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61.
该文将循环神经网络(recurrent neural network,RNN)应用于雷达临近预报。使用预测循环神经网络(predictive RNN)架构,利用雷达历史组合反射率因子建模,给出雷达组合反射率因子未来1 h的预报结果。预测循环神经网络的核心是在长短时记忆单元(long short-term memory,LSTM)中增加时空记忆模块,能够提取雷达回波不同尺度的空间特征,配合循环神经网络架构,可以有效解决反射率因子预测问题。北京大兴雷达和广州雷达长时间序列的独立检验结果和2个强对流天气个例检验结果表明:该方法和传统的基于交叉相关法的1 h雷达外推临近预报相比,在20 dBZ和30 dBZ检验项目内,临界成功指数(CSI)可以提升0.15~0.30,命中率(POD)提高0.15~0.25,虚警率(FAR)降低0.15~0.20,该方法对反射率因子强度变化有一定预报能力。 相似文献
62.
基于深度学习的地震数据噪声压制方法是当前地震数据去噪处理的重要方向。深度学习方法突破了传统滤波处理的局限,在对常规地震数据的噪声压制中表现出效率高、信噪分离效果好的特点。但针对深部弱有效反射数据,当前的深度学习方法特征提取能力有限,难以取得较好的去噪效果。笔者等结合深反射地震数据特点,针对当前深度学习噪声压制方法在特征提取及对数据集依赖上的局限,提出了基于注意力循环生成对抗网络(Attention Cycle- Consistent Generative Adversarial Networks,A- CGAN)的深反射地震数据随机噪声压制方法。借助循环一致生成对抗网络(Cycle- Consistent Generative Adversarial Networks,Cycle- GAN)的域映射思想,降低对数据集的要求。为了构建适用于深反射地震数据的去噪网络,从3个方面对Cycle- GAN进行改进:在Cycle- GAN的生成器(去噪器)中加入残差结构和注意力机制,用于加深网络深度和提高其特征提取能力;在Cycle- GAN的鉴别器中使用块判决,提高鉴别精度和准确度;在损失函数部分加入感知一致性损失函数,提升网络模型恢复纹理细节信息的能力。通过合成地震数据和实际深反射地震数据测试,验证了优化算法的有效性,体现了良好的应用价值。 相似文献
63.
This study compares how humans and neural networks classify climate types. Human subjects were asked to classify climates from monthly temperature and precipitation patterns. To model their learning process, the same data were used to produce input vectors that trained a pattern associator neural network. Both human subjects and the neural network classified climates accurately after 10 rounds of supervised learning. The neural network successfully modeled the rate of human learning and the ability to learn specific climate categories. Moreover, the neural network weights used to classify climates correspond to distinct visual characteristics in temperature and precipitation. These results suggest that neural networks can model the formation of visual categories. 相似文献
64.
65.
Learning about learning: lessons from public engagement and deliberation on urban river restoration 总被引:1,自引:0,他引:1
JUDITH PETTS 《The Geographical journal》2007,173(4):300-311
This paper provides a new discussion of how people learn through deliberative processes, drawing upon empirical analysis of a novel public engagement process for urban river restoration. Such critical evaluation is rare and yet will be crucial to both theoretical development and learning about engagement practice, not least in a policy area subject to strong regulatory drivers for public participation. The analysis supports two important learning mechanisms – the use of 'gatekeepers' of knowledge, interests and values, and the privileging of narrative. It provides new evidence of instrumental and communicative learning about shared priorities and criteria for effective river restoration that evolved through the deliberative process and directly informed the restoration scheme. It is important to question whether and how such site or context-specific learning might inform other restoration schemes. Finally, the paper questions the often ignored issue of expert learning, not least the issue of the link between individual and organizational learning. 相似文献
66.
A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG.
The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of
advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the
two different numerical models and a set of different initial conditions in combination with distinct boundary conditions.
Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess
taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the
uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection
in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic
forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts. 相似文献
67.
Deriving rules from activity diary data: A learning algorithm and results of computer experiments 总被引:1,自引:0,他引:1
Theo A. Arentze Frank Hofman Harry J.P. Timmermans 《Journal of Geographical Systems》2001,3(4):325-346
Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time.
Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography
and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the
further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm
for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the
theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns
of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves
on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to
fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust
for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically
tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical
data.
Received: 31 January 2001 / Accepted: 13 September 2001 相似文献
68.
利用一种新的神经网络模型识别点状地图符号 总被引:1,自引:0,他引:1
黄文骞 《武汉大学学报(信息科学版)》1996,(1)
着重讨论了用一种新的神经网络模型识别点状地图符号的过程,主要包括网络的结构特点和学习算法以及学习训练过程,并验证了用该网络进行点状地图符号识别的有效性。 相似文献
69.
Mozheng Wei 《大气科学进展》1996,13(1):67-90
ALow-orderModelofTwo-dimensionalFluidDynamicsontheSurfaceofaSphereMozhengWei(CRCforSouthernHemisphereMeteorology,CSIRODivisio... 相似文献
70.
基于SWCWARMS模式9km×9km分辨率逐小时降水预报,通过时间滞后集合预报方法构建多个集合成员,使用SAL评分值计算相应集合成员的权重系数,进行不等权集合平均,从而得到新的逐小时降水预报。利用SAL、TS和BIAS检验方法对四川省2019年8月的逐小时降水量时间滞后集合预报及相应的SWCWARMS模式最新时次预报进行对比分析,结果表明:(1)时间滞后集合预报SAL检验的L值和A值都较模式预报更接近于0,较好地改善了降水位置和强度的整体预报水平;(2)时间滞后集合预报对逐小时降水晴雨TS评分提升明显,评分提高百分率在10%左右,有效地减小了模式在晴雨方面的空报;(3)临近预报时效,时间滞后集合预报方法对于大量级降水预报也有较好的订正效果。 相似文献