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1.
为建立高精度的边坡位移预测模型,采用相空间重构(PSR)将边坡位移时间序列数据转换为多维数据,同时构造小波核函数改进的支持向量机模型,建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机(SVM)模型、小波支持向量机(WSVM)模型和基于相空间重构的支持向量机(PSR-SVM)模型预测结果进行对比,通过平均绝对误差(MAE)、平均绝对误差百分比(MAPE)和均方根误差(RMSE)3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明,PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型,边坡位移预测的精度明显提升。 相似文献
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Peng Yue Fan Gao Boyi Shangguan Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274
ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献
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Olac Fuentes 《Experimental Astronomy》2001,12(1):21-31
In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data. 相似文献
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Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms 总被引:1,自引:0,他引:1
In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices.
This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts
their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature,
surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results
show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive
accuracy error up to 35%.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
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J. William Miller Jr. 《Mathematical Geology》1991,23(2):201-218
A computer simulation method has been developed to find efficient drilling grids for mineral deposits. A well-known ore deposit is used as a model to develop an efficient pattern for undiscovered ore bodies in the same area or in other prospects where similar geometry is suspected. The model for this study is the Austinville, Virginia deposit, a Mississippi Valley-type deposit composed of 17 ore bodies totaling 34 million short tons (30 million metric tons). The method employs a computer program that simulates drilling the model deposit with different patterns, including various levels of follow-up drilling. Follow-up holes are drilled in fences at one half the original spacing around holes in the grid that show ore-grade mineralization. Each pattern is drilled 100 times from random starting locations to provide a range of outcomes of drilling, including the best, worst, and most likely. For this study, patterns of 100 drill holes were composed of 10 fences spaced 1000–5000 feet (305–1524 m) apart, each with 10 holes spaced 200–1000 feet (61–305 m) apart. In all, 25 grids were used with zero to three levels of follow-up drilling. The 600/2000 grid, with drill holes spaced 600 feet (183 m) apart in fences spaced 2000 feet (610 m) apart, was compared with the 200/5000 grid because they represented contrasting outcomes. The 600/2000 grid penetrated many ore bodies consistently but with few multiple hits to individual ore bodies; whereas the 200/5000 grid inconsistently penetrated few ore bodies with many multiple hits. The 600/2000 grid was more efficient than the 200/5000 grid at hitting large ore bodies of 1,000,000 short tons or greater (900,000 metric tons or greater) and was made more effective by adding one cycle of follow-up drilling. The 600/2000 grid had a 97% chance of hitting one or more large ore bodies with at least one drill hole per ore body, and the 200/5000 grid had a 64% chance. Once hit, there was an 82% chance that the largest ore body would be penetrated by three or more holes when using the 600/2000 grid and an 88% chance using the 200/5000 grid. 相似文献
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重点分析了近几年我国水平定向钻进铺管钻机和施工市场的发展趋势,结合国外的水平定向钻进市场发展分析,总结了我国水平定向钻进技术与市场的发展态势。 相似文献