Visualising a knowledge mapping of information systems investment evaluation

Information systems (IS) facilitate organisations to increase responsiveness and reduce the costs of their supply chain. This paper seeks to make a contribution through exploring and visualising knowledge mapping from the perspective of IS investment evaluation. The evaluation of IS is regarded as a challenging and complex process, which becomes even more difficult with the increased complexity of IS. The intricacy of IS evaluation, however, is due to numerous interrelated factors (e.g. costs, benefits and risks) that have human or organisational dimensions. With this in mind, there appears to be an increasing need to assess investment decision-making processes, to better understand the often far-reaching implications associated with technology adoption and interrelated knowledge components (KC). Through the identification and extrapolation of key learning issues from the literature and empirical findings, organisations can better improve their business processes and thereby their effectiveness and efficiency, while preventing others from making costly oversights that may not necessarily be only financial. In seeking to enlighten the often obscure evaluation of IS investments, this paper attempts to inductively emphasise the dissemination of knowledge and learning through the application of a fuzzy Expert System (ES) based knowledge mapping technique (i.e. Fuzzy Cognitive Map [FCM]). The rationale for exploring knowledge and IS investment evaluation is that a knowledge map will materialise for others to exploit during their specific technology evaluation. This is realised through conceptualising the explicit and tacit investment drivers. Among the several findings drawn from this research, the key resulting knowledge mapping through FCM demonstrated the complex, multifaceted and emergent behaviour of causal relationships within the knowledge area. The principal relationships and knowledge within IS investment evaluation are illustrated as being determined by a blend of managerial and user perspectives.     

基于APIC的高精度定时器设计

在操作系统开发过程中需要一定精度的计时器支持。在Windows实时扩展改造过程中,针对原有定时精度低和波动较大的问题,提出一种基于高级可编程中断控制器(APIC)的高精度定时器设计方案。利用CPU片上结构APIC的计数寄存器编程,构建高精度时钟。运用内核驱动建立内核调度管理,通过内存映射提高用户态到内核态的数据传输速度,保证实时性能。利用DLL提供一组和内核交互的定时器接口供用户使用。实验结果表明,该方案有效解决了定时精度和稳定性问题,构造的定时器性能稳定,具有良好的实用性。     

基于奇偶树型交互学习机与遗传算法的密文优化系统

为使图像加密系统具备优化功能,并解决当前遗传算法无法实现全局最优、收敛速率慢等问题,提出奇偶树型交互学习机耦合全局离散遗传算法的密文优化系统。定义权值更新机制,耦合混沌映射,构造奇偶树型交互学习机及其互扰模型。将切断型轮盘赌择取机制引入均匀交叉算子中,以图像分块的相邻像素相关系数和密文信息熵为目标,根据权重理论设计加权适应度函数,提出一种全局离散遗传算法,最终形成"初始加密-密文优化"的加密结构。实验结果表明,与超混沌算法、离散遗传算法、元胞自动机相比,该系统的加密质量较好,并且具备全局优化功能,可优化所有迭代结果,使最终输出密文的信息熵最大,相关系数最小。     

一种基于同义词词林的中文大规模本体映射方案

本体映射是解决本体异构问题的重要途径和手段,中文知识是网络开放知识库的重要组成部分,但现有的中文本体映射系统在面对大规模本体映射任务时,显得效率较低且可用性不高,目前仍缺乏针对中文大规模本体映射的相关系统。为了解决中文大规模本体的映射问题,设计并实现了一个面向中文的大规模本体映射系统。首先,提出了一种基于拟核力场势函数的大规模本体压缩方法;其次,提出了一种基于同义词词林的中文概念等价关系确定算法;再次,实现了大规模中文本体映射的原型系统;最后,将本系统与相似度计算相关典型算法进行比较,证明其具备一定的可用性和较高的总体性能。     

基于Java 3D的地球空间环境可视化研究

根据空间科学研究对交互性和Web化的发展需求,将Java 3D应用于该领域。深入剖析了Java 3D的主要几何类和实现细节;改进并实现了一种矩形网格等值线生成算法,解决了二义性和等值点在网格顶点的问题;介绍了一种生成规则网格的简单算法,并选用合适的Java 3D类加以实现;介绍了一种数值———颜色映射算法,并将空间环境数据映射为RGB颜色信息。综合运用这些算法,对地球空间环境中若干物理要素模型进行了二维和三维可视化。可视化结果与国内外空间环境模型研究成果基本一致,说明上述算法正确,有较高的参考价值。     

基于形变模型的多视图三维人脸重建

本文提出了基于形变模型的多视图三维人脸重建方法,将人脸形变模型与同一人脸在不同视点下的多幅图像进行匹配,从而重建出具有较强真实感的三维人脸模型。本文将对基于形变模型的多视图三维人脸重建方法进行详细的阐述,并把实验结果与单视图重建出的三维人脸模型进行了对比,从而体现出多视图重建的优势所在。     

基于方程式逆序数的软件水印算法*

针对方程式重排序等算法存在的程序运行速度、信息隐藏量等问题,提出基于方程式操作数系数排列逆序数的软件水印算法。重新排列那些可以相互交换的操作数,使各操作数的系数按照一定次序排列。通过排列的逆序数和二进制数的一一对应关系来隐藏水印。这一方法无须向程序中添加任何代码,并且能有效提高隐藏数据的效率,程序的规模和速度并不受影响。     

Map Reduce inspired loop mapping for coarse-grained reconfigurable architecture

Our work investigates how to map loops efficiently onto Coarse-Grained Reconfigurable Architecture （CGRA）. This paper examines the properties of CGRA and builds MapReduce inspired models for the loop parallelization problem. The proposed model has a more detailed performance metric and a more flexible unrolling scheme that can unroll different loop levels with different factors. A Geometric Programming based approach is proposed to resolve the optimization problem of loop parallelization problem. The proposed approach can find the optimal unrolling factor for each level loop, resulting in better parallelization of loops. Experimental results show that the proposed approach achieved up to 44% performance gain compared to the state-of-the-art loop mapping scheme.     

An homogeneous space geometry for simultaneous localisation and mapping

Simultaneous Localisation and Mapping (SLAM) is the archetypal chicken and egg problem: Localisation of a robot with respect to a map requires an estimate of the map, while mapping an environment from data acquired by a robot requires an estimate of the robot localisation. The nonlinearity and co-dependence of the SLAM problem has made it an ongoing research problem for more than thirty years. The present paper details recent advances in understanding the SLAM problem, specifically the existence of an underlying geometry and symmetry structure that provides significant insight into the difficulties that have plagued many SLAM algorithms. To demonstrate the power of the geometric insight we derive a constant gain observer for the SLAM problem that; that does not depend on linearisation, has globally asymptotically stable error dynamics, is very robust, and operates in dynamic environments (estimating the landmark velocities as states in the observer).     

基于IMU与激光雷达紧耦合的混合匹配算法

针对线、面特征匹配的激光雷达测距与地图构建算法(Lightweight and Ground-Optimized Lidar Odometry And Mapping,LeGO-LOAM)在自动导引运输车(Automated Guided Vehicle,AGV)室内室外实时建图与定位时,易出现激光里程计累积误差大和旋转估计不准确等问题,本工作采用惯性测量单元(Inertial Measurement Unit,IMU)与激光雷达紧耦合的LeGO-LOAM算法,通过IMU为激光雷达提供的初始位姿信息,构建IMU与激光雷达联合误差函数,实现位姿共同迭代优化.其中,对于室外结构化信息较少时,在点对点的迭代最近点算法(Iterative Closest Point,ICP)较高定位精度的基础上,结合LeGO-LOAM算法和ICP算法互补性,进一步提出基于IMU与激光雷达紧耦合的混合匹配算法:当环境中结构信息较多时,激光里程计采用LeGO-LOAM算法,而当环境中结构化信息较少时采用ICP算法.实验结果表明,基于IMU与激光雷达紧耦合的混合匹配算法可有效降低激光里程计相对位姿误差和累积误差,提高AGV小车定位精度以消除部分地图重影.