全文获取类型
收费全文 | 336篇 |
免费 | 34篇 |
国内免费 | 26篇 |
专业分类
测绘学 | 63篇 |
大气科学 | 45篇 |
地球物理 | 102篇 |
地质学 | 87篇 |
海洋学 | 58篇 |
天文学 | 10篇 |
综合类 | 6篇 |
自然地理 | 25篇 |
出版年
2024年 | 4篇 |
2023年 | 7篇 |
2022年 | 19篇 |
2021年 | 7篇 |
2020年 | 12篇 |
2019年 | 11篇 |
2018年 | 14篇 |
2017年 | 3篇 |
2016年 | 2篇 |
2015年 | 10篇 |
2014年 | 9篇 |
2013年 | 10篇 |
2012年 | 5篇 |
2011年 | 20篇 |
2010年 | 14篇 |
2009年 | 25篇 |
2008年 | 35篇 |
2007年 | 24篇 |
2006年 | 18篇 |
2005年 | 19篇 |
2004年 | 21篇 |
2003年 | 19篇 |
2002年 | 15篇 |
2001年 | 11篇 |
2000年 | 15篇 |
1999年 | 11篇 |
1998年 | 8篇 |
1997年 | 4篇 |
1996年 | 5篇 |
1995年 | 7篇 |
1994年 | 8篇 |
1993年 | 2篇 |
1991年 | 2篇 |
排序方式: 共有396条查询结果,搜索用时 15 毫秒
91.
This paper proposes a saturated tracking controller for underactuated autonomous marine surface vehicles with limited torque. First, a second-order open-loop error dynamic model is developed in the actuated degrees of freedom to simplify the design procedure. Then, a saturated tracking controller is designed by utilizing generalized saturation functions to reduce the risk of actuator saturation. This, in turn, improves the transient performance of the control system. A multi-layer neural network and adaptive robust control techniques are also employed to preserve the controller robustness against unmodeled dynamics and environmental disturbances induced by waves and ocean currents. A Lyapunov stability analysis shows that all signals of the closed-loop system are bounded and tracking errors are semi-globally uniformly ultimately bounded. Finally, simulation results are provided for a hovercraft vehicle to illustrate the effectiveness of the proposed controller as a qualified candidate for real implementations in offshore applications. 相似文献
92.
Accurate water levels modeling and prediction is essential for safety of coastal navigation and other maritime applications. Water levels modeling and prediction is traditionally developed using the least-squares-based harmonic analysis method that estimates the harmonic constituents from the measured water levels. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the current state-of-the-art artificial neural network has recently been developed for water levels prediction from short water level measurements. However, a highly nonlinear and efficient wavelet network model is proposed and developed in this paper for water levels modeling and prediction using short water level measurements. Water level measurements (about one month and a week) from six different tide gauges are employed to develop the proposed model and investigate the atmospheric changes effect. It is shown that the majority of error values, the differences between water level measurements and the modeled and predicted values, fall within the −5 cm and +5 cm range and root-mean-squared (RMS) errors fall within 1–6 cm range. A comparison between the developed highly nonlinear wavelet network model and the harmonic analysis method and the artificial neural networks shows that the RMS of the developed wavelet network model when compared with the RMS of the harmonic analysis method is reduced by about 70% and when compared with the RMS of the artificial neural networks is reduced by about 22%. It is also worth noting that if the atmospheric changes effect (meteorological effect) of the air pressure, the air temperature, the relative humidity, wind speed and wind direction are considered, the performance accuracy of the developed wavelet network model is improved by about 20% (based on the estimated RMS values). 相似文献
93.
The process of scour around submarine pipelines laid on mobile beds is complicated due to physical processes arising from the triple interaction of waves/currents, beds and pipelines. This paper presents Artificial Neural Network (ANN) models for predicting the scour depth beneath submarine pipelines for different storm conditions. The storm conditions are considered for both regular and irregular wave attacks. The developed models use the Feed Forward Back Propagation (FFBP) Artificial Neural Network (ANN) technique. The training, validation and testing data are selected from appropriate experimental data collected in this study. Various estimation models were developed using both deep water wave parameters and local wave parameters. Alternative ANN models with different inputs and neuron numbers were evaluated by determining the best models using a trial and error approach. The estimation results show good agreement with measurements. 相似文献
94.
The paper presents an approach towards a medium-term (decades) modelling of water levels and currents in a shallow tidal sea by means of combined hydrodynamic and neural network models. The two-dimensional version of the hydrodynamic model Delft3D, forced with realistic water level and wind fields, is used to produce a two-year-database of water levels and currents in the study area. The linear principal component analysis (PCA) of the results is performed to reveal dominating spatial patterns in the analyzed dataset and to significantly reduce the dimensionality of the data. It is shown that only a few principal components (PCs) are necessary to reconstruct the data with high accuracy (over 95% of the original variance). Feed-forward neural networks are set up and trained to effectively simulate the leading PCs based on water level and wind speed and direction time series in a single, arbitrarily chosen point in the study area. Assuming that the spatial modes resulting from the PCA are ‘universally’ applicable to the data from time periods not modelled with Delft3D, the trained neural networks can be used to very effectively and reliably simulate temporal and spatial variability of water levels and currents in the study area. The approach is shown to be able to accurately reproduce statistical distribution of water levels and currents in various locations inside the study area and thus can be viewed as a reliable complementary tool e.g., for computationally expensive hydrodynamic modelling. Finally, a detailed analysis of the leading PCs is performed to estimate the role of tidal forcing and wind (including its seasonal and annual variability) in shaping the water level and current climate in the study area. 相似文献
95.
96.
97.
Using the characteristic
values of sunspot number variations during the descent and ascent of solar cycles,a neural network is designed to
make long-term predications of the ascending period and the maximum smoothed monthly mean
sunspot number for the Solar Cycle 23. Moreover,the factor of geomagnetic disturbance is also added as an
input. The trained and tested results from Solar Cycle 12 to 22 have been obtained.
Finally,the
predictions of the ascending period and the maximum smoothed monthly mean sunspot number
are given for Solar Cycle 23. 相似文献
98.
以陕西地区的地震为例,探讨了人工神经网络方法在地震预报中的应用。预报因子采用Keilis-Borok提出的地震流函数。结果表明,人工神经网络方法能够较好地学习复杂的预报因子和预报对象的关系,模拟地震预报问题,预报效果也较好,有广阔的应用前景。 相似文献
99.
A. S. Tawadrou P. D. Katsabani 《Fragblast: International Journal for Blasting and Fragmentation》2005,9(4):233-242
This paper is an application of artificial neural networks (ANNs) in the prediction of the geometry of surface blast patterns in limestone quarries. The built model uses 11 input parameters which affect the design of the pattern. These parameters are: formation dip, blasthole diameter, blasthole inclination, bench height, initiation system, specific gravity of the rock, compressive and tensile strength, Young's modulus, specific energy of the explosive and the average resulting fragmentation size. Detailed data from a previous investigation were used to train and verify the network and predict burden and spacing of a blast. The built model was used to conduct parametric studies to show the effect of blasthole diameter and bench height on pattern geometry. 相似文献
100.