使用报表脚本实现报表续打套打

报表文件的续打套打是一个常见的需求,通过程序的参数设置和计算操控报表脚本从而实现规范的报表续打可以报表规范的续打。     

A binary approximating method for graspable region determination of biped climbing robots

For a biped pole-climbing robot (BiPCR) with grippers, it is an essential demand to determine the target grasp configuration for climbing and transiting between poles, with the graspable region as a priori knowledge. The graspable region on the target pole is critically important for climbing path planning and motion control. To efficiently compute the graspable region for a BiPCR, we propose a novel binary approximating method in this paper. This method may also be applied to generate the three-dimensional (3-D) workspace of a manipulator with constant orientation. The grasping problem and the concept of graspable region for a BiPCR are first introduced. The binary approximating method and the corresponding algorithms are then presented to generate the graspable region. Additional constraints on a biped climbing robot with five degrees of freedom (DoFs) are presented as a supplement to the algorithm. A series of comprehensive simulations are conducted with the five-DoF and six-DoF climbing robots to verify the effectiveness of the proposed method. Finally, the dexterity of biped climbing robots with different DoFs is discussed.     

Two-dimensional bilinear preserving projections for image feature extraction and classification

Two-dimensional locality preserving projections (2DLPP) was recently proposed to extract features directly from image matrices based on locality preserving criterion. A significant drawback of 2DLPP is that it only works on one direction (left or right) to reduce the dimensionality of the image matrices and thus too many coefficients are needed for image representation in low-dimensional subspace. In this paper, we propose a novel method called two-dimensional bilinear preserving projections (2DBPP) for image feature extraction. We generalized the image-based (2D-based) feature extraction techniques into bilinear cases, in which 2DLPP is a special case of our proposed method. In order to obtain the bilinear projections, we proposed an iteration method by solving the corresponding generalized eigen-equations. Moreover, analyses show that 2DBPP has stronger locality preserving abilities than 2DLPP. By using the label information and defining different local neighborhood graphs, the proposed framework is further extended to supervised case. Experiments on three databases show that 2DBPP and its supervised extension are superior to some other image-based state-of-the-art techniques.     

Coordinating efficiency and equity in disaster relief logistics via information updates

This paper addresses a multi-supplier, multi-affected area, multi-relief, and multi-vehicle relief allocation problem in disaster relief logistics. A multi-objective optimisation model based on disaster scenario information updates is proposed in an attempt to coordinate efficiency and equity through timely and appropriate decisions regarding issues such as vehicle routing and relief allocation. An optimal stopping rule is also proposed to determine the optimum period of delay before responding to disaster, because decision making requires accurate disaster information. The main contribution of this paper is solving relief allocation problem in a novel way by correlating operational research with statistical decision making and Bayesian sequential analysis. Finally, a case is presented based on the post-disaster rescue in Eastern China after supertyphoon Saomai to test the applicability and show the potential advantages of the proposed model.     

An FAR-SW based approach for webpage information extraction

Automatically identifying and extracting the target information of a webpage, especially main text, is a critical task in many web content analysis applications, such as information retrieval and automated screen reading. However, compared with typical plain texts, the structures of information on the web are extremely complex and have no single fixed template or layout. On the other hand, the amount of presentation elements on web pages, such as dynamic navigational menus, flashing logos, and a multitude of ad blocks, has increased rapidly in the past decade. In this paper, we have proposed a statistics-based approach that integrates the concept of fuzzy association rules (FAR) with that of sliding window (SW) to efficiently extract the main text content from web pages. Our approach involves two separate stages. In Stage 1, the original HTML source is pre-processed and features are extracted for every line of text; then, a supervised learning is performed to detect fuzzy association rules in training web pages. In Stage 2, necessary HTML source preprocessing and text line feature extraction are conducted the same way as that of Stage 1, after which each text line is tested whether it belongs to the main text by extracted fuzzy association rules. Next, a sliding window is applied to segment the web page into several potential topical blocks. Finally, a simple selection algorithm is utilized to select those important blocks that are then united as the detected topical region (main texts). Experimental results on real world data show that the efficiency and accuracy of our approach are better than existing Document Object Model (DOM)-based and Vision-based approaches.     

A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.     

Fixed-time Group Consensus of Nonlinear Multi-agent Systems via Pinning Control

This paper deals with the fixed-time group consensus problem for multi-agent systems (MASs) subjected to exogenous disturbances. Firstly, two pinning control algorithms are constructed for MASs, which not only reduce the number of controllers but also achieve expected tracking consensus. Secondly, fixed-time group consensus is ensured by utilizing the algebraic graph theory, Lyapunov stability and fixed-time control technique. Finally, simulations are finally given for demonstrate the availability of the derived results.

     

多平台数据容灾系统的研究与设计*

基于对Windows和Linux平台卷管理系统的特性分析,提出一种基于卷复制的数据容灾系统模型,并论述了其系统结构、工作原理以及关键技术.该系统的主要特点是采用模块嵌入技术实现系统与平台的有机结合,同时支持多种复制模式以保证数据的一致性,通过加密、压缩传输以提高数据安全性并降低带宽要求,另外提供灾难恢复机制,保证了主数据系统在遭受灾难的情况下可以方便地迁移和回迁.理论和实验均证明,本数据容灾系统较好地保证了数据可靠性和可用性.     

EL-CID电磁铁心故障检测试验的应用研究

为了检验发电机定子铁心质量,在制造过程中和安装现场,对发电机定子铁心进行试验是必不可少的.目前国内针对定子铁心更多地是采用常规试验方法,但是常规铁损试验所需的设备容量大、试验线路复杂,而且经济性较差、效率低下.而采用电磁定子铁芯故障探测仪(EL-CID)铁心试验只需提供约4％的额定磁通量,即可完成对铁心故障的诊断,其所需的励磁电源容量较小且容易配备和携带.为了更好地利用EL-CID检测方式进行铁心质量的判定,本文通过模拟多种故障状况下的试验,论证了EL-CID的使用条件和影响因素,为实际工作中EL-CID的应用提供更好的参考依据.