MS Project 4.0 forWindows使用初探

本文介绍了微软公司项目管理软件Project4．0Forwindows的几点使用小经验。     

Robustness of declarative modeling languages: Improvements via probability-one homotopy

Robustness issues with steady-state initialization remain a barrier in the practical use of declarative modeling languages for multi-domain modeling of large, complex, and heterogeneous technical systems. The objective of this paper is to illustrate how probability-one homotopy, an established method from topology, can solve this issue. This is achieved by establishing a framework for application-specific probability-one homotopy in declarative modeling languages. The analysis is based on domain-specific probability-one homotopy maps, which were reformulated in a declarative fashion. Additionally, a novel probability-one homotopy map and associated coercivity proof is introduced for a class of thermo-fluid dynamics problems. It was found that the approach enables robust initialization for declarative modeling languages on several test cases and leads to a concise declarative problem formulation.     

会计电算化初始设置论述

系统初始化设置是应用会计软件的核心步骤，初始化设置是否恰当，将在很大程度上影响日后核算工作。文章从软件应用的角度阐述了初始化设置的方法。     

Quadratic optimization fine tuning for the Support Vector Machines learning phase

This work presents a comparative analysis of specific, rather than general, mathematical programming implementation techniques of the quadratic optimization problem (QP) based on Support Vector Machines (SVM) learning process. Considering the Karush–Kuhn–Tucker (KKT) optimality conditions, we present a strategy of implementation of the SVM-QP following three classical approaches: (i) active set, also divided in primal and dual spaces, methods, (ii) interior point methods and (iii) linearization strategies. We also present the general extension to treat large-scale applications consisting in a general decomposition of the QP problem into smaller ones, conserving the exact solution approach. In the same manner, we propose a set of heuristics to take into account for a better than a random selection process for the initialization of the decomposition strategy. We compare the performances of the optimization strategies using some well-known benchmark databases.     

FCM聚类算法的改进

模糊C均值（FCM）算法广泛地应用于模式识别、图像分割等领域。根据FCM算法存在对初始解敏感且迭代过程中计算量大的问题,本文提出了一种改进的算法：先通过精简数据集,减少算法迭代的时间;再使用密度函数法得到FCM算法的初始聚类中心,以减少FCM算法收敛所需的迭代次数。实验结果表明,改进后的算法较好地解决了类中心的初值化问题,提高了算法的收敛速度和运行效率。     

提高水平集方法初始化计算速度的研究

本文对水平集方法的初始化步骤进行了改进。在水平集方法中，初始化是一个很重要的步骤。它包括计算图像平面中每个点的初始水平集距离，同时找到与当前点距离最近的曲线上的点。这是一个费时的过程，影响到整个水个集方法的计算速度。本文基于快速推进法提出了一种快速初始化方法。这种方法只需要Ｏ（Ｎ）的时间就能够遍历所有的图像点，完成初始化。最后，对三种不同的初始化方法：直接法、快速推进法和本文的方法在计算速度上作了对比。实验表明，最终得到理想的分割结果的情况下，本文的方法在时间上优于其他两种方法。     

基于自适应水平集方法的运动目标跟踪

在对复杂背景条件下的非刚体目标进行跟踪时,水平集方法是一种非常有效的方法。针对水平集的初始化问题,提出了一种自适应初始化的水平集方法。首先采用粒子滤波对目标进行跟踪,得到运动目标的粗外接矩形轮廓;然后以外接矩形轮廓的质心为基础进行水平集曲线演化,求取目标的精确轮廓信息,并将轮廓的提取结果反馈到跟踪框架中;最后,动态更新参考模板,以改进似然函数。实验结果表明,本方法能够适应非刚体目标在运动中的自由变化,使得跟踪更加精确。     

求解带约束的多峰函数优化问题的新演化算法

求解带约束的多峰函数优化问题在科学研究和工程应用中具有重要的现实意义。本文在对郭涛算法和“两阶段子空间演化算法”改进的基础上,通过采用混沌初始化和混沌变异算子,给出了一种求解带约束条件的多峰函数优化问题的有效的演化算法。通过对具有多峰的复杂函数的测试表明:该算法在求解质量和收敛速度方面都得到了很好的结果,表现出较强的鲁棒性。     

Low computation and low latency algorithms for distributed sensor network initialization

In this paper, we show how an underlying system’s state vector distribution can be determined in a distributed heterogeneous sensor network with reduced subspace observability at the individual nodes. The presented algorithm can generate the initial state vector distribution for networks with a variety of sensor types as long as the collective set of measurements from all the sensors provides full state observability. Hence the network, as a whole, can be capable of observing the target state vector even if the individual nodes are not capable of observing it locally. Initialization is accomplished through a novel distributed implementation of the particle filter that involves serial particle proposal and weighting strategies that can be accomplished without sharing raw data between individual nodes. If multiple events of interest occur, their individual states can be initialized simultaneously without requiring explicit data association across nodes. The resulting distributions can be used to initialize a variety of distributed joint tracking algorithms. We present two variants of our initialization algorithm: a low complexity implementation and a low latency implementation. To demonstrate the effectiveness of our algorithms we provide simulation results for initializing the states of multiple maneuvering targets in smart sensor networks consisting of acoustic and radar sensors. Prepared through collaborative participation in the Advanced Sensors Consortium sponsored by the US Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-02-0008.