JPEG隐密分析中的重压缩检测

重压缩检测是多级隐密分析中关键的预处理部分,高准确率的重压缩检测是隐密分析获得更高性能的重要前提条件.深入研究了重压缩对于JPEG图像各种特征的影响,基于此提出了一种融合直方图分布特征、Benford特征、DFT特征的重压缩检测算法.仿真实验表明,该算法具有更高的检测率,能够适用于JPEG多级隐密分析中的重压缩检测.     

一种多核处理器存储层次性能评估模型

一种用于评估多核处理器存储层次性能的模型,使用排队论建模,求解速度快,可以在设计早期给出不同配置参数对处理器整体性能的影响,从而调整存储层次结构,优化设计.     

基于群决策的多Agent资源管理模型

在大部分系统中,资源都是有限供给的.因此,为了提高系统的性能,保证资源使用的效率与公平,必须对资源进行有效的管理.而合理分配资源和解决资源使用冲突是系统资源管理的核心问题.基于群决策方法提出了一个多Agent资源管理模型,以动态优先级的方式灵活地解决了系统资源分配与使用冲突问题.     

A Galerkin finite element scheme for time–space fractional diffusion equation

In this paper, a Galerkin finite element scheme to approximate the time–space fractional diffusion equation is studied. Firstly, the fractional diffusion equation is transformed into a fractional Volterra integro-differential equation. And a second-order fractional trapezoidal formula is used to approach the time fractional integral. Then a Galerkin finite element method is introduced in space direction, where the semi-discretization scheme and fully discrete scheme are given separately. The stability analysis of semi-discretization scheme is discussed in detail. Furthermore, convergence analysis of semi-discretization scheme and fully discrete scheme are given in details. Finally, two numerical examples are displayed to demonstrate the effectiveness of the proposed method.     

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.     

Adaptive sparse coding on PCA dictionary for image denoising

Sparse coding is a popular technique in image denoising. However, owing to the ill-posedness of denoising problems, it is difficult to obtain an accurate estimation of the true code. To improve denoising performance, we collect the sparse coding errors of a dataset on a principal component analysis dictionary, make an assumption on the probability of errors and derive an energy optimization model for image denoising, called adaptive sparse coding on a principal component analysis dictionary (ASC-PCA). The new method considers two aspects. First, with a PCA dictionary-related observation of the probability distributions of sparse coding errors on different dimensions, the regularization parameter balancing the fidelity term and the nonlocal constraint can be adaptively determined, which is critical for obtaining satisfying results. Furthermore, an intuitive interpretation of the constructed model is discussed. Second, to solve the new model effectively, a filter-based iterative shrinkage algorithm containing the filter-based back-projection and shrinkage stages is proposed. The filter in the back-projection stage plays an important role in solving the model. As demonstrated by extensive experiments, the proposed method performs optimally in terms of both quantitative and visual measurements.     

Majority Clusters‐Density Ordered Weighting Averaging: A Family of New Aggregation Operators in Group Decision Making

In the process of aggregation, it is necessary, especially for group decision‐making (GDM) problems, to consider distributed characteristic hidden in aggregates. In this case, clustering has been a common way for discovering the implicit distributed structures. This paper mainly investigates the characteristic of majority clusters, rather than majority elements and develops a new class of aggregation operators denominated majority clusters density‐ordered weighting averaging (MC‐DOWA) operators. Furthermore, we discuss properties of these operators and calculate the associated weights. Finally, a numerical example is provided to illustrate the application of the MC‐DOWA operators, and the aggregations are compared with those of the other three aggregation operators: majority additive‐OWA (MA‐OWA), dependent OWA (DOWA) and cluster‐based DOWA (Clus‐DOWA) operators.     

Evaluation of visual saliency analysis algorithms in noisy images

Most existing visual saliency analysis algorithms assume that the input image is clean and does not have any disturbances. However, this situation is not always the case. In this paper, we provide an extensive evaluation of visual saliency analysis algorithms in noisy images. We analyze the noise immunity of saliency analysis algorithms by evaluating the performances of the algorithms in noisy images with increasing noise scales and by studying the effects of applying different denoising methods before performing saliency analysis. We use 10 state-of-the-art saliency analysis algorithms and 7 typical image denoising methods on 4 eye fixation datasets and 2 salient object detection datasets. Our experiments show that the performances of saliency analysis algorithms decrease with increasing image noise scales in general. An exception is that the nonlinear features (NF) integrated algorithm shows good noise immunity. We also find that image denoising methods can greatly improve the noise immunity of the algorithms. Our results show that the combination of NF and Median denoising method works best on eye fixation datasets and the combination of saliency optimization (SO) and color block-matching and 3D filtering (C-BM3D) method works best on salient object detection datasets. The combination of SO and Average denoising method works best for applications wherein time efficiency is a major concern for both types of datasets.