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.     

A general topology-based framework for adaptive insertion of cohesive elements in finite element meshes

Large-scale simulation of separation phenomena in solids such as fracture, branching, and fragmentation requires a scalable data structure representation of the evolving model. Modeling of such phenomena can be successfully accomplished by means of cohesive models of fracture, which are versatile and effective tools for computational analysis. A common approach to insert cohesive elements in finite element meshes consists of adding discrete special interfaces (cohesive elements) between bulk elements. The insertion of cohesive elements along bulk element interfaces for fragmentation simulation imposes changes in the topology of the mesh. This paper presents a unified topology-based framework for supporting adaptive fragmentation simulations, being able to handle two- and three-dimensional models, with finite elements of any order. We represent the finite element model using a compact and “complete” topological data structure, which is capable of retrieving all adjacency relationships needed for the simulation. Moreover, we introduce a new topology-based algorithm that systematically classifies fractured facets (i.e., facets along which fracture has occurred). The algorithm follows a set of procedures that consistently perform all the topological changes needed to update the model. The proposed topology-based framework is general and ensures that the model representation remains always valid during fragmentation, even when very complex crack patterns are involved. The framework correctness and efficiency are illustrated by arbitrary insertion of cohesive elements in various finite element meshes of self-similar geometries, including both two- and three-dimensional models. These computational tests clearly show linear scaling in time, which is a key feature of the present data-structure representation. The effectiveness of the proposed approach is also demonstrated by dynamic fracture analysis through finite element simulations of actual engineering problems.

Evolving BlenX programs to simulate the evolution of biological networks

We present a formal approach to study the evolution of biological networks. We use the Beta Workbench and its BlenX language to model and simulate networks in connection with evolutionary algorithms. Mutations are done on the structure of BlenX programs and networks are selected at any generation by using a fitness function. The feasibility of the approach is illustrated with a simple example.     

Small sample improvements in the threshold cointegration test using residual-based moving block bootstrap

A residual-based moving block bootstrap procedure for testing the null hypothesis of linear cointegration versus cointegration with threshold effects is proposed. When the regressors and errors of the models are serially and contemporaneously correlated, our test compares favourably with the Sup LM test proposed by Gonzalo and Pitarakis. Indeed, shortcomings of the former motivated the development of our test. The small sample performance of the bootstrap test is investigated by Monte Carlo simulations, and the results show that the test performs better than the Sup LM test.     

一种实用的5阶电荷泵锁相环性能分析与设计仿真

本文在分析5阶锁相环基本原理和线性化数学模型的基础上,给出了一种实用的4阶环路滤波器的设计方法,用Matlab编程计算锁相环的参数;用ADS工具对系统性能进行仿真,仿真结果与预期结果相吻合;比较了具有相同环路带宽和相位裕度下的高阶锁相环的杂散性能。这对5阶电荷泵锁相环的系统设计和仿真有一定的指导意义。     

基于模糊控制的无刷直流电机建模仿真新方法

为提高无刷直流电动机(BLDCM)控制系统的精度,本文根据电机的数学模型,基于Matlab仿真平台构建了BLDCM电流、转速模糊PI复合控制的双闭环控制系统的仿真模型。该模型采用了一种基于分段线性化的思想,利用Simulink中的Look-Up Table对梯形波感应电动势和电磁转矩进行快速建模的方法。仿真结果表明,该模型准确易行、控制精度高,为实际BLDCM控制系统的设计和调试提供了有效的工具。     

汽包水位粒子群-PID优化控制

对循环流化床锅炉汽包水位控制系统,采用串级三冲量PID控制,应用粒子群优化算法对PID控制器进行参数整定,并获得了最优参数。仿真结果表明,与传统整定方法相比,这种参数整定方案使汽包水位控制系统具有更好的控制品质,提高了系统的静动态特性。     

一种在线测算交流永磁同步电机定子电感参数的方法

本文介绍了一种采用准电流定向控制在线测算交流永磁同步电机定子电感参数的新方法,该方法利用通用的PWM调制的电压型逆变器在电机定子d轴上施加了一个正负交替的小电压,与d轴上获得的正负交替的小电流一起推算出定子电感参数。该法具有测试时间短和电机无需堵转的特点。它所涉及到的数学计算简单易于数字编程实现。MATLAB仿真结果证明了该方法的可行性和正确性。     

虚拟人运动中受扰的平衡保持算法

为了更加真实且实时地模拟运动中虚拟人恢复平衡的反应动作,提出一种针对受到外界作用力扰动的虚拟人平衡保持算法.首先通过虚拟人质心位移和速度来判断平衡性;然后借助生物力学的研究成果设计了具有人体特性的虚拟人运动受扰后的平衡保持方法,并用动力学进行模拟,驱动虚拟人完成平衡恢复.实验结果表明:该算法计算效率高,符合人体的生物力学特性,并且具有良好的交互性与较好的视觉效果,适用于虚拟人动画合成.     

基于Agent的分布仿真平台原型系统设计与实现

基于Agent的分布仿真是基于Agent的建模与仿真ABMS(Agent-Based Modeling and Simulation)研究的重要组成部分。在提出的基于Agent的分布仿真软件框架和通信系统设计与实现的基础上,利用Java语言和面向对象的方法设计实现了一个基于Agent的分布仿真平台原型系统ADSimE。介绍了该分布式仿真平台的主要实现细节,给出了全系统的主要Agent类的UML设计,重点阐述了保守策略仿真Agent类和乐观策略仿真Agent类的设计、处理流程以及KQML消息解析的实现细节。最后以闭合排队网络为例,说明了怎样在该环境下进行基于Agent的分布仿真应用的开发。