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介绍了非织造布自动温被的开发过程、结构及性能特点 ,结合实际应用结果 ,通过与传统材料的对比分析 ,探讨了非织造布自动温被在各种大棚上应用的可行性和发展前景。 相似文献
123.
黄河流域多沙粗沙区植被覆盖变化与减水减沙效益分析 总被引:14,自引:0,他引:14
通过遥感手段和数字流域模型,研究了黄河流域多沙粗沙区近10来年的植被覆盖变化及其减水减沙效益。研究表明,多沙粗沙区90年代末期与80年代末期相比,植被覆盖度增加区域大约占50%,植被覆盖度没变化区域约占48%。植被覆盖度的变化导致了流域水沙条件的变化,90年代末期植被覆盖条件下,植被措施的减水减沙百分比不高;不同的降雨过程,植被措施的减水减沙效益有一定的差别;从不同降雨过程多年平均看,近10来年植被措施年均减水2.63亿m3,减沙1.13亿t,减水减沙比为2.75,即减1亿t沙的同时减水2.75亿m3,减水百分比为7.61,减沙百分比为10.56,减沙效益大于减水效益。 相似文献
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SWAT模型的参数灵敏度分析——以贡嘎山海螺沟不同植被类型流域为例 总被引:1,自引:0,他引:1
目前分布式水文模型在我国各种流域的水文过程模拟中已得到广泛的应用,为了深入了解不同分布式水文模型参数在水文循环过程中的影响程度,文中针对贡嘎山海螺沟4种不同植被类型流域,采用扰动分析方法定义模型参数的灵敏度并根据灵敏度的取值分为5级,基于SWAT(Soil&Water Assessment Tool)分布式水文模型研究了模型参数灵敏度在年内夏季和冬季的变化及其对水文过程模拟结果的影响。计算结果表明:冬季模型参数的灵敏度普遍偏高,会出现枯水期径流过程模拟误差较大的现象,而夏季模型参数的灵敏度相对降低;但在同时模拟汛期和枯期的径流过程情况下,前者的误差会小于后者。通过对SWAT模型参数在不同植被类型流域的灵敏度分析,将会有助于减少水文模拟的不确定性或能获得更好的估计结果。 相似文献
125.
WU Long-hua YANG Xiao-li College of Water Conservancy Hydropower Engineering Hohai University Nanjing China 《水动力学研究与进展(B辑)》2011,(6):723-729
The bending rigidity of submerged vegetation is closely related with vegetative drag force. This work aims at determining the effects of flow conditions and characteristics of vegetation on the bending rigidity of submerged vegetation. Based on the dimensional analysis method, the factors influencing the bending rigidity of individual submerged vegetation were analyzed. The relationship between the relative bending rigidity and its influencing factors was investigated by experimental observation, and a rela... 相似文献
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The Bangkok Metropolitan Region (BMR), located in the Chao Phraya River basin delta, is particularly vulnerable to floods, with susceptibility heightened by geographical aspects and rapid urbanization. This study aimed to assess spatiotemporal flood exposure and allow proper flood-risk recognition among all stakeholders through a three-phase flood exposure assessment. First, land use and land cover (LULC) changes were identified based on a 30-year Landsat time series. Second, built-up areas that overlapped with past flood inundation maps were designated as flood exposure areas. Third, a rainfall-runoff inundation (RRI) model simulated the 2011 Thailand Flood, the largest on record, by analyzing inundation depth implications across three decades. The findings revealed a dramatic increase in the use of built-up areas and the associated flood exposure. In 1992, built-up areas accounted for approximately 20% of the total area, sharply increasing to nearly 45% by 2022, according to the LULC classification. The flood exposure increased from 648.83 km2 in 1992 to 1681.26 km2 by 2022, demonstrating a linear trend. Notably, the catastrophic 2011 flood did not inhibit urbanization in flood-prone areas, highlighting the need for robust policies, such as the segmentation of flood-risk zones, to mitigate future exposure in the region. 相似文献
129.
Agricultural robots rely on semantic segmentation for distinguishing between crops and weeds to perform selective treatments and increase yield and crop health while reducing the amount of chemicals used. Deep‐learning approaches have recently achieved both excellent classification performance and real‐time execution. However, these techniques also rely on a large amount of training data, requiring a substantial labeling effort, both of which are scarce in precision agriculture. Additional design efforts are required to achieve commercially viable performance levels under varying environmental conditions and crop growth stages. In this paper, we explore the role of knowledge transfer between deep‐learning‐based classifiers for different crop types, with the goal of reducing the retraining time and labeling efforts required for a new crop. We examine the classification performance on three datasets with different crop types and containing a variety of weeds and compare the performance and retraining efforts required when using data labeled at pixel level with partially labeled data obtained through a less time‐consuming procedure of annotating the segmentation output. We show that transfer learning between different crop types is possible and reduces training times for up to 80%. Furthermore, we show that even when the data used for retraining are imperfectly annotated, the classification performance is within 2% of that of networks trained with laboriously annotated pixel‐precision data. 相似文献
130.
To investigate the structural compatibility of eco-porous concrete (EPC) and vegetation, a simulation method (sand penetration coefficient) of structure compatibility between the eco-porous concrete (EPC) and vegetation was put forward. Furthermore, the relation between sand penetration coefficient and pore characteristics were analyzed, and the technology of acquiring equivalent pore size was optimized. The results present that the new simulation method, characterized by sand penetration coefficient, can effectively assess the ability of eco-porous concrete (EPC) to allow the root system to pass through. In addition, the real pore structure shape by “rubbing technology” can be easily and quickly obtained. 相似文献