24,430 research outputs found
Wind turbine condition assessment through power curve copula modeling
Power curves constructed from wind speed and active power output measurements provide an established method of analyzing wind turbine performance. In this paper it is proposed that operational data from wind turbines are used to estimate bivariate probability distribution functions representing the power curve of existing turbines so that deviations from expected behavior can be detected. Owing to the complex form of dependency between active power and wind speed, which no classical parameterized distribution can approximate, the application of empirical copulas is proposed; the statistical theory of copulas allows the distribution form of marginal distributions of wind speed and power to be expressed separately from information about the dependency between them. Copula analysis is discussed in terms of its likely usefulness in wind turbine condition monitoring, particularly in early recognition of incipient faults such as blade degradation, yaw and pitch errors
Warranty Data Analysis: A Review
Warranty claims and supplementary data contain useful information about product quality and reliability. Analysing such data can therefore be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy and forecasting future warranty claims needed for preparing fiscal plans. In the last two decades, considerable research has been conducted in warranty data analysis (WDA) from several different perspectives. This article attempts to summarise and review the research and developments in WDA with emphasis on models, methods and applications. It concludes with a brief discussion on current practices and possible future trends in WDA
El aprendizaje de audacity para la edición y producción de contenidos didácticos digitales
En la sociedad actual resulta indispensable
proporcionar una capacitación adecuada a los
futuros docentes para que puedan desarrollar
metodologías innovadoras, donde las TIC y los
recursos didácticos digitales desempeñan un
papel clave y permiten que los conocimientos
y habilidades del alumno tengan un desarrollo
exitoso. Esta investigación se aborda desde
una metodología cuantitativa, mediante el
uso de un cuestionario creado ad hoc sobre
aprendizaje y la evaluación de la herramienta
Audacity para la creación de recursos
didácticos digitales en el Grado de Educación
Infantil de la Universidad de Córdoba. Los
resultados muestran una valoración positiva
de la experiencia vivida, así como de la
herramienta estudiada.In present society it is essential to provide
the necessary training to future teachers so
they can accomplish innovative teaching
and learning methodologies, where ICT and
digital learning resources play a key role that
will enable the student’s knowledge and skills
to be successfully developed. This research is
approached from a quantitative methodology,
by using a questionnaire created ad hoc about
the learning and the assessment of the Audacity
software tool for creating digital didactic
resources, in the Early Childhood Education
Degree from the University of Cordoba. The
results obtained show a positive assessment
of the tool studied and its subsequent use for
audiovisual productions
Recommended from our members
A Copula-Based Joint Model of Commute Mode Choice and Number of Non-Work Stops during the Commute
At the time of publication A. Portoghese, E. Spissu, and I. Meloni were at University of Cagliari, and C.R. Bhat and N. Eluru were at the University of Texas at Austin.In this paper, in the spirit of a tour-based frame of analysis, we examine the commute mode choice
and the number of non-work stops during the commute. Understanding the mode and activity stop
dimensions of weekday commute travel is important since the highest level of weekday traffic
congestion in urban areas occurs during the commute periods. The paper employs a copula-based
joint multinomial logit – ordered modeling framework in which commute mode choice is modeled
using a multinomial logit formulation and the number of commute stops is modeled using an ordered
response formulation. The data used in this study are drawn from the “Time use” multipurpose
survey conducted between 2002 and 2003 by the Turin Town Council and the Italian National
Institute of Statistics (ISTAT) in the Greater Turin metropolitan area of Italy. The results highlight
the importance of accommodating the inter-relationship between commute mode choice and
commute stops behavior. The results also point to the stronger effect of household responsibilities
and demographic characteristics in the Italian context compared to the US context.Civil, Architectural, and Environmental Engineerin
Multiscale Granger causality
In the study of complex physical and biological systems represented by
multivariate stochastic processes, an issue of great relevance is the
description of the system dynamics spanning multiple temporal scales. While
methods to assess the dynamic complexity of individual processes at different
time scales are well-established, multiscale analysis of directed interactions
has never been formalized theoretically, and empirical evaluations are
complicated by practical issues such as filtering and downsampling. Here we
extend the very popular measure of Granger causality (GC), a prominent tool for
assessing directed lagged interactions between joint processes, to quantify
information transfer across multiple time scales. We show that the multiscale
processing of a vector autoregressive (AR) process introduces a moving average
(MA) component, and describe how to represent the resulting ARMA process using
state space (SS) models and to combine the SS model parameters for computing
exact GC values at arbitrarily large time scales. We exploit the theoretical
formulation to identify peculiar features of multiscale GC in basic AR
processes, and demonstrate with numerical simulations the much larger
estimation accuracy of the SS approach compared with pure AR modeling of
filtered and downsampled data. The improved computational reliability is
exploited to disclose meaningful multiscale patterns of information transfer
between global temperature and carbon dioxide concentration time series, both
in paleoclimate and in recent years
Evaluating the ecological realism of plant species distribution models with ecological indicator values
Species distribution models (SDMs) are routinely applied to assess current as well as future species distributions, for example to assess impacts of future environmental change on biodiversity or to underpin conservation planning. It has been repeatedly emphasized that SDMs should be evaluated based not only on their goodness of fit to the data, but also on the realism of the modelled ecological responses. However, possibilities for the latter are hampered by limited knowledge on the true responses as well as a lack of quantitative evaluation methods. Here we compared modelled niche optima obtained from European-scale SDMs of 1,476 terrestrial vascular plant species with empirical ecological indicator values indicating the preferences of plant species for key environmental conditions. For each plant species we first fitted an ensemble SDM including three modeling techniques (GLM, GAM and BRT) and extracted niche optima for climate, soil, land use and nitrogen deposition variables with a large explanatory power for the occurrence of that species. We then compared these SDM-derived niche optima with the ecological indicator values by means of bivariate correlation analysis. We found weak to moderate correlations in the expected direction between the SDM-derived niche optima and ecological indicator values. The strongest correlation occurred between the modelled optima for growing degree days and the ecological indicator values for temperature. Correlations were weaker for SDM-derived niche optima with a more distal relationship to ecological indicator values (notably precipitation and soil moisture). Further, correlations were consistently highest for BRT, followed by GLM and GAM. Our method gives insight into the ecological realism of modelled niche optima and projected core habitats and can be used to improve SDMs by making a more informed selection of environmental variables and modeling techniques
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