适应性Web缓存的研究

近年来,由于Web缓存技术对缓解因特网上热点现象的有效性,它已迅速得到了研究人员和业界的关注。适应性Web缓存(adaptive Web caching)由于能够根据用户的不同访问模式,自适应地调整热点数据在缓存系统中的分布,自动均衡整个缓存系统的负载,因而成为了缓存技术研究的一个新的热点。本文介绍了适应性Web缓存领域的研究状况,详细分析了基于组播和基于单播这两种主要的适应性缓存技术。最后,指出了适应性Web缓存研究存在的问题和值得进一步改进的方向。     

Discrete Adaptive Sliding Mode Control of a State-Space System with a Bounded Disturbance

This paper presents the discrete adaptive sliding mode control of a state-space system in the presence of a bounded disturbance. The delta form of the discrete state-space model is used as it closely resembles that of the continuous model. The control law takes into account of the effect of the disturbance by using its approximate value. The system behavior in the vicinity of the sliding surface is studied. It is shown that the adaptive controller leads to a stable closed-loop system. Also, simulation results are presented to illustrate the features of the proposed adaptive control strategy.     

Intelligent adaptive control for MIMO uncertain nonlinear systems

This paper investigates an intelligent adaptive control system for multiple-input–multiple-output (MIMO) uncertain nonlinear systems. This control system is comprised of a recurrent-cerebellar-model-articulation-controller (RCMAC) and an auxiliary compensation controller. RCMAC is utilized to approximate a perfect controller, and the parameters of RCMAC are on-line tuned by the derived adaptive laws based on a Lyapunov function. The auxiliary compensation controller is designed to suppress the influence of residual approximation error between the perfect controller and RCMAC. Finally, two MIMO uncertain nonlinear systems, a mass–spring–damper mechanical system and a Chua’s chaotic circuit, are performed to verify the effectiveness of the proposed control scheme. The simulation results confirm that the proposed intelligent adaptive control system can achieve favorable tracking performance with desired robustness.     

MANET与Internet互联的自适应网关发现策略研究

网关发现是MANET和Internet互联的关键技术.自适应网关发现策略是在混合网关发现策略的基础上对TTL(hops)等参数动态调整以适应MANET的动态拓扑和不同网络环境的一种网关发现策略.本文指出自适应网关发现策略的关键问题是如何找到主动方法和被动方法之间的理想操作点.文章系统地介绍了目前自适应网关发现策略的研究进展,第一次给出了自适应网关发现策略的分类方法,最后结合该领域的研究现状指出自适应网关发现策略的一个发展方向,即在对TTL和advertisement interval等因素进行综合考虑的同时进一步控制Reactive Zone的范围.     

On the stability of neural networks with arbitrary weights

Dynamical properties of a general neural network are discussed. A condition for the neural activation dynamics being stable is derived. The stability condition does not put any symmetry restriction on the weights. The transitions from stable to non-stable dynamics are analysed and their analogy to phase transitions in statistical mechanics discussed. In general, the network dynamics can violate the stability condition. To avoid that happening, a method is introduced which makes the dynamics adaptive such that the stability condition is sustained. Relations to some previous works on stability are discussed.     

一种构建COTS中间件的自适应Web服务方法

自适应Web服务运行在COTS中间件平台具有特别的挑战性。通过引入业务Web服务与自适应组件之间的管理代理,给出一种构建COTS中间件的自适应Web服务的解决方案。管理代理可以定制并利用现有的中间件机制无缝地集成到一个COTS平台。通过一个基于.Net WCF的Web服务应用来论证此架构。结果表明,该方案在性能和灵活性方面是有效的。     

基于遗传算法的DM-GA组合测试数据生成方法

测试数据生成是组合软件测试的重要部分,生成高质量的测试数据对于软件测试具有重要意义.针对两两组合测试数据生成问题,结合传统遗传算法,加入了精英策略和自适应变异概率,提出了DM-GA( dynamic mutation rates-genetic algorithm)算法,改善了传统遗传算法容易陷入局部最优以及收敛速度慢等不足,并取得了良好的效果.实验结果表明DM-GA算法可以作为一种较理想的两两组合测试数据生成方法.     

医学图像中粘连细胞分割方法研究

研究粘连细胞图像的分割问题。针对医学细胞图像中,细胞经常发生粘连,细胞粘连部分由于发生像素灰度混合,造成图像不清晰。传统的应用灰度的分割算法由于不能很好的区分粘连部分的灰度,不能完整的分割细胞的问题。为了解决上述问题,提出了自适应阀值调整的粘连细胞分割方法。通过求得粘连部位的细胞像素的具体特征,运用阀值的自适应变化区分粘连部位像素之间的不同,对像素进行划分,克服了传统方法像素划分不清的弊端。实验表明,方法能够有效的分割医学图像中的粘连细胞,取得了比较好的效果。     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

A framework to design a human-centred adaptive manufacturing system for aging workers

The so-called smart manufacturing systems (SMS) combine smart manufacturing technologies, cyber-physical infrastructures, and data control to realize predictive and adaptive behaviours. In this context, industrial research focused mainly on improving the manufacturing system performance, almost neglecting human factors (HF) and their relation to the production systems. However, in order to create an effective smart factory context, human performance should be included to drive smart system adaptation in efficient and effective way, also by exploiting the linkages between tangible and intangible entities offered by Industry 4.0. Furthermore, modern companies are facing another interesting trend: aging workers. The age of workers is generally growing up and, consequently, the percentage of working 45–64 years old population with different needs, capabilities, and reactions, is increasing. This research focuses on the design of human-centred adaptive manufacturing systems (AMS) for the modern companies, where aging workers are more and more common. In particular, it defines a methodology to design AMS able to adapt to the aging workers’ needs considering their reduced workability, due to both physical and cognitive functional decrease, with the final aim to improve the human-machine interaction and the workers’ wellbeing. The paper finally presents an industrial case study focusing on the woodworking sector, where an existing machine has been re-designed to define a new human-centred AMS. The new machine has been engineered and prototyped by adopting cyber-physical systems (CPS) and pervasive technologies to smartly adapt the machine behaviour to the working conditions and the specific workers’ skills, tasks, and cognitive-physical abilities, with the final aim to support aging workers. The achieved benefits were expressed in terms of system usability, focusing on human-interaction quality.