位移量 ,这位移量中已不含系统误差的影响 ,达到将系统误差与形变信息分离的目的。     

盐渍土浸水载荷试验基准梁环境误差分析与试验研究

文中分析了盐渍土浸水载荷试验过程中温度、基准梁设置方式、浸水对基准梁的影响，初步研究了影响基准梁变形的环境误差影响机理，通过现场试验，研究了温度应力对基准梁变形的影响程度，提出了减少误差的方法。     

全站仪自由设站法的探讨与分析

本文讨论和分析了2～4个已知控制点的全站仪自由设站法,并给出了点位坐标和点位误差的直接计算法,同时绘制了点位误差等值线示意图,通过一个计算实例表明,直接计算得到的结果基本上与严密平差一致,提出一些实用的说明,供参考。     

高精度GPS滑坡监测网建立研究

用静态GPS边坡监测做实例,通过实测数据,分析研究了不同基线数即网形变化对GPS控制网质量的影响,确定基准站静态GPS布设网形式变形监测是建立高精度GPS滑坡监测网的有效方法,可以兼顾精度、可靠性和灵敏度及效益的有效统一,结合滑坡及其变形特性、变形监测的特点等,分析了GPS测量的有关误差来源及其特性,并从滑坡GPS变形监测网建立的点位选择、基准网确定以及监测网有关质量指标等关键问题进行理论探讨,体现出GPS监测网的高精度性能。     

测角后方交会最佳点位的探讨

本文对测角后方交会的最佳点位进行了讨论。分析了影响测角后交点位误差的因素；给出了对称测角后交最佳交会角的公式；利用无约束最优化共轭梯度法，计算出一般测角后交的最佳交会角和最小点位方主；得出寻求测角后交最佳点位的几点规律。     

地形图检查中地物点点位中误差计算的"无纸化"

本文介绍了在地形图质量检查中,利用VB、Lisp等工具把外业采集的点位坐标和地形图上内业获取的对应的点位坐标导入Excel中,再利用Excel的统计计算功能计算出图幅的点位中误差。     

Energy-savings predictions for building-equipment retrofits

Energy-consumption data collected from two equipment-retrofit projects before and after the retrofits was used to develop a model that estimates energy savings from retrofit projects. The computation method used in the model is based on Artificial Neural Networks (ANN). The model integrates weather variables, specific equipment-usage and occupancy data, and building-operation schedules into the pre-retrofit energy-usage pattern. It then estimates the energy usage of the pre-retrofit equipment in the post-retrofit period by using weather data, occupancy, and building-operation schedules in the post-retrofit period. The difference between the recorded energy usage of the post-retrofit equipment and the predicted energy usage of the pre-retrofit equipment in the post-retrofit period is the estimate of energy savings. For the two retrofit projects used in the ANN model, the coefficient of correlation varied from 0.957 to 0.844; the root mean square error varied from 6.81% to 16.4%; and the mean absolute error varied from 5.31% to 9.95%. Additionally, the sensitivity of the model to the input variables was analyzed with one of the retrofit project data. Dry bulb temperature, wet bulb temperature, and time (representing building-occupancy and equipment-operation schedule) were determined as the most effective variables in the ANN model. The research and findings are presented in this paper.     

Prediction of bed load sediments using different artificial neural network models

Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radial based function (RBF), and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed. The optimum models were found through 102 data sets of flow discharge, flow velocity, water surface slopes, flow depth, and mean grain size. The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data. The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models. The accuracy performance of all models was evaluated and interpreted using different statistical error criteria, analytical graphs and confusion matrixes. Although the ANN models predicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network error was outperform than others.     

浅议工业园区供水企业降低购销差率

以工业园区供水企业购销差率为研究对象,通过对近2年购销水量和差率数据进行分析,发现购水量增长幅度和差率之间存在一定的关联.在此基础上进一步对电磁流量计的选型和实际使用中影响计量准确性的因素进行分析,OPTIFLUX2300电磁流量计的流速范围应在0.3～12 m/s,而根据园区日均水量折算的平均流速仅为0.034 m/s,因此造成流量计未能在最佳工况条件下工作,计量的准确性无法保证.此外,工业园区用水波动较大,基于电磁流量计的工作原理,在用水量突然增大时由于流速不稳定,造成切割磁感应强度不同,感应电流大小不断变化,导致误差进一步增大.     

Principal component analysis of electricity use in office buildings

Principal component analysis was conducted on five major climatic variables—dry-bulb temperature, wet-bulb temperature, global solar radiation, clearness index and wind speed. Twenty-eight year (1996–2000) long-term measured weather data were considered. A two-component solution was obtained, which could explain 80% of the variance in the original weather data. Monthly electricity consumption data recorded during a 5-year period (1979–2006) were gathered from 20 fully air-conditioned office buildings with centralised HVAC systems in subtropical Hong Kong. Electricity use per unit gross floor area ranged from 163 to 389 kWh/m². These consumption data were correlated with the corresponding principal components using linear multiple regression techniques. The coefficient of determination (R²) varied from 0.76 to 0.95 indicating reasonably strong correlation. It was found that the regression models developed could give a reasonably good indication (mostly within 3%) of the annual electricity use, but the monthly estimates might differ from the actual consumption by up to 9%. Attempt was also made to develop a general regression model for the 20 buildings, which had an R² of 0.84 with a maximum mean-biased error of 18.6% and a maximum root-mean-square error of 21.4%.     

基于改进BP网络的盾构隧道开挖地表沉降预测

盾构隧道施工中引起的地表沉降是衡量开挖方式是否合适的关键指标。文中在介绍BP神经网络及盾构施工引起变形情况的基础上,对基于BP神经网络的盾构隧道开挖引起的地表沉降预测进行了研究,考虑了训练样本中奇异数据的剔除,采用变步长的方法,并选取适当的动量项系数,综合考虑各种影响因素,建立了盾构隧道开挖引起的地表沉降预测的BP网络模型,并对广州地铁二号线进行了具体的预测分析。分析结果表明:理论计算结果与工程实际情况一致,误差小于5%,所建立的预测模型是令人满意的。     

桥梁高墩位移延性能力计算方法研究

本文总结了现行位移延性能力计算方法,对不同参数桥梁高墩计算模型采用Pushover法进行分析并与公式法比较,讨论了两种方法在计算桥梁高墩位移延性能力时的适用性,并指出位移延性能力计算偏差对抗震设计有较大的影响。     

EDG无核密度仪标定模型与测试误差的试验研究

 EDG无核密度仪是一种无损快速的压实土干密度测试仪器,其在国内的应用经验较少。为研究EDG法干密度测试的准确性和适宜性,从EDG的测试机制出发,基于黄土试样的EDG法、灌砂法和烘干法测试结果,分析EDG法干密度测试产生误差的原因,以及测试误差大小。结果表明土的复合阻抗和电容电阻比均受密度和体积含水量两者的影响是造成EDG法测试产生误差的原因;EDG法干密度测试的误差大小并不是恒定值,而是在某些特定含水率和干密度区间内测试误差较小,其他区间测试误差较大;保证土料的均匀性,控制土料的含水率在较小区间内变化是EDG法干密度测试适用的前提条件;工程实践中宜针对采用的标定模型进行测试误差分析,明确标定模型适用的含水率和干密度范围。     

Hadoop支持下海量出租车轨迹数据预处理技术研究

海量出租车轨迹数据预处理是轨迹数据挖掘和应用的前提。出租车轨迹数据是典型的大数据,传统的数据处理技术无法解决大规模出租车轨迹数据误差分析和处理问题,文章在分析轨迹数据误差来源和误差类型的基础上,提出基于Hadoop的海量出租车轨迹数据预处理模型,使用Hive实现轨迹数据误差统计分析,设计MapReduce并行处理程序实现轨迹数据预处理。实验结果表明,该模型可以有效解决大规模出租车轨迹数据预处理问题,处理方式可靠性较高,大大提高了轨迹数据预处理效率,为后期轨迹数据深入挖掘和分析奠定了基础。     

基于神经网络的激光扫描数据拟合

针对扫描仪采集数据时可能存在偶然误差或系统误差的问题,对采用神经网络进行激光数据的高程拟合过程作了深入探讨,实例证明该方法能很好地处理数据中的偶然误差和系统误差,提高高程拟合精度。     

应用Db10分析减水剂对水泥水化的影响

利用TAM-AIR热活性微量热仪测定了掺不同减水剂水泥水化过程的水化放热曲线,并用Db10小波对放热曲线进行分析.结果表明:掺新型聚羧酸减水剂(SPC)水泥的水化曲线放热峰比掺萘系减水剂(NSF)和不掺减水剂的水泥分别滞后了171.3,235.9 min.对各放热曲线进行分解与重构发现,掺SPC试样的各近似系数比掺NSF试样和空白样小,重构得到的信号误差大,表明掺SPC比掺NSF对水泥水化的影响大.减水剂可有效延缓水化放热峰出现,掺SPC水泥水化放热过程比掺NSF水泥更加温和,有利于后期水泥强度的发展.