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141.
142.
Many video service sites headed by YouTube know what content requires copyright protection. However, they lack a copyright
protection system that automatically distinguishes whether uploaded videos contain legal or illegal content. Existing protection
techniques use content-based retrieval methods that compare the features of video. However, if the video encoding has changed
in resolution, bit-rate or codec, these techniques do not perform well. Thus, this paper proposes a novel video matching algorithm
even if the type of encoding has changed. We also suggest an intelligent copyright protection system using the proposed algorithm.
This can serve to automatically prevent the uploading of illegal content. The proposed method has represented the accuracy
of 97% with searching algorithm in video-matching experiments and 98.62% with automation algorithm in copyright-protection
experiments. Therefore, this system could form a core technology that identifies illegal content and automatically excludes
access to illegal content by many video service sites. 相似文献
143.
In this paper we have proposed a dynamic pricing scheme for the contributing peers in the Video on Demand (VoD) system. The
scheme provides an effective mechanism to maximize the profit through the residual resources of the contributing peers. A
utilization function is executed for each contributing peer to estimate the utility factor based on the parameters such as
initial setup cost, holding cost, chaining cost and salvage cost. In this paper, we urge an effective dynamic pricing algorithm
that efficiently utilizes a range of parameters with a varying degree of complexity. The key findings of the algorithm are
(i) each contributing peers are benefitted by the monetary based on its resource contributions to the VoD system and (ii)
a high degree of social optimum is established by proficiently aggregating the contributing peer’s resources with the overall
resources of the VoD system. We validate our claim by simulating the proposed dynamic pricing scheme with other standard pricing
schemes such as altruism, cost model and game theory perspective. The result of our dynamic pricing scheme shows the best
utility factor than other standard pricing schemes. 相似文献
144.
Wei-Lan Suo Bo Feng Zhi-Ping Fan 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(3):471-483
The decision making trial and evaluation laboratory (DEMATEL) method is a useful tool for analyzing correlations among factors
using crisp values. However, the crisp values are inadequate to model real-life situations due to the fuzziness and uncertainty
that are frequently involved in judgments of experts. The aim of this paper is to extend the DEMATEL method to an uncertain
linguistic environment. In this paper, the correlation information among factors provided by experts is in the form of uncertain
linguistic terms. A formula is first presented to transform correlation information from uncertain linguistic terms to trapezoidal
fuzzy numbers. Then, we aggregate the transformed correlation information of each expert into group information using the
operations of trapezoidal fuzzy numbers. The importance and classification of factors are determined via fuzzy matrix operations.
Furthermore, a causal diagram is constructed to vividly show the different roles of factors. Finally, an example is used to
illustrate the procedure of the proposed method. 相似文献
145.
146.
Tae Hyon Kim Kiyohiro Goto Hiroki Igarashi Kazuyuki Kon Noritaka Sato Fumitoshi Matsuno 《Artificial Life and Robotics》2012,16(4):514-518
Recently, many research projects and competitions have attempted to find an autonomous mobile robot that can drive in the
real world. In this article, we consider a path-planning method for an autonomous mobile robot that would be safe in a real
environment. In such a case, it is very important for the robot to be able to identify its own position and orientation in
real time. Therefore, we applied a localization method based on a particle filter. Moreover, in order to improve the safety
of such autonomous locomotion, we improved the path-planning algorithm and the generation of the trajectory so that it can
consider a region with a limited maximum velocity. In order to demonstrate the validity of the proposed method, we participated
in the Real World Robot Challenge 2010. The experimental results are given. 相似文献
147.
A K-nearest neighbours method based on imprecise probabilities 总被引:1,自引:1,他引:0
Sebastien Destercke 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(5):833-844
K-nearest neighbours algorithms are among the most popular existing classification methods, due to their simplicity and good
performances. Over the years, several extensions of the initial method have been proposed. In this paper, we propose a K-nearest
neighbours approach that uses the theory of imprecise probabilities, and more specifically lower previsions. We show that
the proposed approach has several assets: it can handle uncertain data in a very generic way, and decision rules developed
within this theory allow us to deal with conflicting information between neighbours or with the absence of close neighbour
to the instance to classify. We show that results of the basic k-NN and weighted k-NN methods can be retrieved by the proposed approach. We end with some experiments on the classical data sets. 相似文献
148.
Yiqiao Cai Jiahai Wang Jian Yin 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(2):303-330
Differential evolution (DE) is a simple and powerful population-based search algorithm, successfully used in various scientific
and engineering fields. However, DE is not free from the problems of stagnation and premature convergence. Hence, designing
more effective search strategies to enhance the performance of DE is one of the most salient and active topics. This paper
proposes a new method, called learning-enhanced DE (LeDE) that promotes individuals to exchange information systematically.
Distinct from the existing DE variants, LeDE adopts a novel learning strategy, namely clustering-based learning strategy (CLS).
In CLS, there are two levels of learning strategies, intra-cluster learning strategy and inter-cluster learning strategy.
They are adopted for exchanging information within the same cluster and between different clusters, respectively. Experimental
studies over 23 benchmark functions show that LeDE significantly outperforms the conventional DE. Compared with other clustering-based
DE algorithms, LeDE can obtain better solutions. In addition, LeDE is also shown to be significantly better than or at least
comparable to several state-of-art DE variants as well as some other evolutionary algorithms. 相似文献
149.
Zhibin Wu Jiuping Xu 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(4):577-589
In group decision making (GDM) using linguistic preference relations to obtain the maximum degree of agreement, it is desirable
to develop a consensus process prior to the selection process. This paper proposes two consensus models with linguistic information
to support the GDM consensus reaching process. Two different distance functions between linguistic preference relations are
introduced to measure both individual consistency and group consensus. Based on these measures, the consensus reaching models
are developed. The two models presented have the same concept that the expert whose preference is farthest from the group
preference needs to update their opinion according to the group preference relation. In addition, the convergence of the models
is proved. After achieving the predefined consensus level, each expert’s consistency indexes are still acceptable under the
condition that the initial preference relations are of satisfactory consistency. Finally, an example is given to show the
effectiveness of the models and to verify the theoretical results. 相似文献
150.
Yong Song Yi-bin Li Cai-hong Li Gui-fang Zhang 《International Journal of Control, Automation and Systems》2012,10(1):166-172
This article demonstrates that Q-learning can be accelerated by appropriately specifying initial Q-values using dynamic wave
expansion neural network. In our method, the neural network has the same topography as robot work space. Each neuron corresponds
to a certain discrete state. Every neuron of the network will reach an equilibrium state according to the initial environment
information. The activity of the special neuron denotes the maximum cumulative reward by following the optimal policy from
the corresponding state when the network is stable. Then the initial Q-values are defined as the immediate reward plus the
maximum cumulative reward by following the optimal policy beginning at the succeeding state. In this way, we create a mapping
between the known environment information and the initial values of Q-table based on neural network. The prior knowledge can
be incorporated into the learning system, and give robots a better learning foundation. Results of experiments in a grid world
problem show that neural network-based Q-learning enables a robot to acquire an optimal policy with better learning performance
compared to conventional Q-learning and potential field-based Qlearning. 相似文献