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Three different surface modifiers, octadecyl trimethyl ammonium (ODTMA), octadecyl primary ammonium (ODPA), and decanediamine (DDA) were used to modify Na+? montmorillonite (MMT), and the resultant organoclays were coded as ODTMA‐MMT, ODPA‐MMT, DDA‐MMT, respectively. Rigid PU foams/organoclay composites were prepared by directly using organoclay as the blowing agent without the addition of water. Investigation shows that the morphology of the nanocomposites is greatly dependent on the surface modifiers of clay used in the composites. In detail, DDA‐MMT is partially exfoliated in the PU matrix with the smallest cell size, while two others are intercalated in the PU matrices with smaller cell sizes. The sequence of their cell sizes is pristine PU foams > rigid PU foams/ODTMA‐MMT > rigid PU foams/ODPA‐MMT > rigid PU foams/DDA‐MMT, and the average cell size of rigid PU foams/DDA‐MMT composites decreases evidently from 0.30 to 0.07 mm. Moreover, all rigid PU foams/organoclay composites show remarkable enhanced compressive and tensile strengths as well as dynamic properties than those of PU foams, and the enhancement degree coincides well with the relative extent of internal hydrogen bonding of materials and gallery spacing of organoclay. For example, in the case of rigid PU foams/DDA‐MMT composite, 214% increase in compressive strength and 148% increase in tensile strength compared with those of pure PU foams were observed. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007 相似文献
323.
Software systems can be represented as complex networks and their artificial nature can be investigated with approaches developed in network analysis.Influence maximization has been successfully applied on software networks to identify the important nodes that have the maximum influence on the other parts.However,research is open to study the effects of network fabric on the influence behavior of the highly influential nodes.In this paper,we construct class dependence graph(CDG)networks based on eight practical Java software systems,and apply the procedure of influence maximization to study empirically the correlations between the characteristics of maximum influence and the degree distributions in the software networks.We demonstrate that the artificial nature of CDG networks is reflected partly from the scale free behavior:the in-degree distribution follows power law,and the out-degree distribution is lognormal.For the influence behavior,the expected influence spread of the maximum influence set identified by the greedy method correlates significantly with the degree distributions.In addition,the identified influence set contains influential classes that are complex in both the number of methods and the lines of code(LOC).For the applications in software engineering,the results provide possibilities of new approaches in designing optimization procedures of software systems. 相似文献
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The preparation of very hydrophobic poly(vinylidene fluoride) (PVDF) membranes was explored by using two methods. The first one was the modified phase inversion method using a water/N,N‐dimethylacetamide (DMAc) mixture instead of pure water as a soft precipitation bath. The second method was a precipitation‐bath free method, that is, the PVDF/DMAc casting solution underwent gelation in the open air instead of being immersed into a precipitation bath. The morphology of the surface and cross section of the membranes was investigated by using scanning electron microscopy (SEM). It was found that the membranes exhibited certain micro‐ and nanoscale hierarchical roughness on the surface, which brought about an enhanced hydrophobicity of the membrane. The contact angle (CA) of the samples obtained by the second method was as high as 150° with water. The conventional phase inversion method preparing PVDF porous membrane using pure water as precipitation bath usually results in an asymmetric membrane with a dense skin layer having a CA close to that of a smooth PVDF surface. The modified approach avoided the formation of a skin‐layer and resulted in a porous and highly hydrophobic surface of PVDF. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 98: 1358–1363, 2005 相似文献
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K-shell分解法能快速识别复杂网络中的关键节点,但是无法辨别同壳层内节点重要性的差异,并且低估了处于网络边缘位置的高度值节点的重要性。针对这两个问题,提出一种基于K-shell位置和两阶邻居的节点重要性评估方法。该方法根据K-shell分解过程中节点移除的顺序细化节点的全局位置信息,然后综合考虑节点的局部拓扑结构信息和全局位置信息,利用两步长内邻居节点的K-shell位置信息度量节点的重要性。在八个真实网络上用传染病模型进行仿真实验,结果表明,所提方法与其他五种相关方法相比能更准确有效地评估并区分节点的重要性。 相似文献
329.
偏标记学习是一种重要的弱监督学习框架。在偏标记学习中,每个实例与一组候选标记相关联,它的真实标记隐藏在候选标记集合中,且在学习过程中不可获知。为了消除候选标记对学习过程的影响,提出了一种融合实例语义差别最大化和流型学习的偏标记学习方法(partial label learning by semantic difference and manifold learning, PL-SDML)。该方法是一个两阶段的方法:在训练阶段,基于实例的语义差别最大化准则和流型学习方法为训练实例生成标记置信度;在预测阶段,使用基于最近邻投票的方法为未知实例预测标记类别。在四组人工改造的UCI数据集中,在平均70%的情况下优于其他对比算法。在四组真实偏标记数据集中,相比其他对比算法,取得了0.3%~13.8%的性能提升。 相似文献
330.
合成孔径雷达(Synthetic Aperture Radar, SAR)作为一种主动微波传感器,被广泛应用于遥感对地观测。然而,传统上的雷达散射模型是基于几何光学近似的,忽略了地物目标之间的相互作用,即多次散射效应。为了充分挖掘和利用毫米波雷达影像中目标电磁散射特性信息,亟需对典型目标的多次散射效应进行建模仿真和验证。基于矩量法(Method of Moments, MoM)分析了球体、二面角等目标表面等效电流分布与分段成像结果的对应关系,并利用后向投影(Back-Projection, BP)算法进行电磁仿真成像,总结了单/双基地雷达模式对成像中散射机制的影响。结果表明:目标表面等效电流分布以及分段等效合成孔径张角会随入射角度发生改变,其中分段等效合成孔径张角会影响方位向分辨率;双基地雷达成像结果中包含更丰富的电磁散射信息。本研究可为SAR系统设计和验证、典型目标回波特性数据收集以及如何基于高解析度SAR影像进行目标识别等研究提供借鉴与参考。 相似文献