基于分块思想的最小化总变差去噪新算法

现有的图像分块方法在处理前将源噪声图像分割成不同的小块,分别独立进行去噪,最后将所有小块拼接。该方法所得图像的去噪效果有一定程度的提高,但在相邻分块结合处存在不连续。对去噪方法的现状进行了分析,并结合最小化总变差及分块思想,提出新的去噪方法:在每小块处理过程中,分块值参与判断终止,整幅图像值计算去噪图像;对各块所得去噪图像取均值,得到最终的结果;对新算法进行优化,提高了算法运行效率。分析实验结果表明,该方法在计算效率和信噪比上均有显著提高。     

基于CUDA的泥石流模拟计算研究

针对泥石流仿真过程中海量数据计算问题,采用CUDA技术即结合CPU与GPU的优点研究了一种协同计算方法以提高数据计算效率和仿真性能。同时,搭建了基于GPU的泥石流仿真计算平台,对优化的CUDA并行计算方法进行验证。实验结果表明,该方法对海量数据的计算具有快速准确、低成本、低功耗的特点,能为灾害预测提供及时准确的决策支持,满足了高密集型计算的需求。     

Learning algorithm for multimodal optimization

We present a new evolutionary algorithm—“learning algorithm” for multimodal optimization. The scheme for reproducing a new generation is very simple. Control parameters, of the length of the list of historical best solutions and the “learning probability” of the current solutions being moved towards the current best solutions and towards the historical ones, are used to assign different search intensities to different parts of the feasible area and to direct the updating of the current solutions. Results of numerical tests on minimization of the 2D Schaffer function, the 2D Shubert function and the 10D Ackley function show that this algorithm is effective and efficient in finding multiple global solutions of multimodal optimization problems.     

Mean square asymptotic behavior of stochastic neural networks with infinitely distributed delays

In this paper, according to classic -matrix method, integral–differential inequality technique and Ito formula, we study asymptotic behavior in mean square sense of stochastic neural networks with infinitely distributed delays by establishing a generalized Halanay inequality. This is a new means for investigating asymptotic behavior of stochastic differential equation. Some useful results are derived. Especially, our methods can be extended to research p-moment asymptotic behavior easily. At last, example and simulations demonstrate the power of our methods.     

Synchronization analysis of coupled connected neural networks with mixed time delays

In this paper, the global exponential synchronization of coupled connected neural networks with both discrete and distributed delays is investigated under mild condition, assuming neither the differentiability and strict monotonicity for the activation functions nor the diagonal for the inner coupling matrices. By employing a new Lyapunov–Krasovskii functional, applying the theory of Kronecker product of matrices and the linear matrix inequality (LMI) technique, several delay-dependent sufficient conditions in LMI form are obtained for global exponential synchronization of such systems. Moreover, the decay rate is estimated. The proposed LMI approach has the advantage of considering the difference of neuronal excitatory and inhibitory efforts, which is also computationally efficient as it can be solved numerically using efficient Matlab LMI toolbox, and no tuning of parameters is required. In addition, the proposed results generalize and improve the earlier publications. An example with simulation is given to show the effectiveness of the obtained results.     

Structure identification of Bayesian classifiers based on GMDH

This paper introduces group method of data handing (GMDH) theory to Bayesian classification, and proposes GMBC algorithm for structure identification of Bayesian classifiers. The algorithm combines two structure identification ideas of search & scoring and dependence analysis, and is able to accomplish the process of adaptive structure identification. We experimentally test two versions of Bayesian classifiers (GMBC-BDe and GMBC-BIC) over 25 data sets. The results show that, the structure identification of the two Bayesian classifiers especially GMBC-BDe is very effective. And when the data sets contain lots of noise, the superiority of Bayesian classifiers learned by GMBC is more obvious. Finally, giving a classification domain without any prior information about the noise, we recommend adopting GMBC-BDe rather than GMBC-BIC.     

Automatic classification of Tamil documents using vector space model and artificial neural network

Automatic text classification based on vector space model (VSM), artificial neural networks (ANN), K-nearest neighbor (KNN), Naives Bayes (NB) and support vector machine (SVM) have been applied on English language documents, and gained popularity among text mining and information retrieval (IR) researchers. This paper proposes the application of VSM and ANN for the classification of Tamil language documents. Tamil is morphologically rich Dravidian classical language. The development of internet led to an exponential increase in the amount of electronic documents not only in English but also other regional languages. The automatic classification of Tamil documents has not been explored in detail so far. In this paper, corpus is used to construct and test the VSM and ANN models. Methods of document representation, assigning weights that reflect the importance of each term are discussed. In a traditional word-matching based categorization system, the most popular document representation is VSM. This method needs a high dimensional space to represent the documents. The ANN classifier requires smaller number of features. The experimental results show that ANN model achieves 93.33% which is better than the performance of VSM which yields 90.33% on Tamil document classification.     

面向入侵检测的改进BMHS模式匹配算法

模式匹配算法是入侵检测系统中使用较多的一种算法。分析几种常用模式匹配算法,提出一种基于BMHS的改进算法——N-BMHS算法。该算法对字符串进行双向模式匹配,引入一个新的数组s记录模式串中字符的出现次数,次数为1时可以利用已匹配的信息来辅助得到模式串的最大移动距离。实验结果表明,改进的模式匹配算法能减少比较次数,有效提高匹配速度。     

Linux文件加／解密过滤驱动的设计与实现

应用Linux内核中的系统调用劫持技术和模块动态装载机制,研究与开发Linux下的文件过滤驱动。通过对文件标识的注入与识别,采用blowfish加密算法,设计和实现内核级的文件加/解密服务,并对软件的性能进行分析。结果表明,在内核级增添软件功能,可以提高软件的运行效率,而计算机主机系统的安全性在内核机制的安全保障下得以增强。     

Error Analysis Methods for Group Decision Making Based on Hesitant Fuzzy Preference Relation

Hesitant fuzzy preference relation (HFPR) is an effective way to depict the decision makers’ preferences over the objects (alternatives or attributes) in the process of group decision making. Each component of the HFPR is characterized by several possible values and can express the decision makers’ hesitant information comprehensively. To make a decision with the HFPR, it is very necessary to find a proper technique for deriving the priority weights from the HFPR. In this paper, we use the error analysis as a tool to develop several straightforward methods for the priorities of the HFPR. We first define the expected value and the average value of each hesitant fuzzy element in the HFPR. Then based on the error analysis, we come up with the interval midpoint method, the average value method, and the difference method to derive the priority weights from the HFPR. After that, we discuss the relations among these methods, and utilize them and the possibility degree formula to develop an approach to decision making with the HFPR. Finally, we demonstrate the effectiveness and practicality of our approach through a case study concerning the investment problem in liquor enterprise.