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Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values. 相似文献
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不间断电源(UPS)是大型指挥中心核心装备安全、稳定运行的可靠保障,而针对大型指挥中心如何科学、合理地设计UPS供电系统却鲜有研究。针对大型指挥中心大型设备多且集中、对供电可靠性要求高等实际情况,分析了UPS种类和集中式、分散式两种供电方式的优缺点;根据UPS供电方案的设计流程,提出了大型指挥中心UPS供电方案设计方法,对UPS类型、供电方式和电源容量的选择进行探讨。最后,对UPS安装环境、维护保养要求以及常见故障的处理方法提出了建议。该研究为相关建设单位如何根据设备布局和使用需求来设计UPS供电系统提供有益参考。 相似文献
4.
Social robotics poses tough challenges to software designers who are required to take care of difficult architectural drivers like acceptability, trust of robots as well as to guarantee that robots establish a personalized interaction with their users. Moreover, in this context recurrent software design issues such as ensuring interoperability, improving reusability and customizability of software components also arise. Designing and implementing social robotic software architectures is a time-intensive activity requiring multi-disciplinary expertise: this makes it difficult to rapidly develop, customize, and personalize robotic solutions. These challenges may be mitigated at design time by choosing certain architectural styles, implementing specific architectural patterns and using particular technologies. Leveraging on our experience in the MARIO project, in this paper we propose a series of principles that social robots may benefit from. These principles lay also the foundations for the design of a reference software architecture for social robots. The goal of this work is twofold: (i) Establishing a reference architecture whose components are unambiguously characterized by an ontology thus allowing to easily reuse them in order to implement and personalize social robots; (ii) Introducing a series of standardized software components for social robots architecture (mostly relying on ontologies and semantic technologies) to enhance interoperability, to improve explainability, and to favor rapid prototyping. 相似文献
5.
Detecting topic-based communities in social networks: A study in a real software development network
In social network analysis, a key issue is the detection of meaningful communities. This problem consists of finding groups of people who are both connected and semantically aligned. In the software development context, identifying communities considering both collaborations between developers and their skills can help to address critical elements or issues in a project. However, a large amount of data and the lack of data structure make it difficult to analyze these networks’ content. In this paper, we propose a framework for detecting overlapping semantic communities and their influential members. We also propose an ontology to extract topics of interest through tag enrichment in a Q&A forum. An evaluation was conducted in a large network of software developers built with Stack Overflow’s data, showing that the proposed framework and ontology can find real communities of developers. The evaluation indicates that their members are semantic aligned and still active in the detected topics of interest, and the quantitative analysis showed that the detected communities have high internal connectivity. 相似文献
6.
As blockchain technology is gaining popularity in industry and society, solutions for Verification and Validation (V&V) of blockchain-based software applications (BC-Apps) have started gaining equal attention. To ensure that BC-Apps are properly developed before deployment, it is paramount to apply systematic V&V to verify their functional and non-functional requirements. While existing research aims at addressing the challenges of engineering BC-Apps by providing testing techniques and tools, blockchain-based software development is still an emerging research discipline, and therefore, best practices and tools for the V&V of BC-Apps are not yet sufficiently developed. In this paper, we provide a comprehensive survey on V&V solutions for BC-Apps. Specifically, using a layered approach, we synthesize V&V tools and techniques addressing different components at various layers of the BC-App stack, as well as across the whole stack. Next, we provide a discussion on the challenges associated with BC-App V&V, and summarize a set of future research directions based on the challenges and gaps identified in existing research work. Our study aims to highlight the importance of BC-App V&V and pave the way for a disciplined, testable, and verifiable BC development. 相似文献
7.
参数依赖型软件是指初始化时读取并解析配置参数,并据此进行任务处理的软件,航天测控软件是典型的参数依赖型软件。航天测控软件具有明显的领域软件特征,多采用领域工程分析技术,实现业务处理逻辑和具体任务参数的分离,达到仅通过修改任务配置参数而适应高强度型号任务的目的。通过对参数依赖型软件架构、应用模式的分析,提出一种对参数依赖特性进行验收测试、参数更动测试的流程、策略和方法。并基于该方法,对远程数据交互软件进行了参数依赖特性测试,测试结果表明,该方法具有测试覆盖性强、测试重点突出、测试效率高的特点。 相似文献
8.
为简化不同软件开发技术处理界面中组件关系的算法,增强使用数学模型解决软件开发中算法设计的能力,全面分析和总结直角坐标系在界面设计中的应用十分必要。通过对不同开发技术处理界面设计的分析,界面中组件位置关系实质上都可以转化为坐标关系的计算。本文列举了直角坐标系在Python编程、Android编程、Java编程和Unity编程中的应用,以期给相应的算法设计提供借鉴思路。 相似文献
9.
Textual bridge inspection reports are important data sources for supporting data-driven bridge deterioration prediction and maintenance decision making. Information extraction methods are available to extract data/information from these reports to support data-driven analytics. However, directly using the extracted data/information in data analytics is still challenging because, even within the same report, there exist multiple data records that describe the same entity, which increases the dimensionality of the data and adversely affects the performance of the analytics. The first step to address this problem is to link the multiple records that describe the same entity and same type of instances (e.g., all cracks on a specific bridge deck), so that they can be subsequently fused into a single unified representation for dimensionality reduction without information loss. To address this need, this paper proposes a spectral clustering-based method for unsupervised data linking. The method includes: (1) a concept similarity assessment method, which allows for assessing concept similarity even when corpus or semantic information is not available for the application at hand; (2) a record similarity assessment method, which captures and uses similarity assessment dependencies to reduce the number of falsely-linked records; and (3) an improved spectral clustering method, which uses iterative bi-partitioning to better link records in an unsupervised way and to address the transitive closure problem. The proposed data linking method was evaluated in linking records extracted from ten bridge inspection reports. It achieved an average precision, recall, and F-1 measure of 96.2%, 88.3%, and 92.1%, respectively. 相似文献
10.
低速率拒绝服务(LDoS)攻击是一种拒绝服务(DoS)攻击改进形式,因其攻击平均速率低、隐蔽性强,使得检测LDoS攻击成为难点。针对上述难点,提出了一种在软件定义网络(SDN)的架构下,基于加权均值漂移-K均值算法(WMS-Kmeans)的LDoS攻击检测方法。首先,通过获取OpenFlow交换机的流表信息,分析并提取出SDN环境下LDoS攻击流量的六元组特征;然后,利用平均绝对值百分比误差作为均值漂移聚类中欧氏距离的权值,以此产生的簇心作为K-Means的初始中心对流表进行聚类,从而实现LDoS攻击的检测。实验结果表明:在SDN环境下,所提方法对LDoS攻击具有较好的检测性能,平均检测率达到99.29%,平均误警率和平均漏警率分别为1.97%和0.69%。 相似文献