68,408 research outputs found

    PRISE: An Integrated Platform for Research and Teaching of Critical Embedded Systems

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    In this paper, we present PRISE, an integrated workbench for Research and Teaching of critical embedded systems at ISAE, the French Institute for Space and Aeronautics Engineering. PRISE is built around state-of-the-art technologies for the engineering of space and avionics systems used in Space and Avionics domain. It aims at demonstrating key aspects of critical, real-time, embedded systems used in the transport industry, but also validating new scientific contributions for the engineering of software functions. PRISE combines embedded and simulation platforms, and modeling tools. This platform is available for both research and teaching. Being built around widely used commercial and open source software; PRISE aims at being a reference platform for our teaching and research activities at ISAE

    Innovative teaching of IC design and manufacture using the Superchip platform

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    In this paper we describe how an intelligent chip architecture has allowed a large cohort of undergraduate students to be given effective practical insight into IC design by designing and manufacturing their own ICs. To achieve this, an efficient chip architecture, the “Superchip”, has been developed, which allows multiple student designs to be fabricated on a single IC, and encapsulated in a standard package without excessive cost in terms of time or resources. We demonstrate how the practical process has been tightly coupled with theoretical aspects of the degree course and how transferable skills are incorporated into the design exercise. Furthermore, the students are introduced at an early stage to the key concepts of team working, exposure to real deadlines and collaborative report writing. This paper provides details of the teaching rationale, design exercise overview, design process, chip architecture and test regime

    Experimental Validation of a Marine Current Turbine Simulator: Application to a Permanent Magnet Synchronous Generator-Based System Second-Order Sliding Mode Control

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    This paper deals with the experimental validation of a Matlab-Simulink simulation tool of marine current turbine (MCT) systems. The developed simulator is intended to be used as a sizing and site evaluation tool for MCT installations. For that purpose, the simulator is evaluated within the context of speed control of a permanent magnet synchronous generatorbased (PMSG) MCT. To increase the generated power, and therefore the efficiency of an MCT, a nonlinear controller has been proposed. PMSG has been already considered for similar applications, particularly wind turbine systems using mainly PI controllers. However, such kinds of controllers do not adequately handle some of tidal resource characteristics such as turbulence and swell effects. Moreover, PMSG parameter variations should be accounted for. Therefore, a robust nonlinear control strategy, namely second-order sliding mode control, is proposed. The proposed control strategy is inserted in the simulator that accounts for the resource and the marine turbine models. Simulations using tidal current data from Raz de Sein (Brittany, France) and experiments on a 7.5-kW real-time simulator are carried out for the validation of the simulator.ThÚse financée par Brest Métropole Océan

    Experimental Validation of a Marine Current Turbine Simulator: Application to a Permanent Magnet Synchronous Generator-Based System Second-Order Sliding Mode Control

    Get PDF
    This paper deals with the experimental validation of a Matlab-Simulink simulation tool of marine current turbine (MCT) systems. The developed simulator is intended to be used as a sizing and site evaluation tool for MCT installations. For that purpose, the simulator is evaluated within the context of speed control of a permanent magnet synchronous generatorbased (PMSG) MCT. To increase the generated power, and therefore the efficiency of an MCT, a nonlinear controller has been proposed. PMSG has been already considered for similar applications, particularly wind turbine systems using mainly PI controllers. However, such kinds of controllers do not adequately handle some of tidal resource characteristics such as turbulence and swell effects. Moreover, PMSG parameter variations should be accounted for. Therefore, a robust nonlinear control strategy, namely second-order sliding mode control, is proposed. The proposed control strategy is inserted in the simulator that accounts for the resource and the marine turbine models. Simulations using tidal current data from Raz de Sein (Brittany, France) and experiments on a 7.5-kW real-time simulator are carried out for the validation of the simulator.ThÚse financée par Brest Métropole Océan

    Model-based machine learning to identify clinical relevance in a high-resolution simulation of sepsis and trauma

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    Introduction: Sepsis is a devastating, costly, and complicated disease. It represents the summation of varied host immune responses in a clinical and physiological diagnosis. Despite extensive research, there is no current mediator-directed therapy, nor a biomarker panel able to categorize disease severity or reliably predict outcome. Although still distant from direct clinical translation, dynamic computational and mathematical models of acute systemic inflammation and sepsis are being developed. Although computationally intensive to run and calibrate, agent-based models (ABMs) are one type of model well suited for this. New analytical methods to efficiently extract knowledge from ABMs are needed. Specifically, machine-learning techniques are a promising option to augment the model development process such that parameterization and calibration are performed intelligently and efficiently. Methods: We used the Keras framework to train an Artificial Neural Network (ANN) for the purpose of identifying critical biological tipping points at which an in silico patient would heal naturally or require intervention in the Innate Immune Response Agent-Based Model (IIRABM). This ANN, determines simulated patient “survival” from cytokine state based on their overall resilience and the pathogenicity of any active infections experienced by the patient, defined by microbial invasiveness, toxigenesis, and environmental toxicity. These tipping points were gathered from previously generated datasets of simulated sweeps of the 4 IIRABM initializing parameters. Results: Using mean squared error as our loss function, we report an accuracy of greater than 85% with inclusion of 20% of the training set. This accuracy was independently validated on withheld runs. We note that there is some amount of error that is inherent to this process as the determination of the tipping points is a computation which converges monotonically to the true value as a function of the number of stochastic replicates used to determine the point. Conclusion: Our method of regression of these critical points represents an alternative to traditional parameter-sweeping or sensitivity analysis techniques. Essentially, the ANN computes the boundaries of the clinically relevant space as a function of the model’s parameterization, eliminating the need for a brute-force exploration of model parameter space. In doing so, we demonstrate the successful development of this ANN which will allows for an efficient exploration of model parameter space

    Overview on agent-based social modelling and the use of formal languages

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    Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft
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