6,695 research outputs found
Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data
Sparse sensor placement is a central challenge in the efficient
characterization of complex systems when the cost of acquiring and processing
data is high. Leading sparse sensing methods typically exploit either spatial
or temporal correlations, but rarely both. This work introduces a new sparse
sensor optimization that is designed to leverage the rich spatiotemporal
coherence exhibited by many systems. Our approach is inspired by the remarkable
performance of flying insects, which use a few embedded strain-sensitive
neurons to achieve rapid and robust flight control despite large gust
disturbances. Specifically, we draw on nature to identify targeted
neural-inspired sensors on a flapping wing to detect body rotation. This task
is particularly challenging as the rotational twisting mode is three
orders-of-magnitude smaller than the flapping modes. We show that nonlinear
filtering in time, built to mimic strain-sensitive neurons, is essential to
detect rotation, whereas instantaneous measurements fail. Optimized sparse
sensor placement results in efficient classification with approximately ten
sensors, achieving the same accuracy and noise robustness as full measurements
consisting of hundreds of sensors. Sparse sensing with neural inspired encoding
establishes a new paradigm in hyper-efficient, embodied sensing of
spatiotemporal data and sheds light on principles of biological sensing for
agile flight control.Comment: 21 pages, 19 figure
Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control
In this work, we explore finite-dimensional linear representations of
nonlinear dynamical systems by restricting the Koopman operator to an invariant
subspace. The Koopman operator is an infinite-dimensional linear operator that
evolves observable functions of the state-space of a dynamical system [Koopman
1931, PNAS]. Dominant terms in the Koopman expansion are typically computed
using dynamic mode decomposition (DMD). DMD uses linear measurements of the
state variables, and it has recently been shown that this may be too
restrictive for nonlinear systems [Williams et al. 2015, JNLS]. Choosing
nonlinear observable functions to form an invariant subspace where it is
possible to obtain linear models, especially those that are useful for control,
is an open challenge.
Here, we investigate the choice of observable functions for Koopman analysis
that enable the use of optimal linear control techniques on nonlinear problems.
First, to include a cost on the state of the system, as in linear quadratic
regulator (LQR) control, it is helpful to include these states in the
observable subspace, as in DMD. However, we find that this is only possible
when there is a single isolated fixed point, as systems with multiple fixed
points or more complicated attractors are not globally topologically conjugate
to a finite-dimensional linear system, and cannot be represented by a
finite-dimensional linear Koopman subspace that includes the state. We then
present a data-driven strategy to identify relevant observable functions for
Koopman analysis using a new algorithm to determine terms in a dynamical system
by sparse regression of the data in a nonlinear function space [Brunton et al.
2015, arxiv]; we show how this algorithm is related to DMD. Finally, we
demonstrate how to design optimal control laws for nonlinear systems using
techniques from linear optimal control on Koopman invariant subspaces.Comment: 20 pages, 5 figures, 2 code
Using online primary sources to foster historical thinking
This paper describes an ongoing initiative to enhance learner digital literacies by fostering historical thinking using digitized primary sources. Engaging with primary sources is central to the development of authentic critical historical thinking. In the past thirty years, millions of primary sources have been digitized by libraries and archives and has created a wealth of rich content for historians and history students. However the sheer scale of sources material, websites and questions about source quality make it a challenging research environment for learners. Based on current tutor and student feedback, additional support material that could increase access to these valuable open educational resources would be well received. The initiative is creating a set of learning materials which will support the use of online primary sources and enhance the learner experience. These learning materials will support flexible/off-campus learners and their development of research skills in the six BA in Humanities (Open Education) history modules. The learning materials are comprised of:
An interactive guide to online primary sources
An accompanying social bookmarking web page- Diigo
An online tutorial to practise finding, evaluating and using online primary source
Morphing of Triangular Meshes in Shape Space
We present a novel approach to morph between two isometric poses of the same
non-rigid object given as triangular meshes. We model the morphs as linear
interpolations in a suitable shape space . For triangulated 3D
polygons, we prove that interpolating linearly in this shape space corresponds
to the most isometric morph in . We then extend this shape space
to arbitrary triangulations in 3D using a heuristic approach and show the
practical use of the approach using experiments. Furthermore, we discuss a
modified shape space that is useful for isometric skeleton morphing. All of the
newly presented approaches solve the morphing problem without the need to solve
a minimization problem.Comment: Improved experimental result
Effect of organic and conventional cultivation techniques on yield, phenolic content, and sensory parameters in two carrot varieties
conference paperCarrots are one of the most important field grown vegetables in Ireland with a farm gate value of 16 million euros in 2007. They contain health promoting bioactive compounds including carotenoids, phenolics and
polyacetylenes. Organically grown vegetables are often perceived as healthier and to have better flavour. The objective of this study was to determine levels of phenolics and flavonoids in organic and conventionally
grown carrots, and to determine if they can be distinguished by taste.The Department of Agriculture, Fisheries and Food
(FIRM 06/NITARFC6) is gratefully acknowledged for
financial support of this wor
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Applying Stage-Based Theory to engage female students in university sport
Purpose: University sport is recognized by many as a key area of university business and one of increasing importance, as seen by universities prioritizing sport within their university strategic plans as well as national funding bodies investing in university sport. Whilst sport is rising on the agenda, engaging all students in sport is a key challenge for universities. This paper examines the factors that enable and inhibit female students’ participation in university sport and active recreation using an interpretivist qualitative design. The paper also identified specific behaviour change techniques that could be used within interventions to increase participation rates. Method: Six focus groups were carried out. Data were analysed verbatim using a constant comparative process of analysis. Results: Findings revealed several emergent themes to help inform theory-based interventions to engage more female students in sport. Conclusion: University sport is an important behaviour for students to undertake. The paper identified a number of avenues for universities to pursue in order to achieve this aim
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