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1.
56Fe(n,p)56Mn通常作为标准反应来监测中子场通量,该反应截面数据的准确性直接影响到活化法测量结果的精确度,进而影响到实验待测物理量的精度。本文开展了56Fe(n,p)56Mn反应截面实验测量数据评价工作与协方差计算工作,首先系统分析EXFOR中现有的56Fe(n,p)56Mn反应截面实验测量数据,对实验数据进行了归纳总结分析,并从中子源、测量方法、探测器类型等方面对56Fe(n,p)56Mn直接测量实验数据进行评价。然后,拟合给出适用入射中子能量区间为295~35 MeV的激发曲线。随后,针对评价中重点推荐的实验数据开展了关联协方差矩阵的计算工作。最后,使用核反应计算程序TALYS对56Fe(n,p)56Mn激发曲线进行了调参计算并和评价数据进行了比较分析。该工作拓展了现有的中子活化反应截面实验数据的评价方法,结果提高了35 MeV以下中子诱发56Fe(n,p)56Mn反应的评价数据精度。 相似文献
2.
目前通过3D扫描仪获取的点云仍旧存在一些缺陷:点云含有噪声,点云在不同方向上分布不均匀等.本文针对上述问题开展研究.主要工作为提出一种新的算法用于在点云上进行高质量的重采样,即使用较为稀疏的重采样点集去表达较为密集的原始点云的几何形状,同时重采样点集的分布可以满足用户预先指定的目标分布,并具备一定的蓝噪声性质.在最优传输理论的基础之上,本文方法将传统的点云重采样问题转化为一个最优化问题,并在点云上构建离散网格,使得针对网格的受限制的Power剖分方法能够迁移至点云上.随后利用交叉优化框架对该优化问题进行求解,并对每一个重采样点执行精确的容积约束.大量实验结果表明,本文算法输出的重采样点集可以实现精确自适应控制密度的目标,并且具备较好的蓝噪声性质. 相似文献
3.
In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications. 相似文献
4.
Hosam El- El-Sofany 《计算机系统科学与工程》2022,43(2):573-589
Most user authentication mechanisms of cloud systems depend on the credentials approach in which a user submits his/her identity through a username and password. Unfortunately, this approach has many security problems because personal data can be stolen or recognized by hackers. This paper aims to present a cloud-based biometric authentication model (CBioAM) for improving and securing cloud services. The research study presents the verification and identification processes of the proposed cloud-based biometric authentication system (CBioAS), where the biometric samples of users are saved in database servers and the authentication process is implemented without loss of the users’ information. The paper presents the performance evaluation of the proposed model in terms of three main characteristics including accuracy, sensitivity, and specificity. The research study introduces a novel algorithm called “Bio_Authen_as_a_Service” for implementing and evaluating the proposed model. The proposed system performs the biometric authentication process securely and preserves the privacy of user information. The experimental result was highly promising for securing cloud services using the proposed model. The experiments showed encouraging results with a performance average of 93.94%, an accuracy average of 96.15%, a sensitivity average of 87.69%, and a specificity average of 97.99%. 相似文献
5.
Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,
there is no standard guideline for data transformation from relational database to
NoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processing
time when compared to the relational database query processing time. However,
these approaches produced redundant tables in the resulted schema that in turn
consume large unnecessary storage size and produce high query processing time
due to the generated schema with redundant column families in the transformed
column-oriented database. In this paper, an efficient data transformation technique
from relational database to column-oriented database is proposed. The proposed
schema transformation technique is based on the combination of denormalization
approach, data access pattern and multiple-nested schema. In order to validate the
proposed work, the proposed technique is implemented by transforming data from
MySQL database to HBase database. A benchmark transformation technique is
also performed in which the query processing time and the storage size are compared. Based on the experimental results, the proposed transformation technique
showed significant improvement in terms query processing time and storage space
usage due to the reduced number of column families in the column-oriented
database. 相似文献
6.
Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues. Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations. This paper proposes a comprehensive review of security services and threats. Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms. A threat model has been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats. 相似文献
7.
采用三维激光扫描非接触倾斜测量法,可以高效获取矿仓中精矿产品的物料堆积形态。通过优化精矿盘点扫描测量流程,精准计算出矿仓库存量,提高了精矿盘点数据的准确性、测量的高效性和操作的安全性。 相似文献
8.
M.T. Dlamini J.H.P. Eloff H.S. Venter M.M. Eloff 《International Transactions in Operational Research》2022,29(1):372-395
For businesses to benefit from the many opportunities of cloud computing, they must first address a number of security challenges, such as the potential leakage of confidential data to unintended third parties. An inter-VM (where VM is virtual machine) attack, also known as cross-VM attack, is one threat through which cloud-hosted confidential data could be leaked to unintended third parties. An inter-VM attack exploits vulnerabilities between co-resident guest VMs that share the same cloud infrastructure. In an attempt to stop such an attack, this paper uses the principles of logical analysis to model a solution that provides physical separation of VMs belonging to conflicting tenants based on their levels of conflict. The derived mathematical model is founded on scientific principles and implemented using four conflict-aware VM placement algorithms. The resultant algorithms consider a tenant's risk appetite and cost implications. The model offers guidance to VM placement and is validated using a proof of concept. A cloud simulation tool was used to test and evaluate the effectiveness and efficiency of the model. The findings reflect that the introduction of the proposed model introduced a time lag in the time it took to place VM instances. On top of this, it was also discovered that the number and size of the VM instances has an effect on the VM placement performance. The findings further illustrate that the conflict tolerance level of a VM has a direct impact on the time it took to place. 相似文献
9.
随着空间数据体量的持续增长,空间数据所蕴含的价值巨大.传统的数据感知存储技术和处理分析方法已然不能充分挖掘海量空间数据的价值.因此,空间数据智能,一个专注于海量空间数据的研究与应用的多学科交叉的领域,正扮演着越来越重要的角色.介绍了空间数据智能的概念、空间数据智能领域所面临的技术挑战及空间数据智能的关键技术,同时介绍了空间数据智能在社会生活中的典型应用场景,最后对空间数据智能研究的发展做出了展望. 相似文献
10.
在现有OpenStack云平台与Docker容器技术的集成方案中,基于容器初始资源请求的调度模型由于未充分考虑容器运行时的实际资源使用情况,导致资源利用率较低。为满足云计算领域的高资源利用率和低成本需求,构建基于OpenStack云平台的Docker调度模型(DSM),将其与OpenStack的Keystone、Glance以及Neutron组件的API进行交互,获取创建容器所需的镜像、网络等资源,同时调用Docker Engine提供的API部署容器,对容器生命周期进行高效灵活管控。通过融合初始化模块、资源实时感知模块、容器调度模块、资源实时监测模块和容器迁移模块,并在容器调度模块中利用资源可用度评估与优先级决策调度机制为容器选择最优的计算节点,实现OpenStack云平台中资源的高效利用。实验结果表明,与经典Nova-Docker和Yun集成方案采用的调度模型相比,DSM调度模型在CPU和内存利用率上至少提升38.54、30.17个百分点和38.40、28.69个百分点。 相似文献