parametric models

Military vehicles have to travel, at least from time to time, on the national road network. Whenever that happens, the legal requirements have to be met with respect to the systems that contribute to the traffic safety. One of these systems is the braking system. On the other hand, most of the military vehicles arent that up-to-date that the rest of the traffic partners are. Therefore, a military automotive engineer should have at hand a fast method to diagnose the technical status of the braking system even the checked vehicle is rather old. It would be also even better if the faulty party were accurately identified. The paper aims at providing a method helps the diagnosing teams to quickly compare the behaviour of an actual vehicle braking system status with a general model that had been acquired by tests. Since nobody provided that kind of information during 70s, our model derived from a large number of tests that have been performed on good technical condition vehicles. Key word...

In the present article our aim is to model the HIV infected adult patients' dataset. A retrospective cohort study was conducted in Zewditu Referral Hospital located in Addis Ababa, Ethiopia. Records of patients enrolled between September 2010 and August 2014 were reviewed continuously using patients'Antiretroviral Therapy (ART) unique identification numbers as reference. Kaplan-Meier survival curves and Log-Rank test were used to compare the survival experience of different category of patients. Then we attempted to model the above data with the help of four parametric models namely; Exponential, Weibull, Gompertz, and Log-logistic. All fitted models were compared separately by using AIC and log likelihood. The log-logistic model gave a better description of the time-to-death of HIV infected adult patients than the other models. Based on log-logistic model, age, weight, and functional status, TB screen, World Health Organization (WHO) clinical stage and educational level were found to be the most prognostic factors of time-to-death. Furthermore a high risk of death of patients was found to be associated with lower initial weight, WHO clinical stage IV, lower CD4 count, being ambulatory, bedridden, and TB screened and illiterate.

Describing complex ecosystems as networks of interacting components has proved fruitful-revealing many distinctive patterns and dynamics of ecological systems. Of these patterns, three have often been brought up in literature, including species degree distribution, compartmentalization and nestedness, due largely to their implications for the functionality and stability of communities. Here, using 61 empirical antagonistic networks, we aim to settle the inconsistency in literature by (i) fitting their node degree distributions to five different parametric models and identifying the one fits the best, (ii) measuring the levels of nestedness and compartmentalization of these 61 networks and testing their significance using different null models, and (iii) exploring how network connectance affects these three network architecture metrics. This research showed that most antagonistic networks do not display power law degree distributions and that resource species are generally uniformly distributed. We also clearly showed that the conclusion of whether a network is significantly compartmentalized or nested depends largely on the null model used.

ABSTRACT Recently, much attention has been devoted to image-based scene representations. They allow one to construct an arbitrary view of a 3D scene by the interpolation (transfer) from a sparse set of real 2D (reference) images, rather than by rendering an explicit 3D model. While many authors address mainly the purely geometrical aspect of the task, we focus on the problem of how to select the optimal set of reference views. Selection of reference views from a dense set of real primary views is posed as a selection and fitting of parametric models. The selected set must minimize a weighted sum of the number of reference views and the total fit error. We propose two different algorithms for solving this optimization problem. The experimental results on synthetic and real data indicate the feasibility of the approach for 1-DOF camera movement. We discuss the possibility to extend one of the algorithms for more general case

We present a framework for modeling parametric variation in planar parts and for efficiently computing approximations of their tolerance envelopes. Part features are specified by algebraic equations defining their position and shape as a function of parameters whose nominal values vary along tolerance intervals. Their tolerance envelopes model perfect form Least and Most Material Conditions (LMC/MMC). We derive geometric properties of the tolerance envelopes and describe efficient algorithms for computing first-order linear approximations with successive accuracy. We show that the tolerance envelope of a parametric arc-line polygonal part with features has segments and can be computed in time, where is the maximum number of non-zero partial feature functions derivatives evaluated at nominal parameter values. Our implementation shows that the algorithms are practical on part models with tens of parameters.

Page 1. An Extension to Design of Experiment for Design Optimization with Implicit Parametric Models and Virtual Prototypes Gary G. Wang and Z. Dong Department of Mechanical Engineering University of Victoria Victoria, British Columbia, VSW 3P6 Canada ABSTRACT ...

An artificial neural network (ANN) based helicopter identification system is proposed. The feature vectors are based on both the tonal and the broadband spectrum of the helicopter signal, ANN pattern classifiers are trained using various parametric spectral representation techniques. Specifically, linear prediction, reflection coefficients, cepstrum, and line spectral frequencies (LSF) are compared in terms of recognition accuracy and robustness against additive noise. Finally, an 8-helicopter ANN classifier is evaluated. It is also shown that the classifier performance is dramatically improved if it is trained using both clean data and data corrupted with additive noise.

The widely used Cox proportional hazards regression model for the analysis of censored survival data has limited utility when either hazard functions themselves are of primary interest, or when relative times instead of relative hazards are the relevant measures of association. Parametric regression models are an attractive option in situations such as this, although the choice of a particular model from the available families of distributions can be problematic. The generalized gamma (GG) distribution is an extensive family that contains nearly all of the most commonly used distributions, including the exponential, Weibull, log normal and gamma. More importantly, the GG family includes all four of the most common types of hazard function: monotonically increasing and decreasing, as well as bathtub and arc-shaped hazards. We present here a taxonomy of the hazard functions of the GG family, which includes various special distributions and allows depiction of effects of exposures on hazard functions. We applied the proposed taxonomy to study survival after a diagnosis of clinical AIDS during different eras of HIV therapy, where proportionality of hazard functions was clearly not fulfilled and flexibility in estimating hazards with very different shapes was needed. Comparisons of survival after AIDS in different eras of therapy are presented in terms of both relative times and relative hazards. Standard errors for these and other derived quantities are computed using the delta method and checked using the bootstrap. Description of standard statistical software (Stata, SAS and S-Plus) for the computations is included and available at http://statepi.jhsph.edu/software.