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1.

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

  相似文献   
2.
窦双团  付利平  贾楠  王天放 《应用光学》2022,43(6):1165-1174
基于微通道板电子倍增电荷分割型阳极的成像探测器常用于行星大气、气辉等微弱信号探测。针对探测器读出电路增益不相等导致探测器成像产生畸变的问题,利用理论分析结合MATLAB仿真揭示了不同畸变图像的形成机制,在该基础上提出了一种探测器读出电路增益补偿方法减小探测器成像畸变。通过MATLAB仿真和实验测试结果表明该文提出的方法能够有效减小由于读出电路增益不相等导致的探测器成像畸变。  相似文献   
3.
In this work we consider a poroelastic, flexible material that may deform largely, which is situated in an incompressible fluid driven by the Navier–Stokes equations in two or three space dimensions. By a variational approach we show existence of weak solutions for a class of such coupled systems. We consider the unsteady case, this means that the PDE for the poroelastic solid involves the Fréchet-derivative of a non-convex functional as well as (second order in time) inertia terms.  相似文献   
4.
As a representative of traditionally fermented Chinese medicine, Massa Medicata Fermentata (MMF) shows the functions of invigorating the spleen and stomach and promoting digestion, which plays an important role in the treatment of gastrointestinal diseases. The fermentation mechanism and the key factors that affect the quality of MMF have not been revealed yet, which has become an urgent issue that limits its clinical application. This article aims to systematically and comprehensively reveal the transformation of physical properties and the dynamic trend of chemical components including substrate components, volatile components, and lactic acid as anaerobic fermentation product during MMF fermentation. Along with obvious hyphae growth observed for MMF, the weight of MMF decreased, and the moisture and temperature increased. Through the quantified 14 components from substrate, ferulic acid increased from 45.53 ± 6.94 to 141.89 ± 78.40 μg/g, while glycosides and phenolic acids declined except caffeic acid. Also, within the 66 volatile components analyzed, alcohols and acids increased, while aldehydes and ketones decreased. Lactic acid was not detected in the fermentation substrate, but an apparent increase in lactic acid content was observed along with the increased fermentation days, resulting in 2.54 ± 0.15 mg/g on day 8. Based on the tested components, the fermentation process of MMF was discriminated into three distinct stages by principal component analysis, and an optimal fermentation time of four days was proposed. The results of this study will be of great significance to clarify the characteristics of fermentation and conduce to improving quality standards of MMF.  相似文献   
5.
Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.  相似文献   
6.
In this text, we study factorizations of polynomials over the tropical hyperfield and the sign hyperfield, which we call tropical polynomials and sign polynomials, respectively. We classify all irreducible polynomials in either case. We show that tropical polynomials factor uniquely into irreducible factors, but that unique factorization fails for sign polynomials. We describe division algorithms for tropical and sign polynomials by linear terms that correspond to roots of the polynomials.  相似文献   
7.
The effects of air sparging (0–16 L min−1) and mechanical mixing (0–400 rpm) on enhancing the sonochemical degradation of rhodamine B (RhB) was investigated using a 28 kHz sonoreactor. The degradation of RhB followed pseudo first-order kinetics, where sparging or mixing induced a large sonochemical enhancement. The kinetic constant varied in three stages (gradually increased → increased exponentially → decreased slightly) as the rate of sparging or mixing increased, where the stages were similar for both processes. The highest sonochemical activity was obtained with sparging at 8 L min−1 or mixing at 200 rpm, where the standing wave field was significantly deformed by sparging and mixing, respectively. The cavitational oxidation activity was concentrated at the bottom of the sonicator when higher sparging or mixing rates were employed. Therefore, the large enhancement in the sonochemical oxidation was attributed mainly to the direct disturbance of the ultrasound transmission and the resulting change in the cavitation-active zone in this study. The effect of the position of air sparging and mixing was investigated. The indirect inhibition of the ultrasound transmission resulted in less enhancement of the sonochemical activity. Moreover, the effect of various sparging gases including air, N2, O2, Ar, CO2, and an Ar/O2 (8:2) mixture was compared, where all gases except CO2 induced an enhancement in the sonochemical activity, irrespective of the concentration of dissolved oxygen. The highest activity was obtained with the Ar/O2 (8:2) mixture. Therefore, it was revealed that the sonochemical oxidation activity could be further enhanced by applying gas sparging using the optimal gas.  相似文献   
8.
木材是人们生活中必不可少的可再生资源,同时在建筑、工艺、家具、结构材料等方面有着举足轻重的地位。市场中常见的木材品种繁多,其品质和价格千差万别,使用智能化技术对木材进行正确的分类不仅可以防止不法商贩“以次充好”,也可以大幅度降低木材分类人员的工作难度。通过木材的遗传信息和解剖学信息可以得到较为准确的木材分类结果,这类方法识别工艺相对复杂,对非专业人员并不友好。借助木材切面的图像信息或光谱信息可以简单方便地对木材进行分类,然而由于不同种木材之间存在的近似性,这类方法往往分类精度不高或只适用于某些阔叶木材。提出了一种基于木材横切面图像信息和光谱信息的多特征木材分类算法,首先分别采集木材横切面的光谱信息以及图像信息;再使用Segnet图像分割方法将待分类样本分成含管孔木材和不含管孔木材两组,并对含管孔样本组中的木材进行管孔分割;然后对含管孔样本组中的木材提取管孔特征、光谱特征以及纹理特征,对无管孔样本组木材提取光谱特征和纹理特征;最后根据这些特征使用支持向量机分别对木材进行分类并记录其木材的分类结果,对分类结果不一致的样本使用相似性判据判断最佳分类结果。为了验证该方法的有效性,以20种常见的阔叶木材和针叶木材的混合样本集为研究对象,对其进行了分类。实验结果显示三种特征均可以对木材进行分类,单独使用光谱特征、纹理特征以及管孔特征对木材进行分类的最高正确率分别为93.00%,89.33% 和69.23%,通过相似测度的判断后三个特征可以相互补充从而进一步提高木材的分类正确率,最高正确率可达98.00%。综上所述,该方法可以对包含阔叶木材和针叶木材的混合样本集中的木材进行分类,木材横切面的光谱特征、纹理特征以及管孔特征可以相互补充,从而使分类正确率进一步的提高。与目前的主流木材分类方法进行对比,发现该算法的分类正确率高于其他算法。  相似文献   
9.
The acoustic radiation force resulting from acoustic waves have been extensively studied for the contact-free generation of organized patterning arrays. The precise arrangement of microscopic objects clustered at the pressure nodes is critical to the development of functional structures and patterned surfaces. However, the size of the clusters is restricted by the saturation limit of the acoustic nodes. Here, we present a bulk acoustic wave (BAW) platform, which employs a two-dimensional acoustic wave to propel particles of various sizes. Experimentally, when particles are large, significant acoustic energy is scattered and partly absorbed by the matched layers in front of the sensors. The acoustic radiation force from a convergent acoustic pressure field agglomerates the large polystyrene (PS) particles towards the central region instead of the pressure nodes. The parametric analysis has been performed to assess the transition in the particles from clustering at the organized nodal arrays to agglomerating in the central region, which is a function of particle size, particle concentration, and load voltage. Statistically, the particles can agglomerate with a cluster ratio greater than 70%, and this ratio can be improved by increasing the load power/voltage supplied to the transducers. With its ability to perform biocompatible, label-free, and contact-free self-assembly, this concept offers a new possibility in the fabrication of colloidal layers, the recreation of tissue microstructure, the development of organoid spheroid cultures, the migration of microorganisms, and the assembly of bioprinting materials.  相似文献   
10.
Utilising cavitation for enhancing oxidative desulphurization has been investigated for nearly-two decades with recent investigations shifting focus from low-capacity acoustic cavitation (AC) to scalable hydrodynamic cavitation (HC). This work focuses on developing a viable means for removing thiophene’s from fuels. In the first phase of this work, use of vortex based HC devices for removal of single and dual ring thiophenes from dodecane was investigated. HC was shown to be able to remove single ring thiophene from dodecane without using any external catalyst or additives. However, in absence of catalyst or additives, it was not possible to remove dual ring thiophenes such as dibenzothiophene using HC. Therefore, in the second phase of this work, various strategies based on use of catalyst or additives to augment cavitation based process were investigated. AC based experiments were opted for shortlisting suitable catalysts and additives for intensifying cavitation based processes. The influence of using oxidant (H2O2) and carboxylic acid catalysts on efficacy of removal of dual ring thiophenes is presented. Several conditions were tested, and the optimal volumetric ratios of 0.95 v/v % H2O2 and 6.25 v/v % HCOOH was identified and utilised throughout the remainder of the study. Regeneration of extractant which accumulates oxidised sulphur species from dodecane was also investigated using AC. The additives and process conditions reported in this work are useful for enhancing desulphurization performance.  相似文献   
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