931 research outputs found
Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in ‘Daily Diary’ tags
Smart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals operate. However, interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information. This work presents a single program, FRAMEWORK 4, that uses a particular sensor constellation described in the?Daily Diary? tag (recording tri-axial acceleration, tri-axial magnetic field intensity, pressure and e.g. temperature and light intensity) to determine the 4 key elements considered pivotal within the conception of the tag. These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal operates. The program takes the original data recorded by the Daily Dairy and transforms it into dead-reckoned movements,template-matched behaviours, dynamic body acceleration-derived energetics and positionlinked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space.Fil: Walker, James S.. Swansea University. College Of Sciences; Reino UnidoFil: Jones, Mark W.. Swansea University. College Of Sciences; Reino UnidoFil: Laramee, Robert S.. Swansea University. College Of Sciences; Reino UnidoFil: Holton, Mark D.. Swansea University; Reino UnidoFil: Shepard, Emily L. C.. Swansea University. College Of Sciences; Reino UnidoFil: Williams, Hannah J.. Swansea University. College Of Sciences; Reino UnidoFil: Scantlebury, D. Michael. The Queens University Of Belfast; IrlandaFil: Marks, Nikki, J.. The Queens University Of Belfast; IrlandaFil: Magowan, Elizabeth A.. The Queens University Of Belfast; IrlandaFil: Maguire, Iain E.. The Queens University Of Belfast; IrlandaFil: Grundy, Ed. Swansea University. College Of Sciences; Reino UnidoFil: Di Virgilio, Agustina Soledad. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Patagonia Norte. Instituto de Investigación En Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue; ArgentinaFil: Wilson, Rory P.. Swansea University. College Of Sciences; Reino Unid
Bayesian Estimation of Grain Scale Elastic-Plastic Intrinsic Material Properties via Spherical Indentation Measurements and the Exploration of Design of Experiments Strategies
This thesis is focused on establishing and demonstrating a statistical framework for the objective fusion of data acquired from multiscale experiments and multiscale models performed to understand and predict the intrinsic material behavior. What makes this difficult is that the experimental data often provides information only on derived quantities
from the material response (only these can be measured at present) and not directly the parameters present in the physics-based multiscale materials constitutive models. Consequently, one has to use sophisticated statistical theories to estimate the values of the
critically needed material parameters and quantify rigorously the implicit uncertainty in this quantification. A mathematical framework that addresses this gap and its unique capabilities are demonstrated through the extraction of single crystal elastic-plastic constants for thermodynamic phases present in the microstructure of a metallic alloy and
the extraction of laminate level properties for multi-laminate composite system.Ph.D
The development of object oriented Bayesian networks to evaluate the social, economic and environmental impacts of solar PV
Domestic and community low carbon technologies are widely heralded as valuable means for delivering sustainability outcomes in the form of social, economic and environmental (SEE) policy objectives. To accelerate their diffusion they have benefited from a significant number and variety of subsidies worldwide. Considerable aleatory and epistemic uncertainties exist, however, both with regard to their net energy contribution and their SEE impacts. Furthermore the socio-economic contexts themselves exhibit enormous variability, and commensurate uncertainties in their parameterisation. This represents a significant risk for policy makers and technology adopters.
This work describes an approach to these problems using Bayesian Network models. These are utilised to integrate extant knowledge from a variety of disciplines to quantify SEE impacts and endogenise uncertainties. A large-scale Object Oriented Bayesian network has been developed to model the specific case of solar photovoltaics (PV) installed on UK domestic roofs. Three specific model components have been developed. The PV component characterises the yield of UK systems, the building energy component characterises the energy consumption of the dwellings and their occupants and a third component characterises the building stock in four English urban communities.
Three representative SEE indicators, fuel affordability, carbon emission reduction and discounted cash flow are integrated and used to test the model s ability to yield meaningful outputs in response to varying inputs. The variability in the percentage of the three indicators is highly responsive to the dwellings built form, age and orientation, but is not just due to building and solar physics but also to socio-economic factors. The model can accept observations or evidence in order to create scenarios which facilitate deliberative decision making.
The BN methodology contributes to the synthesis of new knowledge from extant knowledge located between disciplines . As well as insights into the impacts of high PV penetration, an epistemic contribution has been made to transdisciplinary building energy modelling which can be replicated with a variety of low carbon interventions
Tone classification of syllable -segmented Thai speech based on multilayer perceptron
Thai is a monosyllabic and tonal language. Thai makes use of tone to convey lexical information about the meaning of a syllable. Thai has five distinctive tones and each tone is well represented by a single F0 contour pattern. In general, a Thai syllable with a different tone has a different lexical meaning. Thus, to completely recognize a spoken Thai syllable, a speech recognition system has not only to recognize a base syllable but also to correctly identify a tone. Hence, tone classification of Thai speech is an essential part of a Thai speech recognition system.;In this study, a tone classification of syllable-segmented Thai speech which incorporates the effects of tonal coarticulation, stress and intonation was developed. Automatic syllable segmentation, which performs the segmentation on the training and test utterances into syllable units, was also developed. The acoustical features including fundamental frequency (F0), duration, and energy extracted from the processing syllable and neighboring syllables were used as the main discriminating features. A multilayer perceptron (MLP) trained by backpropagation method was employed to classify these features. The proposed system was evaluated on 920 test utterances spoken by five male and three female Thai speakers who also uttered the training speech. The proposed system achieved an average accuracy rate of 91.36%
Modeling the development of pronunciation in infant speech acquisition.
Pronunciation is an important part of speech acquisition, but little attention has been given to the mechanism or mechanisms by which it develops. Speech sound qualities, for example, have just been assumed to develop by simple imitation. In most accounts this is then assumed to be by acoustic matching, with the infant comparing his output to that of his caregiver. There are theoretical and empirical problems with both of these assumptions, and we present a computational model- Elija-that does not learn to pronounce speech sounds this way. Elija starts by exploring the sound making capabilities of his vocal apparatus. Then he uses the natural responses he gets from a caregiver to learn equivalence relations between his vocal actions and his caregiver's speech. We show that Elija progresses from a babbling stage to learning the names of objects. This demonstrates the viability of a non-imitative mechanism in learning to pronounce
Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data
Technological advancements over the past decade have increased our ability to collect data to previously unimaginable volumes [Kei02]. Understanding temporal patterns is key to gaining knowledge and insight. However, our capacity to store data now far exceeds the rate at which we are able to understand it [KKEM10]. This phenomenon has led to a growing need for advanced solutions to make sense and use of an ever-increasing data space. Abstract temporal data provides additional challenges in its, representation, size, and scalability, high dimensionality, and unique structure.One instance of such temporal data is acquired from smart sensor tags attached to freely roaming animals recording multiple parameters at infra-second rates which are becoming commonplace, and are transforming biologists understanding of the way wild animals behave.The excitement at the potential inherent in sophisticated tracking devices has, however, been limited by a lack of available software to advance research in the field. This thesis introduces methodologies to deal with the problem of the analysis of the large, multi-dimensional, time-dependent data acquired. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information.We present several contributions to the field of time-series visualisation, that is, the visualisation of ordered collections of real value data attributes at successive points in time sampled at uniform time intervals. Traditionally, time-series graphs have been used for temporal data. However, screen resolution is small in comparison to the large information space commonplace today. In such cases, we can only render a proportion of the data.It is widely accepted that the effective interpretation of large temporal data sets requires advanced methods and interaction techniques. In this thesis, we address these issues to enhance the exploration, analysis, and presentation of time-series data for movement ecologists in their smart sensor data analysis
VAST: An ASKAP Survey for Variables and Slow Transients
The Australian Square Kilometre Array Pathfinder (ASKAP) will give us an
unprecedented opportunity to investigate the transient sky at radio
wavelengths. In this paper we present VAST, an ASKAP survey for Variables and
Slow Transients. VAST will exploit the wide-field survey capabilities of ASKAP
to enable the discovery and investigation of variable and transient phenomena
from the local to the cosmological, including flare stars, intermittent
pulsars, X-ray binaries, magnetars, extreme scattering events, interstellar
scintillation, radio supernovae and orphan afterglows of gamma ray bursts. In
addition, it will allow us to probe unexplored regions of parameter space where
new classes of transient sources may be detected. In this paper we review the
known radio transient and variable populations and the current results from
blind radio surveys. We outline a comprehensive program based on a multi-tiered
survey strategy to characterise the radio transient sky through detection and
monitoring of transient and variable sources on the ASKAP imaging timescales of
five seconds and greater. We also present an analysis of the expected source
populations that we will be able to detect with VAST.Comment: 29 pages, 8 figures. Submitted for publication in Pub. Astron. Soc.
Australi
The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory
We describe the near real-time transient-source discovery engine for the
intermediate Palomar Transient Factory (iPTF), currently in operations at the
Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system
the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for
PSF-matching, image subtraction, detection, photometry, and machine-learned
(ML) vetting of extracted transient candidates. We also review the performance
of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively
unconfused regions, "bogus" candidates from processing artifacts and imperfect
image subtractions outnumber real transients by ~ 10:1. This can be
considerably higher for image data with inaccurate astrometric and/or
PSF-matching solutions. Despite this occasionally high contamination rate, the
ML classifier is able to identify real transients with an efficiency (or
completeness) of ~ 97% for a maximum tolerable false-positive rate of 1% when
classifying raw candidates. All subtraction-image metrics, source features, ML
probability-based real-bogus scores, contextual metadata from other surveys,
and possible associations with known Solar System objects are stored in a
relational database for retrieval by the various science working groups. We
review our efforts in mitigating false-positives and our experience in
optimizing the overall system in response to the multitude of science projects
underway with iPTF.Comment: 66 pages, 21 figures, 7 tables, accepted by PAS
Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI
Historical materials are abundant. Yet, piecing together how human knowledge
has evolved and spread both diachronically and synchronically remains a
challenge that can so far only be very selectively addressed. The vast volume
of materials precludes comprehensive studies, given the restricted number of
human specialists. However, as large amounts of historical materials are now
available in digital form there is a promising opportunity for AI-assisted
historical analysis. In this work, we take a pivotal step towards analyzing
vast historical corpora by employing innovative machine learning (ML)
techniques, enabling in-depth historical insights on a grand scale. Our study
centers on the evolution of knowledge within the `Sacrobosco Collection' -- a
digitized collection of 359 early modern printed editions of textbooks on
astronomy used at European universities between 1472 and 1650 -- roughly 76,000
pages, many of which contain astronomic, computational tables. An ML based
analysis of these tables helps to unveil important facets of the
spatio-temporal evolution of knowledge and innovation in the field of
mathematical astronomy in the period, as taught at European universities
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