An experimental study of adaptive testing for software reliability assessment

Adaptive testing is a new form of software testing that is based on the feedback and adaptive control principle and can be treated as the software testing counterpart of adaptive control. Our previous work has shown that adaptive testing can be formulated and guided in theory to minimize the variance of an unbiased software reliability estimator and to achieve optimal software reliability assessment. In this paper, we present an experimental study of adaptive testing for software reliability assessment, where the adaptive testing strategy, the random testing strategy and the operational profile based testing strategy were applied to the Space program in four experiments. The experimental results demonstrate that the adaptive testing strategy can really work in practice and may noticeably outperform the other two. Therefore, the adaptive testing strategy can serve as a preferable alternative to the random testing strategy and the operational profile based testing strategy if high confidence in the reliability estimates is required or the real-world operational profile of the software under test cannot be accurately identified.     

Experiences of Using Generative Communications to Support Adaptive Mobile Applications

Attention has recently begun to focus on the use of asynchronous paradigms to support adaptive mobile applications. To investigate this issue the authors have developed an asynchronous distributed systems platform based on the tuple space paradigm [19] coupled with extensions to support operation in mobile environments. This paper presents our experiences of developing and using this platform. The benefits of the tuple space approach are highlighted and we discuss in some detail the design, implementation and performance of our platform. We subsequently focus on the critical issues of the tuple space API and the level of support for adaptation which can be provided without compromising the elegance and simplicity of the paradigm. The paper concludes with an analysis of the suitability of platforms based on the tuple space paradigm for use in mobile environments.     

Optimal design methods for a digital human–computer interface based on human reliability in a nuclear power plant part 1: Optimization method for monitoring unit layout

This is the first in a series of papers describing the optimal design method for a digital human-computer interface of a nuclear power plant (NPP) from three different points based on human reliability. The purpose of this series is to propose different optimization methods from varying perspectives to decrease human factor events that arise from the defects of a human–computer interface. The present paper mainly discusses the optimization method for the layout of monitoring units.The layout of relative positions among different functional blocks in a digital human–computer interface influences the time required to search information. The risk of an event increases with increases in the time required to search for information because of the limited time available during a nuclear emergency. To avoid the risk of such an event, the authors propose an optimization method for the layout of monitoring units based on human reliability for a digital human–computer interface of a NPP. In the optimal design process, the authors propose a linear reversal genetic hybridization method that uses the Bayesian method as an adaptive function and takes human reliability as the optimized criterion. To quantitatively obtain the probability of human reliability, the authors use dynamic simulative functions including time and human factors. Finally, an experiment is conducted. The results indicate that the linear reversal genetic hybridization method has good stability and sensitivity and that the proposed optimization method has good accuracy and convergence.     

基于Eclipse平台的车辆自适应巡航控制仿真

自适应巡航控制是一种先进的汽车辅助驾驶系统,可以减轻驾驶员的工作量并且可以提高驾驶的便利性和安全性。目前一般都是通过MatLab仿真曲线来检验车辆自适应巡航控制算法的控制效果,但该方式不够直观形象。基于Eclipse平台,用JAVA语言搭建一个具有动态效果的车辆巡航控制仿真平台。该平台可以模拟多种典型驾驶工况,直观有效地展现车辆自适应巡航控制算法的结果。最后,在该仿真平台上设计最优PD控制算法和智能驱动驾驶（IDM）控制算法验证车辆自适应巡航控制结果。结果表明,该Eclipse仿真平台能够有效地模拟多种驾驶工况,且能够直观有效地验证多种车辆自适应巡航控制算法的结果。     

Radiative cooling in numerical astrophysics: The need for adaptive mesh refinement

Energy loss through optically thin radiative cooling plays an important part in the evolution of astrophysical gas dynamics and should therefore be considered a necessary element in any numerical simulation. Although the addition of this physical process to the equations of hydrodynamics is straightforward, it does create numerical challenges that have to be overcome in order to ensure the physical correctness of the simulation. First, the cooling has to be treated (semi-)implicitly, owing to the discrepancies between the cooling timescale and the typical timesteps of the simulation. Secondly, because of its dependence on a tabulated cooling curve, the introduction of radiative cooling creates the necessity for an interpolation scheme. In particular, we will argue that the addition of radiative cooling to a numerical simulation creates the need for extremely high resolution, which can only be fully met through the use of adaptive mesh refinement.     

基于DSA的无线传感器网络自适应MAC协议研究

针对工业监测用无线传感器网络高可靠和低功耗的要求,基于TDMA协议提出了一种动态时隙分配(DSA)的自适应介质访问控制(DSA-MAC)协议,通过估计链路质量动态调整节点的时隙长度,提高数据传输的可靠性。该协议中加入了基于接收信号强度指示(RSSI)的自适应功率调整机制,实现网络的低功耗要求。利用CC2530硬件平台实现DSA-MAC协议,并在工业现场进行测试,结果表明:该协议提高了数据传输率并降低了网络能耗,能够适应工业环境的应用需求。     

基于Ptolemy Ⅱ的无线传感器网络自适应通信体系结构

针对现有的异构无线传感器网络(WSN)体系结构中,不同的数据链路层缺乏通用架构的问题,提出在网络体系中增加“属性装配层”的概念。采用Ptolemy Ⅱ建模仿真平台,将属性装备层与数据链路层分别建模,首先采用属性装备层中的属性工厂模块分类各种通信协议原型,封装上层应用程序;然后采用装配工厂模块生产数据包头,分发于不同的网络;最后形成自适应的无线传感器网络体系。该体系融合了异构网络数据链路层,兼容了现有的平台、通信协议和网络机制,适用于未来的通信协议和机制。实验结果证明,基于该体系的通信系统内存占用和时间开销相对较小,具有自适应能力。     

基于RTLinux的AFM实时反馈控制系统

为了提高原子力显微镜(AFM)的测量性能,利用RTLinux良好的实时性,设计了一种开放式的反馈控制平台,完成了基于RTLinux的AFM实时反馈控制系统的软硬件设计,并在PC上具体实现这种实时反馈系统。测试结果表明,该系统能较好地实现AFM在Z方向上的实时反馈控制。利用该平台良好的开放性,可以将各种先进的控制方法应用于AFM系统中,以提高原子力显微镜的扫描速度和精度。此外,该基于RT-Linux的AFM系统还可以扩展成为一个开放式的纳米操作平台。     

基于贝叶斯网络的系统可靠性分析平台

贝叶斯网络是一种进行不确定性推理和分析的有效工具,针对系统可靠性分析问题,建立了一种基于贝叶斯网络的系统可靠性分析平台。所建立的分析平台将贝叶斯网络应用于系统可靠性分析中,把系统各组部件抽象成节点,从而构成贝叶斯网络模型;通过推理和分析算法找到影响系统可靠性的薄弱环节;通过计算某个部件发生变化时对整个系统的影响,得出各个节点的重要度,给出合理的优化方案,提升系统可靠性。通过对平视显示器实例进行分析,计算了每个组部件对整个系统的影响程度,证明该分析平台在利用贝叶斯网络对系统可靠性分析上的可行性。     

基于数据交换与同步的作业调度方案

多机作业调度问题是一个经典的NP难问题,在应用中由于实际需要,会出现各种约束和变形,调度问题的研究成果决定着系统的性能.DataTurbo是作者参与的一个用于解决分布式数据迁移、集成和融合的平台,该平台承担着大数据量的分布式传输任务.在DataTurbo平台基础上,提出一种适用于数据交换与同步的分布式作业调度方案,并构建一个灵活的分布式调度算法框架,解决相关的调度问题.该调度方案是一种在线的、可并发的、作业可分解的多机调度方案.仿真实验结果显示,该调度方案在任务负载大、调度点稀疏情况下优势明显,能适用于数据交换同步作业,可作为数据交换与同步作业的动态调度方案,并为相关启发式算法建立基础模型.     

Optimal and adaptive testing for software reliability assessment

Optimal software testing is concerned with how to test software such that the underlying testing goal is achieved in an optimal manner. Our previous work shows that the optimal testing problem for software reliability growth can be treated as closed-loop or feedback control problem, where the software under test serves as a controlled object and the software testing strategy serves as the corresponding controller. More specifically, the software under test is modeled as controlled Markov chains (CMCs) and the control theory of Markov chains is used to synthesize the required optimal testing strategy. In this paper, we show that software reliability assessment can be treated as a feedback control problem and the CMC approach is also applicable to dealing with the optimal testing problem for software reliability assessment. In this problem, the code of the software under test is frozen and the software testing process is optimized in the sense that the variance of the software reliability estimator is minimized. An adaptive software testing strategy is proposed that uses the testing data collected on-line to estimate the required parameters and selects next test cases. Simulation results show that the proposed adaptive software testing strategy can really work in the sense that the resulting variance of the software reliability estimate is much smaller than that resulting from the random testing strategies. The work presented in this paper is a contribution to the new area of software cybernetics that explores the interplay between software and control.     

基于IP网络的自适应QoS管理方案研究

目前实时音频、视频多媒体应用已经开始进入IP网络,但还有许多问题没有得到很好地解决,其中一个关键问题是多媒体服务的QoS问题、TCP／IP协议本身只提供一种“Best-effort”级别的服务,对QoS支持很少,“Best-effort”级别的服务往往会导致实时应用出现延迟抖动、分组丢失率高,从而极大的影响了实时应用的运行效果,因此必须研究可行的、高效的基于IP网络的QoS控制机制。IP网络QoS已成为分布式多媒体和网络通信的重要研究热点和难点课题。本文在IEＴＦ Intserv与Diffserv相结合的体系结构基础上,提出了一种适用于IP网络的自适应QoS管理框架,与Intserv或Diffserv模型不同的是,该QoS管理框架引入优先级节和自适应概念,QoS俦权处理采用基于多媒体优先级节的算法,在传输控制上采用了自适应QoS控制算法。它在应用层上完成,因此独立于底层网络协议。本文首先讨论了基本概念和函数,然后提出一种基于优先级节的自适应QoS管理框架和优先级调度流程。提出一种基于IP的自适应QoS协商机制,基本思想是基于RSVP（资源预留协议）。在提出QoS请求时进行QoS映射,然后启动适应性函数和资源管理函数进行协商,直到获得一组在当前资源状况下最佳的QoS指标。最后,本文还讨论了此方案的有效性并在自行开发的实验平台GUT上进行的QoS实验。实验表明应用该方法可以根据网络当前状态自适应地整多媒体实时应用的QoS需求。     

深度学习平台体系架构及其关键技术

针对AI模型生产和训练,传统基于脚本的物理单机或集群方式存在训练推理割裂、资源利用不充分、计算环境难迁移、训练流程冗长等问题,提出了一种面向深度学习模型训练的平台体系架构,架构分为数据平台层、计算平台层、训练套件层以及管理平台层四层,并逐层进行分析。在关键技术上,首先提出了训练推理一体化框架,采用抽象的计算流图屏蔽网络结构差异,并进行图优化;其次,提出了GPU状态感知的自适应资源匹配机制,采用环形消除算法解决通信成本线性增长问题;同时,提出基于启发式算法的标签匹配调度算法,以提高资源利用率;并且,通过租户管理和容灾机制的建立保障了系统平台的安全可靠性。最终搭建仿真平台验证其可用性、安全可靠性和拓展性。通过深度学习平台的应用,可以帮助企业更简易快捷地训练定制化模型和使用个性化服务,加速AI生产落地,推动AI技术和整个生态的繁荣发展。     

一种自适应的企业级容错系统的建构

为满足对安全关键领域日益增长的可靠性需求,本文提出一种自适应的基于Cluster技术的企业级容错解决方案。该方案可根据监控状态数据,自适应切换双机热备份与双机对等备份模式,以达到从整体上提高系统可靠性的目的。     

An approach to improve the performance of adaptive predictive control systems: theory,simulations and experiments

In this paper an approach to improve the overall performance of indirect adaptive control systems tailored for non-linear stable plants is presented. The approach involves a commutation of a linear time-varying robustness filter in the feedback path of the control loop in synchronization with an adaptive controller. The algorithm is framed in the celebrated IMC structure for predictive control systems. It can automatically suit to structural changes in the system as order and dead-time, and can deal with plants with zero dynamics. The convergence and stability of the system is analysed in details. It is shown through numeric simulations and experimentation on a heat exchanger with cooling system, that undesired transients due to abrupt and significative changes in the dynamics can be efficiently damped down by the developed control algorithm, achieving a high-quality performance in steady state.     

Observer-based optimal fault-tolerant control for offshore platforms

This paper is concerned with an observer-based optimal fault-tolerant control for an offshore steel jacket platform. The dynamic characteristics of the actuator faults under consideration are formulated by an exogenous system. Based on a dynamic fault observer designed, a feedforward and feedback optimal fault-tolerant controller is developed to improve the reliability of the offshore platform. The controller can be obtained by solving an algebraic Riccati equation and Sylvester equations, respectively. It is shown through simulation results that the proposed control scheme is effective to guarantee the reliability of the offshore platform with the actuator faults. The vibration amplitudes of the displacement, the velocity of the offshore platform and the required control force under the proposed fault-tolerant controller can be reduced significantly.     

High‐precision control of tracked field robots in the presence of unknown traction coefficients

Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off‐track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this paper presents the development, application, and experimental results of a real‐time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high‐frequency (milliseconds) updates. A real‐time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in‐field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time‐varying parameters. The capabilities of the real‐time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. The mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.     

分布式系统可靠性模型研究

为描述分布式开发环境中的软件可靠性增长情况,提出一种分布式开发环境下软件系统的可靠性模型。在实际的软件可靠性数据集上,通过使用适应性度量和AIC准则,对所提模型进行验证和评价。实验结果证明,该模型能捕获大量的可靠性成长曲线,具备较强的适用性和灵活性,且使用该模型所描述的分布式软件开发环境更符合实际情况。     

基于移动代理的动态路由算法的研究

文章在分析了当今几种路由算法的不足和研究了移动代理优越性的基础上,提出了一种基于移动代理的动态路由协议算法的设计方案．该算法用移动代理去发现网络的短期状态,并用服务代理保存网络的长期运行状况．该文还用JAVA和IBM公司的Aglets移动代理开发平台构造了一个简单网络模型,对此算法进行了验证,证明了它的有效性．     

基于P2P的自适应多媒体服务发布平台

研究一种基于P2P的多媒体服务发布平台,用于提供多媒体数据的传输和处理服务。平台将多媒体服务分割成小的服务元,并将它们按预先定义的规则离散到一个P2P网络中去,对服务元可以是:对多媒体数据进行的编码转换,对音频/视频数据进行的有损压缩,或者提供具有QoS保证的数据传输与路由等。平台根据用户终端特性和具体的服务定制生成服务需求,在P2P网络中动态的查找并连接服务元,以构建一个可以提供对应服务的服务链路,因此平台具有自适应的特性。平台基于内容可索引网络(Content Addressable Network),目标系统通过理论分析和局域网内的仿真进行验证。