17,146 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
Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges
Human-swarm interaction (HSI) involves a number of human factors impacting
human behaviour throughout the interaction. As the technologies used within HSI
advance, it is more tempting to increase the level of swarm autonomy within the
interaction to reduce the workload on humans. Yet, the prospective negative
effects of high levels of autonomy on human situational awareness can hinder
this process. Flexible autonomy aims at trading-off these effects by changing
the level of autonomy within the interaction when required; with
mixed-initiatives combining human preferences and automation's recommendations
to select an appropriate level of autonomy at a certain point of time. However,
the effective implementation of mixed-initiative systems raises fundamental
questions on how to combine human preferences and automation recommendations,
how to realise the selected level of autonomy, and what the future impacts on
the cognitive states of a human are. We explore open challenges that hamper the
process of developing effective flexible autonomy. We then highlight the
potential benefits of using system modelling techniques in HSI by illustrating
how they provide HSI designers with an opportunity to evaluate different
strategies for assessing the state of the mission and for adapting the level of
autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling
Conference, Canberra, Australi
Attention Allocation Aid for Visual Search
This paper outlines the development and testing of a novel, feedback-enabled
attention allocation aid (AAAD), which uses real-time physiological data to
improve human performance in a realistic sequential visual search task. Indeed,
by optimizing over search duration, the aid improves efficiency, while
preserving decision accuracy, as the operator identifies and classifies targets
within simulated aerial imagery. Specifically, using experimental eye-tracking
data and measurements about target detectability across the human visual field,
we develop functional models of detection accuracy as a function of search
time, number of eye movements, scan path, and image clutter. These models are
then used by the AAAD in conjunction with real time eye position data to make
probabilistic estimations of attained search accuracy and to recommend that the
observer either move on to the next image or continue exploring the present
image. An experimental evaluation in a scenario motivated from human
supervisory control in surveillance missions confirms the benefits of the AAAD.Comment: To be presented at the ACM CHI conference in Denver, Colorado in May
201
Distributed State Machine Supervision for Long-baseline Gravitational-wave Detectors
The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of
two identical yet independent, widely-separated, long-baseline
gravitational-wave detectors. Each Advanced LIGO detector consists of complex
optical-mechanical systems isolated from the ground by multiple layers of
active seismic isolation, all controlled by hundreds of fast, digital, feedback
control systems. This article describes a novel state machine-based automation
platform developed to handle the automation and supervisory control challenges
of these detectors. The platform, called \textit{Guardian}, consists of
distributed, independent, state machine automaton nodes organized
hierarchically for full detector control. User code is written in standard
Python and the platform is designed to facilitate the fast-paced development
process associated with commissioning the complicated Advanced LIGO
instruments. While developed specifically for the Advanced LIGO detectors,
Guardian is a generic state machine automation platform that is useful for
experimental control at all levels, from simple table-top setups to large-scale
multi-million dollar facilities.Comment: Version 2: 11 pages, 9 figures. Submitted to Review of Scientific
Instrument
NASA space station automation: AI-based technology review
Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures
Improving situation awareness of a single human operator interacting with multiple unmanned vehicles: first results
In the context of the supervision of one or several unmanned vehicles by a human operator, the design of an adapted user interface is a major challenge. Therefore, in the context of an existing experimental set up composed of a ground station and heterogeneous unmanned ground and air vehicles we aim at redesigning the human-robot interactions to improve the operator's situation awareness. We base our new design on a classical user centered approach
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