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211.
The ionospheric eclipse factor method (IEFM) and its application to determining the ionospheric delay for GPS 总被引:4,自引:1,他引:3
A new method for modeling the ionospheric delay using global positioning system (GPS) data is proposed, called the ionospheric
eclipse factor method (IEFM). It is based on establishing a concept referred to as the ionospheric eclipse factor (IEF) λ
of the ionospheric pierce point (IPP) and the IEF’s influence factor (IFF) . The IEF can be used to make a relatively precise distinction between ionospheric daytime and nighttime, whereas the IFF
is advantageous for describing the IEF’s variations with day, month, season and year, associated with seasonal variations
of total electron content (TEC) of the ionosphere. By combining λ and with the local time t of IPP, the IEFM has the ability to precisely distinguish between ionospheric daytime and nighttime, as well as efficiently
combine them during different seasons or months over a year at the IPP. The IEFM-based ionospheric delay estimates are validated
by combining an absolute positioning mode with several ionospheric delay correction models or algorithms, using GPS data at
an international Global Navigation Satellite System (GNSS) service (IGS) station (WTZR). Our results indicate that the IEFM
may further improve ionospheric delay modeling using GPS data. 相似文献
212.
This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ 3). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ 3 time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ 3 data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ 3 predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ 3 time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ 3 reveal the best accuracy for LS + AR. 相似文献
213.
On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms 总被引:2,自引:1,他引:1
The multivariate total least-squares (MTLS) approach aims at estimating a matrix of parameters, Ξ, from a linear model (Y−E
Y
= (X−E
X
) · Ξ) that includes an observation matrix, Y, another observation matrix, X, and matrices of randomly distributed errors, E
Y
and E
X
. Two special cases of the MTLS approach include the standard multivariate least-squares approach where only the observation
matrix, Y, is perturbed by random errors and, on the other hand, the data least-squares approach where only the coefficient matrix
X is affected by random errors. In a previous contribution, the authors derived an iterative algorithm to solve the MTLS problem
by using the nonlinear Euler–Lagrange conditions. In this contribution, new lemmas are developed to analyze the iterative
algorithm, modify it, and compare it with a new ‘closed form’ solution that is based on the singular-value decomposition.
For an application, the total least-squares approach is used to estimate the affine transformation parameters that convert
cadastral data from the old to the new Israeli datum. Technical aspects of this approach, such as scaling the data and fixing
the columns in the coefficient matrix are investigated. This case study illuminates the issue of “symmetry” in the treatment
of two sets of coordinates for identical point fields, a topic that had already been emphasized by Teunissen (1989, Festschrift
to Torben Krarup, Geodetic Institute Bull no. 58, Copenhagen, Denmark, pp 335–342). The differences between the standard least-squares
and the TLS approach are analyzed in terms of the estimated variance component and a first-order approximation of the dispersion
matrix of the estimated parameters. 相似文献
214.
215.
变形分析的神经网络技术应用实例 总被引:1,自引:0,他引:1
大型工程施工过程中的变形监测、分析与预报极为重要。变形分析建模的方法很多,神经网络技术的应用是其中之一。文中结合某深基坑工程的监测资料和工作经验,运用神经网络BP算法进行预测分析。简述BP神经网络的基本概念,介绍基坑变形分析的BP神经网络的具体模型结构,将神经网络方法预报结果与实测数据对比效果较好。该成果对生产实践具有参考价值。 相似文献
216.
217.
SPOT-5影像的高分辨率和高信息量的性质,使其得到了广泛的应用,特别是在全国第二次土地利用调查中得到了更加深入的应用。而数字正射影像的制作是SPOT-5遥感影像应用的关键所在。本文在介绍DOM制作的基础上,依据当前获取GCP主要的三种途径采用ERDAS遥感图像处理软件进行了实验,并对最终的结果精度进行分析,总结了DOM制作过程的相关问题。 相似文献
218.
GIS技术在旅游业中的应用——兼谈旅游地理信息系统及其研究进展与展望 总被引:1,自引:0,他引:1
刘德辉 《测绘与空间地理信息》2008,31(1):90-92
本文对地理信息系统的组成与功能做了简单的概述,在此基础上介绍了GIS在旅游业发展中的主要作用及应用现状,并就旅游地理信息系统的结构与功能及其研究进展和发展趋势做了探讨。 相似文献
219.
连续运行GPS定位服务系统的探讨 总被引:1,自引:0,他引:1
朱国峰 《测绘与空间地理信息》2008,31(2):108-110
介绍了连续运行GPS定位服务的概念、实现模式及其在测绘行业中的应用。 相似文献
220.
针对大样本集的训练问题和动态训练样本的模型更新问题,提出了动态最小二乘支持向量机学习算法.该算法充分利用已建好的模型,逐渐加入新样本,并可删除位于任何位置的非支持向量,避免了矩阵求逆运算,保证了算法的高效率.大坝变形及电离层延迟两个时间序列的预报实例表明,该算法具有计算时间短、预报精度高的特点. 相似文献