首页 | 本学科首页   官方微博 | 高级检索  
文章检索
  按 检索   检索词:      
出版年份:   被引次数:   他引次数: 提示:输入*表示无穷大
  收费全文   35851篇
  免费   6750篇
  国内免费   4349篇
电工技术   1644篇
技术理论   2篇
综合类   3532篇
化学工业   1290篇
金属工艺   936篇
机械仪表   1317篇
建筑科学   4014篇
矿业工程   2005篇
能源动力   469篇
轻工业   596篇
水利工程   744篇
石油天然气   1761篇
武器工业   137篇
无线电   3352篇
一般工业技术   2002篇
冶金工业   2520篇
原子能技术   66篇
自动化技术   20563篇
  2024年   219篇
  2023年   2188篇
  2022年   3538篇
  2021年   3560篇
  2020年   3340篇
  2019年   3122篇
  2018年   1377篇
  2017年   1192篇
  2016年   1241篇
  2015年   1380篇
  2014年   2214篇
  2013年   1690篇
  2012年   2111篇
  2011年   2339篇
  2010年   1954篇
  2009年   1903篇
  2008年   1764篇
  2007年   1808篇
  2006年   1623篇
  2005年   1456篇
  2004年   1085篇
  2003年   961篇
  2002年   842篇
  2001年   640篇
  2000年   605篇
  1999年   483篇
  1998年   407篇
  1997年   327篇
  1996年   278篇
  1995年   201篇
  1994年   169篇
  1993年   146篇
  1992年   117篇
  1991年   61篇
  1990年   64篇
  1989年   63篇
  1988年   36篇
  1987年   31篇
  1986年   47篇
  1984年   14篇
  1979年   16篇
  1966年   14篇
  1965年   24篇
  1964年   25篇
  1963年   23篇
  1961年   17篇
  1959年   18篇
  1958年   16篇
  1957年   22篇
  1955年   23篇
排序方式: 共有10000条查询结果,搜索用时 15 毫秒
1.
卢东祥 《电子科技》2023,36(3):81-86
为了进一步提高城市道路交通网络的通行效率,粒子群优化和神经网络等多种智能优化算法受到越来越多的关注。近年来,深度学习技术的普及与应用大幅提升了城市交通网络的节点识别效率,而交通网络的节点调度又扩展了深度学习技术的应用。文中详细分析了交通节点调度所面临的关键问题,归纳并总结了相关网络节点分配的研究现状。在此基础上,深入研讨了城市交通网络节点调度与深度学习的应用前景,并对交通网络节点分配优化策略的未来研究方向进行了展望。  相似文献   
2.
针对变压器故障诊断准确率低和稳定性差的问题,文中提出了一种改进麻雀搜索算法优化贝叶斯网络的变压器故障诊断方法。首先,通过计算互信息建立最大支撑树并进行定向处理得到贝叶斯网络初始结构即初始种群。然后,在算法中引入一种新的合作机制和正弦余弦算法,提高算法收敛速度和全局搜索能力,并利用油中溶解气体分析,创建基于改进麻雀搜索算法优化贝叶斯网络的变压器故障诊断模型。最后,为了证明所提方法的优越性,将所提的方法与现有变压器故障诊断方法进行对比。结果表明,文中所提出的方法故障诊断率最高,可以更精准地对变压器进行故障诊断。  相似文献   
3.
Geogrids embedded in fill materials are checked against pullout failure through standard pullout testing methodology. The test determines the pullout interaction coefficient which is critical in fixing the embedment length of geogrids in mechanically stabilized earth walls. This paper proposes prediction of pullout interaction coefficient using data driven machine learning regression algorithms. The study primarily focusses on using extreme gradient boosting (XGBoost) method for prediction. A data set containing 220 test results from the literature has been used for training and testing. Predicted results of XGBoost have been compared with the results of random forest (RF) ensemble learning based algorithm. The predictions of XGBoost model indicates 85% accuracy and that of RF model shows 77% accuracy, indicating significantly superior and robust prediction through XGBoost above RF model. The importance analysis indicates that normal stress is the most significant factor that influences the pullout interaction coefficients. Subsequently pullout tests have been performed on geogrid embedded in four different fill materials at three normal stresses. The proposed XGBoost model gives 90% accuracy in prediction of pullout interaction coefficient compared to laboratory test results. Finally, an open-source graphical user interface based on the XGBoost model has been created for preliminary estimation of the pullout interaction coefficient of geogrid at different test conditions.  相似文献   
4.
In this study, the infrastructure leakage index (ILI) indicator that is preferred frequently by the water utilities with sufficient data to determine the performances of water distribution systems is modeled for the first time through the three different methodologies using different input data. In addition to the variables in the literature used for the classical ILI calculations, the age parameter is also included in the models. In the first step, the ILI values have been estimated via multiple linear regression (MLR) using water supply quantity, water accrual quantity, network length, service connection length, number of service connections, and pressure variables. Secondly, the Artificial Neural Network (ANN) approach has been applied with raw data to improve the ILI prediction performance. Finally, the data set has been standardized with the Z-Score method for increasing the learning power of the ANN models, and then the ANN predictions have been made by converting the data through the principal component analysis (PCA) method to minimize complexity by reducing the data set size. The model predictions have been evaluated via mean square error, G-value, mean absolute error, mean bias error, and adjusted-R2 model performance scale. When the model outputs obtained at the end of the study are evaluated together with the classical ILI calculations, it is seen that the successful ILI predictions with three and four variables, including the age parameter, rather than six variables, have been made through the PC-ANN method. Water utilities with insufficient physical and operational data for ILI indicator calculation can make network performance evaluations by predicting the ILI through the models suggested in this study with high accuracy in a reliable way.  相似文献   
5.
An experimental analysis regarding the distribution of the cutting fluid is very difficult due to the inaccessibility of the contact zone within the bore hole. Therefore, suitable simulation models are necessary to evaluate new tool designs and optimize drilling processes. In this paper the coolant distribution during helical deep hole drilling is analyzed with high-speed microscopy. Micro particles are added to the cutting fluid circuit by a developed high-pressure mixing vessel. After the evaluation of suitable particle size, particle concentration and coolant pressure, a computational fluid dynamics (CFD) simulation is validated with the experimental results. The comparison shows a very good model quality with a marginal difference for the flow velocity of 1.57% between simulation and experiment. The simulation considers the kinematic viscosity of the fluid. The results show that the fluid velocity in the chip flutes is low compared to the fluid velocity at the exit of the coolant channels of the tool and drops even further between the guide chamfers. The flow velocity and the flow pressure directly at the cutting edge decrease to such an extent that the fluid cannot generate a sufficient cooling or lubrication. With the CFD simulation a deeper understanding of the behavior and interactions of the cutting fluid is achieved. Based on these results further research activities to improve the coolant supply can be carried out with great potential to evaluate new tool geometries and optimize the machining process.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00383-w  相似文献   
6.
The identification of spreading influence nodes in social networks, which studies how to detect important individuals in human society, has attracted increasing attention from physical and computer science, social science and economics communities. The identification algorithms of spreading influence nodes can be used to evaluate the spreading influence, describe the node’s position, and identify interaction centralities. This review summarizes the recent progress about the identification algorithms of spreading influence nodes from the viewpoint of social networks, emphasizing the contributions from physical perspectives and approaches, including the microstructure-based algorithms, community structure-based algorithms, macrostructure-based algorithms, and machine learning-based algorithms. We introduce diffusion models and performance evaluation metrics, and outline future challenges of the identification of spreading influence nodes.  相似文献   
7.
Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.  相似文献   
8.
9.
Just-in-time defect prediction can remind software developers and managers to verify and fix bugs at the moment they appeared, thus improving the effectiveness and validity of bug fixing. Existing studies mainly focus on just-in-time prediction for software files (JIT-F). JIT-F is a binary classification problem, which classifies (hence predicts) a file change as buggy or clean. This article provides a detailed analysis of just-in-time defect prediction for software hunks (JIT-H), which predicts bugs at a finer level of granularity, and hence further improves the efficiency of bug fixing. Classification is performed using the ensemble technique of bagging—aggregated combinations of random under sampling plus multiple classifiers (J48 and Random Forest). An empirical study with 10 open source projects was conducted to validate the effectiveness of JIT-H. Experimental results show that JIT-H is effective at predicting defects in software hunk changes. Compared with JIT-F, JIT-H is more cost effective. Additionally, analysis on the change features indicates that Text Vector features and hunk change level features are of more importance than features in other groups and levels.  相似文献   
10.
Tablets, smartphones, and wearables have limited resources. Applications on these devices employ a graphical user interface (GUI) for interaction with users. Language runtimes for GUIs employ dynamic memory management using garbage collection (GC). However, GC policies and algorithms are designed for data centers and cloud computing, but they are not necessarily ideal for resource-constrained embedded devices. In this article, we present GUI GC, a JavaFX GUI benchmark, which we use to compare the performance of the four GC policies of the Eclipse OpenJ9 Java runtime on a resource-constrained environment. Overall, our experiments suggest that the default policy Gencon registered significantly lower execution times than its counterparts. The region-based policy, Balanced, did not fully utilize blocking times; thus, using GUI GC, we conducted experiments with explicit GC invocations that measured significant improvements of up to 13.22% when multiple CPUs were available. Furthermore, we created a second version of GUI GC that expands on the number of controllable load-stressing dimensions; we conducted a large number of randomly configured experiments to quantify the performance effect that each knob has. Finally, we analyzed our dataset to derive suitable knob configurations for desired runtime, GC, and hardware stress levels.  相似文献   
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号