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51.
During summer and autumn 2007, a 11 GHz microwave radiometer was deployed in an experimental tree plantation in Sardinilla, Panama. The opacity of the tree canopy was derived from incoming brightness temperatures received on the ground. A collocated eddy-covariance flux tower measured water vapor fluxes and meteorological variables above the canopy. In addition, xylem sapflow of trees was measured within the flux tower footprint. We observed considerable diurnal differences between measured canopy opacities and modeled theoretical opacities that were closely linked to xylem sapflow. It is speculated that dielectric changes in the leaves induced by the sapflow are causing the observed diurnal changes. In addition, canopy intercepted rain and dew formation also modulated the diurnal opacity cycle. With an enhanced canopy opacity model accounting for water deposited on the leaves, we quantified the influence of canopy stored water (i.e. intercepted water and dew) on the opacity. A time series of dew formation and rain interception was directly monitored during a period of two weeks. We found that during light rainfall up to 60% of the rain amount is intercepted by the canopy whereas during periods of intense rainfall only 4% were intercepted. On average, 0.17 mm of dew was formed during the night. Dew evaporation contributed 5% to the total water vapor flux measured above the canopy. 相似文献
52.
Sebastian GunrebenAuthor Vitae 《Performance Evaluation》2011,68(3):237-255
Contention resolution schemes in optical burst switched networks (OBS) as well as contention avoidance schemes delay burst delivery and change the burst arrival sequence. The burst arrival sequence usually changes the packet arrival sequence and degrades the upper layer protocols performance, e.g., the throughput of the transmission control protocol (TCP).In this paper, we present and analyze a detailed burst reordering model for two widely applied burst assembly strategies: time-based and random selection. We apply the IETF reordering metrics and calculate explicitly three reordering metrics: the reordering ratio, the reordering extent metric and the TCP relevant metric. These metrics allow estimating the degree of reordering in a certain network scenario. They estimate the buffer space at the destination to resolve reordering and quantify the number of duplicate acknowledgements relevant for investigations on the transmission control protocol.We show that our model reflects the burst/packet reordering pattern of simulated OBS networks very well. Applying our model in a network emulation scenario, enables investigations on real protocol implementations in network emulation environments. It therefore serves as a substitute for extensive TCP over OBS network simulations with a focus on burst reordering. 相似文献
53.
Fault detection for Byzantine quorum systems 总被引:1,自引:0,他引:1
Alvisi L. Malkhi D. Pierce E. Reiter M.K. 《Parallel and Distributed Systems, IEEE Transactions on》2001,12(9):996-1007
In this paper, we explore techniques to detect Byzantine server failures in asynchronous replicated data services. Our goal is to detect arbitrary failures of data servers in a system where each client accesses the replicated data at only a subset (quorum) of servers in each operation. In such a system, some correct servers can be out-of-date after a write and can therefore, return values other than the most up-to-date value in response to a client's read request, thus complicating the task of determining the number of faulty servers in the system at any point in time. We initiate the study of detecting server failures in this context, and propose two statistical approaches for estimating the risk posed by faulty servers based on responses to read requests 相似文献
54.
Vivanco F Mas S Darde VM De la Cuesta F Alvarez-Llamas G Barderas MG 《Proteomics. Clinical applications》2007,1(9):1102-1122
The characterization of patients with acute coronary syndromes (ACS) at the molecular and cellular levels provides a novel vision for understanding the pathological and clinical expression of the disease. Recent advances in proteomic technologies permit the evaluation of systematic changes in protein expression in many biological systems and have been extensively applied to cardiovascular diseases (CVD). The cardiovascular system is in permanent intimate contact with blood, making blood-based biomarker discovery a particularly worthwhile approach. Thus, proteomics can potentially yield novel biomarkers reflecting CVD, establish earlier detection strategies, and monitor response to therapy. Here we review the different proteomic strategies used in the study of atherosclerosis and the novel proteins differentially expressed and secreted by atherosclerotic lesions which constitute novel potential biomarkers (HSP-27, Cathepsin D). Special attention is paid to MS-Imaging of atheroma plaque and the generation, for the first time, of 2-D images of lipids, showing the distribution of these molecules in the different areas of the atherosclerotic lesions. In addition new potential biomarkers have been identified in plasma (amyloid A1α, transtherytin), circulating cells (protein profile in monocytes from ACS patients) and individual cells constituents of atheroma plaques (endothelial, VSMC, macrophages) which provide novel insights into vascular pathophysiology. 相似文献
55.
Evaluation of Crop Models for Simulating and Optimizing Deficit Irrigation Systems in Arid and Semi-arid Countries Under Climate Variability 总被引:2,自引:2,他引:0
Sebastian?KlossEmail author Raji?Pushpalatha Kefasi?J.?Kamoyo Niels?Schütze 《Water Resources Management》2012,26(4):997-1014
The variability of fresh water availability in arid and semi-arid countries poses a serious challenge to farmers to cope with
when depending on irrigation for crop growing. This has shifted the focus onto improving irrigation management and water productivity
(WP) through controlled deficit irrigation (DI). DI can be conceived as a strategy to deal with these challenges but more
knowledge on risks and chances of this strategy is urgently needed. The availability of simulation models that can reliably
predict crop yield under the influence of soil, atmosphere, irrigation, and agricultural management practices is a prerequisite
for deriving reliable and effective deficit irrigation strategies. In this context, this article discusses the performance
of the crop models CropWat, PILOTE, Daisy, and APSIM when being part of a stochastic simulation-based approach to improve
WP by focusing primarily on the impact of climate variability. The stochastic framework consists of: (i) a weather generator
for simulating regional impacts of climate variability; (ii) a tailor-made evolutionary optimization algorithm for optimal
irrigation scheduling with limited water supply; and (iii) the above mentioned models for simulating water transport and crop
growth in a sound manner. The results present stochastic crop water production functions (SCWPFs) that can be used as basic
tools for assessing the impact on the risk for the potential yield due to water stress and climate variability. Example simulations
from India, Malawi, France and Oman are presented and the suitability of these crop models to be employed in a framework for
optimizing WP is evaluated. 相似文献
56.
Sebastian Scherer Lyle Chamberlain Sanjiv Singh 《Robotics and Autonomous Systems》2012,60(12):1545-1562
Helicopters are valuable since they can land at unprepared sites; however, current unmanned helicopters are unable to select or validate landing zones (LZs) and approach paths. For operation in unknown terrain it is necessary to assess the safety of a LZ. In this paper, we describe a lidar-based perception system that enables a full-scale autonomous helicopter to identify and land in previously unmapped terrain with no human input.We describe the problem, real-time algorithms, perception hardware, and results. Our approach has extended the state of the art in terrain assessment by incorporating not only plane fitting, but by also considering factors such as terrain/skid interaction, rotor and tail clearance, wind direction, clear approach/abort paths, and ground paths.In results from urban and natural environments we were able to successfully classify LZs from point cloud maps. We also present results from 8 successful landing experiments with varying ground clutter and approach directions. The helicopter selected its own landing site, approaches, and then proceeds to land. To our knowledge, these experiments were the first demonstration of a full-scale autonomous helicopter that selected its own landing zones and landed. 相似文献
57.
Pernkopf F Wohlmayr M Tschiatschek S 《IEEE transactions on pattern analysis and machine intelligence》2012,34(3):521-532
We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first. 相似文献
58.
This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach. 相似文献
59.
This research is an effort towards providing higher level Design for Environment (DFE) tools for a broad industrial region. Issues ranging from the levels immediately above existing design tools, to the envisioned highest level for a broad geographical region are discussed. A tool for the regional planning of the DFE activities is proposed, considering a model construction based on material flows across the industry. The Multi-Lifecycle approach is supported by organizing the input/output flows for industries, potentially utilizing waste material, side products and recycling. Capitalizing on the conceptual integration of the design and process activities, an Abstract Design Environment is used for the design of the basically process oriented material flow tool. Within the context, the relations among design, process and flow-modeling concepts are discussed. 相似文献
60.
Bayesian Landmark Learning for Mobile Robot Localization 总被引:10,自引:0,他引:10
To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (e.g., what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL outperforms two other recent approaches to mobile robot localization. 相似文献