18,269 research outputs found
Modeling large scale species abundance with latent spatial processes
Modeling species abundance patterns using local environmental features is an
important, current problem in ecology. The Cape Floristic Region (CFR) in South
Africa is a global hot spot of diversity and endemism, and provides a rich
class of species abundance data for such modeling. Here, we propose a
multi-stage Bayesian hierarchical model for explaining species abundance over
this region. Our model is specified at areal level, where the CFR is divided
into roughly one minute grid cells; species abundance is observed at
some locations within some cells. The abundance values are ordinally
categorized. Environmental and soil-type factors, likely to influence the
abundance pattern, are included in the model. We formulate the empirical
abundance pattern as a degraded version of the potential pattern, with the
degradation effect accomplished in two stages. First, we adjust for land use
transformation and then we adjust for measurement error, hence
misclassification error, to yield the observed abundance classifications. An
important point in this analysis is that only of the grid cells have been
sampled and that, for sampled grid cells, the number of sampled locations
ranges from one to more than one hundred. Still, we are able to develop
potential and transformed abundance surfaces over the entire region. In the
hierarchical framework, categorical abundance classifications are induced by
continuous latent surfaces. The degradation model above is built on the latent
scale. On this scale, an areal level spatial regression model was used for
modeling the dependence of species abundance on the environmental factors.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS335 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
A rest time-based prognostic framework for state of health estimation of lithium-ion batteries with regeneration phenomena
State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framewor
Multiple-Change-Point Modeling and Exact Bayesian Inference of Degradation Signal for Prognostic Improvement
Prognostics play an increasingly important role in modern engineering systems for smart maintenance decision-making. In parametric regression-based approaches, the parametric models are often too rigid to model degradation signals in many applications. In this paper, we propose a Bayesian multiple-change-point (CP) modeling framework to better capture the degradation path and improve the prognostics. At the offline modeling stage, a novel stochastic process is proposed to model the joint prior of CPs and positions. All hyperparameters are estimated through an empirical two-stage process. At the online monitoring and remaining useful life (RUL) prediction stage, a recursive updating algorithm is developed to exactly calculate the posterior distribution and RUL prediction sequentially. To control the computational cost, a fixed-support-size strategy in the online model updating and a partial Monte Carlo strategy in the RUL prediction are proposed. The effectiveness and advantages of the proposed method are demonstrated through thorough simulation and real case studies
- ā¦