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1.
滚动轴承性能退化评估是预诊断的提前和基础,对在役滚动轴承实施在线状态监测和性能退化评估具有重要意义。针对概率相似度量评估方法存在模型复杂、容易过早饱和等现象,提出一种基于自回归时序 (autoregressive model,简称AR)模型和多元状态估计(multivariate state estimation technique, 简称MSET)的滚动轴承性能在线评估方法,其中AR模型用于提取轴承振动信号的状态特征,MSET模型用于重构AR模型系数。首先,提取正常运行状态下振动信号的AR模型系数构建MSET模型的历史记忆矩阵;其次,将待测信号的AR系数作为观测向量输入MSET模型中得到重构后的估计向量;最后,由原始AR系数和重构AR系数分别构造自回归模型,并各自完成对待测信号的时序建模,将两自回归模型所得残差序列的均方根值之差作为性能劣化程度指标。离散实验数据和全寿命疲劳实验数据分析结果表明,该方法能够有效检测早期故障,且具有与轴承故障发展趋势一致性更好等优点。  相似文献   
2.
近年来随着高性能计算技术的不断发展,依托先进的超级计算机和数学物理计算方法,对核反应堆开展多物理、多尺度计算成为前沿研究热点。根据反应堆堆芯多物理耦合分析需求,研究了多物理耦合算法,构建了基于中子输运、燃耗、热工子通道的堆芯多物理耦合系统,完成耦合程序开发,实现中子物理、燃耗、热工子通道的多物理耦合计算。利用压水堆组件模型与快堆模型开展输运-燃耗耦合计算测试和核-热耦合计算测试,初步验证了耦合系统功能。  相似文献   
3.
LiNbO3 crystals activated by Sm3+ and co-doped with Zr4+ (Sm:Zr:LN) or Hf4+ (Sm:Hf:LN) were prepared by the Czochralski method. Detailed investigation on spectroscopic properties was conducted on the frame of Judd-Ofelt (J-O) theory. The J-O intensity parameters Ωi (i = 2, 4, 6), fluorescence branching ratios and radiative lifetime of excited level 4G5/2 were determined. Furthermore, the thermal stability of the strong orange-red emissions obtained under near-UV excitation in both crystals was evaluated. As high as 100% and 97% of integrated intensities at room temperature in Sm:Zr:LN and Sm:Hf:LN respectively were retained at 423 K, demonstrating the suppressed thermal attenuation. The temperature sensing performance based on fluorescence intensity ratio strategy was degraded at higher temperatures with relatively low sensitivities, while the shift of CIE chromaticity coordinates of Sm:Zr:LN and Sm:Hf:LN in the orange-red region was insignificant, demonstrating the color constancy with increasing temperature. With the efficient and thermally stable orange-red luminescence, Sm:Zr:LN and Sm:Hf:LN could serve as promising candidate materials for near-UV excited white light-emitting diodes.  相似文献   
4.
海上油田大斜度井日益增多,而大斜度井固井质量评价对井控安全、完井射孔、后期老井开窗侧钻等关系重大。随钻声波测井工具不受井斜限制、操作简单,可获得高质量的声波数据资料。本项目将随钻声波测井技术与完井刮管作业相结合,在刮管作业的同时进行固井质量评价,并在渤海油田某区块成功运用,获得很好效果。  相似文献   
5.
Cellulose microfibers (CMFs) having surfaces modified with polydopamine (PDPA) and octadecylamine (ODA) were prepared, and their reinforcing abilities for polypropylene (PP) were investigated. The PDPA coating was made via self-polymerization of dopamine (P-CMF), and subsequent alkylation was conducted by the reaction with ODA (OP-CMF). The modified CMFs exhibited improved dispersibility in the PP matrix due to the reduced hydrophilicity. The OP-CMF/PP composite prepared by batch mixing had a higher tensile modulus compared to that for the pure PP and composites with unmodified CMFs. However, excess alkylation lowered the tensile modulus, and the presence of an optimal degree of alkylation was demonstrated. The CMF/PP-IM composites fabricated by injection molding exhibited improved tensile properties compared to those prepared by batch mixing. Both the tensile modulus and yield stress were increased by increasing the CMF content and improved by the surface modification of the CMFs.  相似文献   
6.
近年来,随着油藏开采进一步深入,开发难度越来越大.裂缝性油藏在注水开发过程中,注入水沿储层微裂缝方向快速突进,油井暴性水淹,导致开发效果变差.常规井间地震解释中,解释结果受到多种因素的影响,很多细小的不连续反射轴会被平滑掉,出现小断层、微裂缝遗漏等现象.结合相干体解释技术,减少解释人员的主观判断干预和经验因素,更客观、合理地对微裂缝及小断层位置进行刻画.以中国长庆油田某地区井间地震勘探实例为基础,结合常规地震反射剖面和地震相干技术,进行小断层、微裂缝综合解释,提高断层的解释精度,并结合吸水剖面验证其真实性与可靠性.  相似文献   
7.

Sampling or task jitter affects the performance of digital control systems but realistic simulation of this effect has not been possible to date. Our previous work has developed a novel method to simulate sampling jitter in MATLAB/Simulink simulation software where the jitter is generated randomly. What has been missing is a way to capture sampling jitter from a target platform and then feed this timing information into the simulation. This paper presents a low-cost and novel solution to these problems. The method uses an Arduino board to capture task jitter from two different hardware platforms with multiple stressing conditions. Then the recorded performance data is used to drive realistic simulations of a control system. Measurement shows that the task jitter data does not follow any specific random distribution such as Gaussian or Uniform. Furthermore, very occasional timing patterns, which may not be picked up while testing a real system, can result in extreme controller responses. This novel method allows comparisons of different platforms and reduces the effort required to choose the most appropriate platform for full implementation.

  相似文献   
8.
Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.  相似文献   
9.
Fire spread and growth on real‐scale four cushion mock‐ups of residential upholstered furniture (RUF) were investigated with the goal of identifying whether changes in five classes of materials (barrier, flexible polyurethane foam, polyester fiber wrap, upholstery fabric, and sewing thread), referred to as factors, resulted in statistically significant changes in burning behavior. A fractional factorial experimental design plus practical considerations yielded a test matrix with 20 material combinations. Experiments were repeated a minimum of two times. Measurements included fire spread rates derived from video recordings and heat release rates (HRRs). A total of 13 experimental parameters (3 based on the videos and 10 on the HRR results), referred to as responses, characterized the measurements. Statistical analyses based on Main Effects Plots (main effects) and Block Plots (main effects and factor interactions) were used. The results showed that three of the factors resulted in statistically significant effects on varying numbers of the 13 responses. The Barrier and Fabric factors had the strongest main effects with roughly comparable magnitudes. Foam was statistically significant for fewer of the responses and its overall strength was weaker than for Barrier and Fabric. No statistically significant main effects were identified for Wrap or Thread. Multiple two‐term interactions between factors were identified as being statistically significant. The Barrier*Fabric interaction resulted in the highest number of and strongest statistically significant effects. The existence of two‐term interactions means that it will be necessary to consider their effects in approaches designed to predict the burning behavior of RUF.  相似文献   
10.
在自动驾驶场景中,对前车尾灯的检测是一个广泛且具有研究意义的问题。Darknet53是YOLOv3的特征提取网络,其使用5个残差单元对原始图像进行特征提取并采用三尺度的特征图进行融合预测,尺寸越小对大目标的特征表达能力越强。因为尾灯检测属于小目标检测,所以本文舍去Darknet53的最后一个残差单元,同时增加小尺度特征提取残差单元的重复次数。针对K-means聚类算法存在k值难以确定以及对初始聚类中心敏感的问题,本文使用K-means+〖KG-*3〗+聚类算法获取anchor值,同时结合IOU距离度量指标。实验结果表明,改进后的YOLOv3网络上尾灯检测的准确率和检测速度都要高于改进前的,mAP由79.63%提高到89.32%,单张图片检测时间由0.014 s缩短到0.01 s。对比其他主流目标检测框架,本文改进的YOLOv3模型具有优越的检测性能。  相似文献   
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