电子经纬仪水平角观测测回数的确定

电子经纬仪、全站仪克服了光学经纬仪测角时的许多误差,但目前电子经纬仪水平角观测仍然要按照光学经纬仪的操作规程进行,因为没有针对电子经纬仪的操作规范。通过对大量实验数据的分析,寻找电子经纬仪水平角观测测角精度与测回数关系,并将之与光学经纬仪的操作规范进行对比,从而确定电子经纬仪不同等级水平角观测的测回数。利用这一结论可以充分发挥电子经纬仪的优越性,节省作业时间,提高工作效率。

三角高程测距导线垂直角测回数的思考

目前四等三角高程测距导线采用规范不同,测回数也不一样。随着仪器设备精度的提高,达到规范要求的精度,就不需要测量这么多测回数,比如,四等三角高程导线垂直角测回数可以减少到两个测回,同样可以满足四等三角高程测距导线的精度,并且可以减少部分工作量。

地面激光扫描技术在建筑变形测量中的应用探讨

已有不少文献针对激光扫描在建筑变形测量中的应用进行了研究,但是国家和行业尚无三维激光扫描技术规程出台。本文通过应用调研、扫描仪的标称精度统计分析,以及具有针对性的试验,明确了激光扫描测量建筑变形的作业方法,确立了选用仪器精度、扫描测量的测回数、标靶入射角度等技术指标,为激光扫描技术在建筑变形测量中的工程应用提供了参考依据。

经纬仪水平角观测精度的研究

如何评定经纬仪水平角观测的精度 ,探求按菲律罗公式计算的测角中误差与各测站平差后单位权方向中误差的平均值和不同测回数之间合适的数学表达式 ,是三角测量中具有实际意义的重要研究课题。本文根据 2 3个高精度短边三角网和 3个城市二等网的实测资料统计结果 ,对有关经验估算公式 ,系统与偶然误差的分析、检验、影响比例和最适宜测回数等进行了研究。

小海子水库地形测量平面控制网的建立

本文总结了小海子水库地形测量平面控制网的建立,给出了在大面积、长距离、大比例尺测图范围内,采用不同的平面控制网确保测图精度。经对所测三角网各种短边精度的统计分析,探讨影响短边测角精度的有效途径,使测角精度达到最或然值。

三角高程跨河水准测量的限差计算

三角高程跨河水准测量需要同时进行对向（双向）观测。以消除地球曲率和大气折光的影响。但三角高程测量中照准和仪器的误差只能通过增加观测次数来减少其误差，这就需要根据全站仪竖直角的测角读数限差、跨河水准的距离和水准测量的精度等级来计算对向观测的双测回数，然后根据所取的双测回数确定高差读数之问较差的限差。在实际测量过程中，为满足各项限差，规定1个单向测回观测必须正镜读数8次，然后倒镜读数8次，这样形成1组观测值。

后方交会在施工测量中的应用与分析

基于2个已知点的边角后方交会,采用了测角、测边后方交会的计算公式得出计算结果,并对平面部分进行精度分析与评定,测角、测边严格按照规范进行具体操作。在此基础上按照二等施测方法施测一条闭合导线,成功地完成了控制点的加密工作。

论地面激光扫描技术在建筑变形测量中的应用

激光扫描技术是20世纪90年代中期激光应用研究的重大突破,它改变了传统单点采集数据的作业模式,能快速自动连续获取点云数据,从而提高数据采集效率。该技术已经在文物考古、建筑测绘、地形测量、交通事故调查、城市规划测量、三维仿真等领域得到了推广应用。

浅谈隔点设站法测高程在玛坞阿娜大坝工程的应用

本文介绍了利用全站仪进行隔点设站法测量高程的方法。通过介绍该方法在阿尔及亚玛坞阿纳大坝工程的应用情况,供同行参考。

Data-driven facial animation based on manifold Bayesian regression

Driving facial animation based on tens of tracked markers is a challenging task due to the complex topology and to the non-rigid nature of human faces. We propose a solution named manifold Bayesian regression. First a novel distance metric, the geodesic manifold distance, is introduced to replace the Euclidean distance. The problem of facial animation can be formulated as a sparse warping kernels regression problem, in which the geodesic manifold distance is used for modelling the topology and discontinuities of the face models. The geodesic manifold distance can be adopted in traditional regression methods, e.g. radial basis functions without much tuning. We put facial animation into the framework of Bayesian regression. Bayesian approaches provide an elegant way of dealing with noise and uncertainty. After the covariance matrix is properly modulated, Hybrid Monte Carlo is used to approximate the integration of probabilities and get deformation results. The experimental results showed that our algorithm can robustly produce facial animation with large motions and complex face models.

Statistical Downscaling Prediction of Summer Precipitation in Southeastern China

A statistical downscaling approach based on multiple-linear-regression(MLR) for the prediction of summer precipitation anomaly in southeastern China was established,which was based on the outputs of seven operational dynamical models of Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction(DEMETER) and observed data.It was found that the anomaly correlation coefficients(ACCs) spatial pattern of June-July-August(JJA) precipitation over southeastern China between the seven models and the observation were increased significantly;especially in the central and the northeastern areas,the ACCs were all larger than 0.42(above 95% level) and 0.53(above 99% level).Meanwhile,the root-mean-square errors(RMSE) were reduced in each model along with the multi-model ensemble(MME) for some of the stations in the northeastern area;additionally,the value of RMSE difference between before and after downscaling at some stations were larger than 1 mm d-1.Regionally averaged JJA rainfall anomaly temporal series of the downscaling scheme can capture the main characteristics of observation,while the correlation coefficients(CCs) between the temporal variations of the observation and downscaling results varied from 0.52 to 0.69 with corresponding variations from-0.27 to 0.22 for CCs between the observation and outputs of the models.

Context Information and Fragments Based Cross-Domain Word Segmentation

A new joint decoding strategy that combines the character-based and word-based conditional random field model is proposed.In this segmentation framework,fragments are used to generate candidate Out-of-Vocabularies(OOVs).After the initial segmentation,the segmentation fragments are divided into two classes as "combination"(combining several fragments as an unknown word) and "segregation"(segregating to some words).So,more OOVs can be recalled.Moreover,for the characteristics of the cross-domain segmentation,context information is reasonably used to guide Chinese Word Segmentation(CWS).This method is proved to be effective through several experiments on the test data from Sighan Bakeoffs 2007 and Bakeoffs 2010.The rates of OOV recall obtain better performance and the overall segmentation performances achieve a good effect.

Parameter selection in time series prediction based on nu-support vector regression

The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.

Prediction of cavity growth rate during underground coal gasification using multiple regression analysis

During underground coal gasification （UCG）, whereby coal is converted to syngas in situ, a cavity is formed in the coal seam. The cavity growth rate （CGR） or the moving rate of the gasification face is affected by controllable （operation pressure, gasification time, geometry of UCG panel） and uncontrollable （coal seam properties） factors. The CGR is usually predicted by mathematical models and laboratory experiments, which are time consuming, cumbersome and expensive. In this paper, a new simple model for CGR is developed using non-linear regression analysis, based on data from 1 l UCG field trials. The empirical model compares satisfactorily with Perkins model and can reliably predict CGR.

Online residual useful life prediction of large-size slewing bearings A data fusion method

To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.

Dverview and Main Advances in Permutation Tests for Linear Regression Models

When the population, from which the samples are extracted, is not normally distributed, or if the sample size is particularly reduced, become preferable the use of not parametric statistic test. An alternative to the normal model is the permutation or randomization model. The permutation model is nonparametric because no formal assumptions are made about the population parameters of the reference distribution, i.e., the distribution to which an obtained result is compared to determine its probability when the null hypothesis is true. Typically the reference distribution is a sampling distribution for parametric tests and a permutation distribution for many nonparametric tests. Within the regression models, it is possible to use the permutation tests, considering their ownerships of optimality, especially in the multivariate context and the normal distribution of the response variables is not guaranteed. In the literature there are numerous permutation tests applicable to the estimation of the regression models. The purpose of this study is to examine different kinds of permutation tests applied to linear models, focused our attention on the specific test statistic on which they are based. In this paper we focused our attention on permutation test of the independent variables, proposed by Oja, and other methods to effect the inference in non parametric way, in a regression model. Moreover, we show the recent advances in this context and try to compare them.

An Intelligent Early Warning Method of Press-Assembly Quality Based on Outlier Data Detection and Linear Regression

Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.

架空索道的角度测量

在《有色金属》矿山部分,1976年第5期'架空索道的直线测量'一文中(下面简称'前文'),讨论了索道的直线测量方法及索道直线测量的限差公式。本文再来讨论一下索道直线角度测量的有关问题。

地勘测量规范几项限差的探讨

本文根据误差理论及测量方案设计理论,分析了一级导线中测回数的确定及2C互差限差的确定,比较并论证了不同规范的记录取位规定,指出了《地质矿产勘查测量规范》中几项规定的过严之处。

Statistical Diagnosis and Gross Error Test for Semiparametric Linear Model

This paper systematically studies the statistical diagnosis and hypothesis testing for the semiparametric linear regression model according to the theories and methods of the statistical diagnosis and hypothesis testing for parametric regression model.Several diagnostic measures and the methods for gross error testing are derived.Especially,the global and local influence analysis of the gross error on the parameter X and the nonparameter s are discussed in detail;at the same time,the paper proves that the data point deletion model is equivalent to the mean shift model for the semiparametric regression model.Finally,with one simulative computing example,some helpful conclusions are drawn.