全文获取类型
收费全文 | 1287篇 |
免费 | 108篇 |
国内免费 | 13篇 |
专业分类
电工技术 | 38篇 |
综合类 | 6篇 |
化学工业 | 301篇 |
金属工艺 | 26篇 |
机械仪表 | 65篇 |
建筑科学 | 64篇 |
矿业工程 | 3篇 |
能源动力 | 85篇 |
轻工业 | 120篇 |
水利工程 | 28篇 |
石油天然气 | 24篇 |
无线电 | 122篇 |
一般工业技术 | 194篇 |
冶金工业 | 42篇 |
原子能技术 | 8篇 |
自动化技术 | 282篇 |
出版年
2024年 | 7篇 |
2023年 | 40篇 |
2022年 | 48篇 |
2021年 | 93篇 |
2020年 | 85篇 |
2019年 | 95篇 |
2018年 | 123篇 |
2017年 | 120篇 |
2016年 | 95篇 |
2015年 | 58篇 |
2014年 | 95篇 |
2013年 | 153篇 |
2012年 | 82篇 |
2011年 | 87篇 |
2010年 | 53篇 |
2009年 | 41篇 |
2008年 | 27篇 |
2007年 | 19篇 |
2006年 | 20篇 |
2005年 | 7篇 |
2004年 | 10篇 |
2003年 | 8篇 |
2002年 | 2篇 |
2001年 | 3篇 |
2000年 | 3篇 |
1999年 | 5篇 |
1998年 | 6篇 |
1997年 | 7篇 |
1996年 | 3篇 |
1995年 | 1篇 |
1994年 | 3篇 |
1992年 | 1篇 |
1991年 | 3篇 |
1988年 | 1篇 |
1987年 | 1篇 |
1985年 | 1篇 |
1977年 | 2篇 |
排序方式: 共有1408条查询结果,搜索用时 15 毫秒
31.
32.
Maryam Amiri Hassan Bakhshandeh Amnieh Mahdi Hasanipanah Leyli Mohammad Khanli 《Engineering with Computers》2016,32(4):631-644
Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R 2), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations. 相似文献
33.
Many environments and scenarios contain rough and irregular terrain and are inaccessible or hazardous for humans. Robotic automation is preferred in lieu of placing humans at risk. Legged locomotion is more advantageous in traversing complex terrain but requires constant monitoring and correction to maintain system stability. This paper presents a multi-legged reactive stability control method for maintaining system stability under external perturbations. Assuming tumbling instability and sufficient friction to prevent slippage, the reactive stability control method is based solely on the measured foot forces normal to the contact surface, reducing computation time and sensor information. Under external perturbations, the reactive stability control method opts to either displace the CG or the foot contacts of the robot based on the measured foot force distribution. Details describing the reactive stability control method are discussed including algorithms and an implementation example. An experimental demonstration of the reactive stability control method is presented. The experiment was conducted on a hexapod robot platform retrofitted with a tiny computer and force sensitive resistors to measure the foot forces. The experimental results show that the presented reactive stability control strategy prevents the robot from tipping over under external perturbation. 相似文献
34.
Multiple kernel learning (MKL) approach has been proposed for kernel methods and has shown high performance for solving some real-world applications. It consists on learning the optimal kernel from one layer of multiple predefined kernels. Unfortunately, this approach is not rich enough to solve relatively complex problems. With the emergence and the success of the deep learning concept, multilayer of multiple kernel learning (MLMKL) methods were inspired by the idea of deep architecture. They are introduced in order to improve the conventional MKL methods. Such architectures tend to learn deep kernel machines by exploring the combinations of multiple kernels in a multilayer structure. However, existing MLMKL methods often have trouble with the optimization of the network for two or more layers. Additionally, they do not always outperform the simplest method of combining multiple kernels (i.e., MKL). In order to improve the effectiveness of MKL approaches, we introduce, in this paper, a novel backpropagation MLMKL framework. Specifically, we propose to optimize the network over an adaptive backpropagation algorithm. We use the gradient ascent method instead of dual objective function, or the estimation of the leave-one-out error. We test our proposed method through a large set of experiments on a variety of benchmark data sets. We have successfully optimized the system over many layers. Empirical results over an extensive set of experiments show that our algorithm achieves high performance compared to the traditional MKL approach and existing MLMKL methods. 相似文献
35.
Elnaz Bigdeli Mahdi Mohammadi Bijan Raahemi Stan Matwin 《Pattern Analysis & Applications》2017,20(1):183-199
Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback–Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods. 相似文献
36.
The structural properties of networked control systems with both bandwidth limitations and delays are investigated. Sufficient conditions are given for controllability (stabilizability) and reconstructibility (detectability). Our results enhance previous works by capturing bandwidth limitations and delays simultaneously. The adopted modeling framework could be readily used in control and estimation methods, including optimal and predictive schemes. It also facilitates the use of scheduling optimization algorithms in conjunction with the control scheme presented. 相似文献
37.
Determining the Main Factors in Declining the Urmia Lake Level by Using System Dynamics Modeling 总被引:6,自引:2,他引:4
Urmia Lake in Iran is the second largest saline lake in the world. This ecosystem is the home for different species. Due to
various socio-economical and ecological criteria, Urmia Lake has important role in the Northwestern part of the country but
it has faced many problems in recent years. Because of droughts, overuse of surface water resources and dam constructions,
water level has decreased in such a way that one quarter of the lake has changed to saline area in the last 10 years. The
purpose of this research is to determine the main factors which reduce the lake’s water level. To this end, a simulation model,
based on system dynamics method, is developed for the Urmia Lake basin to estimate the lake’s level. After successful verification
of the model, results show that (among the proposed factors) changes in inflows due to the climate change and overuse of surface
water resources is the main factor for 65% of the effect, constructing four dams is responsible for 25% of the problem, and
less precipitation on lake has 10% effect on decreasing the lake’s level in the recent years. In the future, the model also
can be used by managers as a decision support system to find the effects of building new dams or other infrastructures. 相似文献
38.
Farzad Hashemzadeh Iraj Hassanzadeh Mahdi Tavakoli Ghasem Alizadeh 《Journal of Intelligent and Robotic Systems》2012,68(3-4):245-259
In this paper, we introduce a new adaptive controller design scheme for nonlinear telerobotic systems with varying time delays where the delays and their variation rates are unknown. The designed controller has the ability to synchronize the state behaviors of the local and the remote robots. In this paper, asymptotic stability in the presence of varying time delays is of interest. Using the proposed controller, asymptotic stability of the bilateral telerobotic system subject to any bounded yet unknown varying delay with a bounded yet unknown rate of change can be guaranteed. Besides the varying time delay, the proposed adaptive controller has the ability to adapt to the parameter variations in the local and the remote robots’ dynamics. It is shown that position and velocity errors between the local and the remote manipulators converge to the zero asymptotically, thus ensuring teleoperation transparency. Experimental and simulation results with a pair of PHANToM haptic devices and a pair of planar manipulators under varying time delays in the communication channel demonstrate the effectiveness of the proposed scheme. 相似文献
39.
Implementation of genetic algorithm in an embedded microcontroller-based polarization control system
Ghazaleh Mamdoohi Ahmad Fauzi Abas Khairulmizam Samsudin Noor Hisham Ibrahim Ariya Hidayat Mohd Adzir Mahdi 《Engineering Applications of Artificial Intelligence》2012,25(4):869-873
Implementation of genetic algorithm in a PIC32MX microcontroller-based polarization control system is proposed and demonstrated. The controller measures the signal intensity that is used to estimate the genetic value. This process is controlled by the genetic algorithm rather than referring to the Look-Up-Table as implemented in existing solutions. To reach optimum performance, the code is optimized by using the best genetic parameters so that the fastest execution time can be achieved. An ability of genetic algorithm to work efficiently in polarization control system possesses many advantages including easy code construction, low memory consumption and fast control speed. Genetic algorithm is intelligent enough to be used for endless polarization stabilization and in the worst case, able to stabilize the polarization changes in 300 μs. In the best case the response time can reach 17 μs. 相似文献
40.
Mahdi Ghasemi-Varnamkhasti Seyed Saeid MohtasebiMaria Luz Rodriguez-Mendez Jesus LozanoSeyed Hadi Razavi Hojat AhmadiConstantin Apetrei 《Expert systems with applications》2012,39(4):4315-4327
Sensory evaluation is the application of knowledge and skills derived from several different scientific and technical disciplines, physiology, chemistry, mathematics and statistics, human behavior, and knowledge about product preparation practices. This research was aimed to evaluate aftertaste sensory attributes of commercial non-alcoholic beer brands (P1, P2, P3, P4, P5, P6, P7) by several chemometric tools. These attributes were bitter, sour, sweet, fruity, liquorice, artificial, body, intensity and duration. The results showed that the data are in a good consistency. Therefore, the brands were statistically classified in several categories. Linear techniques as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were performed over the data that revealed all types of beer are well separated except a partial overlapping between zones corresponding to P4, P6 and P7. In this research, for the confirmation of the groups observed in PCA and in order to calculate the errors in calibration and in validation, PLS-DA technique was used. Based on the quantitative data of PLS-DA, the classification accuracy values were ranked within 49-86%. Moreover, it was found that the classification accuracy of LDA was much better than PCA. It shows that this trained sensory panel can discriminate among the samples except an overlapping between two types of beer. Also, two types of artificial networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Back Propagation (BP) learning method. The highest classification success rate (correct predicted number over total number of measurements) of about 97% was obtained for RBF followed by 94% for BP. The results obtained in this study could be used as a reference for electronic nose and electronic tongue in beer quality control. 相似文献