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为解决城市信号自组织控制中,由相邻路口间各自最优通行效率冲突造成的路口群整体通行效率难以继续提升的问题,提出一种城市元胞路网下自组织控制规则参数的整定方法. 首先,利用城市路网元胞传输(CTM)模型模拟路网交通流动态状态信息;然后,基于元胞路网实时信息,以选定局域路网中各路口元胞的自组织控制规则参数为设计空间;最后,建立各路口相互协调下通行量最大化的目标函数,完成各路口自组织控制规则参数的整定. 仿真结果表明:在基于城市元胞路网实时信息条件的整定方法下,不仅解决了自组织控制中相邻路口间互相制约通行效率的问题,并且有效提高了自组织单元中所有路口的通行效率. 该方法为面向复杂城市路网的实时城市交通信号自组织控制系统提供可借鉴的智能化方法与工程化理论. 相似文献
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污水处理是一个复杂的非线性过程,化学需氧量(chemical oxygen demand,COD)是评价污水处理效果的关键指标之一。COD的传统测量方法耗时长、成本高,基于传统神经网络的软测量方法提高了COD参数的测量速度但精度较差。针对这些问题,设计一种结合自组织特征映射 (self-organizing map, SOM)和径向基函数(radial basis function, RBF)神经网络的COD参数软测量方法。该方法利用SOM网络聚类数据样本,根据所得聚类结果确定RBF网络的隐层节点数及节点的数据中心,综合提高RBF网络的收敛速度和拟合精度。利用污水处理厂部分水样数据建立COD软测量模型,模型仿真和硬件在线测试结果表明,相对于传统的BP、RBF等网络,基于SOM-RBF神经网络的COD软测量方法测量时间短、预测精度较高,具有较为广阔的应用前景。 相似文献
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Vehicle ad hoc networks (VANETs) have attracted great interests from both industry and academia, but a number of issues, particularly security, have not been readily addressed. Intrusion Detection System (IDS) as one of the most important approaches to protect network security has been studied adequately in previous literatures. However, the performance of IDSs still needs to be improved to adapt the scenario of VANETs which are very fast moving and highly dynamic. In this paper, we propose a novel IDS that is able to be appropriately used in the wireless and dynamic networks, like VANETs. It mainly contains a novel feature extraction algorithm and a classifier based on an improved growing hierarchical self-organizing map (I-GHSOM) for IDS in VANETs. The proposed feature extraction algorithm is used to quickly extract distinct features from vehicle messages for IDS’s training and test. In the proposed algorithm, two key features including the differences of traffic flow and of position are extracted. The former feature is calculated according to the range of the distance between vehicles, while both a voting filter mechanism and a semi-cooperative mechanism are designed to get the latter feature. Furthermore, in the I-GHSOM-based classifier, for quickly attaining precise classification results, two novel mechanisms (relabeling and recalculating mechanisms) are proposed to relabel the units of GHSOM and check whether the balance of GHSOM structure is broken or not. Simulation results show that the proposed IDS is better than others in the measurement of accuracy, stability, processing efficiency and message scales. 相似文献
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Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help of an MRI scanner. With the slice images obtained using an MRI scanner, certain image processing techniques are utilized to have a clear anatomy of brain tissues. One such image processing technique is hybrid self-organizing map (SOM) with fuzzy K means (FKM) algorithm, which offers successful identification of tumor and good segmentation of tissue regions present inside the tissues of brain. The proposed algorithm is efficient in terms of Jaccard Index, Dice Overlap Index (DOI), sensitivity, specificity, peak signal to noise ratio (PSNR), mean square error (MSE), computational time and memory requirement. The algorithm proposed through this paper has better data handling capacities and it also performs efficient processing upon the input magnetic resonance (MR) brain images. Automatic detection of tumor region in MR (magnetic resonance) brain images has a high impact in helping the radio surgeons assess the size of the tumor present inside the tissues of brain and it also supports in identifying the exact topographical location of tumor region. The proposed hybrid SOM-FKM algorithm assists the radio surgeon by providing an automated tissue segmentation and tumor identification, thus enhancing radio therapeutic procedures. The efficiency of the proposed technique is verified using the clinical images obtained from four patients, along with the images taken from Harvard Brain Repository. 相似文献
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为了得到结构更加紧凑、泛化性能更强的自组织模糊神经网络,提出了基于粒子滤波(particle filter,PF)的自组织模糊神经网络训练算法.其能够对模糊规则进行自动生成和增删.文中给出了模糊规则生成准则,应用误差率下降方法作为模糊规则增删策略,删除作用不大的规则.建立了以隶属函数宽度参数为状态,以理想输出为量测的动力学模型,利用PF对参数进行了学习.最后,对两个实例进行了仿真,从仿真结果可以看出,与D-FNN、SOFNN、EKF-SOFNN等算法相比,其在结构紧凑性以及泛化性能上都得到了提高,从而证明了PF-SOFNN的有效性. 相似文献
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