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   54310篇
  免费   11044篇
  国内免费   5790篇
电工技术   7374篇
技术理论   2篇
综合类   5869篇
化学工业   1754篇
金属工艺   952篇
机械仪表   3984篇
建筑科学   1482篇
矿业工程   663篇
能源动力   3110篇
轻工业   1182篇
水利工程   738篇
石油天然气   1110篇
武器工业   575篇
无线电   12154篇
一般工业技术   4342篇
冶金工业   682篇
原子能技术   301篇
自动化技术   24870篇
  2024年   123篇
  2023年   1135篇
  2022年   1928篇
  2021年   2445篇
  2020年   2656篇
  2019年   3052篇
  2018年   1618篇
  2017年   2133篇
  2016年   2208篇
  2015年   2676篇
  2014年   3836篇
  2013年   3456篇
  2012年   4650篇
  2011年   4870篇
  2010年   3925篇
  2009年   3981篇
  2008年   3980篇
  2007年   4134篇
  2006年   3385篇
  2005年   2945篇
  2004年   2336篇
  2003年   1831篇
  2002年   1477篇
  2001年   1210篇
  2000年   938篇
  1999年   709篇
  1998年   571篇
  1997年   516篇
  1996年   414篇
  1995年   355篇
  1994年   293篇
  1993年   250篇
  1992年   205篇
  1991年   159篇
  1990年   149篇
  1989年   101篇
  1988年   79篇
  1987年   52篇
  1986年   58篇
  1985年   65篇
  1984年   53篇
  1983年   47篇
  1982年   32篇
  1981年   22篇
  1980年   17篇
  1979年   10篇
  1978年   7篇
  1977年   7篇
  1960年   4篇
  1959年   7篇
排序方式: 共有10000条查询结果,搜索用时 15 毫秒
1.
风电和光伏发电具有间歇性和随机性,为了降低在多源联合发电系统中的弃风弃光率,采用含氢储能系统和火电机组配合来平滑风电和光电机组出力。文中以系统运行成本最小和弃电惩罚成本最小为目标,以系统功率平衡、火电机组出力和爬坡、热备用、风电和光电出力及储能系统储氢罐容量、电解槽和燃料电池功率等为约束条件构建了多源联合发电系统日前调度模型。通过YALMIP工具箱对模型进行编程,并调用CPLEX对编写的程序进行求解。对含有风电、光电、火电机组以及储能系统的多源联合发电系统进行算例分析,通过对比有无储能系统的弃风弃光量和系统总运行成本,证明了含氢储能系统可以有效降低系统的弃风弃光率,并提高系统的经济性。  相似文献   
2.
光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。  相似文献   
3.
Geogrid reinforcement can significantly improve the uplift bearing capacity of anchor plates. However, the failure mechanism of anchor plates in reinforced soil and the contribution of geogrids need further investigation. This paper presents an experimental study on the anchor uplift behavior in geogrid-reinforced soil using particle image velocimetry (PIV) and the high-resolution optical frequency domain reflectometry (OFDR). A series of model tests were performed to identify the relationship between the failure mechanism and various factors, such as anchor embedment ratio, number of geogrid layers, and their location. The test results indicate that soil deformation and the uplift resistance of anchor plates are substantially influenced by anchor embedment ratio and location of geogrids, whereas the number of geogrid layers has limited influence. In reinforced soil, increasing the embedment ratio greatly improves the ultimate bearing capacities of anchor plates and affects the interlock between the soil and geogrids. As the embedment depth increases, the failure surfaces gradually change from a vertical slip surface to a bulb-shaped surface that is limited within the soil. The strain monitoring data shows that the deformations of geogrids are symmetrical, and the peak strains of geogrids can characterize the reinforcing effects.  相似文献   
4.
5.
铬矿中砷的测定方法对完善铬矿检测方法和质量评价手段有重要意义。在0.5 g样品中加入2 g过氧化钠,于650 ℃熔融20 min,熔融物经盐酸酸化溶解,以10%盐酸为载流,15 g/L硼氢化钾溶液为还原剂,采用氢化物发生-原子荧光光谱法(HG-AFS)进行测定,建立了铬矿中砷测定的新方法。实验表明,砷质量浓度在0.025~100 μg/L范围内与荧光强度呈线性关系,相关系数为1.000 0,方法检出限为0.05 μg/g,定量限为0.17 μg/g。对产地为南非、土耳其、巴基斯坦等19个国家共2 649个铬矿样品的成分进行统计,发现铬矿主要成分为铬、铁、镁、铝和硅;干扰试验表明,这些样品中的共存元素均不干扰测定。按照实验方法对3个不同砷含量的铬矿样品进行精密度和加标回收试验,结果的相对标准偏差(RSD,n=11)为1.4%~6.0%,回收率为96%~109%。将实验方法应用于与铬矿基体近似的铁矿标准物质中砷的测定,测定值与认定值基本一致。  相似文献   
6.
In this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM-BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self-organization, and self-configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM-BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy-efficient clustering approach consolidated game theory as well as dual-cluster-head mode for WSNs energy-aware clustering by cuckoo optimization approach (EECM-COA), energy-aware clustering-based routing using multi-path reliable transmission with routing and control board (EECM-RCB-MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM-AR-TORA-FCS), passive multi-hop clustering algorithm (EECM-PMC), dynamic source routing protocol based on genetic algorithm-bacterial foraging optimization (DSR-GA-BFO).  相似文献   
7.
This paper plans to develop an intelligent super resolution model with the linkage of Wavelet lifting scheme and Deep learning algorithm. Before initiating the resolution procedure, the entire HR images are converted into Low Resolution (LR) images using bicubic interpolation-based downsampling and upsampling. Further, the Wavelet lifting scheme helps to generate the four subbands of each image like LR wavelet Sub-Bands for LR images, and High Resolution (HR) wavelet Sub-Bands for HR images. The residual image is generated by taking the difference between the LR wavelet Sub-Bands and HR wavelet Sub-Bands images. The proposed model involves two main phases: Training phase and Testing. The training phase trains the residual image of all images by Deep Convolutional Neural Network with LR wavelet Sub-Bands as input and residual image as target. On the other hand, in testing phase, the LR wavelet Sub-Bands query image is subjected to Deep Convolutional Neural Network, which outputs the concerned residual image. This generated residual image is summed with LR wavelet Sub-Bands image, followed by inverse wavelet lifting scheme to obtain the final super resolution image. The main contribution of this paper is to improve the conventional Deep Convolutional Neural Network by optimizing the number of hidden layer, and hidden neurons using modified Whale Optimization Algorithm called Average Fitness Enabled Whale Optimization Algorithm by considering the objective of maximizing the Peak Signal-to-Noise Ratio. Finally, the proposed method achieves an improved quality of the results which is comparable the existing models.  相似文献   
8.
In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of features that includes Gray Level Co-occurrence Matrix (GLCM) based features, Histograms of Positive Shearlet Coefficients (HPSC), and Histograms of Negative Shearlet Coefficients (HNSC) are estimated. The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers; k-Nearest Neighbor (kNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers. The output of individual trained classifiers for a testing input is hybridized to take a final decision. The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data (REMBRANDT) database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.  相似文献   
9.
Software testing plays a pivotal role in entire software development lifecycle. It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required. These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite, fault-tolerance, defects due to uncovered-statements and overall-performance at the time of execution. With this objective, the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization. The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate (90.78% to 100%) and failure detection rate (44.56% to 55.01%). Results proved MOMPSO outperformed the other stated algorithms.  相似文献   
10.
铜转炉吹炼是火法炼铜的关键工序,其终点判断与炉寿、铜产率和直收率紧密相关,目前现有人工经验、仪器测定和物料平衡法等终点判断方法均存在一定的局限性。理论上铜转炉吹炼造渣期终点与渣含Fe是否达标有关,而不同Fe含量渣样呈现不同的图像特征,鉴于此,基于图形识别的特征向量提取原理,分别采用卷积神经网络(CNN)算法与支持向量机(SVM)算法,构建了铜转炉吹炼造渣期渣含Fe预测模型,为图像识别技术在铜转炉吹炼终点判断中的应用奠定数模基础。两种模型的实例分析表明,卷积神经网络的训练集预测准确率98%,测试集预测准确率约50%;支持向量机模型的训练集预测准确率99%,测试集预测准确率62%。  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