全文获取类型
收费全文 | 24795篇 |
免费 | 3548篇 |
国内免费 | 2481篇 |
专业分类
电工技术 | 497篇 |
技术理论 | 2篇 |
综合类 | 1951篇 |
化学工业 | 718篇 |
金属工艺 | 391篇 |
机械仪表 | 571篇 |
建筑科学 | 1712篇 |
矿业工程 | 9729篇 |
能源动力 | 471篇 |
轻工业 | 242篇 |
水利工程 | 295篇 |
石油天然气 | 466篇 |
武器工业 | 51篇 |
无线电 | 1581篇 |
一般工业技术 | 787篇 |
冶金工业 | 1532篇 |
原子能技术 | 39篇 |
自动化技术 | 9789篇 |
出版年
2024年 | 91篇 |
2023年 | 459篇 |
2022年 | 982篇 |
2021年 | 1246篇 |
2020年 | 1348篇 |
2019年 | 841篇 |
2018年 | 812篇 |
2017年 | 826篇 |
2016年 | 927篇 |
2015年 | 944篇 |
2014年 | 1612篇 |
2013年 | 1350篇 |
2012年 | 2047篇 |
2011年 | 1946篇 |
2010年 | 1516篇 |
2009年 | 1666篇 |
2008年 | 1751篇 |
2007年 | 1820篇 |
2006年 | 1608篇 |
2005年 | 1407篇 |
2004年 | 1201篇 |
2003年 | 1140篇 |
2002年 | 728篇 |
2001年 | 544篇 |
2000年 | 490篇 |
1999年 | 338篇 |
1998年 | 256篇 |
1997年 | 181篇 |
1996年 | 163篇 |
1995年 | 137篇 |
1994年 | 110篇 |
1993年 | 91篇 |
1992年 | 69篇 |
1991年 | 57篇 |
1990年 | 23篇 |
1989年 | 24篇 |
1988年 | 24篇 |
1987年 | 16篇 |
1986年 | 17篇 |
1985年 | 2篇 |
1984年 | 1篇 |
1983年 | 1篇 |
1982年 | 2篇 |
1981年 | 4篇 |
1980年 | 1篇 |
1979年 | 3篇 |
1978年 | 1篇 |
1976年 | 1篇 |
排序方式: 共有10000条查询结果,搜索用时 15 毫秒
1.
A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone. 相似文献
2.
In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability. 相似文献
3.
In this paper, we strive to propose a self-interpretable framework, termed PrimitiveTree, that incorporates deep visual primitives condensed from deep features with a conventional decision tree, bridging the gap between deep features extracted from deep neural networks (DNNs) and trees’ transparent decision-making processes. Specifically, we utilize a codebook, which embeds the continuous deep features into a finite discrete space (deep visual primitives) to distill the most common semantic information. The decision tree adopts the spatial location information and the mapped primitives to present the decision-making process of the deep features in a tree hierarchy. Moreover, the trained interpretable PrimitiveTree can inversely explain the constituents of the deep features, highlighting the most critical and semantic-rich image patches attributing to the final predictions of the given DNN. Extensive experiments and visualization results validate the effectiveness and interpretability of our method. 相似文献
4.
Safa Meraghni Labib Sadek Terrissa Meiling Yue Jian Ma Samir Jemei Noureddine Zerhouni 《International Journal of Hydrogen Energy》2021,46(2):2555-2564
Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data. 相似文献
5.
6.
本文分析了乌兰矿投产前期采矿现状及存在的主要问题,针对该矿所处蒙古国经济落后、投资风险大的现实状况,为避免生产中断、规避投资风险,早日回收前期投资考虑,采取了双斜坡道开拓、全尾胶结充填、高端壁空场嗣后充填采矿、多中段组合式连续开采等系列技术应对方案。大大降低了一次性投资规模及投资风险,前期投资得以快速回笼的同时,矿山产能也充分释放,确保了矿山的持续稳定,取得了较好的经济和社会效益。为海外地下近地表矿体开采矿山规避投资风险提供了很好的技术方案借鉴。 相似文献
7.
《International Journal of Hydrogen Energy》2022,47(36):16121-16131
Ammonia is considered as a promising hydrogen or energy carrier. Ammonia absorption or adsorption is an important aspect for both ammonia removal, storage and separation applications. To these ends, a wide range of solid and liquid sorbents have been investigated. Among these, the deep eutectic solvent (DES) is emerging as a promising class of ammonia absorbers. Herein, we report a novel type of DES, i.e., metal-containing DESs for ammonia absorption. Specifically, the NH3 absorption capacity is enhanced by ca. 18.1–36.9% when a small amount of metal chlorides, such as MgCl2, MnCl2 etc., are added into a DES composed of resorcinol (Res) and ethylene glycol (EG). To our knowledge, the MgCl2/Res/EG (0.1:1:2) DES outperforms most of the reported DESs. The excellent NH3 absorption performances of metal–containing DESs have been attributed to the synergy of Lewis acid–base and hydrogen bonding interactions. Additionally, good reversibility and high NH3/CO2 selectivity are achieved over the MgCl2/Res/EG (0.1:1:2) DES, which enables it to be a potential NH3 absorber for further investigations. 相似文献
8.
Most real-world vehicle nodes can be structured into an interconnected network of vehicles. Through structuring these services and vehicle device interactions into multiple types, such internet of vehicles becomes multidimensional heterogeneous overlay networks. The heterogeneousness of the overlays makes it difficult for the overlay networks to coordinate with each other to improve their performance. Therefore, it poses an interesting but critical challenge to the effective analysis of heterogeneous virtual vehicular networks. A variety of virtual vehicular networks can be easily deployed onto the native network by applying the concept of SDN (Software Defined Networking). These virtual networks reflect their heterogeneousness due to their different performance goals, and they compete for the same physical resources of the underlying network, so that a sub-optimal performance of the virtual networks may be achieved. Therefore, we propose a Deep Reinforcement Learning (DRL) approach to make the virtual networks cooperate with each other through the SDN controller. A cooperative solution based on the asymmetric Nash bargaining is proposed for co-existing virtual networks to improve their performance. Moreover, the Markov Chain model and DRL resolution are introduced to leverage the heterogeneous performance goals of virtual networks. The implementation of the approach is introduced, and simulation results confirm the performance improvement of the latency sensitive, loss-rate sensitive and throughput sensitive heterogeneous vehicular networks using our cooperative solution. 相似文献
9.
Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities. 相似文献
10.
Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted. 相似文献