基于约束放松的自适应Web服务选取方法的研究

已有服务选取都是把问题规划为针对某个模型寻求最优解或者次优解的过程,不考虑用户约束对Web服务选取的影响,而在SLA模型中,用户对服务的选取是有约束的,如果约束不满足,选取算法直接结束,要求用户重新给出约束定义,增加了用户与选取过程的交互.为了减少用户的操作次数,本文为用户的约束定义了约束放松模型,对于约束不能满足的情况下,自动地对已有模型进行条件放松,定义了基于分布式约束满足问题求解的服务选取过程,将约束放松模型应用于选取过程,进行自适应的选取.通过与已有的选取算法进行实验比较,本文提出的自适应组合服务选取算法,考虑了用户的约束条件,在选取失败时,基于放松模型进行自动约束条件的更新,进行服务的重新选取,减少了与用户交互次数,有效提高了有约束的组合服务选取的执行效率.     

基于近似熵的VDR人声识别技术研究

利用话者识别原理和语音数字信号处理技术对人声建模方法进行研究,建立了基于GMM模型的VDR环境下的人声识别基准系统;从分析影响人声识别率因素的角度出发,指出传统算法的不足,并提出一种基于近似熵的语音端点检测算法。理论分析和实验结果证明:新算法能有效屏蔽大动态冲击性噪声,解决了语音的虚检现象,并且在低信噪比0 dB情况下的识别率提升66%。     

基于计算听觉场景分析和语者模型信息的语音识别鲁棒前端研究

传统抗噪算法无法解决人声背景下语音识别(Automatic speech recognition, ASR)系统的鲁棒性问题. 本文提出了一种基于计算听觉场景分析(Computational auditory scene analysis, CASA)和语者模型信息的混合语音分离系统. 该系统在CASA框架下, 利用语者模型信息和因子最大矢量量化(Factorial-max vector quantization, MAXVQ)方法进行实值掩码估计, 实现了两语者混合语音中有效地分离出目标说话人语音的目标, 从而为ASR系统提供了鲁棒的识别前端. 在语音分离挑战(Speech separation challenge, SSC)数据集上的评估表明, 相比基线系统, 本文所提出的系统的语音识别正确率提高了15.68%. 相关的实验结果也验证了本文提出的多语者识别和实值掩码估计的有效性.     

基于复杂性科学基本概念的MAS涌现性量化研究

在MAS(Multi-Agent System)领域,面向涌现的MAS研究将MAS看成一类特殊的复杂系统,其关注点是MAS宏观层面的涌现性问题.这类研究需要借用复杂性科学研究中的一些基本概念描述、阐述MAS涌现性研究中的问题.文中着重对几个常用的基本概念及其量化研究做一概览,以深化对它们的认识,并结合这些概念及其量化研究在MAS涌现性研究中的应用特点,阐明它们对面向涌现的MAS研究的作用,以促进进一步的研究.这些概念包括复杂性、混沌边缘、自组织、涌现、适应和进化等.     

A comprehensive solution for application‐level adaptation

Driven by the emergence of mobile and pervasive computing there is a growing demand for context‐aware software systems that can dynamically adapt to their run‐time environment. We present the results of project MADAM that has delivered a comprehensive solution for the development and operation of context‐aware, self‐adaptive applications. The main contributions of MADAM are (a) a sophisticated middleware that supports the dynamic adaptation of component‐based applications, and (b) an innovative model‐driven development methodology that is based on abstract adaptation models and corresponding model‐to‐code transformations. MADAM has demonstrated the viability of a general, integrated approach to application‐level adaptation. We discuss our experiences with two real‐world case studies that were built using the MADAM approach. Copyright © 2008 John Wiley & Sons, Ltd.     

System supports for protocol and application adaptation in vertical handoffs

In overlay networks, the network characteristics before and after a vertical handoff would be drastically different. Consequently, in this paper, we propose an end‐to‐end based scheme to support protocol and application adaptation in vertical handoffs. First, we proposed a Vertical‐handoff Aware TCP, called VA‐TCP. VA‐TCP can identify the packet losses caused by vertical handoffs. If segments losses are due to vertical handoffs, VA‐TCP only retransmits the missing segments but does not invoke the congestion control procedure. Moreover, VA‐TCP dynamically estimates the bandwidth and round‐trip time in a new network. Based on the estimated bandwidth and round‐trip time, VA‐TCP adjusts its parameters to respond to the new network environment. Second, during a vertical handoff, applications also need to be adapted accordingly. Therefore, we design a programming interface that allows applications to be notified upon and adapt to changing network environments. To support our interface, we utilize the signal mechanism to achieve kernel‐to‐user notification. Nevertheless, signals cannot carry information. Thus, we implement the shared memory mechanism between applications and the kernel to facilitate parameters exchange. Finally, we also provide a handoff‐aware CPU scheduler so that tasks that are interested in the vertical‐handoff event are given preference over other processes to attain a prompt response for new network conditions. We have implemented a prototype system on the Linux kernel 2.6. From the experimental results, our proposed protocol and application adaptation mechanisms are shown to effectively improve the performance of TCP and applications during vertical handoffs. Copyright © 2008 John Wiley & Sons, Ltd.     

CASE-BASED REASONING FOR MANAGING NONCOMPLIANCE WITH CLINICAL GUIDELINES

Despite the recognized advantages that can be obtained in clinical practice when following clinical guidelines (GL), situations of noncompliance with them may emerge. Keeping track of such deviations from the default GL execution, and documenting the physician's motivations, would clearly be an added value. Moreover, repeated alterations of GL actions (or flow) may highlight the need for an adaptation of the GL itself to the local reality, or may even indicate an improper or weak initial GL definition.
In this article, we propose an approach for managing noncompliance with GL, based on the case-based reasoning methodology. In front of a new noncompliance case, our tool allows the physician to retrieve past situations similar to the current one, and to decide whether to reapply the same GL modifications adopted in them. Moreover, the tool is able to learn indications from the ground noncompliance cases that can be deployed for local adaptation, and possibly, for suggesting more formal GL revisions to be carried out by a committee of expert physicians.     

Nonlinear system identification using optimized dynamic neural network

In this paper, both off-line architecture optimization and on-line adaptation have been developed for a dynamic neural network (DNN) in nonlinear system identification. In the off-line architecture optimization, a new effective encoding scheme—Direct Matrix Mapping Encoding (DMME) method is proposed to represent the structure of neural network by establishing connection matrices. A series of GA operations are applied to the connection matrices to find the optimal number of neurons on each hidden layer and interconnection between two neighboring layers of DNN. The hybrid training is adopted to evolve the architecture, and to tune the weights and input delays of DNN by combining GA with the modified adaptation laws. The modified adaptation laws are subsequently used to tune the input time delays, weights and linear parameters in the optimized DNN-based model in on-line nonlinear system identification. The effectiveness of the architecture optimization and adaptation is extensively tested by means of two nonlinear system identification examples.     

一种非平稳环境下的自适应变步长盲源分离算法

提出一种基于不完整自然梯度的变步长约束算法,用来处理非平稳环境下的瞬时盲源分离问题.该算法利用系统上的扰动对代价函数进行约束,对算法中的约束因子采用自适应形式,根据分离情况对约束因子进行自适应调整,以加快收敛速度.同时,引入基于代价函数梯度的变步长,使其具有更好的跟踪性能.仿真结果表明,在非平稳环境下,所提出的算法在提高收敛速度的同时可以有效分离源信号而不产生严重的稳态误差.     

情境感知和兴趣适应的农业信息推荐模型

个性化推荐是解决农户需求和农业信息服务契合度的关键技术。传统基于行为的推荐算法能挖掘农户兴趣但不能反映其兴趣的迁移,时间权重的引入较好地实现自适应机制。地域、时间以及农业生产门类等情境信息直接影响农户的兴趣偏好,已有算法却融入度不足。综合情境因素、浏览行为和兴趣改变等多维因素,设计情景感知和兴趣适应的农业信息推荐模型。实验结果显示,改进后的算法能显著提升推荐的准确率和有效性。