Real-time rendering of algebraic B-spline surfaces via Bzier point insertion

This paper presents a GPU-based real-time raycasting algorithm for piecewise algebraic surfaces in terms of tensor product B-splines.3DDDA and depth peeling algorithms are employed to traverse the piecewise surface patches along each ray.The intersection between the ray and the patch is reduced to the root-finding problem of the univariate Bernstein polynomial.The polynomial is obtained via Chebyshev sampling points interpolation.An iterative and unconditionally convergent algorithm called B′ezier point insertion is proposed to find the roots of the univariate polynomials.The B′ezier point insertion is robust and suitable for the SIMD architecture of GPU.Experimental results show that the proposed root-finding algorithm performs better than other root-finding algorithms,such as B′ezier clipping and B-spline knot insertion.Our rendering algorithm can display thousands of piecewise algebraic patches of degrees 6–9 in real time and can achieve the semi-transparent rendering interactively.     

Multi-degree reduction of tensor product Bézier surfaces with conditions of corners interpolations

This paper studies the multi-degree reduction of tensor product B(?)zier surfaces with any degree interpolation conditions of four corners, which is urgently to be resolved in many CAD/CAM systems. For the given conditions of corners interpolation, this paper presents one intuitive method of degree reduction of parametric surfaces. Another new approximation algorithm of multi-degree reduction is also presented with the degree elevation of surfaces and the Chebyshev polynomial approximation theory. It obtains the good approximate effect and the boundaries of degree reduced surface can preserve the prescribed continuities. The degree reduction error of the latter algorithm is much smaller than that of the first algorithm. The error bounds of degree reduction of two algorithms are also presented .     

Discrete Interpolation Surface

In this paper,a method to construct a surface with point interpolation and normal interpolation is presented.An algorithm to construct the discrete interpolation is also presented,which has the time complexity O(Nlog N),where N in the number of scattered points.     

Linearly and Quadratically Separable Classifiers Using Adaptive Approach

This paper presents a fast adaptive iterative algorithm to solve linearly separable classification problems in R n.In each iteration,a subset of the sampling data (n-points,where n is the number of features) is adaptively chosen and a hyperplane is constructed such that it separates the chosen n-points at a margin and best classifies the remaining points.The classification problem is formulated and the details of the algorithm are presented.Further,the algorithm is extended to solving quadratically separable classification problems.The basic idea is based on mapping the physical space to another larger one where the problem becomes linearly separable.Numerical illustrations show that few iteration steps are sufficient for convergence when classes are linearly separable.For nonlinearly separable data,given a specified maximum number of iteration steps,the algorithm returns the best hyperplane that minimizes the number of misclassified points occurring through these steps.Comparisons with other machine learning algorithms on practical and benchmark datasets are also presented,showing the performance of the proposed algorithm.     

A new method of mesh simplification for 3-Dimension terrain using Laplace operator

This paper proposes a new method to simplify mesh in 3D terrain.The 3D terrain is presented by digital elevation model.First,Laplace operator is introduced to calculate sharp degree of mesh point,which indicates the variation trend of the terrain.Through setting a critical value of sharp degree,feature points are selected.Second,critical mesh points are extracted by an recursive process,and constitute the simplified mesh.Third,the algorithm of linear-square interpolation is employed to restore the characteristics of the terrain.Last,the terrain is rendered with color and texture.The experimental results demonstrate that this method can compress data by 16% and the error is lower than 10%.     

High-Order Two-Dimension Cluster Competitive Activation Mechanisms Used for Performing Symbolic Logic Algorithms of Problem Solving

This paper presents a neural network approach,based on high-order twodimension temporal and dynamically clustering competitive activation mechanisms,to implement parallel searching algorithm and many other symbolic logic algorithms.This approach is superior in many respects to both the common sequential algorithms of symbolic logic and the common neural network used for optimization problems.Simulations of problem solving examples prove the effectiveness of the approach.     

An Algorithm of B-Spline Surface Interpolation

A surface interpolation algorithm is presented. By using a special kind of knot vector. a B-spline surface can be constructed to interpolate an array of m ×n positions, including parameter u and v tangent vectors and twist vector at each positions. Single surface interpolation approach is easier to ensure the smoothness of the interpolating surface than multi-patches method. This algorithm can be used to solve the approximating problem of B-spline approximation of general parametric surface.     

C~2 quartic spline surface interpolation

This paper discusses the problem of constructing C2 quartic spline surface interpolation. Decreasing the continuity of the quartic spline to C2 offers additional freedom degrees that can be used to adjust the precision and the shape of the interpolation surface. An approach to determining the freedom degrees is given, the continuity equations for constructing C2 quartic spline curve are discussed, and a new method for constructing C2 quartic spline surface is presented. The advantages of the new method are that the equations that the surface has to satisfy are strictly row diagonally dominant, and the discontinuous points of the surface are at the given data points. The constructed surface has the precision of quartic polynomial. The comparison of the interpolation precision of the new method with cubic and quartic spline methods is included.     

Splitting touching cells based on concave-point and improved watershed algorithms

Splitting touching cells is important for medi- cal image processing and analysis system. In this paper, a novel strategy is proposed to separate ellipse-like or circle- like touching cells in which different algorithms are used ac- cording to the concave-point cases of touching domains. In the strategy, a concave-point extraction and contour segmen- tation methods for cells in series and in parallel are used for the images with distinct concave points, and an improved watershed algorithm with multi-scale gradient and distance transformation is adopted for the images with un-distinct or complex concave points. In order to visualize each whole cell, ellipse fitting is used to process the segments. Experimental results show that, for the cell images with distinct concave points, both of the two algorithms can achieve good separat- ing results, but the concave-point based algorithm is more ef- ficient. However, for the cell images with unobvious or com- plex concave points, the improved watershed based algorithm can give satisfying segmenting results.     

Adaptive control of uncertain time-delay chaotic

This work investigates adaptive control of a large class of uncertain time-delay chaotic systems （UTCSs） with unknown general perturbation terms bounded by a polynomial （unknown gains）, Associated with the different cases of known and unknowl system matrices, two corresponding adaptive controllers are proposed to stabilize unstable fixed points of the systems by means of Lyapunov stability theory and linear matrix inequafities （LMI） which can be solved easily by convex optimization algorithms, Two examples are used for examining the effectiveness of the proposed methods.     

Deterministic Perturbation Partial Volume Interpolation:Algorithm and Analysis

1IntroductionEssentially,image registration based on mutual information is a problem of multi-parameteroptimization[1].Therefore,the target function is usually unsmooth.This cumbers registration badly.Local extremes are always ascribed to two facts[2].One…     

基于自适应扰动的疯狂蝴蝶算法

蝴蝶优化算法作为新提出的自然启发算法,其寻优方式模拟了蝴蝶利用嗅觉来确定花蜜或交配对象位置的行为。针对蝴蝶优化算法求解精度不高和收敛速度慢等问题,提出一种基于自适应扰动的疯狂蝴蝶算法（CIBOA）。首先,在自身认知飞行部分引入自适应惯性权重,平衡算法的局部与全局搜索能力;其次,在全局最优位置引入扰动策略,避免算法陷入局部最优;最后,在花蜜位置引入疯狂因子以增加种群多样性,获取更好的最优解。通过8个基准函数对5种算法搜索性能在10、30和50维的情况下进行对比分析,仿真实验结果表明改进算法的综合表现要优于其他算法。     

图像配准中确定性扰动PV插值算法

基于互信息的图像配准方法具有自动化程度高、配准精度高等优点，已被广泛应用于医学图像的配准，图像插值是图像配准过程中一个十分关键的步骤，详细分析了各类插值算法的优缺点，提出了一种确定性扰动PV插值算法，在利用PV插值时考虑了随机扰动涉及到扰动点周围9个网格点，并且通过扰动确定化计算PV插值法的权值．实验结果表明：该算法能够避免网格点和非网格点上的局部极值，有效地改善了基于互信息的目标函数的性能，使函数更加平滑。     

多段扰动的共享型乌鸦算法

乌鸦搜索算法作为新提出的元启发式智能算法,其寻优方式模拟了乌鸦间相互跟随窃食的行为。为了提高算法的收敛精度、后期搜索能力等,基于传统乌鸦搜索算法提出一种新的混合乌鸦搜索算法,其核心思想是在算法中加入共享机制,改进原始算法中随机追踪的位置更新方式,降低搜索盲目性,提高收敛速度;在不同的迭代阶段对全局最优位置进行大小不同的扰动操作,有效提高了跳出局部最优的概率,保证算法全局搜索能力与局部搜索能力的平衡。最后通过8个基准函数对5种算法搜索性能在10、30、50维的情况下进行对比分析,结果表明该改进算法的综合表现要优于其他算法。     

串行生产线的参数优化

对随机离散事件系统模型,用实验(或模拟)方法进行扰动分析(Perturbation Analysisi, 简称PA),对固定的样本,得到性能指标(设为J(θ))对可调参数θ的梯度dJ(θ)/dθ的估计. 用固定长度的观测值(如L个顾客)估计dJ(θ)/dθ,将估计值代入随机逼近算法,递推地求最优 参数,得到了基于扰动分析的优化算法.实验结果表明.这种优化算法,有较好的收敛速度. 对串行生产线,提出每离开L个顾客递推一次参数的优化算法,并证明了这种算法可收敛到 使J(θ)达极小的θ.     

t-分布扰动策略和变异策略的花授粉算法

为了解决传统花授粉算法(FPA)收敛速度慢、易陷入局部最优、寻优精度低等缺陷,提出了一种t-分布扰动策略和变异策略的花授粉算法(t MFPA).首先利用混沌映射初始化花朵个体的位置,然后在全局授粉过程中,利用t-分布扰动的随机个体和莱维飞行共同实现个体位置更新,加快收敛速度的同时提高搜索空间的多样性;在局部授粉过程中,加入具有两个差分向量的变异策略和小概率策略,结合两种策略使算法能够跳出局部最优.实验结果表明,t MFPA相比于FPA和其他启发式智能算法具有更好的寻优精度和收敛速度,相对于其他改进算法具有更好的收敛性能.     

基于改进混合蜂群算法的非线性电路谐波平衡分析

针对射频电路非线性分析中谐波平衡方程求解问题,提出一种基于高斯扰动、锦标赛选择策略以及拟牛顿局部寻优算子的改进混合蜂群算法,该算法在搜索方程中引入基于当前全局最优解的高斯扰动,能有效防止算法陷入局部最优并加快算法收敛;跟随蜂采用锦标赛选择策略在一定程度上避免了算法的早熟现象;采用拟牛顿算子进行局部寻优,可使算法快速收敛。实验结果表明,改进混合蜂群算法成功应用于谐波平衡方程求解,与其他求解算法对比,收敛时间较短,性能较优。     

基于当代学习离散粒子群算法的多机器人任务分配*

针对多机器人协同控制中的任务分配问题,首先综合考虑机器人完成任务的效率、机器人自身能力以及任务本身性质各因素,建立了多机器人任务分配的数学模型。而后提出一种基于当代学习机制的离散粒子群算法进行高效求解,该算法设计了准确的粒子运动方程,并加入扰动算子保持粒子多样性,使其迅速跳出局部最优,增加了算法空间探索能力。实验结果表明：在小规模任务数情况下,算法能精确寻到最优,稳定性表现极佳且优于现有算法。在中大规模任务数情况下算法也表现出强寻优能力,实验验证了模型的合理性和算法的优越性。     

An improved SPSA algorithm for system identification using fuzzy rules for training neural networks

Simultaneous perturbation stochastic approximation (SPSA) belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation (FASPA) algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks (NNs). Accordingly, a multilayer perceptron (MLP) network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.     

基于近似约简与最优采样的集成剪枝

集成学习被广泛用于提高分类精度, 近年来的研究表明, 通过多模态扰乱策略来构建集成分类器可以进一步提高分类性能. 本文提出了一种基于近似约简与最优采样的集成剪枝算法(EPA_AO). 在EPA_AO中, 我们设计了一种多模态扰乱策略来构建不同的个体分类器. 该扰乱策略可以同时扰乱属性空间和训练集, 从而增加了个体分类器的多样性. 我们利用证据KNN (K-近邻)算法来训练个体分类器, 并在多个UCI数据集上比较了EPA_AO与现有同类型算法的性能. 实验结果表明, EPA_AO是一种有效的集成学习方法.