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对于移动对象历史轨迹索引,现有的方案绝大多数都基于室外空间,难以直接应用于室内空间中;同时,未将对象本身作为一个独立的维度加以索引,无法提供高效的对象轨迹查询方式。对此,提出了一个室内环境下的移动对象索引结构DR-tree来对移动数据的位置、时间、对象三个维度进行索引,并将位置维与对象维解耦,将三维索引转换为两个二维索引,同时给出查询优化方案。实验结果表明,与现有的室内环境下的索引方案RTR-tree相比,该结构不仅能够提供高效的时空查询,而且还能提供高效的对象轨迹查询。 相似文献
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84.
《Ergonomics》2012,55(7):1019-1029
This paper briefly reviews research studies of interest to environmental ergonomists. It includes some recent work on the health effects of office lighting, especially the effects of daylighting, fluorescent lighting and full-spectrum lighting. It also covers studies of indoor air quality in offices, especially investigations of localized air filtration and the sick building syndrome. It argues the value of a systematic, ergonomics approach to designing the built environment. 相似文献
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86.
Location estimation or localization is one of the key components in IoT applications such as remote health monitoring and smart homes. Amongst device-free localization technologies, passive infrared (PIR) sensors are one of the promising options due to their low cost, low energy consumption, and good accuracy. However, most of the existing systems are complexly designed and difficult to deploy in real life, in addition, there is no public dataset available for researchers to benchmark their proposed localization and tracking methods. In this paper, we propose a system and a dataset collected from our PIR system consisting of commercial-of-the-shelf (COTS) sensors without any modification. Our dataset includes profile data of 36 classes that have over 1,000 samples of different walking directions and test data consisting of multiple scenarios with a sequence length of over 2,000 timesteps. To evaluate our system and dataset, we implement various deep learning methods such as CNN, RNN, and CNN–RNN. Our results prove the applicability and feasibility of our system and illustrate the viability of deep learning methods for PIR-based localization and tracking. We also show that our dataset can be converted for coordinate estimation so that deep learning methods and particle filter approaches can be applied to estimate coordinates. As a result, the best performer achieves a distance error of 0.25 m. 相似文献
87.
Prediction of parking space availability in real time 总被引:1,自引:0,他引:1
Intelligent parking reservation (IPR) systems allow customers to select a parking facility according to their preferences, rapidly park their vehicle without searching for a free stall, and pay their reservation in advance avoiding queues. Some IPR systems interact with in-vehicle navigation systems and provide users with information in real time such as capacity, parking fee, and current parking utilization. However, few of these systems provide information on the forecast utilization at specific hours - a process that requires the study of the competition between parking alternatives for the market share. This paper proposes a methodology for predicting real-time parking space availability in IPR architectures. This methodology consists of three subroutines to allocate simulated parking requests, estimate future departures, and forecast parking availability. Parking requests are allocated iteratively using an aggregated approach as a function of simulated drivers’ preferences, and parking availability. This approach is based on a calibrated discrete choice model for selecting parking alternatives. A numerical comparison between a one-by-one simulation-based forecast and the proposed aggregated approach indicates that no significant discrepancies exists, validating and suggesting the use of the less time consuming proposed aggregated methodology. Results obtained from contrasting predictions with real data yielded small average error availabilities. The forecast improves as the system registers arrivals and departures. Thus, the forecast is adequate for potential distribution in real-time using different media such as Internet, navigation systems, cell phones or GIS. 相似文献
88.
Pablo Giménez Benjamín Molina Jaime Calvo-Gallego Manuel Esteve Carlos E. Palau 《Computers in Industry》2014
Sensor Web Enablement (SWE) technologies have been successfully applied to a great variety of outdoor scenarios but, in practical terms, little effort has been applied for indoor environments, and even less in the field of industrial applications. This article presents an intelligent SWE application for an indoor and industrial scenario, with the aim of improving and increasing the levels of human safety. The base low-level architecture is built on top of wireless sensor networks (WSN) connected to a Sensor Observation Service (SOS). Higher layers in the architecture include services that make real time decisions based on the collected data. Both simulation and experimental results are presented. The paper shows the viability of our approach in terms of performance, scalability, modularity and safety. 相似文献
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90.
基于蜂窝网的室内定位由于与通信网络共用基础设施,因此具有覆盖范围广、无需基础设施再投资等突出优点,已成为电信运营商级室内定位的首选,是5G通信领域的研究热点之一。在蜂窝网室内定位场景中,基站的布局将直接影响接收首径的数量、到达时间TOA(Time of Arrivaling)和测量误差等要素,从而影响定位精度。据此,文中提出一种面向室内定位的基站选择优化方法,以减小由于基站布局引入的误差。首先,引入TOA信息去除TDOA定位的虚定位点;其次,针对不同基站选择方案得到的定位结果,利用二次聚类的思想去除孤立点,并根据聚类结果中样本节点数量最多的类确定定位点的位置。实验结果表明,与其他优化方法相比,所提方法的室内定位平均误差降低了15.49%。 相似文献