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1.
ZHANG Xuhui LI Huan GONG Zheng ZHOU Zeng DAI Weiqi WANG Lizhu Samuel DARAMOLA 《地理学报(英文版)》2021,31(12):1852-1872
It is common to obtain the topography of tidal flats by the Unmanned Aerial Vehicle(UAV) photogrammetry,but this method is not applicable in tidal creeks.The residual water will lead to inaccurate depth inversion results,and the topography of tidal creeks mainly de-pends on manual survey.The present study took the tidal creek of Chuandong port in Jiangsu Province,China,as the research area and used UAV oblique photogrammetry to reconstruct the topography of the exposed part above the water after the ebb tide.It also proposed a Trend Prediction Fitting (TPF) method for the topography of the unexposed part below the water to obtain a complete 3D topography.The topography above the water measured by UAV has the vertical precision of 12 cm.When the TPF method is used,the cross-section should be perpendicular the central axis of the tidal creek.A polynomial function can be adapted to most shape of sections,while a Fourier function obtains better results in asym-metrical sections.Compared with the two-order function,the three-order function lends itself to more complex sections.Generally,the TPF method is more suitable for small,straight tidal creeks with clear texture and no vegetation cover. 相似文献
2.
车辆碾压作用下戈壁地表起尘浓度 总被引:1,自引:1,他引:0
通过野外观测车辆碾压作用下戈壁地表PM10释放浓度,探讨其主要影响因素。针对戈壁区砾石、砂砾、砂质和砂土4种路面类型,应用TSI粉尘仪多次重复观测车辆碾压产生的PM10浓度,进而阐明碾压次数、车速和地表水分等因素对起尘浓度的影响。结果表明:4种路面的平均起尘浓度依次为5.339、9.089、16.944、50.251 mg·m-3,是自然状态下的5~50倍;起尘浓度随碾压次数增多而增大,其中砂土路情况下的增长最显著;随土层厚度的增大呈现出幂函数的增长趋势;降雨后路面起尘浓度显著减小,且降雨量会影响降雨后起尘浓度的变化趋势。最后提出了针对性的建议和措施以减弱车辆碾压导致的戈壁地表起尘。 相似文献
3.
An axisymmetric underwater vehicle (UV) at a steady drift angle experiences the complex three-dimensional crossflow separation. This separation arises from the unfavorable circumferential pressure gradient developed from the windward side toward the leeward side. As is well known, the separated flow in the leeward side gives rise to the formation of a pair of vortices, which affects considerably the forces and moments acting on the UV. In this regard, the main purpose of the present study is to evaluate the role of the leeward vortical flow structure in the hydrodynamic behavior of a shallowly submerged UV at a moderate drift angle traveling beneath the free surface. Accordingly, the static drift tests are performed on the SUBOFF UV model using URANS equations coupled with a Reynolds stress turbulence model. The simulations are carried out in the commercial code STARCCM+ at a constant advance velocity based on Froude number equal to Fn = 0.512 over submergence depths and drift angles ranging from h = 1.1D to h = ∞ and from β = 0 to β = 18.11°, respectively. The validation of the numerical model is partially conducted by using the existing experimental data of the forces and moment acting on the totally submerged bare hull model. Significant interaction between the low-pressure region created by the leeward vortical flow structure and the free surface is observed. As a result of this interaction, the leeward vortical flow structure appears to be largely responsible for the behavior of the forces and moments exerted on a shallowly submerged UV at steady drift. 相似文献
4.
Unmanned Underwater Vehicles (UUVs) are increasingly being used in advanced applications that require them to operate in tandem with human divers and around underwater infrastructure and other vehicles. These applications require precise control of the UUVs which is challenging due to the non-linear and time varying nature of the hydrodynamic forces, presence of external disturbances, uncertainties and unexpected changes that can occur within the UUV’s operating environment. Adaptive control has been identified as a promising solution to achieve desired control within such dynamic environments. Nevertheless, adaptive control in its basic form, such as Model Reference Adaptive Control (MRAC) has a trade-off between the adaptation rate and transient performance. Even though, higher adaptation rates produce better performance they can lead to instabilities and actuator fatigue due to high frequency oscillations in the control signal. Command Governor Adaptive Control (CGAC) is a possible solution to achieve better transient performance at low adaptation rates. In this study CGAC has been experimentally validated for depth control of a UUV, which is a unique challenge due to the unavailability of full state measurement and a greater thrust requirement. These in turn leads to additional noise from state estimation, time-delays from input noise filters, higher energy expenditure and susceptibility to saturation. Experimental results show that CGAC is more robust against noise and time-delays and has lower energy expenditure and thruster saturation. In addition, CGAC offers better tracking, disturbance rejection and tolerance to partial thruster failure compared to the MRAC. 相似文献
5.
针对当前采用PID控制器控制无人驾驶救援车伺服系统时存在的轨迹跟踪精度不高,误差控制性能较差,灵活性、平稳性和安全性能不佳等问题,提出并设计基于BP神经网络整定PID控制器的无人驾驶救援车伺服控制系统,建立突发地震灾害中无人驾驶救援车伺服控制系统驱动模型,并以此模型作为被控对象;根据系统期望输出值与实际输出值构成的控制偏差获得PID控制规律,并通过调节PID控制器控制参数实现系统控制,在此基础上,采用BP神经网络通过对无人驾驶救援车伺服控制系统性能的学习,构建基于BP神经网络整定的PID控制器,并采用梯度下降法修正控制器加权系数,通过在线调整BP神经网络加权系数即可实现控制器的自适应调整,控制突发地震灾害中无人驾驶救援车实施救援。实验结果表明,设计的基于BP神经网络整定PID控制器的无人驾驶救援车伺服系统可有效提高轨迹跟踪精度,具有较好的灵活性,且能够保证驾驶员的安全和车辆平稳行驶。 相似文献
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7.
使用无人机实施测绘航空摄影时,由于无人机相对航高较低,地面起伏会对无人机影像的分辨率、覆盖范围、重叠度造成较大的影响,影像成果会出现分辨率不足、重叠度不够、覆盖漏洞等缺陷。针对这一情况,本文提出了一种利用数字微分正解法的计算方法,借助DEM准确计算每张影像的覆盖范围,并使用FME软件高效生成全部影像的覆盖范围。经过实际使用,验证了该方法可以在航线设计阶段准确预测并分析全部影像的覆盖范围、重叠度,因此可及时发现设计问题并调整航线。该方法可以有效减少因地形起伏造成的影像覆盖缺陷,减少返工现象,从整体上提高了作业效率。 相似文献
8.
在大数据的背景下,充分利用北斗卫星导航系统(BDS)的定位功能,以无人船作为用户端,在水质数据采集和污染源位置的问题中积极探索新的实践方案,迅速、准确地找到污染源,减少河流污染物污染的时间,且无人船上装有净化模块,在发现污染时可作简易处理。在无人船上安装水质分析仪,水质分析仪的传输模块中安装经给防水处理的北斗卫星定位芯片和WiFi数据传输器,进而对河流的污染物种类及浓度进行分析,并采用大数据的计算方法,计算出污染源位置,向云平台反映污染源位置分析结果与污染物处理方法。通过BDS,将数据按照位置区域划分,并将能到达污染源的最短路径发送到处理人的移动设备上。本文通过对基于BDS定位的水质污染监测可视化系统进行分析,以期迅速找出污染源,减少水质污染现象的发生。 相似文献
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10.
Soil surface roughness (SSR) is an important factor in controlling sediment and runoff generation, influencing directly a wide spectrum of erosion parameters. SSR is highly variable in time and space under natural conditions, and characterizing SSR to improve the parameterization of hydrological and erosion models has proved challenging. Our study uses recent technological and algorithmic developments in capturing and processing close aerial sensing data to evaluate how high-resolution imagery can assist the temporally and spatially explicit monitoring of SSR. We evaluated the evolution of SSR under natural rainfall and growing vegetation conditions on two arable fields in Denmark. Unmanned aerial vehicle (UAV) photogrammetry was used to monitor small field plots over 7 months after seeding of winter wheat following conventional and reduced tillage treatments. Field campaigns were conducted at least once a month from October until April, resulting in nine time steps of data acquisition. Structure from motion photogrammetry was used to derive high-resolution point clouds with an average ground sampling distance of 2.7 mm and a mean ground control point accuracy of 1.8 mm. A comprehensive workflow was developed to process the point clouds, including the detection of vegetation and the removal of vegetation-induced point cloud noise. Rasterized and filtered point clouds were then used to determine SSR geostatistically as the standard deviation of height, applying different kernel sizes and using semivariograms. The results showed an influence of kernel size on roughness, with a value range of 0.2–1 cm of average height deviation during the monitoring period. Semivariograms showed a measurable decrease in sill variance and an increase in range over time. This research demonstrated multiple challenges to measuring SSR with UAV under natural conditions with increasing vegetation cover. The proposed workflow represents a step forward in tackling those challenges and provides a knowledge base for future research. © 2020 John Wiley & Sons, Ltd. 相似文献