76,994 research outputs found
Enhancing the EAST-ADL error model with HiP-HOPS semantics
EAST-ADL is a domain-specific modelling language for the engineering of automotive embedded systems. The language has abstractions that enable engineers to capture a variety of information about design in the course of the lifecycle ā from requirements to detailed design of hardware and software architectures. The specification of the EAST-ADL language includes an error model extension which documents language structures that allow potential failures of design elements to be specified locally. The effects of these failures are then later assessed in the context of the architecture design. To provide this type of useful assessment, a language and a specification are not enough; a compiler-like tool that can read and operate on a system specification together with its error model is needed. In this paper we integrate the error model of EAST-ADL with the precise semantics of HiP-HOPS ā a state-of-the-art tool that enables dependability analysis and optimization of design models. We present the integration concept between EAST-ADL structure and HiP-HOPS error propagation logic and its transformation into the HiP-HOPS model. Source and destination models are represented using the corresponding XML formats. The connection of these two models at tool level enables practical EAST-ADL designs of embedded automotive systems to be analysed in terms of dependability, i.e. safety, reliability and availability. In addition, the information encoded in the error model can be re-used across different contexts of application with the associated benefits for cost reduction, simplification, and rationalisation of dependability assessments in complex engineering designs
Model-based dependability analysis : state-of-the-art, challenges and future outlook
Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis
Model transformations and Tool Integration
Model transformations are increasingly recognised as being of significant importance to many areas of software development and integration. Recent attention on model transformations has particularly focused on the OMGs Queries/Views/Transformations (QVT) Request for Proposals (RFP). In this paper I motivate the need for dedicated approaches to model transformations, particularly for the data involved in tool integration, outline the challenges involved, and then present a number of technologies and techniques which allow the construction of flexible, powerful and practical model transformations
Space-Time Evolution of the Oscillator, Rapidly moving in a random media
We study the quantum-mechanical evolution of the nonrelativistic oscillator,
rapidly moving in the media with the random vector fields. We calculate the
evolution of the level probability distribution as a function of time, and
obtain rapid level diffusion over the energy levels. Our results imply a new
mechanism of charmonium dissociation in QCD media.Comment: 32 pages, 13 figure
Bond-Propagation Algorithm for Thermodynamic Functions in General 2D Ising Models
Recently, we developed and implemented the bond propagation algorithm for
calculating the partition function and correlation functions of random bond
Ising models in two dimensions. The algorithm is the fastest available for
calculating these quantities near the percolation threshold. In this paper, we
show how to extend the bond propagation algorithm to directly calculate
thermodynamic functions by applying the algorithm to derivatives of the
partition function, and we derive explicit expressions for this transformation.
We also discuss variations of the original bond propagation procedure within
the larger context of Y-Delta-Y-reducibility and discuss the relation of this
class of algorithm to other algorithms developed for Ising systems. We conclude
with a discussion on the outlook for applying similar algorithms to other
models.Comment: 12 pages, 10 figures; submitte
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