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HJ-1B卫星红外多光谱相机星上定标精度分析 总被引:1,自引:0,他引:1
介绍了"环境一号"B(HJ-1B)卫星红外多光谱相机在轨辐射定标的目的以及星上定标的原理,分析了星上定标算法的精度。利用星上零级数据对星上定标系统的测控精度和工作稳定性进行研究,并在此基础上分析了定标数据的处理精度。HJ-1B卫星发射以来共对红外相机热红外通道进行了7次星上定标,为了监测定标系统的精度变化,文章利用MODIS-31、32通道辐亮度对HJ-1B卫星上定标系统精度进行检验,考虑MODIS传感器本身定标的不确定度恒定,分析结果表明自HJ-1B卫星发射以来星上定标系统相对精度发生了衰减。 相似文献
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Sriharsha Madhavan Junqiang Sun Xiaoxiong Xiong 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2021,67(10):3059-3071
Dust detection using remotely sensed measurements has been one of the challenging problems encountered by atmospheric scientists. MODerate Resolution Imaging Spectroradiometer (MODIS) on the Terra (T) and Aqua (A) platforms have been a versatile sensor for well over 21 and 18 years respectively, and have been extremely useful in the retrieval of aerosol information over the entire globe. The MODIS radiances from the Level 1B in general are expected to be within 5% accuracy in the reflective wavelengths and within 1% in the thermal emissive wavelengths. In this paper, we evaluate the sensitivity of previously developed dust detection technique based on thermal emissive wavelengths, which correspond to MODIS bands 20, 29, 31, and 32 respectively. The Thermal Emissive Dust Index (TEDI) performed very comparably to the traditional Aerosol Optical Thickness (AOT) retrievals by MODIS reflective channels. Since the MODIS Thermal Emissive Bands (TEB) are well calibrated on-orbit using a BlackBody (BB) source, the calibration of these long wave infrared bands is quite robust. As A-MODIS continues to perform well beyond its designed lifetime of 6 years, the instrument has undergone various levels of degradation during its mission time. As a consequence, it is imperative to check the impacts of calibration on the higher-level retrievals. In this paper, we rigorously analyze the sensitivity of TEDI due to the impact of calibration by the afore-mentioned TEB. The perturbation of the dominant (linear) calibration term demonstrated the following: first, there was a correlation in the sensitivity of the TEDI due to the uncertainty in the linear calibration term. Based on a perturbation in the linear calibration term for all aforementioned bands over a range of ±5% yielded the TEDI sensitivity to vary from approximately ?3.2% to about ?3.6%. When considering the uncertainty in each individual band significant changes were observed. The least change was observed for the perturbation in the calibration of band 20 with the TEDI sensitivity and the largest sensitivity in TEDI was observed in the perturbation of band 31 calibration. Thus, in the case of TEDI, noticeable sensitivity due to calibration uncertainty was observed in bands 29, 31, and 32, reiterating the importance of the TEB calibration in these bands. Also, the dust detection scheme based on A-MODIS was successfully transferred to the follow-on sensors such as Suomi (SNPP) and NOAA 20 (N20) VIIRS. The results presented in this paper would be extremely helpful in understanding impacts of calibration on the higher-level products for both current and future missions based on the MODIS heritage. Finally, the work also identifies the importance of radiometric fidelity in maintaining the accuracy of the dust detection. Results presented will show drastic improvement of the Saharan dust detection after the reduction of the electronic crosstalk in the 8.5 µm channel of T-MODIS. 相似文献
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本文所介绍的控制仪是实现对风洞应变天平自补偿静校设备进行自动加载和自动位置补偿的装置。文中扼要地介绍了设计思想、技术指标及其主要组成部件和电路原理图,还总结了控制仪在调试过程中排除电路干扰的若干措施。 相似文献
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7月14日,我国第一颗业务型静止气象卫星风云2号C在北京正式在轨交付。中国航天科技集团公司作为研制单位,将该卫星交付给应用单位——中国气象局。国防科工委主任张云川出席交付仪式,总装备部副部长张建启、国防科工委副主任兼国家航天局局长孙来燕、中国航天科技集团公司总经理张庆伟、中国气象局局长秦大河出席交付仪式并讲话。 相似文献
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Adaptive boosting for SAR automatic target recognition 总被引:3,自引:0,他引:3
Sun Y. Liu Z. Todorovic S. Li J. 《IEEE transactions on aerospace and electronic systems》2007,43(1):112-125
The paper proposed a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition (MSTAR) public release database. First MSTAR image chips are represented as fine and raw feature vectors, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) network as the base learner. Since the RBF network is a binary classifier, the multiclass problem was decomposed into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF network for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature 相似文献