Search results for "Estimation"
showing 10 items of 924 documents
Asylum Migration, Borders, and Terrorism in a Structural Gravity Model
2021
This article has benefited from very helpful comments from two anonymous reviewers and the Academic Editor Inmaculada Martinez. The authors would like to acknowledge the financial support from Junta de Andalucia (SEJ 413), from Generalitat Valenciana (GV Prometeo 2018/102 and GV/2020/012), the Spanish Ministry of Science, Innovation and Universities (RTI2018-100899-B-I00, co-financed with FEDER), and the Kellogg Institute for International Studies (University of Notre Dame).
A Dataset of Annotated Omnidirectional Videos for Distancing Applications
2021
Omnidirectional (or 360°) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360° videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360° image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some point…
Clinical evaluation of automated capillary refill time estimation in dogs and cats
2019
In this study, we clinically evaluated a pulse oximeter-based device for automated capillary refill time (CRT) estimation in dogs and cats. CRT can reveal conditions like shock or anemia in dogs and cats. However, visual CRT estimation has low repeatability, and the available optical systems for automated estimation are not suitable for pets. We evaluated a custom-made portable CRT measuring device on various measurement sites of 12 dogs and 11 cats with parallel visual CRT estimation on the gum by treating veterinarian. The capillary refill was also recorded by a video camera for reference. The visual and video procedures were moderately correlated with the coefficient of 0.61; visual CRT …
DAE-GP
2020
Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper presents DAE-GP, a new EDA-GP which uses denoising autoencoder long short-term memory networks (DAE-LSTMs) as probabilistic model. DAE-LSTMs are artificial neural networks that first learn the properties of a parent population by mapping promising candidate solutions to a latent space and reconstructing the candidate solutions from the latent space. The trained model is then used to sample new offspring solutions. We show on a generalization of t…
An analysis of the bias of variation operators of estimation of distribution programming
2018
Estimation of distribution programming (EDP) replaces standard GP variation operators with sampling from a learned probability model. To ensure a minimum amount of variation in a population, EDP adds random noise to the probabilities of random variables. This paper studies the bias of EDP's variation operator by performing random walks. The results indicate that the complexity of the EDP model is high since the model is overfitting the parent solutions when no additional noise is being used. Adding only a low amount of noise leads to a strong bias towards small trees. The bias gets stronger with an increased amount of noise. Our findings do not support the hypothesis that sampling drift is …
What makes a citrus farmer go 'organic'? Empirical evidence from Spanish citrus farming
2012
Organic farming is increasing its share of total world food output and receiving grow-ing support from policymakers concerned with agricultural sustainability issues. This paper studies the characteristics of citrus farmers in the Spanish region of Valencia that affect their probability of becoming organic farmers. A fair understanding of these characteristics may help policymakers improve the design of agricultural policies aimed at supporting organic citrus practices. As regards the methodology, a probit model is estimated with information from a sample of conventional and organic citrus farmers obtained from a survey designed for a larger research project aimed at analysing Valencian cit…
The Comparison of Software Reliability Assessment Models
2015
Abstract The reliability of the software represents one of the most important attributes of software quality, and the estimation of the reliability of the software is a problem hard to solve with accuracy. Nevertheless, in order to manage the quality of the software and of the standard practices in an organization, it is important to achieve an estimation of the reliability as accurate as possible. In the present work there are described the principles and techniques which underlie the estimation of the reliability of the software, starting from the definition of the concepts which express the attributes of software quality. It is taken into account the issue of the estimation of a software…
Optimization of Delayed-State Kalman-Filter-based Algorithm via Differential Evolution for Sensorless Control of Induction Motors
2010
This paper proposes the employment of the differential evolution (DE) to offline optimize the covariance matrices of a new reduced delayed-state Kalman-filter (DSKF)-based algorithm which estimates the stator-flux linkage components, in the stationary reference frame, to realize sensorless control of induction motors (IMs). The DSKF-based algorithm uses the derivatives of the stator-flux components as mathematical model and the stator-voltage equations as observation model so that only a vector of four variables has to be offline optimized. Numerical results, carried out using a low-speed training test, show that the proposed DE-based approach is very promising and clearly outperforms a cla…
Flexible space-time process for seismic data
2009
Point processes are well studied objects in probability theory and a powerful tool in statistics for modelling and analyzing the distribution of real phenomena, such as the seismic one. Point processes can be specified mathematically in several ways, for instance, by considering the joint distributions of the counts of points in arbitrary sets or defining a complete intensity function. The conditional intensity function is a function of the point history and it is itself a stochastic process depending on the past up to time t. In this paper some techniques to estimate the intensity function of space-time point processes are developed, by following semi-parametric approaches and diagnostic m…
Central limit theorem for bifurcating Markov chains under L 2 -ergodic conditions
2021
Bifurcating Markov chains (BMC) are Markov chains indexed by a full binary tree representing the evolution of a trait along a population where each individual has two children. We provide a central limit theorem for additive functionals of BMC under L 2-ergodic conditions with three different regimes. This completes the pointwise approach developed in a previous work. As application, we study the elementary case of symmetric bifurcating autoregressive process, which justify the non-trivial hypothesis considered on the kernel transition of the BMC. We illustrate in this example the phase transition observed in the fluctuations.