Type-2 fuzzy Gaussian mixture models

This paper presents a new extension of Gaussian mixture models (GMMs) based on type-2 fuzzy sets (T2 FSs) referred to as T2 FGMMs. The estimated parameters of the GMM may not accurately reflect the underlying distributions of the observations because of insufficient and noisy data in real-world problems. By three-dimensional membership functions of T2 FSs, T2 FGMMs use footprint of uncertainty (FOU) as well as interval secondary membership functions to handle GMMs uncertain mean vector or uncertain covariance matrix, and thus GMMs parameters vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FGMM becomes an interval rather than a precise real number to account for GMMs uncertainty. These interval likelihoods are then processed by the generalized linear model (GLM) for classification decision-making. In this paper we focus on the role of the FOU in pattern classification. Multi-category classification on different data sets from UCI repository shows that T2 FGMMs are consistently as good as or better than GMMs in case of insufficient training data, and are also insensitive to different areas of the FOU. Based on T2 FGMMs, we extend hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). Phoneme classification in the babble noise shows that T2 FHMMs outperform classical HMMs in terms of the robustness and classification rate. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower.     

基于纹理和高斯密度特征的图像检索算法

直接从DCT域中提取图像的特征是提高图像的检索效率的方法.直接从压缩域中提取图像的高斯密度,即计算图像在8个方向上的分段累加值,形成一个8*4的二维向量,再结合图像的纹理特征来进行图像检索.为了验证算法的可行性,建立了10000幅图像的图像库.实验结果表明,该方法能够准确地检索出目标图像,有效地提高了图像检索的精度和速度.     

基于近似高斯金字塔的视觉注意模型快速算法

利用输入图像的近似高斯金字塔,将经典的基于显著性的视觉注意模型改造为时空开销更小的版本,从而使其更加适合在嵌入式实时系统中实现.首先采用矩形窗口近似圆形窗口,矩形平均算子近似高斯卷积核;然后采用“先做行累加,再做列累加”的方法来实现矩形平均算子,并直接采样计算出各个特征通道的显著性分布图,该算法关于输入图像像素点个数具有线性时间复杂度;最后,还给出了在显著性分布图中抑制已提取区域显著性的快速算法.在Berkeley分割图像库上的实验结果表明,该方法极大地减小了系统实现的时空开销,且输出结果的误差在可接受范围内.提出的用矩形窗口近似圆形窗口,用矩形平均算子近似高斯卷积核的方法,还适用于其他需要在嵌入式实时系统中实现的图像处理问题.     

基于高斯滤波C-V模型分割算法的改进

在现实世界经常有一些灰度不均匀的图像给图像分割造成了很大的困难.为了解决由于图像灰度不均匀造成的图像轮廓提取的困难,结合邻域算法和高斯滤波的性质的基础上,提出了基于图像局部区域灰度信息的算法,使曲线的演化过程基于图像的局部信息.同时采用了水平集演化规则化模型,避免了水平集函数的不断初始化.实验结果表明,新算法无论对合成图像还是真实图像,都能提取出准确的物体轮廓.     

基于变步长量化的安全图像隐写

图像隐写通过将信息隐藏在载体图像中进行秘密传送,实现隐蔽通信.提出了VSQS图像隐写算法,利用密钥控制生成高斯序列并取整,用于对经伪随机排列后的图像像素进行变步长量化,根据量化像素与秘密信息之间的关系来修改像素,实现信息的嵌入.同时给出了该算法的一种扩展(称为TLQS隐写).实验结果表明,VSQS和TLQS图像隐写可提供较大的隐藏容量,并能抵抗几种常见的隐写分析方法.     

Reliability-based design optimization of problems with correlated input variables using a Gaussian Copula

The reliability-based design optimization (RBDO) using performance measure approach for problems with correlated input variables requires a transformation from the correlated input random variables into independent standard normal variables. For the transformation with correlated input variables, the two most representative transformations, the Rosenblatt and Nataf transformations, are investigated. The Rosenblatt transformation requires a joint cumulative distribution function (CDF). Thus, the Rosenblatt transformation can be used only if the joint CDF is given or input variables are independent. In the Nataf transformation, the joint CDF is approximated using the Gaussian copula, marginal CDFs, and covariance of the input correlated variables. Using the generated CDF, the correlated input variables are transformed into correlated normal variables and then the correlated normal variables are transformed into independent standard normal variables through a linear transformation. Thus, the Nataf transformation can accurately estimates joint normal and some lognormal CDFs of the input variable that cover broad engineering applications. This paper develops a PMA-based RBDO method for problems with correlated random input variables using the Gaussian copula. Several numerical examples show that the correlated random input variables significantly affect RBDO results.     

一种主动感知的纹理图像检索方法(英文)

基于Contourlet变换和支持向量机提出了一种新的纹理图像检索方法。在这种方法中,能量和广义高斯分布参数被用做Contourlet子带图像的特征。通过这种表示,提出了由一类和二类支持向量机组成的两阶段检索算法来完成感知相似性测度。通过具有640个纹理图像的VisTex库和具有1760个纹理图像的Brodatz库证明了所提方法的有效性。实验结果表明,对于这两个纹理库,新的纹理图像检索方法的平均检索率分别达99.38%和98.07%。     

140 GHz回旋管高转换效率准光模式变换器设计研究（英文）

成功实现高转换效率的140 GHz TE22,6准光模式变换器原型设计。基于周期微扰原理设计Denisov辐射器,实现低边缘绕射的初级出射波束。针对三镜面光路系统,采用全矢物理光学积分作为主要计算手段,围绕主极化场分量进行三级相位修正面迭代优化,实现高出射高斯纯度的模式场转换,其中一级镜的修正有效改善了辐射器出射的不理想性。基于全矢数值仿真确认,相比原二次曲面原型设计,相位修正后的变换器系统的出射高斯纯度从92.7%提高到99.6%,结合98.8%以上的功率传递效率,实现了性能优越的高阶回旋管准光模式变换器原型设计。     

基于GPIO的永磁同步电机模型预测转矩控制

在永磁同步电机模型预测转矩控制(model predictive torque control,MPTC)中,由于目标函数控制变量的量纲不同,为了在代价函数中选取最优的权重系数,提出一种改进高斯变异鸽群算法(Gaussian mutation pigeon-inspired optimization algorithm, GPIO)实现权重系数自整定。首先,基于代价函数最小化原则设计PIO的目标函数,用电流id、iq脉动误差均方根评价权重系数。其次,在传统高斯变异算子的基础上引入自适应调整动态参数实现变异。仿真和实验结果表明,将改进后的算法应用于永磁同步电机模型预测转矩系统,能在较少的迭代次数内整定出最优的权重系数,极大地降低了转矩误差和电流谐波畸变率。