Comparing design of experiments and evolutionary approaches to multi-objective optimisation of sensornet protocols

Tate, Jonathan; Woolford-Lim, Benjamin; Bate, Iain; Yao, Xin

doi:10.1109/cec.2009.4983074

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…DoE has been successfully applied to control simulations of parameterized systems, for various verification purposes: screening [13], sensitivity analysis [6], robust design [1], or multi-objective optimization [12]. Previous work found DoE able to detect important effects and response extremes, for static responses in automotive applications [7], [8].…”

Section: Related Workmentioning

confidence: 99%

Design of experiments for reliable operation of electronics in automotive applications

Rafaila

Decker

Grimm

et al. 2010

2010 Forum on Specification &Amp; Design Languages (FDL 2010)

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Starting from pre-sillicon verification steps, automotive electronics demand for the assurance of a high degree of reliability. A dependable system must comply with requirements even in the presence of sources of variation, which can come from internal or external influences. Therefore, one must study the impact of such variations on the responses of the system and find safe margins for them. The problem amplifies when the effects of variations and the conditions which drive the system into a worst-case change in time.Statistical Design of Experiments and metamodelling strategies are adopted and extended to cope with the problem. A reduced number of simulation runs is found sufficient to characterize time-variant effects and to predict the system's behaviour between the simulated points. Then, the bounds of the signals of interest are estimated and confirmed. The proposed methods are demonstrated on a window lifting automotive system. 1

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Section: Related Workmentioning

confidence: 99%

Design of experiments for reliable operation of electronics in automotive applications

Rafaila

Decker

Grimm

et al. 2010

2010 Forum on Specification &Amp; Design Languages (FDL 2010)

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show abstract

“…In section 3.3 we apply a similar approach with direct interpolation on the data. Alternative approaches to fill the parameter space are Factorial Designs [30] and Latin Hypercube Designs [31], which avoids redundant parameter sets and constructs a RSM with similar accuracy in less simulation runs. In such DOEs typically all parameter sets are defined before the first simulation is run.…”

Section: Design-of-experiments Studiesmentioning

confidence: 99%

Modular Workflow Engine for Distributed Services using Lightweight Java Clients

Vetter,

Lennartz,

Peetz

2009

Preprint

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In this article we introduce the concept and the first implementation of a lightweight client-server-framework as middleware for distributed computing. On the client side an installation without administrative rights or privileged ports can turn any computer into a worker node. Only a Java runtime environment and the JAR files comprising the workflow client are needed. To connect all clients to the engine one open server port is sufficient. The engine submits data to the clients and orchestrates their work by workflow descriptions from a central database. Clients request new task descriptions periodically, thus the system is robust against network failures. In the basic set-up, data up-and downloads are handled via HTTP communication with the server. The performance of the modular system could additionally be improved using dedicated file servers or distributed network file systems. We demonstrate the design features of the proposed engine in real-world applications from mechanical engineering. We have used this system on a compute cluster in design-of-experiment studies, parameter optimisations and robustness validations of finite element structures.

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“…reduce the set of factors to the decisive ones (Kleijnen, 2008;Trocine, Malone, 2001); sensitivity analysis, to offer understanding in a quantitative and predictive way about impact of factors on system performance (Nookala, Ying Chen, Sapatnekar, 2005;Srinivasaiah, Bhat, 2004); to find optimum implementations (Sheldon, Vahid, Lonardi, 2007;Tate et al, 2009); for robust design, to choose factors that ensure minimum response variability (Ayeb, Theuerkauf, Winsel, 2006). Previous work evaluates experimental designs to cover the verification space better or reduce the number of simulation points while keeping reasonable prediction models (Sanchez, 2007).…”

Section: Introductionmentioning

confidence: 99%

Sequential design of experiments for effective model-based validation of electronic control units

Rafaila

Grimm

Decker

et al. 2010

Elektrotech. Inftech.

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Model-based verification of automotive electronic control units (ECUs) must ensure compliance with the target requirements in a short time frame. On the other hand, an increasing number of sources of variability (e.g. operating conditions, block parameters) impact system performance. To reduce the overall verification effort, much focus has been put on performance in simulation, and little on how to plan simulation experiments to yield maximum information with minimum number of runs. This paper shows how the classical framework of statistical Design of Experiments can be extended to perform faster and more reliable multivariable sensitivity and worstcase studies. Simulation experiments of a state of the art ECU modelled in SystemC/SystemC-AMS show significant increase in efficiency as compared to traditional approaches. Ó Springer-Verlag 2010 IntroductionAutomotive ECUs must face ever increasing demands. Requirements relate to functionality, computing power, safety, energy consumption. These all translate into more complexity, both as density of integration and heterogeneity. Pre-silicon verification phases must provide more reliable outcome. On the other hand, overall verification time must be reduced, to shorten development cycles.Solutions based on specification languages such as SystemC manage to speed-up steps of the system-level model-based verification. Raising the abstraction level gains performance in simulation, at the expense of a reduced model accuracy. This is one way to address the issue of long verification times. However, not much work focuses on more efficient and faster methods for multivariable sensitivity and worst-case analyses. These take part in the verification to ensure system reliability when multiple sources of variability (factors) are present.Factors vary in specified windows and can be both internal and external to the verified system. For sensitivity analysis, factors must be identified that have high impact on system outputs/performance (response). Worst-case analysis ensures the target requirements are met, under any factor conditions. In addition, configurations are found that ensure optimum performance. A challenge is how to cover the highly dimensional and continuous verification space with fewer simulation runs, to realize reliable sensitivity and worst-case studies.Simulation practitioners without statistical background often rely on trial-and-error methods (best-guesses, random corner-cases). These can easily end-up in a large number of runs without getting closer to the worst-case, thus they are not reliable. Pessimistic guesses lead to overestimations (oversize the specification range; over-restrict admitted factor variations). Most methods assume little or no impact of design factors on the system response, and do not discover interaction effects, i.e. that can be tracked only by concom-

show abstract

Comparing design of experiments and evolutionary approaches to multi-objective optimisation of sensornet protocols

Cited by 7 publications

References 9 publications

Design of experiments for reliable operation of electronics in automotive applications

Design of experiments for reliable operation of electronics in automotive applications

Modular Workflow Engine for Distributed Services using Lightweight Java Clients

Sequential design of experiments for effective model-based validation of electronic control units

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