图像轮廓提取方法研究

对现有的轮廓提取现状进行分析,把主要的轮廓提取方法划分为先验知识法、数学形态法、基于梯度的方法、水平集方法、活动轮廓模型方法、以及神经动力学方法六大类,并研究这些方法的主要特点。对轮廓提取方法研究进行展望,提出神经动力学方法是轮廓提取方法的发展方向。     

网管系统策略冲突解决的结果中存在的问题及检测与解决方法

当前，多种方法被用来解决网管系统中的策略冲突，不同方法有不同的应用范围，因此，通常需要综合应用多种方法来解决策略之间的冲突．各种方法解决策略冲突解决的过程是相对独立的，其策略冲突解决的结果也是相对独立的．在解决策略冲突的过程中，难以考虑到已经存在的策略冲突解决结果．因此。不同的策略冲突解决方法的结果之间可能存在冲突．分析了网管系统中多策略冲突解决方法的结果中可能存在的冲突，提出了检测及解决这些冲突的方法，并给出了实验结果及结论．     

Relaxed parallel two-stage multisplitting methods ∗

Two classes of relaxed parallel two-stage multisplitting methods based on extrapolated and AOR methods are studied for the solution of nonsingular linear systems, which are called outer relaxed or inner relaxed parallel two-stage multisplitting methods. Convergence of these methods is studied for H-matrix. Furthermore, computational results about these methods on a shared memory multiprocessor are presented. The results show that the methods we proposed are better than the corresponding existed parallel (two-stage) multisplitting methods.     

Vague集向Fuzzy集的转化方法的比较

证明了vague集转化为fuzzy集的三角形法和不确定度加权法都是实用方法,证明了这两种方法是相同的方法,且证明了这两种方法都是加权均值修正法的特殊情况。     

Spectral analysis of nonuniformly sampled data – a review

In this paper, we present a comprehensive review of methods for spectral analysis of nonuniformly sampled data. For a given finite set of nonuniformly sampled data, a reasonable way to choose the Nyquist frequency and the resampling time are discussed. The various existing methods for spectral analysis of nonuniform data are grouped and described under four broad categories: methods based on least squares; methods based on interpolation techniques; methods based on slotted resampling; methods based on continuous time models. The performance of the methods under each category is evaluated on simulated data sets. The methods are then classified according to their capabilities to handle different types of spectrum, signal models and sampling patterns. Finally the performance of the different methods is evaluated on two real life nonuniform data sets. Apart from the spectral analysis methods, methods for exact signal reconstruction from nonuniform data are also reviewed.     

行程时间预测方法研究

行程时间预测,有助于实施高级旅行者信息系统.自20世纪90年代起,已经有多种行程时间预测方法被研发出来.将行程时间预测方法分为模型驱动方法和数据驱动方法两大类.介绍了两种常见的模型驱动方法,即排队论模型和细胞传输模型.数据驱动方法被分类为参数方法和非参数方法：参数方法包括线性回归、自回归集成移动平均和卡尔曼滤波,非参数方法包括神经网络、支持向量回归、最近邻和集成学习方法.对现有行程时间预测方法从源数据、预测范围、准确率、优缺点和适用范围等方面进行了分析总结.针对现有方法的一些缺点,提出了可能的解决方案.给出了一种新颖的数据预处理框架和一个行程时间预测模型,最后指出了未来的研究方向.     

虚拟人皮肤建模技术研究概况与展望

总结了国内外虚拟人皮肤建模技术的研究概况,从逼真性角度将传统的研究方法分为两层结构法和物理结构法两大类进行描述与分析;对各种皮肤建模方法特别是物理结构法进行了概要分析.最后展望了虚拟人皮肤建模技术的几个发展方向.     

ASP.NET页面间传值综述

对ASP.NET中各种页面间传值方法进行综述,阐述实现这些方法所必需的对象的概念、作用和特点,讨论这些方法的优缺点,通过实例代码给出这些方法的基本应用,并从生命周期、应用范围、可传递的数据类型和数据量4个方面对这些方法进行比较和分析。介绍了实现这些方法时常用的2种页面重定向方法。     

人脸画像合成研究的综述与对比分析

人脸画像合成通常是在给定一些训练画像-照片的前提下,将一张输入的人脸照片转换为画像的过程.目前并没有一个系统性的实验对比分析揭示当前此过程面临的挑战以及可能的解决思路.文中对具有代表性的各类方法进行综合深入对比与分析.人脸画像合成方法归纳为2类:数据驱动类方法(即基于样本的方法)和模型驱动类方法.数据驱动方法由3类方法组成:基于子空间学习的方法、基于稀疏表示的方法和基于贝叶斯推断的方法.模型驱动方法直接学习照片到画像的映射关系.文中给出一些之前文献中并未发现的有意义的结论和展望.     

Five-stage Milstein methods for SDEs

In this paper, we consider the problem of computing numerical solutions for Itô stochastic differential equations (SDEs). The five-stage Milstein (FSM) methods are constructed for solving SDEs driven by an m-dimensional Wiener process. The FSM methods are fully explicit methods. It is proved that the FSM methods are convergent with strong order 1 for SDEs driven by an m-dimensional Wiener process. The analysis of stability (with multidimensional Wiener process) shows that the mean-square stable regions of the FSM methods are unbounded. The analysis of stability shows that the mean-square stable regions of the methods proposed in this paper are larger than the Milstein method and three-stage Milstein methods.     

A survey of direct time-integration methods in computational structural dynamics—I. Explicit methods

A comprehensive survey of direct time-integration methods and computational solution procedures for easier computer implementation is given in four parts for dynamic analysis of linear and nonlinear structures.

Part I is exclusively devoted to explicit methods. Popular second order central difference methods (formulation, step-by-step solution procedures, recent developments, computational and stability aspects) are described in detail. Other explicit methods, viz. Runge-Kutta methods, stiffly stable methods, Predictor-Corrector methods and Taylor series schemes are also presented. Techniques for stabilizing numerical computations are given.

In Part II, conventional implicit methods, viz. the Newmark, Wilson-θ and Houbolt methods and their step-by-step solution procedures are given with reference to solution of linear and nonlinear structural dynamics problems. Also presented are Trujillo's modified Newmark-beta method and implicit formulae via weighted residual approach. Computational and stability aspects, desirable characteristics of an ideal solution procedure and salient features of conventional implicit algorithms are discussed.

Part III reviews further developments in implicit methods. In Part IV, mixed implicit-explicit finite element methods and operator-splitting methods are described.

Numerical solution methods surveyed here will be of much use to practicing computational/finite element/structural engineers working in the area of dynamics of structures.     

Clustering of collinear line segments

A number of methods are presented for finding clusters in collinear collections of line segments. The methods are of two kinds — merging methods and splitting methods. Both make use of an evaluation function, and several alternative functions are illustrated. The methods are evaluated using randomly generated clusters on backgrounds containing varying amounts of noise.     

Numerical solution of fuzzy differential equations by predictor-corrector method

In this paper three numerical methods to solve “The fuzzy ordinary differential equations” are discussed. These methods are Adams-Bashforth, Adams-Moulton and predictor-corrector. Predictor-corrector is obtained by combining Adams-Bashforth and Adams-Moulton methods. Convergence and stability of the proposed methods are also proved in detail. In addition, these methods are illustrated by solving two fuzzy Cauchy problems.     

基于特征的视线跟踪方法研究综述

针对基于特征的视线跟踪方法进行了综述.首先对视线跟踪技术的发展、相关研究工作和研究现状进行了阐述; 然后将基于特征的视线跟踪方法分成了两大类:二维视线跟踪方法和三维视线跟踪方法, 从硬件系统配置、误差主要来源、头部运动影响、优缺点等多个方面重点分析了这两类视线跟踪方法, 对近五年现有的部分基于特征的视线跟踪方法进行了对比分析, 并对二维视线跟踪系统和三维视线跟踪系统中的几个关键问题进行了探讨; 此外, 介绍了视线跟踪技术在人机交互、医学、军事、智能交通等多个领域的应用; 最后对基于特征的视线跟踪方法的发展趋势和研究热点进行了总结与展望.     

时间序列特征表示与相似性度量研究综述

时间序列是将同一指标的数值按照时间的先后顺序排列组成的一组随机数列。随着科学技术的蓬勃发展,时间序列在数据挖掘领域中的应用变得越来越广泛。综合分析了近年来时间序列在数据挖掘领域的文献成果,对时间序列特征表示和相似性度量方法进行了阐述。针对时间序列特征表示方法,从非数据适应性方法、数据自适应性方法、基于模型的方法三方面进行说明,对各种主要方法的研究现状、优缺点、适用领域、方法特性以及局限性等进行了比较分析。针对时间序列的相似性度量方法,从基于形状的相似性度量方法、基于模型的相似性度量方法和基于数据压缩的相似性度量方法三方面进行系统描述,对各种主要方法的优缺点、适用领域等进行介绍,并从是否支持非等长时间序列之间的比较、是否支持平移、是否支持三角不等式等方面进行了比较分析。最后,对时间序列的未来研究方向进行了展望。     

Vague值向Fuzzy值转化方法新探

用反例证明文献[1]提出的两种Vague值向Fuzzy值的转化方法是不满足准则1^[2]和准则2^[2]的。但是如果在这两个方法中补充参数的约束条件,则这两种方法皆是实用方法。提出两种Vague值向Fuzzy值新的转化方法,证明了它们是实用方法。     

Methods of planning the electronic workplace

Traditional data processing methods of implementing systems have led to many human problems and have often not been successful. This paper reviews a number of alternative design methods, both centralized methods such as structured design methods, and decentralized or end user-developed systems. It concludes that, whilst the new methods are all more user-centred in concept, they may still fail because the methods necessary to deal with user issues are not widely available. The paper ends by reviewing the required methods which include task analysis, prototype evaluation, interface design and continued user support. Some examples of the required methods are provided.     

Defuzzification block: New algorithms,and efficient hardware and software implementation issues

The defuzzification is a critical block when implementing a fuzzy inference engine due to different variations and also high computational power demands of defuzzification algorithms. These various methods stand for different cost-accuracy trade-off points. Three new implementation friendly defuzzification algorithms are presented in this paper and compared with a complete set of existing defuzzification methods. Some accuracy analysis simulation results and analytic studies are provided to demonstrate that these methods provide acceptable precision with respect to other existing methods. The software models of the proposed and exiting defuzzification methods are developed under three well-known platforms, Intel's Pentium IV, IBM's PowerPC, and TI's C62 DSP to show that new methods gain much lower execution-time and instruction-count with respect to the most common existing methods. The hardware models of all these methods are also developed and synthesized to demonstrate the superiority of the new methods in terms of area, delay, and power consumption with respect to other methods when implemented in hardware.     

基于物理及数据驱动的流体动画研究

主要针对近年来流行的基于物理及数据驱动的各种流体动画模拟算法及其应用给出了一个全面的前沿性综述.首先,对传统的基于物理的流体模拟加速方法进行了综述和总结,同时给出了此类方法中各种算法的优劣性分析;其次,对现有的基于数据驱动的多种算法进行了综述和分析.特别地,将现有的数据驱动方法归结为3类,即数据插值法、数据预计算方法和基于深度学习的方法.并且,进一步讨论了基于数据驱动的流体动画模拟算法的几个关键问题以及其研究趋势与方向.     

Stochastic Approximation Approaches to the Stochastic Variational Inequality Problem

Stochastic approximation methods have been extensively studied in the literature for solving systems of stochastic equations and stochastic optimization problems where function values and first order derivatives are not observable but can be approximated through simulation. In this paper, we investigate stochastic approximation methods for solving stochastic variational inequality problems (SVIP) where the underlying functions are the expected value of stochastic functions. Two types of methods are proposed: stochastic approximation methods based on projections and stochastic approximation methods based on reformulations of SVIP. Global convergence results of the proposed methods are obtained under appropriate conditions.