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61.
In this paper, acoustic emission (AE) intensity analysis was utilized to assess the concrete cover cracking due to steel corrosion in reinforced concrete structures. A total of 30 reinforced concrete prism samples were tested under an accelerated corrosion test coupled with continuous AE monitoring using attached AE sensors. The samples were cast with three concrete cover thicknesses (20, 30 and 40 mm) around steel bars and were exposed to five percentages of steel mass loss: 1, 2, 3, 4, and 5 %. The cover cracking was monitored daily by visual inspection to detect and measure crack widths. Different AE signal parameters were continuously recorded during the tests, including number of hits, signal strength, energy, and amplitude. The acquired AE events were subjected to an intensity analysis of signal strength to estimate historic index (H (t)) and severity (\(S_r)\). In addition, a b value analysis was conducted on all AE data and the results were compared to those obtained from the intensity analysis. The results showed that increasing the cover thickness had no significant impact on AE parameters (number of hits, cumulative signal strength, cumulative energy, amplitude, H (t), and \(S_{r})\) at similar values of crack growth. Nonetheless, varying the cover thickness from 20 to 40 mm resulted in lower crack widths and slightly higher b values at the same levels of steel mass loss. It was also found that both H (t) and \(S_r\) showed a more evident correlation with the values of crack growth than did b values, regardless of cover thickness or percentage of steel mass loss. Finally, an intensity classification chart was developed to quantify the cover crack growth based on the values of H (t) and \(S_{r}\). 相似文献
62.
Katherine E. Ridgley Ahmed A. Abouhussien Assem A. A. Hassan Bruce Colbourne 《Materials and Structures》2018,51(5):119
The key objective of this investigation was to evaluate the abrasion resistance of self-consolidating concrete (SCC) with and without synthetic fibers (SynFs). The abrasion resistance of normal concrete was also investigated in this study for comparison. The abrasion test was performed on concrete specimens according to the rotating-cutter method along with continuous monitoring of acoustic emission (AE) using attached AE sensors. The effects of changing concrete type and incorporating various types (flexible and semi-rigid) and lengths of SynFs on the abrasion behaviour were investigated with the aid of AE analysis. AE signal characteristics such as amplitude, signal strength, number of hits, and duration were gathered during testing. Furthermore, the collected AE data was used to complete b-value analysis as well as intensity analysis resulting in three additional parameters: b-value, severity (Sr), and historic index (H(t)). The results showed that the AE parameters were directly correlated with the abrasion damage in all tested mixtures. Adding SynFs to all SCC mixtures enhanced their abrasion resistance. The flexible fibers variety exhibited better abrasion performance on average than the semi-rigid fibers. Meanwhile, longer fibers showed lower abrasion resistance than the shorter ones with the same type. The results also indicated that AE intensity analysis was able to determine the ranges for H(t) and Sr that identify the extent of damage due to abrasion of SynF-reinforced SCC. 相似文献
63.
Magnetic nanoparticles (NPs) were functionalised with soy protein isolate (SPI) and bovine serum albumin (BSA) for inulinase immobilisation. The results revealed the nanomagnetite size of about 50 nm with a polydispersity index (PDI) of 0.242. The average size of the SPI NPs prepared by using acetone was 80–90 nm (PDI, 0.277), and SPI–BSA NPs was 80–90 nm (PDI, 0.233), and their zeta potential was around −34 mV. The mean diameter of fabricated Fe3 O4 @SPI–BSA NPs was <120 nm (PDI, 0.187). Inulinase was covalently immobilised successfully through glutaraldehyde on Fe3 O4 @SPI–BSA NPs with 80% enzyme loading. Fourier transform infrared spectra, field emission scanning electron microscopy, and transmission electron microscopy images provided sufficient proof for enzyme immobilisation on the NPs. The immobilised inulinase showed maximal activity at 45°C, which was 5°C higher than the optimum temperature of the free enzyme. Also, the optimum pH of the immobilised enzyme was shifted from 6 to 5.5. Thermal stability of the enzyme was considerably increased to about 43% at 75°C, and K m value was reduced to 25.4% after immobilisation. The half‐life of the enzyme increased about 5.13‐fold at 75°C as compared with the free form. Immobilised inulinase retained over 80% of its activity after ten cycles.Inspec keywords: magnetic particles, nanoparticles, proteins, molecular biophysics, nanofabrication, enzymes, Fourier transform spectra, infrared spectra, scanning electron microscopy, field emission ion microscopy, transmission electron microscopy, pH, biochemistry, nanobiotechnology, biomagnetism, electrokinetic effects, iron compoundsOther keywords: magnetic nanoparticles, soy protein isolate, bovine serum albumin, inulinase immobilisation, nanomagnetite, polydispersity index, SPI‐BSA NP, zeta potential, inulinase, glutaraldehyde, enzyme loading, Fourier transform infrared spectra, field emission scanning electron microscopy, transmission electron microscopy images, enzyme immobilisation, pH, size 80 nm to 90 nm, temperature 45 degC, temperature 75 degC, Fe3 O4 相似文献
64.
Mostafizur Rahman Rana Erik Upol Biswas Sultan Mahmud Salman Meem Sahel Syeda Sarita Hassan Mahdy Rahman Chowdhury Mahdy 《Advanced Engineering Materials》2023,25(20):2300438
Electromagnetic wideband absorption is still perceived as a critical and formidable challenge to address with an unambiguous photonic absorber. Subwavelength metamaterial (MM) unit cells with unique and controlled features have recently gained considerable interest. However, meta-atoms, generated using a quantum-inspired pattern distribution, are underwhelming in existing literature to design photonic absorbers and their potential application to manufacture solar sails is still quite uncommon. In this article, to create a flexible, polarization-insensitive, ultrathin, and broadband MM absorber, quantum interference pattern-inspired design is utilized. Herein, a novel approach to fabricating solar sails for the space exploration incorporates the proposed broadband photonic absorber rather than conventional reflectors. The quantum-inspired meta-absorber (QIMA) exhibits an absorption of over 91% for the visible domain, i.e., 380–800 nm under a conventional plane-polarized source. It is shown in the study that broadband absorbers are almost equivalent to excellent reflectors to design the solar sails in terms of the time-averaged force calculated by utilizing the Maxwell stress tensor method. Thus, the QIMA has the potential to be a viable alternative to reflectors in the design of futuristic solar sails for space exploration. The interference theory model is also utilized to assure the dependability of calculated data, and additionally, the standard AM1.5 solar spectrum is utilized to demonstrate the QIMA's solar-harvesting potentiality. 相似文献
65.
Saeed Mohsen Sherif S. M. Ghoneim Mohammed S. Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan 《计算机、材料和连续体(英文)》2023,75(3):5271-5286
Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers. 相似文献
66.
Mohamed Fahim Hassan Mohamed Zribi Hamed M. K. Alazemi 《International Journal of Control, Automation and Systems》2011,9(2):237-248
This paper proposes an optimal recursive estimator to estimate the states of a stochastic discrete time linear dynamic system
when the states of the system are constrained with inequality constraints. The case when the constraints are strictly satisfied
is treated independently from the case when some of the constraints are violated. For the first case, the well known Kalman
filter estimator is used. In the second case, an algorithm which uses a series of successive orthogonalizations on the measurement
subspaces is employed to obtain the optimal estimate. It is shown that the proposed estimator has several attractive properties
such that it is an unbiased estimator. More importantly, compared to other estimator found in the literature, the proposed
estimator needs less computational efforts, is numerically more stable and it leads to a smaller variance. To show the effectiveness
of the proposed estimator, several simulation results are presented and discussed. 相似文献
67.
Detection of rapidly evolving malware requires classification techniques that can effectively and efficiently detect zero-day
attacks. Such detection is based on a robust model of benign behavior and deviations from that model are used to detect malicious
behavior. In this paper we propose a low-complexity host-based technique that uses deviations in static file attributes to
detect malicious executables. We first develop simple statistical models of static file attributes derived from the empirical
data of thousands of benign executables. Deviations among the attribute models of benign and malware executables are then
quantified using information-theoretic (Kullback-Leibler-based) divergence measures. This quantification reveals distinguishing
attributes that are considerably divergent between benign and malware executables and therefore can be used for detection.
We use the benign models of divergent attributes in cross-correlation and log-likelihood frameworks to classify malicious
executables. Our results, using over 4,000 malicious file samples, indicate that the proposed detector provides reasonably
high detection accuracy, while having significantly lower complexity than existing detectors. 相似文献
68.
Yun Sheng Alexei Sourin Gabriela Gonzalez Castro Hassan Ugail 《The Visual computer》2010,26(6-8):975-984
Three-dimensional (3D) representations of complex geometric shapes, especially when they are reconstructed from magnetic resonance imaging (MRI) and computed tomography (CT) data, often result in large polygon meshes which require substantial storage for their handling, and normally have only one fixed level of detail (LOD). This can often be an obstacle for efficient data exchange and interactive work with such objects. We propose to replace such large polygon meshes with a relatively small set of coefficients of the patchwise partial differential equation (PDE) function representation. With this model, the approximations of the original shapes can be rendered with any desired resolution at interactive rates. Our approach can directly work with any common 3D reconstruction pipeline, which we demonstrate by applying it to a large reconstructed medical data set with irregular geometry. 相似文献
69.
Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems. 相似文献
70.
Mehrdad Hakimi-Asiabar Seyyed Hassan Ghodsypour Reza Kerachian 《Applied Soft Computing》2010,10(4):1151-1163
Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained.Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set.In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs).To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river. 相似文献