Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

基于混沌系统和极化恒等式的数字图像加密算法

混沌系统具有初值敏感性,参数敏感性和类随机性等特性。将图像分成大小为8 8的图像子块,利用经典的Logistic方程产生的混沌序列并对其排序生成匹配模板来改变图像子块的位置,然后利用极化恒等式内积分解加密算法改变图像像素的值。因此,基于混沌系统和极化恒等式的数字图像加密算法使图像的像素的位置和值都发生了变化,实现了图像的混淆和扩散。仿真实验表明该方法进行图像加密具有可行性和安全性。     

改进的猴王遗传算法求解组合拍卖最优竞胜标

应用遗传算法求解第一价格封闭组合拍卖问题,编码及约束条件复杂,在交叉和变异的过程中容易产生大量不可行解,并且在选择和精英保留中,一些局部最优点(早熟点)对应的个体容易被大量复制,使群体多样性下降。该文就以上的问题,改进了原有遗传算法求解组合拍卖最优竞胜标的方法,并采用了猴王遗传算法的精英保留策略。实际计算证明本算法编码方式自然并且直观表达了个体和标的的关系。在交叉和变异算子中,产生不可行解的数量下降,更积极的保存了上一代进化的部分成果。而改进猴王遗传算法的精英保留策略,使算法能够以高概率跳出早熟,达到算法速度与准确的平衡。     

遗传算法与文本特征选择方法相结合的研究

对文本特征选择的传统方法进行了深入分析并对各自的优缺点进行了详细的对比;其次,通过利用遗传算法自身的寻优特点,把它与传统特征选择方法相结合,并得出了算法。最后,在对各自算法进行了分析,表明了在性能上有了明显的提高。     

一种改进的应用于噪声数据中的KNN算法

基于实例的KNN算法不可避免地要依赖于数据的质量,但原始数据含有噪声,因而KNN算法的结果势必会因为数据中的噪声而受到严重的影响。事实上,大多噪声都服从一定的模型,而且模型一般是已知的。充分利用数据中的噪声模型,以减小噪声对KNN算法结果的影响。通过实验结果表明该方法是有效的。     

Precise identification of moving vehicular parameters based on improved glowworm swarm optimization algorithm

This paper proposes an indirect method for the identification of moving vehicular parameters using the dynamic responses of the vehicle. The moving vehicle is modelled as 2-DOF system with 5 parameters and 4-DOF system with 12 parameters, respectively. Finite element method is used to establish the equation of the coupled bridge–vehicle system. The dynamic responses of the system are calculated by Newmark direct integration method. The parameter identification problem is transformed into an optimization problem by minimizing errors between the calculated dynamic responses of the moving vehicle and those of the simulated measured responses. Glowworm swarm optimization algorithm (GSO) is used to solve the objective function of the optimization problem. A local search method is introduced into the movement phase of GSO to enhance the accuracy and convergence rate of the algorithm. Several test cases are carried out to verify the efficiency of the proposed method and the results show that the vehicular parameters can be identified precisely with the present method and it is not sensitive to artificial measurement noise.     

基于SQL的多条件选择智能实现算法

B/S架构的信息系统开发中,一般情况下,根据用户的选择,n个条件的输入组合数是2n个。但是,该文中提出的算法,其组合数仅为n到n(n+1)/2,大大简化了编程。     

Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of a variety of surfaces. In this study, prediction models of SEM were constructed by using a generalized regression neural network (GRNN) and genetic algorithm (GA). The SEM components examined include condenser lens 1 and 2 and objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM resolution (R), a face-centered Box–Wilson experiment was conducted. Two sets of data were collected with or without the adjustment of magnification. Root-mean-squared prediction error of optimized GRNN models are GA 0.481 and 1.96×10^-12 for non-adjusted and adjusted data, respectively. The optimized models demonstrated a much improved prediction over statistical regression models. The optimized models were used to optimize parameters particularly under best tuned SEM environment. For the variations in CL2 and OL-Coarse, the highest R could be achieved at all conditions except a larger CL2 either at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained at all conditions but larger CL2 and smaller CL1.     

An inexact hybrid projection-proximal point algorithm for solving generalized mixed variational inequalities

In this paper, we investigate an inexact hybrid projection-proximal method for solving a class of generalized mixed variational inequalities in Hilbert spaces. We construct a general inexact hybrid projection-proximal point algorithm, in which an inexact relaxed proximal point step is followed by a suitable orthogonal projection onto a hyperplane. Under some suitable conditions concerned with the pseudomonotone set-valued mapping T, the nonsmooth convex function f and the step size λ_k, we prove the convergence of the inexact hybrid projection-proximal point algorithm for solving generalized mixed variational inequalities in Hilbert spaces.