开放式实时系统中的自适应调度方法

首先针对开放式实时系统 ,讨论了自适应实时调度的需求情况和自适应技术应用上的关键问题 ;提出了适用于硬实时调度需求的调度参数自适应调整机制 ;重点面向软实时调度需求 ,提出了一种基于模糊控制策略的自适应调度方法 ,它致力于动态跟踪调度对象的负载变化 ,并把截止期错过率控制在期望值附近 .相对于现有方法 ,更适合于解决开放式实时系统中的自适应调度问题

一种开放混合实时系统的开放自适应调度算法

开放计算环境下的实时与非实时任务不确定并发,以及多种实时约束混合的复杂约束系统,即开放混合实时系统的需求越来越广泛.通过引入接收控制、调度服务器、自适应调节机制,提出一种开放环境下的自适应实时系统调度架构——OARtS(open adaptive real-time scheduling).它能适应开放计算环境的不确定性,有控制地接受实时任务运行;可根据系统空闲计算带宽变化,自适应地调节任务的实时等级,使得系统运行在最优的实时性能上;对于软实时任务,可根据其计算带宽需求变化,自适应地调节其计算带宽分配,以适应任务执行时间时变引起的实时不确定性.

实时系统中的非定期任务调度算法综述

非定期任务调度是实时系统中的一个重要研究内容 综述了实时系统中非定期任务调度算法的研究与进展 ,按照这些算法的特征分为基于服务器的算法与基于空闲时间的算法两大类别 ,并着重对每个类别中的不同算法的特征与性能进行了分析 通过对这些算法的比较与分析 ,希望为实时系统的研究与开发者提供有意义的参考 ,最后还给出了非定期任务调度进一步研究的思路与建议

Global stability of Hopfield neural networks

This paper gives a condition for the global stability of a continuous-time hopfield neural network when its activation function maybe not monotonically increasing.

改进的以SMT为基础的实时系统限界模型检测

基于SAT的限界模型检测在处理实时系统时具有很高的复杂度.SMT求解器在计算可满足性的同时,还能处理算术和其他可判定性理论.在对实时系统进行检测时,用SMT求解器代替SAT求解器,系统里的时钟就可以用整型或实型变量表示,时钟约束则可以直接表示成线性算术表达式,从而使整个检测过程更加高效.带时间参数的计算树逻辑(timed computation tree logic,简称TCTL)被用来描述实时系统里的性质.同时,还对检测方法作了相应的改进.

Identification of nonlinear dynamic systems: Time-frequency filtering and skeleton curves

The nonlinear behavior varying with the instantaneous response was analyzed through the joint time-frequency analysis method for a class of S. D. O. F nonlinear system. A masking operator an definite regions is defined and two theorems are presented. Based on these, the nonlinear system is modeled with a special time-varying linear one, called the generalized skeleton linear system (GSLS). The frequency skeleton curve and the damping skeleton curve are defined to describe the main feature of the non-linearity as well. Moreover, an identification method is proposed through the skeleton curves and the time-frequency filtering technique.

Identification of Nonlinear Systems Through Time-Frequency Filtering Technique

In the previous paper, a class of nonlinear system is mapped to a so-called skeleton linear model (SLM) based on the joint time-frequency analysis method. Behavior of the nonlinear system may be indicated quantitatively by the variance of the coefficients of SLM versus its response. Using this model we propose an identification method for nonlinear systems based on nonstationary vibration data in this paper. The key technique in the identification procedure is a time-frequency filtering method by which solution of the SLM is extracted from the response data of the corresponding nonlinear system. Two time-frequency filtering methods are discussed here. One is based on the quadratic time-frequency distribution and its inverse transform, the other is based on the quadratic time-frequency distribution and the wavelet transform. Both numerical examples and an experimental application are given to illustrate the validity of the technique.

Time-Frequency Analysis of Nonlinear Systems: The Skeleton Linear Model and the Skeleton Curves

The joint time-frequency analysis method is adopted to study the nonlinear behavior varying with the instantaneous response for a class of S.D.O.F nonlinear system. A time-frequency masking operator, together with the conception of effective time-frequency region of the asymptotic signal are defined here. Based on these mathematical foundations, a so-called skeleton linear model (SLM) is constructed which has similar nonlinear characteristics with the nonlinear system. Two skeleton curves are deduced which can indicate the stiffness and damping in the nonlinear system. The relationship between the SLM and the nonlinear system, both parameters and solutions, is clarified. Based on this work a new identification technique of nonlinear systems using the nonstationary vibration data will be proposed through time-frequency filtering technique and wavelet transform in the following paper.

A time-varying controllable fault-tolerant field associative memory model and its simulation

A time-varying controllable fault-tolerant field associative memory model and the realization algorithms are proposed. On the one hand, this model simulates the time-dependent changeability character of the fault-tolerant field of human brain's associative memory. On the other hand, fault-tolerant fields of the memory samples of the model can be controlled, and we can design proper fault-tolerant fields for memory samples at different time according to the essentiality of memory samples. Moreover, the model has realized the nonlinear association of infinite value pattern from n dimension space to m dimension space. And the fault-tolerant fields of the memory samples are full of the whole real space R-n. The simulation shows that the model has the above characters and the speed of associative memory about the model is faster.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.