scholarly journals A MULTIVARIATE STATISTICAL MODEL TO SIMULATE STORM EVOLUTION

2018 ◽  
pp. 84
Author(s):  
Andrea Lira-Loarca ◽  
Manuel Cobos ◽  
Asunción Baquerizo ◽  
Miguel A. Losada
Keyword(s):  
Optimization Problem ◽  
Interarrival Time ◽  
Storm Events ◽  
Multivariate Statistical ◽  
Vector Autoregressive ◽  
Storm Duration ◽  
Storm Evolution ◽  
Multivariate Statistical Model ◽  
Useful Life ◽  
Different Levels

The design and management of a coastal structure must take into account not only the different levels of damage along its useful life but also the construction, reparation and dismantling costs. Therefore, it should be addressed as an optimization problem that depends on random multivariate climate variables. In this context it is essential to develop tools that allow the simulation of storms taking into account all the main maritime variables and their evolution (Borgman, 1969). In general, most studies focusing on storm characterization and evolution use geometric shapes like the equivalent triangular storm (Bocotti, 2000; ROM-1.0; 2009) to characterize individual storms. Actual storms have, however, irregular and random histories. In this work, we present a simple and efficient methodology to simulate time-series of storm events including several maritime variables. This methodology includes the use of non-stationary parametric distributions (Solari, 2011) to characterize each variable, a vector autoregressive (VAR) model to describe the temporal dependence between variables, and a copula model to link the seasonal dependency of the storm duration and the interarrival time between consecutive storms.

scholarly journals An Optimization Approach to Selective Editing

2013 ◽  
Vol 29 (4) ◽  
pp. 489-510 ◽  
Author(s):  
Ignacio Arbue´s ◽  
Pedro Revilla ◽  
David Salgado
Keyword(s):  
Measurement Errors ◽  
Optimization Problem ◽  
Optimization Approach ◽  
Multivariate Statistical ◽  
Cross Sectional ◽  
Score Functions ◽  
Interactive Editing ◽  
Multivariate Statistical Model ◽  
The One ◽  
Relationship Of

Abstract We set out two generic principles for selective editing, namely the minimization of interactive editing resources and data quality assurance. These principles are translated into a generic optimization problem with two versions. On the one hand, if no cross-sectional information is used in the selection of units, we derive a stochastic optimization problem. On the other hand, if that information is used, we arrive at a combinatorial optimization problem. These problems are substantiated by constructing a so-called observation-prediction model, that is, a multivariate statistical model for the nonsampling measurement errors assisted by an auxiliary model to make predictions. The restrictions of these problems basically set upper bounds upon the modelled measurement errors entering the survey estimators. The bounds are chosen by subject-matter knowledge. Furthermore, we propose a selection efficiency measure to assess any selective editing technique and make a comparison between this approach and some score functions. Special attention is paid to the relationship of this approach with the editing fieldwork conditions, arising issues such as the selection versus the prioritization of units and the connection between the selective and macro editing techniques. This approach neatly links the selection and prioritization of sampling units for editing (micro approach) with considerations upon the survey estimators themselves (macro approach).

144 The Epidemiology of Hoof-related Lameness in Western Canadian Feedlot Cattle

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 81-82
Author(s):  
Sarah E Erickson ◽  
Murray Jelinski ◽  
Karen S Schwartzkopf-Genswein ◽  
Calvin Booker ◽  
Eugene Janzen
Keyword(s):  
North Carolina ◽  
Total Population ◽  
Health Management ◽  
Statistical Significance ◽  
Feedlot Cattle ◽  
Foot Rot ◽  
Digital Dermatitis ◽  
Multivariate Statistical ◽  
Multivariate Statistical Model ◽  
Control And Prevention

Abstract The epidemiology of hoof-related lameness (HRL) in western Canadian feedlots, with a focus on digital dermatitis (DD), was described and analyzed to help inform recommendations on lameness control and prevention in western Canadian feedlot cattle. The retrospective data in this study were accessed from 28 western Canadian feedlots that placed cattle in 2014–2018, inclusive. The total population for this study was 1,796,176 cattle, with an annual placement average of 12,830 cattle per feedlot. These data were accessed through iFHMS Consolidated Database, provided by Feedlot Health Management Services by TELUS Agriculture, and manipulated using Microsoft® Office Access 365 ProPlus and Microsoft® Office Excel 365 ProPlus. Epidemiological analyses determined that lameness accounts for 25.7% of all treatments in western Canadian feedlots. Of those treatments, 71.7% are localized to the hoof, corresponding to 18.6% of all treatments. The most common HRL diseases are infectious bovine pododermatitis [foot rot (FR)]; digital dermatitis (DD), also known as hairy-heel wart or strawberry foot rot; and toe-tip necrosis syndrome (TTNS). These diseases account for 89.6%, 7.9% and 2.4% of HRL, respectively. Between 2014 and 2018, HRL prevalence ranged between 1.93% and 3.09% of the population, with FR consistently having the highest prevalence and TTNS the lowest. HRL and DD were tested for their associations with several animal-level risk factors using © Ausvet 2021 Epitools software. The resultant crude, univariate odds ratio values, evaluated at 95% confidence, are summarized in Table 1. Based on this analysis, acquisition source has the largest influence on the odds of developing HRL and DD, followed by population size, and placement quarter. Using SAS® (Version 9.4, SAS Institute Inc, Cary, North Carolina) statistical software, these preliminary findings will be subjected to a multivariate statistical model, which will provide adjusted OR values and statistical significance for the data in this study.

Multivariate Statistical Model for Predicting Occurrence and Location of Broken Rails

2003 ◽  
Vol 1825 (1) ◽  
pp. 48-55 ◽  
Author(s):  
C. Tyler Dick ◽  
Christopher P. L. Barkan ◽  
Edward R. Chapman ◽  
Mark P. Stehly
Keyword(s):  
Statistical Model ◽  
Service Failure ◽  
Quantitative Prediction ◽  
Strongly Correlated ◽  
Santa Fe ◽  
Multivariate Statistical ◽  
Hazardous Materials Transportation ◽  
Service Failures ◽  
Multivariate Statistical Model ◽  
Improve Service Quality

Broken rails are the leading cause of major accidents on U.S. railroads and frequently cause delays. A multivariate statistical model was developed to improve the prediction of broken-rail incidences (i.e., service failures). Improving the prediction of conditions that cause broken rails can assist railroads in allocating inspection, detection, and preventive resources more efficiently, to enhance safety, reduce the risk of hazardous materials transportation, improve service quality, and maximize rail assets. The service failure prediction model (SFPM) uses a combination of engineering and traffic data commonly recorded by major railroads. A Burlington Northern Santa Fe Railway database was developed in which the locations of approximately 1,800 service failures over 2 years were recorded. The data on each location were supplemented with information on other engineering and traffic volume parameters. A complementary database with the same parameters was developed for a randomly selected set of locations at which service failures had not occurred. The combined databases were analyzed using multivariate statistical methods to identify the variables and their combinations most strongly correlated with service failures. SFPM accuracy in predicting service failures at specific locations exceeded 85%. Although further validation is necessary, SFPM is promising in the quantitative prediction of broken rails, thereby improving a railroad’s ability to manage its assets and risks.

Time domain damage localization and quantification in seismically excited structures using a limited number of sensors

2016 ◽  
Vol 23 (18) ◽  
pp. 2942-2961 ◽  
Author(s):  
Abdollah Bagheri ◽  
Ali Zare Hosseinzadeh ◽  
Piervincenzo Rizzo ◽  
Gholamreza Ghodrati Amiri
Keyword(s):  
Optimization Problem ◽  
Moment Generating Function ◽  
Discrete Wavelet ◽  
Damage Localization ◽  
Northridge Earthquake ◽  
Optimization Strategy ◽  
Noisy Signals ◽  
Time Histories ◽  
The Moment ◽  
Different Levels

This paper presents a new algorithm to determine the occurrence, location, and severity of damage in structures subjected to earthquakes. The algorithm is based on the analysis of the time series associated with displacement or acceleration, and provided by a limited number of sensors. The algorithm is formulated in terms of an optimization problem. An objective function is defined based on the moment generating function for a segment of the time histories and an evolutionary optimization strategy, based on the competitive optimization algorithm, is employed to detect damage. The efficiency of the proposed method is numerically validated by studying the response of some structures subjected to the 1940 El-Centro earthquake and the 1994 Northridge earthquake. In order to simulate real conditions, different levels of noise are added to the response’s signals, and then the discrete wavelet transform is used to de-noise the signals. Moreover, the robustness of the method is evaluated by considering an error in the model of the structures. Overall, we find that the proposed algorithm detects and localizes damage even in presence of noisy signals and errors in the model.

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