9 research outputs found

    Ingeniera de Tiempo Real en el Dise~no de un Sistema de Supervision y Control para Redes de Energa Ele tri a

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    Este traba jo aborda la problemati a de integra ion de los requisitos de tiempo real en el dise~no de un sistema de supervision y ontrol (SCADA) apli ado alse tor ele tri o. El esfuerzo subya ente se ha orientado a la busqueda y apli a- ion de las te ni as mas apropiadas de la ingeniera de tiempo real, que permitanprede ir matemati amente la respuesta del sistema ante diferentes situa iones de arga de pro esamiento durante los estados de a tividad rti a. Como resultados substan iales se han obtenido: la espe ializa ion de un pro eso de desarrollogeneri o, RUP (Rational Unied Pro ess), enfo andolo a sistemas de tiempo real;una arquite tura SCADA abierta soportada en estandares IEEE e IEC y modelada on UML (Unied Modeling Language); y un onjunto de re omenda ionesde implementa ion, obtenidas a partir del analisis del omportamiento en tiemporeal on RMA (Rate Monotoni Analysis) y la plataforma MAST (Modeling andAnalysis Suite for real-Time appli ations)

    Reuse of safety certification artefacts across standards and domains: A systematic approach

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    Reuse of systems and subsystem is a common practice in safety-critical systems engineering. Reuse can improve system development and assurance, and there are recommendations on reuse for some domains. Cross-domain reuse, in which a previously certified product typically needs to be assessed against different safety standards, has however received little attention. No guidance exists for this reuse scenario despite its relevance in industry, thus practitioners need new means to tackle it. This paper aims to fill this gap by presenting a systematic approach for reuse of safety certification artefacts across standards and domains. The approach is based on the analysis of the similarities and on the specification of maps between standards. These maps are used to determine the safety certification artefacts that can be reused from one domain to another and reuse consequences. The approach has been validated with practitioners in a case study on the reuse of an execution platform from railway to avionics. The results show that the approach can be effectively applied and that it can reduce the cost of safety certification across standards and domains. Therefore, the approach is a promising way of making cross-domain reuse more cost-effective in industry.European Commission's FP7 programm

    Model-based specification of safety compliance needs for critical systems : A holistic generic metamodel

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    Abstract Context: Many critical systems must comply with safety standards as a way of providing assurance that they do not pose undue risks to people, property, or the environment. Safety compliance is a very demanding activity, as the standards can consist of hundreds of pages and practitioners typically have to show the fulfilment of thousands of safety-related criteria. Furthermore, the text of the standards can be ambiguous, inconsistent, and hard to understand, making it difficult to determine how to effectively structure and manage safety compliance information. These issues become even more challenging when a system is intended to be reused in another application domain with different applicable standards. Objective: This paper aims to resolve these issues by providing a metamodel for the specification of safety compliance needs for critical systems. Method: The metamodel is holistic and generic, and abstracts common concepts for demonstrating safety compliance from different standards and application domains. Its application results in the specification of “reference assurance frameworks” for safety-critical systems, which correspond to a model of the safety criteria of a given standard. For validating the metamodel with safety standards, parts of several standards have been modelled by both academic and industry personnel, and other standards have been analysed. We further augment this with feedback from practitioners, including feedback during a workshop. Results: The results from the validation show that the metamodel can be used to specify safety compliance needs for aerospace, automotive, avionics, defence, healthcare, machinery, maritime, oil and gas, process industry, railway, and robotics. Practitioners consider that the metamodel can meet their needs and find benefits in its use. Conclusion: The metamodel supports the specification of safety compliance needs for most critical computer-based and software-intensive systems. The resulting models can provide an effective means of structuring and managing safety compliance information

    Framework of Key Enabling Technologies for Safe and Autonomous Drones'Applications

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    International audienc

    A General Structure for the Analysis Framework of the UML MARTE Profile

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    Abstract. The ongoing underlying work aims to provide a robust and straightforward basis to the UML profile for Modeling and Analysis of Real-Time and Embedded systems (MARTE) issued by the OMG. Particularly, in this paper, we analyze the existing annotating mechanisms of extra-functional properties and some specific requirements of the concerned OMG’s Request For Proposal (RFP) to consistently derive a preliminary framework for the Analysis subprofile. Our proposal provides a flexible mechanism to easily increase and suppress QoS attributes without changing the associated Domain Model and Profile, which covers inclusion of modeling capability for new analysis techniques. Furthermore, we allow the unification of the existing Schedulability and Performance modeling sub-profiles in the pertinent aspects, letting them separated in the specialized ones. At the same time, we attempt to provide a generic framework able to be applied to all the UML Profile for MARTE.

    Towards empathic deep q-learning

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    As reinforcement learning (RL) scales to solve increasingly complex tasks, interest continues to grow in the fields of AI safety and machine ethics. As a contribution to these fields, this paper introduces an extension to Deep Q-Networks (DQNs), called Empathic DQN, that is loosely inspired both by empathy and the golden rule ("Do unto others as you would have them do unto you"). Empathic DQN aims to help mitigate negative side effects to other agents resulting from myopic goal-directed behavior. We assume a setting where a learning agent coexists with other independent agents (who receive unknown rewards), where some types of reward (e.g. negative rewards from physical harm) may generalize across agents. Empathic DQN combines the typical (self-centered) value with the estimated value of other agents, by imagining (by its own standards) the value of it being in the other's situation (by considering constructed states where both agents are swapped). Proof-of-concept results in two gridworld environments highlight the approach's potential to decrease collateral harms. While extending Empathic DQN to complex environments is non-trivial, we believe that this first step highlights the potential of bridge-work between machine ethics and RL to contribute useful priors for norm-abiding RL agents

    Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

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    The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held
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