20,260 research outputs found
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Application of Advanced Early Warning Systems with Adaptive Protection
This project developed and field-tested two methods of Adaptive Protection systems utilizing synchrophasor data. One method detects conditions of system stress that can lead to unintended relay operation, and initiates a supervisory signal to modify relay response in real time to avoid false trips. The second method detects the possibility of false trips of impedance relays as stable system swings “encroach” on the relays’ impedance zones, and produces an early warning so that relay engineers can re-evaluate relay settings. In addition, real-time synchrophasor data produced by this project was used to develop advanced visualization techniques for display of synchrophasor data to utility operators and engineers
A Framework for Evaluating Model-Driven Self-adaptive Software Systems
In the last few years, Model Driven Development (MDD), Component-based
Software Development (CBSD), and context-oriented software have become
interesting alternatives for the design and construction of self-adaptive
software systems. In general, the ultimate goal of these technologies is to be
able to reduce development costs and effort, while improving the modularity,
flexibility, adaptability, and reliability of software systems. An analysis of
these technologies shows them all to include the principle of the separation of
concerns, and their further integration is a key factor to obtaining
high-quality and self-adaptable software systems. Each technology identifies
different concerns and deals with them separately in order to specify the
design of the self-adaptive applications, and, at the same time, support
software with adaptability and context-awareness. This research studies the
development methodologies that employ the principles of model-driven
development in building self-adaptive software systems. To this aim, this
article proposes an evaluation framework for analysing and evaluating the
features of model-driven approaches and their ability to support software with
self-adaptability and dependability in highly dynamic contextual environment.
Such evaluation framework can facilitate the software developers on selecting a
development methodology that suits their software requirements and reduces the
development effort of building self-adaptive software systems. This study
highlights the major drawbacks of the propped model-driven approaches in the
related works, and emphasise on considering the volatile aspects of
self-adaptive software in the analysis, design and implementation phases of the
development methodologies. In addition, we argue that the development
methodologies should leave the selection of modelling languages and modelling
tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition,
self-adaptive application, context oriented software developmen
Resilient Critical Infrastructure Management using Service Oriented Architecture
Abstract—The SERSCIS project aims to support the use of interconnected systems of services in Critical Infrastructure (CI) applications. The problem of system interconnectedness is aptly demonstrated by ‘Airport Collaborative Decision Making’ (ACDM). Failure or underperformance of any of the interlinked ICT systems may compromise the ability of airports to plan their use of resources to sustain high levels of air traffic, or to provide accurate aircraft movement forecasts to the wider European air traffic management systems. The proposed solution is to introduce further SERSCIS ICT components to manage dependability and interdependency. These use semantic models of the critical infrastructure, including its ICT services, to identify faults and potential risks and to increase human awareness of them. Semantics allows information and services to be described in such a way that makes them understandable to computers. Thus when a failure (or a threat of failure) is detected, SERSCIS components can take action to manage the consequences, including changing the interdependency relationships between services. In some cases, the components will be able to take action autonomously — e.g. to manage ‘local’ issues such as the allocation of CPU time to maintain service performance, or the selection of services where there are redundant sources available. In other cases the components will alert human operators so they can take action instead. The goal of this paper is to describe a Service Oriented Architecture (SOA) that can be used to address the management of ICT components and interdependencies in critical infrastructure systems. Index Terms—resilience; QoS; SOA; critical infrastructure, SLA
Adaptive service discovery on service-oriented and spontaneous sensor systems
Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief
Context-aware adaptation in DySCAS
DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met
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Evaluating the resilience and security of boundaryless, evolving socio-technical Systems of Systems
Robustness-Driven Resilience Evaluation of Self-Adaptive Software Systems
An increasingly important requirement for certain classes of software-intensive systems is the ability to self-adapt their structure and behavior at run-time when reacting to changes that may occur to the system, its environment, or its goals. A major challenge related to self-adaptive software systems is the ability to provide assurances of their resilience when facing changes. Since in these systems, the components that act as controllers of a target system incorporate highly complex software, there is the need to analyze the impact that controller failures might have on the services delivered by the system. In this paper, we present a novel approach for evaluating the resilience of self-adaptive software systems by applying robustness testing techniques to the controller to uncover failures that can affect system resilience. The approach for evaluating resilience, which is based on probabilistic model checking, quantifies the probability of satisfaction of system properties when the target system is subject to controller failures. The feasibility of the proposed approach is evaluated in the context of an industrial middleware system used to monitor and manage highly populated networks of devices, which was implemented using the Rainbow framework for architecture-based self-adaptation
Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers
The success of the human-robot co-worker team in a flexible manufacturing
environment where robots learn from demonstration heavily relies on the correct
and safe operation of the robot. How this can be achieved is a challenge that
requires addressing both technical as well as human-centric research questions.
In this paper we discuss the state of the art in safety assurance, existing as
well as emerging standards in this area, and the need for new approaches to
safety assurance in the context of learning machines. We then focus on robotic
learning from demonstration, the challenges these techniques pose to safety
assurance and indicate opportunities to integrate safety considerations into
algorithms "by design". Finally, from a human-centric perspective, we stipulate
that, to achieve high levels of safety and ultimately trust, the robotic
co-worker must meet the innate expectations of the humans it works with. It is
our aim to stimulate a discussion focused on the safety aspects of
human-in-the-loop robotics, and to foster multidisciplinary collaboration to
address the research challenges identified
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