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51.
Creating the brain and interacting with the brain: an integrated approach to understanding the brain
In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the ‘understanding the brain by creating the brain’ approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain–machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop. 相似文献
52.
Evidence suggests that transient visual information, such as animations, may be more challenging to learn than static visualizations. However, when a procedural-manipulative task is involved, our evolved embodied cognition seems to reverse this transitory challenge. Hence, for object manipulative tasks, instructional animations may be more suitable than statics. We investigated this argument further by comparing animations with statics using a Lego task shown to university students, by examining three potential moderators of effectiveness: (a) the environment of manipulation (virtual or physical), (b) the quality of visual information (focused or unfocused), and (c) the presence of hands (no hands or with hands). In Experiment 1 we found an advantage of animation over statics, and no differences among the environments. In Experiment 2, we again observed an animation advantage, a small advantage of focused static information compared to unfocused static information, and a positive effect of not showing the hands. 相似文献
53.
BDI模型能够很好地解决在特定环境下的Agent的推理和决策问题,但在动态和不确定环境下缺少决策和学习的能力。强化学习解决了Agent在未知环境下的决策问题,却缺少BDI模型中的规则描述和逻辑推理。针对BDI在未知和动态环境下的策略规划问题,提出基于强化学习Q-learning算法来实现BDI Agent学习和规划的方法,并针对BDI的实现模型ASL的决策机制做出了改进,最后在ASL的仿真平台Jason上建立了迷宫的仿真,仿真实验表明,在加入Q-learning学习机制后的新的ASL系统中,Agent在不确定环境下依然可以完成任务。 相似文献
54.
近年来,我国传统暴力犯罪与成年人犯罪呈下降态势,但是,犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力,打击各类违法犯罪事件,本文针对犯罪数据,提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类,然后进行数据降维提取关键属性生成特征数据,继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习,从而预测犯罪案由。实验结果表明,与传统方法相比,本文方法具有更好的预测效果,为公安实践工作中类似案件的侦破和预防,提供新的路径支撑。 相似文献
55.
56.
Single image super resolution (SISR) is an important research content in the
field of computer vision and image processing. With the rapid development of deep
neural networks, different image super-resolution models have emerged. Compared to
some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods
using traditional convolutional neural networks, SISR based on generative adversarial
networks (GAN) has achieved the most advanced visual performance. In this review, we
first explore the challenges faced by SISR and introduce some common datasets and
evaluation metrics. Then, we review the improved network structures and loss functions
of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of
different networks are analyzed by multiple comparative experiments. Finally, we
summarize the paper and look forward to the future development trends of GAN-based
perceptual SISR. 相似文献
57.
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
58.
The standardization data for the California Verbal Learning Test-Second Edition (CVLT-II; D. C. Delis, J. H. Kramer, E. Kaplan, & B. A. Ober, 2000) were used to evaluate the base rate of 6 specific discrepancies between various key variables. The results indicated that CVLT-II performance discrepancies should equal or exceed 1 or 1.5 z score points (depending on the individual comparison) in the hypothesized direction to be considered potentially unusual. However, because about 1 in 3 persons in the standardization sample displayed at least 1 such large discrepancy, it is concluded that these base rates should be viewed only as a starting point for interpretation. (PsycINFO Database Record (c) 2010 APA, all rights reserved) 相似文献
59.
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
60.
Masters Rich S. W.; MacMahon Ken M. A.; Pall Hardev S. 《Canadian Metallurgical Quarterly》2004,49(1):79
Objective: This study examined whether disruption of performance is moderated in Parkinson's disease (PD) patients who acquire their motor behaviors in an implicit manner. Method: Twenty-seven patients with PD learned a hammering task in errorless (implicit) or errorful (explicit) conditions and were tested for robustness of motor performance under a secondary task load, which required them to continuously count backward as they performed the hammering task. Results: Patients in the errorless (implicit) motor learning condition exhibited robustness to secondary task loading, whereas patients in the errorful (explicit) motor learning condition did not. Conclusions: Implicit motor learning techniques should be considered by PD rehabilitation specialists in cases in which existing disruption to movements is exacerbated by conscious control. (PsycINFO Database Record (c) 2010 APA, all rights reserved) 相似文献