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21.
This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning
of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed
Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given
visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample
size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive
experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr
and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood
(ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given
sparse data, while CL-AIC should be chosen given moderate or large data sample sizes. 相似文献
22.
北斗三星无源定位技术 总被引:6,自引:2,他引:4
介绍了北斗双星定位系统的特点、功能、系统组成和工作原理,说明了北斗有源定位方式在应用方面的局限性。针对北斗有源定位方式不能无线电静默,和人们对具有无线电隐蔽性的卫星定位的需求,详细介绍一种北斗三星无源定位技术:包括工作原理、实现方法、定位精度分析和目前达到的定位精度。阐述了北斗三星无源定位技术的优点和应用形势。 相似文献
23.
在对铝合金铸件进行X射线实时探伤时,由于采集回来的许多图像的目标物体的轮廓比较模糊,这样在评片时容易造成对铸件的误判和漏判。针对这种现象并结合这类图像噪声源多的特点,在研究常用卷积算法的基础上,采用特殊卷积(特殊的卷积核)操作,同时根据雕刻的艺术底色为灰色,最后给卷积结果加上灰度值128,实验结果表明图像中目标物体的轮廓变得更清楚,同时处理后的图像具有比较好的三维效果,这给工作人员在看图时带来了方便。 相似文献
24.
Effendi Rusli Timothy O. Drews David L. Ma Richard C. Alkire Richard D. Braatz 《Journal of Process Control》2006,16(4):409-417
Robust nonlinear feedforward–feedback controllers are designed for a multiscale system that dynamically couples kinetic Monte Carlo (KMC) and finite difference (FD) simulation codes. The coupled codes simulate the copper electrodeposition process for manufacturing on-chip copper interconnects in electronic devices. The control objective is to regulate the current density subject to the condition that the steady-state fluctuation of the overpotential remains bounded within ±0.01 V. The controller designs incorporate a low-order stochastic model that captures the input–output behavior of the coupled KMC–FD code. The controllers achieve the objectives and the closed-loop responses implemented on the low-order model and the coupled KMC–FD code match well within stochastic variations. The nonlinear feedforward control reduces the rise time of the controller response while the feedback control ensures robustness in the presence of model uncertainty. 相似文献
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Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional
groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal
activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on
the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three
principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that
may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional
rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore
models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation
adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and
multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting
medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric
models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual
medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures
as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for
activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in
some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically,
using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct
good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments
in statistical relational learning.
Editors: Tamás Horváth and Akihiro Yamamoto 相似文献
29.
CMOS光接收机主放大器设计 总被引:1,自引:0,他引:1
利用CMOS工艺设计一种用于SDH STM 4速率级(622 Mb/s)光纤用户网的光接收机放大电路。此电路由输入/输出缓冲、主放大单元、偏置补偿电路4部分组成。通过直接耦合技术提高增益,降低功耗;利用有源电感负载提高系统带宽。采用商用SmartSpice电路仿真软件和CSMC HJ 0.6μm工艺参数对该电路进行仿真。结果表明,该电路在5 V工作电压下中频增益为81 dB,3 dB带宽为470 MHz。 相似文献
30.