The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform

Seibum B. Choi - One of the best experts on this subject based on the ideXlab platform.

  • linearized recursive Least Squares Methods for real time identification of tire road friction coefficient
    IEEE Transactions on Vehicular Technology, 2013
    Co-Authors: Mooryong Choi, Jiwon Oh, Seibum B. Choi
    Abstract:

    The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive Least Squares (LRLS) Methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-road friction coefficient with great accuracy and robustness before tires reach their frictional limits but successfully estimates the two different tire-road friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.

  • Linearized Recursive Least Squares Methods for Real-Time Identification of Tire–Road Friction Coefficient
    IEEE Transactions on Vehicular Technology, 2013
    Co-Authors: Mooryong Choi, Jiwon J. Oh, Seibum B. Choi
    Abstract:

    The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive Least Squares (LRLS) Methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-road friction coefficient with great accuracy and robustness before tires reach their frictional limits but successfully estimates the two different tire-road friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.

Mooryong Choi - One of the best experts on this subject based on the ideXlab platform.

  • linearized recursive Least Squares Methods for real time identification of tire road friction coefficient
    IEEE Transactions on Vehicular Technology, 2013
    Co-Authors: Mooryong Choi, Jiwon Oh, Seibum B. Choi
    Abstract:

    The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive Least Squares (LRLS) Methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-road friction coefficient with great accuracy and robustness before tires reach their frictional limits but successfully estimates the two different tire-road friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.

  • Linearized Recursive Least Squares Methods for Real-Time Identification of Tire–Road Friction Coefficient
    IEEE Transactions on Vehicular Technology, 2013
    Co-Authors: Mooryong Choi, Jiwon J. Oh, Seibum B. Choi
    Abstract:

    The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive Least Squares (LRLS) Methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-road friction coefficient with great accuracy and robustness before tires reach their frictional limits but successfully estimates the two different tire-road friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.

Lynne J. Williams - One of the best experts on this subject based on the ideXlab platform.

  • Chpter 23: Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression
    Computational Toxicology. Methods in Molecular Biology (Methods and Protocols), 2013
    Co-Authors: Herve´ Abdi, Lynne J. Williams
    Abstract:

    As in many fields of scientific endeavor, computational toxicology represents a broad and expanding group of activities. This chapter attempts to summarize ongoing efforts for a number of computational approaches and suggest ways in which these Methods could be applied effectively for improving risk assessment practice going forward in time. Generic issues include QA/QC of data used for computational modeling, graduate education programs for training the next generation of computational modelers with a common language among themselves, and the training in translation of computational toxicology terms for scientists in other related fields and the lay public so that effective communication of modeling data is achieved. Communication with scientists involved in systems biology approaches will be of particular importance. In this regard, it will also be essential to integrate artificial intelligence (AI) programs into future risk assessment programs for the evolution of this field in order to more fully integrate systems biology into mode of action risk analysis. Expanded use of data mining programs for development of testable hypotheses and to facilitate the incorporation of “green chemistry” approaches will reduce the number of chemicals in need of post-manufacture toxicology testing and risk assessment. In summary, it is hoped that the key elements identified in this chapter will help this field to continue to develop in a robust manner and provide the risk assessment community with a much needed set of modern scientific tools.

  • partial Least Squares Methods partial Least Squares correlation and partial Least square regression
    Methods of Molecular Biology, 2013
    Co-Authors: Herve Abdi, Lynne J. Williams
    Abstract:

    Partial Least square (PLS) Methods (also sometimes called projection to latent structures) relate the information presentintwodatatablesthatcollectmeasurementsonthesamesetofobservations. PLSMethodsproceedby derivinglatentvariableswhichare(optimal)linearcombinationsofthevariablesofadatatable.Whenthegoal is tofind the shared information between two tables, the approach is equivalent toa correlation problem and the technique is then called partial Least square correlation (PLSC) (also sometimes called PLS-SVD). In this case there are two sets of latent variables (one set per table), and these latent variables are required to have maximal covariance. When the goal is to predict one data table the other one, the technique is then called partialLeastsquareregression.Inthis casethereisonesetoflatentvariables(derivedfromthepredictor table) and these latent variables are required to give the best possible prediction. In this paper we present and illustratePLSCandPLSRandshowhowthesedescriptivemultivariateanalysistechniquescanbeextendedto deal with inferential questions by using cross-validation techniques such as the bootstrap and permutation tests.

Mateo Vargas - One of the best experts on this subject based on the ideXlab platform.

  • mapping qtls and qtl x environment interaction for cimmyt maize drought stress program using factorial regression and partial Least Squares Methods
    Theoretical and Applied Genetics, 2006
    Co-Authors: Mateo Vargas, Fred A Van Eeuwijk, Jose Crossa, Jeanmarcel Ribaut
    Abstract:

    The study of QTL × environment interaction (QEI) is important for understanding genotype × environment interaction (GEI) in many quantitative traits. For modeling GEI and QEI, factorial regression (FR) models form a powerful class of models. In FR models, covariables (contrasts) defined on the levels of the genotypic and/or environmental factor(s) are used to describe main effects and interactions. In FR models for QTL expression, considerable numbers of genotypic covariables can occur as for each putative QTL an additional covariable needs to be introduced. For large numbers of genotypic and/or environmental covariables, Least square estimation breaks down and partial Least Squares (PLS) estimation procedures become an attractive alternative. In this paper we develop methodology for analyzing QEI by FR for estimating effects and locations of QTLs and QEI and interpreting QEI in terms of environmental variables. A randomization test for the main effects of QTLs and QEI is presented. A population of F2 derived F3 families was evaluated in eight environments differing in drought stress and soil nitrogen content and the traits yield and anthesis silking interval (ASI) were measured. For grain yield, chromosomes 1 and 10 showed significant QEI, whereas in chromosomes 3 and 8 only main effect QTLs were observed. For ASI, QTL main effects were observed on chromosomes 1, 2, 6, 8, and 10, whereas QEI was observed only on chromosome 8. The assessment of the QEI at chromosome 1 for grain yield showed that the QTL main effect explained 35.8% of the QTL + QEI variability, while QEI explained 64.2%. Minimum temperature during flowering time explained 77.6% of the QEI. The QEI analysis at chromosome 10 showed that the QTL main effect explained 59.8% of the QTL + QEI variability, while QEI explained 40.2%. Maximum temperature during flowering time explained 23.8% of the QEI. Results of this study show the possibilities of using FR for mapping QTL and for dissecting QEI in terms of environmental variables. PLS regression is efficient in accounting for background noise produced by other QTLs.

Jiwon J. Oh - One of the best experts on this subject based on the ideXlab platform.

  • Linearized Recursive Least Squares Methods for Real-Time Identification of Tire–Road Friction Coefficient
    IEEE Transactions on Vehicular Technology, 2013
    Co-Authors: Mooryong Choi, Jiwon J. Oh, Seibum B. Choi
    Abstract:

    The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive Least Squares (LRLS) Methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-road friction coefficient with great accuracy and robustness before tires reach their frictional limits but successfully estimates the two different tire-road friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.