基于完全非线性函数的最优跳频序列族构造

基于完全非线性函数,构造了一类新的跳频序列族.根据完全非线性函数的性质,证明了这些跳频序列具有良好的汉明自相关和汉明互相关特性.利用不同的完全非线性函数,可以得到不同的并且具有最优汉明相关性能的跳频序列族.     

具有四值自相关特性的二元序列的构造

本文给出了具有四值自相关特性的二元序列的一种构造方法.对于任意给定的素数p和正整数m、n,当m|n时,我们利用周期为pm-1的具有三值自相关特性的二元序列构造了周期为pn-1的具有四值自相关特性的二元序列,给出了新的二元序列的自相关取值及其分布,同时还讨论了这些序列的陪集不变性和线性复杂度等性质.     

对称布尔函数的算术相关函数

算术相关函数是最近提出的一种研究布尔函数密码学性质的方法,该方法通过定义多元2-adic数上的加法和乘法运算,构建一种新的环结构,实现对经典相关函数的带进位计算的模拟。首先介绍了算术相关函数的定义,并针对具有良好密码学性质的对称布尔函数讨论了其算术相关函数的性质和取值,最后利用对称布尔函数的实值对称性证明了对称布尔函数的算术自相关函数也是一个与向量的重量有关的实值对称函数,至多是n+1值的。     

对称布尔函数的算术相关函数

算术相关函数是最近提出的一种研究布尔函数密码学性质的方法,该方法通过定义多元2-adic数上的加法和乘法运算,构建一种新的环结构,实现对经典相关函数的带进位计算的模拟。首先介绍了算术相关函数的定义,并针对具有良好密码学性质的对称布尔函数讨论了其算术相关函数的性质和取值,最后利用对称布尔函数的实值对称性证明了对称布尔函数的算术自相关函数也是一个与向量的重量有关的实值对称函数,至多是n+1值的。     

β-甘露聚糖酶理化性质与其最适pH的相关性

为了探究影响GH5家族β-甘露聚糖酶最适pH的各种因素,利用该酶的序列信息,构建了基于氨基酸组成的β-甘露聚糖酶理化性质与其最适pH的相关性模型。结果表明:该酶N端氨基酸序列是影响其最适pH的重要因素,在243个理化性质中有17个呈极显著相关,其中7个呈正相关。优化的转移能量参数相关性最高,为0.78:7个正相关的理化性质中,与螺旋相关的理化性质占3个。优化的转移能量参数和螺旋是影响pH稳定性的最重要的因素。所建立的模型为研究该酶酸碱适应性机理提供了一种简单有效的方法。     

边际核函数在生物序列分类中的应用

在核方法的基础上,介绍了一种自潜变量生成模型构造核函数的新方法;并专门探讨了结合生物序列的自身特点,按照该方法提取了几个边际核函数;随后将它们应用于具体的gyrB(旋转酶B亚单位)氨基酸序列分类实验中,实验结果表明边际核具有良好的识别效果及极佳的推广能力,也肯定了此种核构造方法的先进性.     

素平方序列的自相关函数

介绍了一种p^2-序列,它是利用Zp^2上阶数为2的广义割圆类定义的,该序列的线性复杂度最大值为p。利用计算机模拟的方法给出了所有p〈50的p^2-序列的随机性指标,包括周期与拟周期自相关函数以及自相关函数Merit因子等。证明了该序列的周期自相关函数为二值或三值,并据此指出p^2-序列不是一种“好”的伪随机序列。     

AAindex数据库中傅里叶方法的多重氨基酸指数选择

波谱学方法研究蛋白质时,蛋白质序列的特征频率与蛋白质的位点、功能、空间结构对应.基于时间序列傅里叶变换的交叉谱原理,本文提出的“一个蛋白质关于多种氨基酸指数的一致谱”能够获取一个或多个蛋白质序列较显著的特征频率,再由特征频率去筛选与之对应的氨基酸指数.该方法与Lazovi(c)的筛选方法和筛选意义不同.结果显示,筛选出的氨基酸指数对应的性质多数与蛋白质的空间结构性质有关.蛋白质特征频率也许是用于特征提取的好方法,氨基酸指数筛选可被看作蛋白质数字表达的一种优化.     

由2N点m-ary插值细分法构造m带正交小波

为了能够构造具有良好性质的m带正交小波,本文提出一种利用插值细分法构造紧支正交小波的方法.首先基于2N点m-ary插值细分法构造m带紧支正交的可细化函数,该可细化函数的自相关函数是插值细分法的基极限函数;然后根据可细化函数构造正交小波,并研究了正交小波的消失矩性质.构造算例结果表明,利用文中方法可以构造出新的具有更大自由度的含参m带正交小波,并证明了该方法的实用性.     

组合码自相关函数的快速计算

本文论述了二元伪随机序列组合码的一条性质,深入分析了产生这一性质的内在原因,并根据性质提出了一种不用计算机快速计算组合巴克码自相关函数的方法。     

Predicting important classes of chemokine family based on kernel method

Chemokines are a family of chemotactic cytokines that have important physiological roles in a wide range of disease processes. Identifying novel class of chemokines by kernel methods can provide insights for the functional studies of the human uncharacterized proteins. In this study, a support vector machine learning system was trained to predict two main classes of chemokines (CC and CXC classes) based solely on amino acid composition and associated physicochemical properties. Further, the effect of different kernel functions and learning methods were investigated. The cross-validation results demonstrated that this kernel method performed well in identifying two main classes of chemokines.     

利用组合核函数提高核主分量分析的性能

为了提高图像分类的识别率，在对基于核的学习算法中，核函数的构成条件以及不同核函数的特性进行分析和研究的基础上，提出了一种新的核函数——组合核函数，并将它应用于核主分量分析(KPCA)中，以便进行图像特征的提取，由于新的核函数既可以提取全局特征，又可以提取局部特征，因此，可以提高KPCA在图像特征提取中的性能。为了验证所提出核函数的有效性，首先利用新的核函数进行KPCA，以便对手写数字和脸谱等图像进行特征提取，然后利用线性支持向量机(SVM)来进行识别，实验结果显示，从识别率上看，用组合核函数所提取的特征质量比原核函数所提取的特征质量高。     

Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines

Functional variations on the human ghrelin receptor upon mutations have been associated with a syndrome of short stature and obesity, of which the obesity appears to develop around puberty. In this work, we reported a proteometrics analysis of the constitutive and ghrelin-induced activities of wild-type and mutant ghrelin receptors using amino acid sequence autocorrelation (AASA) approach for protein structural information encoding. AASA vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. Genetic algorithm-based multilinear regression analysis (GA-MRA) and genetic algorithm-based least square support vector machines (GA-LSSVM) were used for building linear and non-linear models of the receptor activity. A genetic optimized radial basis function (RBF) kernel yielded the optimum GA-LSSVM models describing 88% and 95% of the cross-validation variance for the constitutive and ghrelin-induced activities, respectively. AASA vectors in the optimum models mainly appeared weighted by hydrophobicity-related properties. However, differently to the constitutive activity, the ghrelin-induced activity was also highly dependent of the steric features of the receptor.     

Operators for transforming kernels into quasi-local kernels that improve SVM accuracy

Motivated by the crucial role that locality plays in various learning approaches, we present, in the framework of kernel machines for classification, a novel family of operators on kernels able to integrate local information into any kernel obtaining quasi-local kernels. The quasi-local kernels maintain the possibly global properties of the input kernel and they increase the kernel value as the points get closer in the feature space of the input kernel, mixing the effect of the input kernel with a kernel which is local in the feature space of the input one. If applied on a local kernel the operators introduce an additional level of locality equivalent to use a local kernel with non-stationary kernel width. The operators accept two parameters that regulate the width of the exponential influence of points in the locality-dependent component and the balancing between the feature-space local component and the input kernel. We address the choice of these parameters with a data-dependent strategy. Experiments carried out with SVM applying the operators on traditional kernel functions on a total of 43 datasets with different characteristics and application domains, achieve very good results supported by statistical significance.     

Online monitoring of nonlinear multivariate industrial processes using filtering KICA–PCA

In this paper, a novel approach for processes monitoring, termed as filtering kernel independent component analysis–principal component analysis (FKICA–PCA), is developed. In FKICA–PCA, first, a method to calculate the variance of independent component is proposed, which is significant to make Gaussian features and non-Gaussian features comparable and to select dominant components legitimately; second, Genetic Algorithm is used to determine the kernel parameter through minimizing false alarm rate and maximizing detection rate; furthermore, exponentially weighted moving average (EWMA) scheme is used to filter the monitoring indices of KICA–PCA to improve monitoring performance. In addition, a novel contribution analysis scheme is developed for FKICA–PCA to diagnosis faults. The feasibility and effectiveness of the proposed method are validated on the Tennessee Eastman (TE) process.     

基于KPCA和CPSO的故障检测方法

提出一种基于核主元分析(KPCA)和混沌粒子优化群(CPSO)算法的非线性故障检测方法。通过核函数完成非线性变换,将变量由非线性的输入空间转换到线性的特征空间来计算主元,构造平方预测误差统计量检测故障是否发生。为避免粒子群算法的早熟现象,利用混沌优化的搜索特性,将CPSO算法应用到KPCA核参数的优化中。变压器故障检测结果表明,与基于PCA、KPCA和 PSO-KPCA的故障检测方法相比,该方法的检测正确率较高。     

复杂内核数据结构程序形式化验证

操作系统内核作为软件系统的基础组件, 其安全可靠是构造高可信软件系统的重要环节, 但是, 在实际的验证工作中, 操作系统内核中全局性质的不变式定义, 复杂数据结构程序的形式化描述和验证仍存在很多困难. 本文针对操作系统内核中满足的全局性质, 在代码层以函数为单位, 用全局不变式进行定义, 并在不同的函数中进行形式化验证, 从而证明各个函数符合操作系统内核的全局性质; 针对操作系统内核中经常使用的复杂数据结构程序, 本文通过扩展形状图理论, 提出一种使用嵌套形状图逻辑的方法来形式化描述复杂数据结构程序, 并对该方法进行了正确性证明, 最终成功验证操作系统内核中关于任务创建与调度, 消息队列创建与操作相关的代码.     

基于距离量度的边际核设计及应用

很多情况下,研究者掌握了一些分类数据的生成信息,这些信息能够为核函数提供有价值的分类特征.已有大量结合生成模型构造核函数的研究,边际核是其中较新的研究成果.以边际核理论为基础,在边际核特征空间中引入特征向量之间的距离作为相似性的量度,构造了基于距离量度的边际核函数.随后将它和原边际核均应用于具体的grbB(旋转酶B亚单位)氨基酸序列分类实验中,实验结果表明:基于距离量度的边际核拥有比原边际核更佳的识别效率,且也具备一定的推广能力.     

A novel hybrid KPCA and SVM with GA model for intrusion detection

A novel support vector machine (SVM) model combining kernel principal component analysis (KPCA) with genetic algorithm (GA) is proposed for intrusion detection. In the proposed model, a multi-layer SVM classifier is adopted to estimate whether the action is an attack, KPCA is used as a preprocessor of SVM to reduce the dimension of feature vectors and shorten training time. In order to reduce the noise caused by feature differences and improve the performance of SVM, an improved kernel function (N-RBF) is proposed by embedding the mean value and the mean square difference values of feature attributes in RBF kernel function. GA is employed to optimize the punishment factor C, kernel parameters σ and the tube size ɛ of SVM. By comparison with other detection algorithms, the experimental results show that the proposed model performs higher predictive accuracy, faster convergence speed and better generalization.     

非线性特征提取和LSSVM在化工过程故障诊断中应用

提出利用非线性特征提取（核主成分分析（KPCA）和核独立成分分析）消除数据的不相关性,降低维数。核主成分分析利用核函数把输入数据映射到特征空间,进行线性主成分分析计算提取特征;核独立成分分析在KPCA白化空间进行线性独立成分分析（ICA）变换提取独立成分。提取的特征作为最小二乘支持向量机分类器的输入,构建融合非线性特征提取和最小二乘支持向量机的智能故障分类方法。研究了该方法应用到某石化企业润滑油生产过程的故障诊断中的有效性和可行性。