校园网网络事件源定位技术研究

为了解决在校园网内部分网络安全事件和故障事件中只能确认攻击和事故的发生,而无法确定攻击和事故的源头这一问题.文章提出一种三步骤的校园网内网网络事件源定位模型.该模型将神经网络和证据理论相结合应用于攻击源定位.提高了校园网内网攻击定位的效率和准确性.     

设备远程监控及故障诊断系统

基于GPRS技术和规则引擎,设计开发了一个高校仪器设备远程监控及故障诊断系统。该系统可扩展性强,能实现事件记录、远程监控、故障自诊断等功能。能够真正意义上实现对设备从采购到报废整个生命周期的管理,提高教育投资效益,减少仪器设备重复购置,降低故障率。本文从系统的组成框架、实现流程、关键功能模块以及应用实例等几个方面来介绍该系统设计思想及实现技术。该系统具有人机界面良好,实用性强,成本投资少等特点。     

石油化工产业区配套服务业发展模式分析

随着以上海石化和上海化学工业区为主体的上海石油化工生产基地及相关产业的发展,与其配套的综合服务业已成为产业区结构与功能提升的重要内容。结合国内外相关案例及区域整体发展状况,探讨并分析了上海石油化工生产基地综合配套服务业的发展模式。对服务性产业发展状况、产业发展战略、规划选址、服务功能等进行了相关研究。     

基于TQM的高校毕业论文信息管理系统的研究与构建

该文提出在毕业论文设计过程引入项目管理机制,采用TQM思想和PDCA的过程管理方法建立基于论文设计工作流的质量管理体系,同时在微软的企业级协同平台SharePoint上对这种质量体系下的论文管理信息系统进行了构建。     

银行业信息科技风险管理能力探究

本文从国内外的信息安全风险管理法律法规出发,全面介绍了银行等金融机构如何构建一套动静结合的信息安全保障体系,全面提升银行业信息科技风险管理能力。     

Cortex—M3的生态物联网数据采集分站设计

以Cortex—M3处理器LM3S811为核心,设计了面向森林生态监测的物联网数据采集终端分站系统。通过单总线数字式温湿度传感器、烟雾传感器、CO2传感器和环境光亮度传感器分别采集温度、湿度、可燃性颗粒物、CO2含量和光照等与林木生态相关的环境信息和林场防火的相关信息。由LM3S81l处理器处理并添加分站ID和校验码后...     

Dynamics of a large scale rigid–flexible multibody system composed of composite laminated plates

A novel computational approach for the dynamic analysis of a large scale rigid–flexible multibody system composed of composite laminated plates is proposed. The rigid parts in the system are described through the Natural Coordinate Formulation (NCF) and the flexible bodies in the system are modeled via the finite elements of Absolute Nodal Coordinate Formulation (ANCF), which can lead to a constant mass matrix for the derived system equation of motion. For modeling composite laminated plates accurately, a new composite laminated plate element of ANCF is proposed and the corresponding efficient formulations for evaluating both the elastic force and its Jacobian of the element are derived from the first Piola–Kirchhoff stress tensor. To improve computational efficiency, the sparse matrix technology and graph theory are used to solve the huge set of linear algebraic equations in the process of integrating the equations of motion by using the generalized-a method, and an OpenMP based parallel scheme is also introduced. Finally, the effectiveness of the proposed approach is validated through two numerical examples. One is the static simulation of a single composite laminated plate under gravity and the other is the dynamic simulations of unfolding process of a satellite system with a pair of complicated antennas.     

Manifold elastic net: a unified framework for sparse dimension reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al., Ann Stat 32(2):407–499, 2004), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: (1) the local geometry of samples is well preserved for low dimensional data representation, (2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, (3) the projection matrix of MEN improves the parsimony in computation, (4) the elastic net penalty reduces the over-fitting problem, and (5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.     

Community discovery using nonnegative matrix factorization

Complex networks exist in a wide range of real world systems, such as social networks, technological networks, and biological networks. During the last decades, many researchers have concentrated on exploring some common things contained in those large networks include the small-world property, power-law degree distributions, and network connectivity. In this paper, we will investigate another important issue, community discovery, in network analysis. We choose Nonnegative Matrix Factorization (NMF) as our tool to find the communities because of its powerful interpretability and close relationship between clustering methods. Targeting different types of networks (undirected, directed and compound), we propose three NMF techniques (Symmetric NMF, Asymmetric NMF and Joint NMF). The correctness and convergence properties of those algorithms are also studied. Finally the experiments on real world networks are presented to show the effectiveness of the proposed methods.