An Intelligent Controller Design for Magnetorheological Damper Based on a Quarter-car Model

This paper presents the control strategies of nonlinear vehicle suspension using a magnetorheological (MR) damper. We used two different approaches for modeling and control of the mechanical and electrical parts of the suspension systems with the MR damper. First, we have formulated and resolved the control problem in order to design the linear feedback dumping force controller for a nonlinear suspension system. Then the values of the control dumping force functions were transformed into electrical control signals by the application of a fuzzy logic control method. The numerical simulations were provided in order to show the effectiveness of this method for the semi-active control of the quarter-car suspension.

Suppression of vibrations in a nonlinear half-car model using a magneto-rheological damper

The present work investigates the nonlinear response of a half-car model. The disturbances of the road are assumed to be sinusoidal. After constructing the bifurcation diagram, we use the 0-1 test to identify chaotic motions. The main objective of this study is to eliminate chaotic behavior of the chassis and reduce its vibrations. To accomplish this, a semi-active vehicle suspension control system, using magneto-rheological dampers, is proposed. The proposed semi-active control strategy consists of two nonlinear control laws: a feedforward control, and a feedback control. They are obtained by considering the SDRE (State Dependent Riccati Equation) control, where the control parameter is the voltage applied to the coils of the magneto-rheological dampers. Numerical results show that the proposed control method is effective in significantly reducing of the chassis vibration, increasing, therefore, passenger comfort.

A Bayesian Spatial Mixture Model for FMRI Analysis

We develop, implement and study a new Bayesian spatial mixture model (BSMM). The proposed BSMM allows for spatial structure in the binary activation indicators through a latent thresholded Gaussian Markov random field. We develop a Gibbs (MCMC) sampler to perform posterior inference on the model parameters, which then allows us to assess the posterior probabilities of activation for each voxel. One purpose of this article is to compare the HJ model and the BSMM in terms of receiver operating characteristics (ROC) curves. Also we consider the accuracy of the spatial mixture model and the BSMM for estimation of the size of the activation region in terms of bias, variance and mean squared error. We perform a simulation study to examine the aforementioned characteristics under a variety of configurations of spatial mixture model and BSMM both as the size of the region changes and as the magnitude of activation changes.

Vehicle Dynamic Safety in Measured Rough Pavement

Dynamic vehicle behavior is used to identify safe traffic speed limits. The proposed methodology is based on the vehicle vertical wheel contact force response excited by measured pavement irregularities on the frequency domain. A quarter-car model is used to identify vehicle dynamic behavior. The vertical elevation of an unpaved road surface has been measured. The roughness spectral density is quantified as ISO Level C. Calculations for the vehicle inertance function were derived by using the vertical contact force transfer function weighed by the pavement spectral density roughness function in the frequency domain. The statistical contact load variation is obtained from the vehicle inertance density function integration. The vehicle safety behavior concept is based on its handling ability properties. The ability to generate tangential forces on the wheel/road contact interface is the key to vehicle handling. This ability is related to tire/pavement contact forces. A contribution to establish a traffic safety speed limit is obtained from the likelihood of the loss of driveability. The results show that at speeds faster than 25 km/h the likelihood of tire contact loss is possible when traveling on the measured road type. DOI: 10.1061/(ASCE)TE.19435436.0000216. (C) 2011 American Society of Civil Engineers.

Correlation of the Roughometer to the High-Speed Laser Profiler, MLR-97-7, 1997

The Iowa Department of Transportation has been using the Bureau of Public Roads (BPR) Roughometer as part of its detour analysis process for more than 20 years. Advances in technology have made the BPR Roughometer obsolete for ride quality testing. High-speed profilers that can collect the profile of the road at highway speeds are the standard ride instruments for determining ride quality on pavements. The objective of the project was to develop a correlation between the BPR Roughometer and the high-speed laser South Dakota type Profiler (SD Profiler). Nineteen pavement sections were chosen to represent the range of types and conditions for detours. Three computer simulation models were tested on the profiler profiles. The first model is the International Ride Index (IRI) which is considered the standard index for reporting ride quality in the United States. The second model is the Ride Number (RN) developed by the University of Michigan Transportation Research Institute and the third model used is a quarter-car simulation of the BPR Roughometer (ASTM E-1170) which should match the speed and range of roadway features experienced by Iowa's BPR Roughometer Unit. The BPR Roughometer quarter-car model provided the best overall correlation with Iowa's BPR Roughometer.

Fitting spatial models in the R package: hglm

We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM) with spatially correlated random effects. A CAR family for conditional autoregressive random effects was implemented. Eigen decomposition of the matrix describing the spatial structure (e.g. the neighborhood matrix) was used to transform the CAR random effectsinto an independent, but heteroscedastic, gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR model.This gives a computationally efficient algorithm for moderately sized problems (e.g. n<5000).

Influência da suspensão na segurança e no conforto de um pulverizador autopropelido

Para avaliar o comportamento da suspensão do pulverizador autopropelido, foram desenvolvidos modelos físicos e matemáticos em função da excitação ocasionada pelas irregularidades do solo. Neste trabalho, estas irregularidades são representadas por obstáculos de uma pista normalizada segundo a norma ISO 5008. As equações do movimento são obtidas a partir dos modelos matemáticos de meio veículo. As simulações numéricas são executadas nos softwares Matlab® e Simulink®. A partir da entrada conhecida, podem-se determinar as características dos elementos da suspensão para obter níveis desejáveis de conforto e segurança. Foram analisadas quatro diferentes configurações do sistema, variando-se a relação de rigidez a partir de um modelo considerado padrão. Constatou-se que o aumento da relação de rigidez resulta na redução da aceleração vertical e no aumento do curso da suspensão, melhorando o conforto e diminuindo a segurança.

Extração automática de contornos de telhados de edifícios em um modelo digital de elevação, utilizando inferência Bayesiana e campos aleatórios de Markov

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise bayesiana de dados espaciais explorando diferentes estruturas de variância

Pós-graduação em Matematica Aplicada e Computacional - FCT

Influência das não linearidades geométricas em sistemas de suspensão com amortecimento assimétrico sob excitação harmônica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A publicidade contra-intuitiva e o efeito ricochete

O propósito deste artigo é compartilhar com o leitor algumas noções conceituais de publicidade contra-intuitiva e observar seus possíveis efeitos na reavaliação, desconstrução de crenças e estereótipos sociais, na estrutura cognitiva do indivíduo receptor. Destaca-se entre os reflexos provavelmente gerados o irônico efeito ricochete, segundo a teoria desenvolvida por Daniel M. Wegner. A aplicação para se discutir o cruzamento da narrativa contra-intuitiva e o efeito ricochete será, neste primeiro momento, pela exemplificação do filme Motorista, peça integrante da campanha publicitária da Fiat do Brasil “Reveja seus conceitos”, para o lançamento do automóvel Palio 2002. Um experimento laboratorial está sendo desenvolvido pelos autores para se mensurar de maneira consistente a apresentação teórico-conceitual exposta

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.

A Bayesian analysis for spatial processes with application to mapping

In geographical epidemiology, maps of disease rates and disease risk provide a spatial perspective for researching disease etiology. For rare diseases or when the population base is small, the rate and risk estimates may be unstable. Empirical Bayesian (EB) methods have been used to spatially smooth the estimates by permitting an area estimate to "borrow strength" from its neighbors. Such EB methods include the use of a Gamma model, of a James-Stein estimator, and of a conditional autoregressive (CAR) process. A fully Bayesian analysis of the CAR process is proposed. One advantage of this fully Bayesian analysis is that it can be implemented simply by using repeated sampling from the posterior densities. Use of a Markov chain Monte Carlo technique such as Gibbs sampler was not necessary. Direct resampling from the posterior densities provides exact small sample inferences instead of the approximate asymptotic analyses of maximum likelihood methods (Clayton & Kaldor, 1987). Further, the proposed CAR model provides for covariates to be included in the model. A simulation demonstrates the effect of sample size on the fully Bayesian analysis of the CAR process. The methods are applied to lip cancer data from Scotland, and the results are compared. ^

Comparison of vibration and rolling noise emission of resilient and solid monobloc railway wheels in underground lines

Flat or worn wheels rolling on rough or corrugated tracks can provoke airborne noise and ground-borne vibration, which can be a serious concern for nearby neighbours of urban rail transit lines. Among the various treatments used to reduce vibration and noise, resilient wheels play an important role. In conventional resilient wheels, a slightly prestressed V­shaped rubber ring is mounted between the steel wheel centre and tyre. The elastic layer enhances rolling noise and vibration suppression, as well as impact reduction on the track. In this paper the effectiveness of resilient wheels in underground lines, in comparison to monobloc ones, is assessed. The analysed resilient wheel is able to carry greater loads than standard resilient wheels used for light vehicles. It also presents a greater radial resiliency and a higher axial stiffness than conventional V­wheels. The finite element method was used in this study. A quarter car model was defined, in which the wheelset was modelled as an elastic body. Several simulations were performed in order to assess the vibrational behaviour of elastic wheels, including modal, harmonic and random vibration analysis, the latter allowing the introduction of realistic vertical track irregularities, as well as the influence of the running speed. Due to numerical problems some simplifications were needed. Parametric variations were also performed, in which the sensitivity of the whole system to variations of rubber prestress and Poisson’s ratio of the elastic material was assessed.Results are presented in the frequency domain, showing a better performance of the resilient wheels for frequencies over 200 Hz. This result reveals the ability of the analyzed design to mitigate rolling noise, but not structural vibrations, which are primarily found in the lower frequency range.

Fault diagnosis of an automotive suspension system

With the development of the embedded application and driving assistance systems, it becomes relevant to develop parallel mechanisms in order to check and to diagnose these new systems. In this thesis we focus our research on one of this type of parallel mechanisms and analytical redundancy for fault diagnosis of an automotive suspension system. We have considered a quarter model car passive suspension model and used a parameter estimation, ARX model, method to detect the fault happening in the damper and spring of system. Moreover, afterward we have deployed a neural network classifier to isolate the faults and identifies where the fault is happening. Then in this regard, the safety measurements and redundancies can take into the effect to prevent failure in the system. It is shown that The ARX estimator could quickly detect the fault online using the vertical acceleration and displacement sensor data which are common sensors in nowadays vehicles. Hence, the clear divergence is the ARX response make it easy to deploy a threshold to give alarm to the intelligent system of vehicle and the neural classifier can quickly show the place of fault occurrence.