数字图像LSB隐写分析统计模型初探

数字图像LS(BLeast Significant Bits)在统计上与二项分布相似,结构上与原宿主图像仍然具有一定的相关性,从而导致经典LSB替换算法产生直方图"阶梯效应"。本文主要从图像像素以及像素间关系的特征函数出发,观察其常见统计模型的变化,为设计有效的图像隐写算法及隐写分析算法提供参考依据。     

Global optimization of tool path for five-axis flank milling with a conical cutter

In this paper, optimum positioning of the conical cutter for five-axis flank milling of slender surfaces is addressed from the perspective of approximating the tool envelope surface to the data points on the design surface following the minimum zone criterion recommended by ANSI and ISO standards for tolerance evaluation. Based on the observation that a conical surface can be treated as a canal surface, i.e. envelope surface of one-parameter family of spheres, the swept envelope of a conical cutter is represented as a sphere-swept surface. Then, an approach is presented to efficiently compute the signed distance between a point in space and the swept surface without constructing the swept surface itself. The first order differential increment of the signed point-to-surface distance with respect to the differential deformation of the tool axis trajectory surface is derived. By using the distance function, tool path optimizations for semi-finish and finish millings are formulated as two constrained optimization problems in a unified framework, and a sequential approximation algorithm along with a hierarchical algorithmic structure is developed for the optimization. Numerical examples are given to confirm the validity and efficiency of the proposed approach. Comparing with the existing approaches, the present one improves the machining accuracy greatly. The rationale developed applies to general rotary cutters.     

Implementation of a strain energy-based nonlinear finite element in the object-oriented environment

The objective of the paper is to describe a novel finite element computational method based on a strain energy density function and to implement it in the object-oriented environment. The original energy-based finite element was put into the known standard framework of classes and handled in a different manner. The nonlinear properties of material are defined with a modified strain energy density function. The local relaxation procedure proposed as a method used to resolve a nonlinear problem is implemented in C++ language. The hexahedral element with eight nodes as well as the adaptation of the nonlinear finite element is introduced. The chosen numerical model is made of nearly incompressible hyperelastic material. The application of the proposed element is shown on the example of a rectangular parallelepiped with a hollow port.     

Numerical solution of Riccati equation using the cubic B-spline scaling functions and Chebyshev cardinal functions

Two numerical techniques are presented for solving the solution of Riccati differential equation. These methods use the cubic B-spline scaling functions and Chebyshev cardinal functions. The methods consist of expanding the required approximate solution as the elements of cubic B-spline scaling function or Chebyshev cardinal functions. Using the operational matrix of derivative, we reduce the problem to a set of algebraic equations. Some numerical examples are included to demonstrate the validity and applicability of the new techniques. The methods are easy to implement and produce very accurate results.     

Some improved results on communication between information systems

To study the communication between information systems, Wang et al. [C. Wang, C. Wu, D. Chen, Q. Hu, C. Wu, Communicating between information systems, Information Sciences 178 (2008) 3228-3239] proposed two concepts of type-1 and type-2 consistent functions. Some properties of such functions and induced relation mappings have been investigated there. In this paper, we provide an improvement of the aforementioned work by disclosing the symmetric relationship between type-1 and type-2 consistent functions. We present more properties of consistent functions and induced relation mappings and improve upon several deficient assertions in the original work. In particular, we unify and extend type-1 and type-2 consistent functions into the so-called neighborhood-consistent functions. This provides a convenient means for studying the communication between information systems based on various neighborhoods.     

On the versatility of radial basis function neural networks: A case study in the field of intrusion detection

Classifiers based on radial basis function neural networks have a number of useful properties that can be exploited in many practical applications. Using sample data, it is possible to adjust their parameters (weights), to optimize their structure, and to select appropriate input features (attributes). Moreover, interpretable rules can be extracted from a trained classifier and input samples can be identified that cannot be classified with a sufficient degree of “certainty”. These properties support an analysis of radial basis function classifiers and allow for an adaption to “novel” kinds of input samples in a real-world application. In this article, we outline these properties and show how they can be exploited in the field of intrusion detection (detection of network-based misuse). Intrusion detection plays an increasingly important role in securing computer networks. In this case study, we first compare the classification abilities of radial basis function classifiers, multilayer perceptrons, the neuro-fuzzy system NEFCLASS, decision trees, classifying fuzzy-k-means, support vector machines, Bayesian networks, and nearest neighbor classifiers. Then, we investigate the interpretability and understandability of the best paradigms found in the previous step. We show how structure optimization and feature selection for radial basis function classifiers can be done by means of evolutionary algorithms and compare this approach to decision trees optimized using certain pruning techniques. Finally, we demonstrate that radial basis function classifiers are basically able to detect novel attack types. The many advantageous properties of radial basis function classifiers could certainly be exploited in other application fields in a similar way.     

Mass function derivation and combination in multivariate data spaces

In this paper, we consider the problem of how to derive mass functions systematically from data samples. We also consider the ensuing problem of how to combine different mass functions derived in this way. We show that a mass function can be efficiently and systematically derived from multivariate data. We also demonstrate that combining mass functions obtained in this manner can be done easily. The way of deriving and combining mass functions is illustrated with a simple example.     

基于SVM用户建模的核函数选择研究

用户建模是从用户偏好数据中建立用户偏好模型的过程,用户偏好数据具有系统运行初期的稀疏性和非线形的特点。支持向量机(Support Vector Machine,简称SVM)具有小样本学习、非线形处理的能力,是合适的用户建模工具。SVM的非线形处理能力主要依赖于核函数,采用不同的核函数进行建模对模型的预测效果有重大影响。本文重点研究核函数的选择对基于SVM建模方法的影响,从中选取了表现较优的小波核函数,构建性能突出的SVM进行用户建模。实验证明该建模方法可以有效地从小样本数据中学习用户偏好信息,建立反映用户真实偏好的用户模型。     

一种基于小波阈值函数的交通图像去噪算法

针对高速运动条件下交通实时监测图像的失真和去噪问题,介绍小波阈值图像去噪过程和传统GCV阈值的原理,引入整数小波变换和合并递推运算的思想,提出一种快速递推GCV阈值的小波图像去噪方法。仿真实验分析了传统GCV阈值和快速递推GCV阈值去噪的效果,并比较了两种算法的复杂度。结果表明,快速递推GCV阈值算法不仅保留了传统GCV阈值算法去噪效果明显的特点,而且很好地解决了算法复杂度较高的问题,具有良好的应用前景。     

活用函数在EXCEL中实现数据库应用

Excel软件在当今社会中已经成为一种必备工具。它除了可以用来制作各种表格与图表、进行简单的运算之外,还可以管理数据,并以此为基础进行数据分析。但是大部分人在使用Excel时,只是简单使用了软件菜单提供的排序、自动筛选等常用功能,以及求和等数学计算函数,在处理最简单的数据表格时相当方便快捷,然而当实际工作对数据的处理提出更复杂的要求时就束手无策了,转而求助于他人或其他软件工具。该文将结合自己在工作中的实践,探讨通过灵活应用函数在Excel中实现数据库应用,解决复杂数据处理问题的一些方法和技巧。