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Optically pumped vapour magnetometers have an orientation dependency in measuring the scalar component of the ambient magnetic field which leads to challenges for integration with mobile platforms. Quantifying the three-dimensional attitude variations (yaw, pitch and roll) of an optically pumped vapour magnetometer, while in-flight and suspended underneath a rotary unmanned aerial vehicle, aids in the successful development of reliable, high-resolution unmanned aerial vehicle magnetometry surveys. This study investigates the in-flight three-dimensional attitude characteristics of a GEM Systems Inc. GSMP-35U potassium vapour magnetometer suspended 3 m underneath a Dà-Jiāng Innovations S900 multi-rotor unmanned aerial vehicle. A series of unmanned aerial vehicle-borne attitude surveys quantified the three-dimensional attitude variations that a simulated magnetometer payload experienced while freely (or semi-rigidly) suspended underneath the unmanned aerial vehicle in fair weather. Analysis of the compiled yaw, pitch and roll data resulted in the design of a specialized semi-rigid magnetometer mount, implemented to limit magnetometer rotation about the yaw axis. A subsequent unmanned aerial vehicle-borne magnetic survey applying this specialized mount resulted in more than 99% of gathered GSMP-35U magnetic data being within industry standards. Overall, this study validates that maintaining magnetometer attitude variations within quantified limits (±5° yaw, ±10° pitch and roll) during flight can yield reliable, continuous and high-resolution unmanned aerial vehicle-borne magnetic measurements. 相似文献
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FU Bo LI Zhi-qiang CHEN Jie FAN Xi-wei LI Xiao-li LI Tao YAO Yuan LIU Yao-hui 《地震地质》2018,40(3):672-684
In order to complete the field investigation to the 25 November 2016 Arketao MW6.6 earthquake, ultra-low altitude remote-sensing data were obtained from miniature unmanned aerial vehicle. The surface rupture surveying has important significance for earthquake research. This paper selects the macro-epicenter of Arketao as the study area. The pictures were obtained with DJI Phantom 3 professional input into the software, the Digital Elevation Model (DEM), Digital Orthophoto Map (DOM) were acquired based on photogrammetry method using the overlapped optical remote-sensing images of UAV. Using these data, we can identify surface ruptures that have vertical dislocation.
We selected six feature points and drew the elevation profile. In the elevation profile map, we chose smooth part of the surface rupture sides and obtained the trend line. A stable point in the surface rupture was selected and the abscissa of the point was taken into the equation of two straight lines. Then subtracting the results of the two equations, we can get the vertical dislocation of the surface rupture. On this basis, we chose six feature points and determined their vertical dislocation, which are between 4.4cm and 10.4cm. What's more, taking Bulungkou Xiang in Xinjiang Uygur Autonomous Region for example, we speculated some surface ruptures that have vertical dislocation. It can provide a new method for identifying surface rupture in the field.
In addition, we get DEM data of the Bulunkou area where ambient conditions are very poor, by using miniature unmanned aerial vehicle and taking 255 photos. Putting those photos into the EasyUAV software, we got the area digital elevation of 2cm resolution. Comparing these data with RTK data, we summarized some practical problems and solutions in the practical operation and evaluated the accuracy of miniature unmanned aerial vehicle data. The Pearson Correlation Coefficient is 0.996 6. In terms of absolute elevation, the average result of UAV and RTK differs by 156.96m. In terms of relative elevation, the average result of UAV and RTK differs by 9.74m. Compared with the previous test of Pishan County, there is a notable divergence in the results. It shows that the data accuracy will be affected to some extent in the cold weather in high elevations. The specific impact needs further exploration. 相似文献
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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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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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AbstractA super-resolution enhancement algorithm was proposed based on the combination of fractional calculus and Projection onto Convex Sets (POCS) for unmanned aerial vehicles (UAVs) images. The representative problems of UAV images including motion blur, fisheye effect distortion, overexposed, and so on can be improved by the proposed algorithm. The fractional calculus operator is used to enhance the high-resolution and low-resolution reference frames for POCS. The affine transformation parameters between low-resolution images and reference frame are calculated by Scale Invariant Feature Transform (SIFT) for matching. The point spread function of POCS is simulated by a fractional integral filter instead of Gaussian filter for more clarity of texture and detail. The objective indices and subjective effect are compared between the proposed and other methods. The experimental results indicate that the proposed method outperforms other algorithms in most cases, especially in the structure and detail clarity of the reconstructed images. 相似文献