点集Bézier曲线

提出了点集Bézier曲线的概念,给出了点集Bézier曲线的性质及细分算法.按照点集算术的定义,当点集是长方形闭域或圆盘时,点集Bézier曲线就是区间Bézier曲线或圆盘Bézier曲线,因此,点集Bézier曲线是对区间Bézier曲线和圆盘Bézier曲线的推广.     

四次保形有理Bézier曲线的构造

本文讨论四次 Bézier曲线的保形性 ,对不保形的四次 Bézier曲线构造了一类四次有理 Bézier曲线的调整方法 ,论述了保形性定理 ,给出了算法和算例     

一类有理Bézier曲线的等距线算法及MATLAB实现

构造一类正则有理Bézier曲线,利用改进的有理de casteljau算法求得这类正则有理n次Bézier曲线各点处的切矢,由此得出各点的单位法矢量,应用于原始曲线等距线的计算.该方法几何意义明显,算法简洁,实践效果比较好.同时给出了用Matlab绘制有理Bézier曲线及其等距线的程序,准确快捷,实践效果较好.     

低阶C-Bézier曲线的升阶

C- Bézier曲线是在 Bézier曲线基础上作了改进 ,因此具有与 Bézier曲线类似的性质 .本文主要研究的是 C- Bézier曲线从 3阶到 4阶 ,4阶到 5阶 ,5阶到 6阶的升阶转换     

G^2有理三次GHI插值算法

本文研究 GHI插值 ,对于给定的切矢和曲率 ,导出了一条分段三次有理 Bézier插值曲线 .该曲线的所有 Bézier点和权因子由已知曲率和切矢直接计算生成 ,最后给出了一个数值实例     

一种混沌多相伪随机序列

提出了一种混沌多相伪随机序列生成方法，推导出通过Logistic映射产生独立同分布多相序列的充分条件，即根据混沌轨迹的概率密度分布把混沌吸引子划分为2n个区域，对混沌轨迹进行采样间隔为n的采样，对照轨道点所处位置与相应的序列元素之间的映射关系，可以得到独立、均匀分布的2n相伪随机序列。数值统计分析支持以上研究结果并表明该序列具有较高的复杂度。此外文中给出了该序列生成的快速算法和一般表达式。该序列可用于信息安全、扩频通信等众多领域。      

有理圆锥曲线段的参数的几何意义

用代数和几何方法, 得到用有理二次或有理三次Bézier曲线表示的圆锥曲线上的点与其参数域上的点所对应的函数关系; 即给出了有理圆锥曲线段的表达式所描述的映射的逆映射公式．这种公式用圆锥曲线段上此点和控制顶点所决定的三角形面积、角度及有理Bézier曲线的权因子来表示, 或用此点和曲线段首末端点相应的参数角度及有理Bézier曲线的权因子来表示. 这些结果对有理Bézier曲线曲面的最佳参数化和重新参数化等算法实现是极其有益的.     

一类参数曲线正则性的判别

Bézier曲线的正则性,完全由它的控制顶点决定.理想的情况是由Bézier曲线的控制顶点的几何关系,就可以判断它的正则性.本文由Bézier曲线的导矢曲线在[0,1]不等于零这些代数条件,推导出了与之等价的Bézier曲线的控制顶点之间的几何关系,即只需知道顶点之间的相对位置或计算相邻线段的斜率就可快速判断Bézier曲线的正则性.最后给出了数值例子.     

两条连续的有理Bézier曲线的逼近合并

目前多项式 Bézier曲线的逼近合并问题已研究得比较深入 ,而有理 Bézier情形主要还是通过两类多项式 h和 H来降阶逼近 ,但是在工业制造中有重要意义的有理 Bézier曲线的合并问题一直缺乏研究 .本文通过控制点的优化扰动将两连续的满足权约束条件的有理 Bézier曲线转化成新的两有理Bézier曲线 ,使它们符合精确合并条件 ;并将合并得到的同阶有理 Bézier曲线看成是原两曲线的有理逼近     

区间Bézier曲线和曲面的升阶

把Bézier曲线、曲面的升阶公式推广到区间Bézier曲线、曲面,证明了在不断升阶的过程中,区间控制顶点的并集收敛到原区间Bézier曲线、曲面.这里的升阶公式可用于将低次的区间Bézier曲线、曲面转换成高次形式,并且升阶可以增加控制顶点的数目,便于更加灵活地对这些区间曲线、曲面作形状控制.由升阶公式和升阶的收敛性可得到一种简洁有效的区间Bézier曲线、曲面的几何作图方法.     

Minimum entropy control of chaos via online particle swarm optimization method

One of the recently developed approaches for control of chaos is the minimum entropy (ME) control technique. In this method an entropy function based on the Shannon definition, is defined for a chaotic system. The control action is designed such that the entropy as a cost function is minimized which results in more regular pattern of motion for the system trajectories. In this paper an online optimization technique using particle swarm optimization (PSO) method is developed to calculate the control action based on ME strategy. The method is examined on some standard chaotic maps with error feedback and delayed feedback forms. Considering the fact that the optimization is online, simulation results show very good effectiveness of the presented technique in controlling chaos.     

Chaotic artificial immune approach applied to economic dispatch of electric energy using thermal units

The economic dispatch problem (EDP) is an optimization problem useful in power systems operation. The objective of the EDP of electric power generation, whose characteristics are complex and highly non-linear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying system constraints. Recently, as an alternative to the conventional mathematical approaches, modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. As special mechanism to avoid being trapped in local minimum, the ergodicity property of chaotic sequences has been used as optimization technique in EDPs. Based on the chaos theory, this paper discusses the design and validation of an optimization procedure based on a chaotic artificial immune network approach based on Zaslavsky’s map. The optimization approach based on chaotic artificial immune network is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. Simulation results and comparisons show that the chaotic artificial immune network approach is competitive in performance with other optimization approaches presented in literature and is also an attractive tool to be used on applications in the power systems field.     

基于直方图初始化的有效的多区域Mumford-Shah图像分割方法（英文）

The Mumford-Shah energy functional is a successful image segmentation model. It is a non-convex variational problem and lacks of good initialization techniques so far. In this paper, motivated by the fact that image histogram is a combination of several Gaussian distributions, and their centers can be considered as approximations of cluster centers, we introduce a histogram-based initialization method to compute the cluster centers. With this technique, we then devise an effective multi-region Mumford-Shah image segmentation method, and adopt the recent proximal alternating minimization method to solve the minimization problem. Experiments indicate that our histogram initialization method is more robust than existing methods,and our segmentation method is very effective for both gray and color images.     

A multilevel image thresholding using the honey bee mating optimization

Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the maximum entropy thresholding (MET) has been widely applied. In this paper, a new multilevel MET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. This proposed method is called the maximum entropy based honey bee mating optimization thresholding (MEHBMOT) method. Three different methods such as the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO) and the Fast Otsu’s method are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed MEHBMOT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other three thresholding methods, the segmentation results using the MEHBMOT algorithm is the best and its computation time is relatively low. Furthermore, the convergence of the MEHBMOT algorithm can rapidly achieve and the results validate that the proposed MEHBMOT algorithm is efficient.     

Fixed-time synchronization of a reaction-diffusion BAM neural network with distributed delay and its application to image encryption

In this paper, the fixed-time synchronization of reaction-diffusion BAM neural networks is investigated, where both discrete and distributed delays are taken into account. Combining Lyapunov stability theory and several integral inequalities, fixed-time synchronization criteria are established. Through sensitivity analysis, we find the key controller parameters that have a great influence on the maximum settling time. Using the chaotic sequences generated by the neural network, the color image can be encrypted by the Arnold Cat Map and the pixel diffusion. Experiments show that the image encryption algorithm designed in this paper has good properties of security and anti-attacking, which meets the requirements for the secure transmission of image information.     

Parameter identification and synchronization of fractional-order chaotic systems

The knowledge about parameters and order is very important for synchronization of fractional-order chaotic systems. In this article, identification of parameters and order of fractional-order chaotic systems is converted to an optimization problem. Particle swarm optimization algorithm is used to solve this optimization problem. Based on the above parameter identification, synchronization of the fractional-order Lorenz, Chen and a novel system (commensurate or incommensurate order) is derived using active control method. The new fractional-order chaotic system has four-scroll chaotic attractors. The existence and uniqueness of solutions for the new fractional-order system are also investigated theoretically. Simulation results signify the performance of the work.     

动力系统实测数据的非线性混沌模型重构

动力系统实测非线性混沌数据的模型重构技术是相空间重构的重要内容。在判定了实测数据的非线性混沌特征,计算了实测数据的分维数,Lyapunov指数,并对其进行了本征值分解和噪声去除及确定其模型阶数以后,提出了一个动力系统实测数据的非线性混沌模型,给出了相应的模型参数辨识方法,并用其确立的混沌模型进行了预测工作,计算结果表明:模型参数辨识方法能迅速地将参数估计值带到多峰目标函数的全局最少值附近,然后再采用优化理论能较准确地求出模型的参数,用得到的混沌模型对系统进行预测工作其预测效果良好,且混沌时序不可能作长期预测。     

Topological sequence entropy for maps of the interval

A result by Franzová and Smítal shows that a continuous map of the interval into itself is chaotic if and only if its topological sequence entropy relative to a suitable increasing sequence of nonnegative integers is positive. In the present paper we prove that for any increasing sequence of nonnegative integers there exists a chaotic continuous map with zero topological sequence entropy relative to this sequence.

     

A block encryption algorithm based on dynamic sequences of multiple chaotic systems

A block encryption algorithm using dynamic sequences generated by multiple chaotic systems is proposed in this paper. In this algorithm, several one-dimension chaotic maps generate pseudo-random sequences, which are independent and approximately uniform. After a series of transformations, the sequences constitute a new pseudo-random sequence uniformly distributing in the value space, which covers the plaintext by executing Exclusive-OR and shifting operations some rounds to form the cipher. This algorithm makes the pseudo-random sequence possess more concealment and noise like characteristic, and overcomes the periodic malpractice caused by the computer precision and single chaotic system. Simulation results show that the algorithm is efficient and useable for the security of communication system.