Relevant bibliographies by topics / Robust model validation

Academic literature on the topic 'Robust model validation'

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Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Robust model validation"

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Ronchetti, Elvezio, Christopher Field, and Wade Blanchard. "Robust Linear Model Selection by Cross-Validation." Journal of the American Statistical Association 92, no. 439 (September 1997): 1017–23. http://dx.doi.org/10.1080/01621459.1997.10474057.

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Wan, Li, and Ying Jin. "Assessment of model validation outcomes of a new recursive spatial equilibrium model for the Greater Beijing." Environment and Planning B: Urban Analytics and City Science 46, no. 5 (September 27, 2017): 805–25. http://dx.doi.org/10.1177/2399808317732575.

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Robust calibration and validation of applied urban models are prerequisites for their successful, policy-cogent use. This is particularly important today when expert assessment is questioned and closely scrutinized. This paper proposes a new model calibration-validation strategy based on a spatial equilibrium model that incorporates multiple time horizons, such that the predictive capabilities of the model can be empirically tested. The model is implemented for the Greater Beijing city region and the model validation strategy is demonstrated over the Census years 2000 to 2010. Through forward/backward forecasting, the model validation helps to verify the stability of the model parameters as well as the predictive capabilities of the recursive equilibrium framework. The proposed modelling strategy sets a new standard for verifying and validating recursive equilibrium models. We also consider the wider implications of the approach.
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Carvalho, Vinícius N., Arinan De P. Dourado, Bruno RF Rende, Aldemir Ap Cavalini, and Valder Steffen. "Experimental validation of a robust model-based balancing approach." Journal of Vibration and Control 25, no. 2 (June 20, 2018): 423–34. http://dx.doi.org/10.1177/1077546318783552.

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Balancing procedures are periodically applied to rotating machines to reduce vibration amplitudes, thus keeping the system operating within acceptable safety limits. Various methods have been developed to balance rotating machines, such as the so-called signal-based balancing techniques. This paper presents the experimental validation of a balancing approach dedicated to rotating machines. This approach is based on a representative mathematical model of the system and it first performs the model updating of the machine. The unbalance condition is obtained through the solution of an inverse problem by taking into account the uncertain parameters of the rotor that can affect the balancing result. This robust balancing methodology is performed by considering a mono-objective optimization method in which the uncertain parameters are represented by random variables. In the present contribution, only the unbalance distribution along the rotor is considered as uncertain information. For this aim, uncertainty was modeled as a Gaussian field and was represented by Monte Carlo simulations. The corresponding experimental investigation considers a rotor system composed of a horizontal steel shaft, three discs, and two self-alignment ball bearings. The obtained results demonstrate that the proposed approach is an effective alternative for the balancing of rotating machines.
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Wittwer, J. W., M. S. Baker, and L. L. Howell. "Robust Design and Model Validation of Nonlinear Compliant Micromechanisms." Journal of Microelectromechanical Systems 15, no. 1 (February 2006): 33–41. http://dx.doi.org/10.1109/jmems.2005.859190.

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Smith, R. S., and J. C. Doyle. "Model validation: a connection between robust control and identification." IEEE Transactions on Automatic Control 37, no. 7 (July 1992): 942–52. http://dx.doi.org/10.1109/9.148346.

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Gevers, Michel, Xavier Bombois, Benoît Codrons, Franky De Bruyne, and Gérard Scorletti. "Model Validation for Robust Control and Controller Validation in a Prediction Error Framework." IFAC Proceedings Volumes 33, no. 15 (June 2000): 19–24. http://dx.doi.org/10.1016/s1474-6670(17)39720-3.

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Stevenson, Samantha, Baylor Fox-Kemper, Markus Jochum, Balaji Rajagopalan, and Stephen G. Yeager. "ENSO Model Validation Using Wavelet Probability Analysis." Journal of Climate 23, no. 20 (October 15, 2010): 5540–47. http://dx.doi.org/10.1175/2010jcli3609.1.

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Abstract A new method to quantify changes in El Niño–Southern Oscillation (ENSO) variability is presented, using the overlap between probability distributions of the wavelet spectrum as measured by the wavelet probability index (WPI). Examples are provided using long integrations of three coupled climate models. When subsets of Niño-3.4 time series are compared, the width of the confidence interval on WPI has an exponential dependence on the length of the subset used, with a statistically identical slope for all three models. This exponential relationship describes the rate at which the system converges toward equilibrium and may be used to determine the necessary simulation length for robust statistics. For the three models tested, a minimum of 250 model years is required to obtain 90% convergence for Niño-3.4, longer than typical Intergovernmental Panel on Climate Change (IPCC) simulations. Applying the same decay relationship to observational data indicates that measuring ENSO variability with 90% confidence requires approximately 240 years of observations, which is substantially longer than the modern SST record. Applying hypothesis testing techniques to the WPI distributions from model subsets and from comparisons of model subsets to the historical Niño-3.4 index then allows statistically robust comparisons of relative model agreement with appropriate confidence levels given the length of the data record and model simulation.
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Smith, R. S. "Model Validation for Robust Control: An Experimental Process Control Application." IFAC Proceedings Volumes 26, no. 2 (July 1993): 133–36. http://dx.doi.org/10.1016/s1474-6670(17)48239-5.

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Namvar, Mehrzad, Alina Voda, and I. D. Landau. "Approaches in identification and model validation for robust control design." IFAC Proceedings Volumes 32, no. 2 (July 1999): 4076–81. http://dx.doi.org/10.1016/s1474-6670(17)56695-1.

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Smith, Roy S. "Model validation for robust control: an experimental process control application." Automatica 31, no. 11 (November 1995): 1637–47. http://dx.doi.org/10.1016/0005-1098(95)00093-c.

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Dissertations / Theses on the topic "Robust model validation"

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Davis, Robert Andrew. "Model validation for robust control." Thesis, University of Cambridge, 1995. https://www.repository.cam.ac.uk/handle/1810/251990.

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Chai, Li. "Multirate periodic systems : robust model validation and stabilization /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202002%20CHAI.

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Al-Takrouri, Saleh Othman Saleh Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Robust state estimation and model validation techniques in computer vision." Publisher:University of New South Wales. Electrical Engineering & Telecommunications, 2008. http://handle.unsw.edu.au/1959.4/41002.

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The main objective of this thesis is to apply ideas and techniques from modern control theory, especially from robust state estimation and model validation, to various important problems in computer vision. Robust model validation is used in texture recognition where new approaches for classifying texture samples and segmenting textured images are developed. Also, a new model validation approach to motion primitive recognition is demonstrated by considering the motion segmentation problem for a mobile wheeled robot. A new approach to image inpainting based on robust state estimation is proposed where the implementation presented here concerns with recovering corrupted frames in video sequences. Another application addressed in this thesis based on robust state estimation is video-based tracking. A new tracking system is proposed to follow connected regions in video frames representing the objects in consideration. The system accommodates tracking multiple objects and is designed to be robust towards occlusions. To demonstrate the performance of the proposed solutions, examples are provided where the developed methods are applied to various gray-scale images, colored images, gray-scale videos and colored videos. In addition, a new algorithm is introduced for motion estimation via inverse polynomial interpolation. Motion estimation plays a primary role within the video-based tracking system proposed in this thesis. The proposed motion estimation algorithm is also applied to medical image sequences. Motion estimation results presented in this thesis include pairs of images from a echocardiography video and a robot-assisted surgery video.
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Sakouvogui, Kekoura. "Robust Capital Asset Pricing Model Estimation through Cross-Validation." Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/29019.

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Limitations of Capital Asset Pricing Model (CAPM) continue to present inconsistent empirical results despite its rm mathematical foundations provided in recent studies. In this thesis, we examine how estimation errors of the CAPM could be minimized using the cross-validation technique, a concept that is widely applied in machine learning (CV-CAPM). We apply our approach to test the assumption of CAPM as a well-diversified portfolio model with data from S&P500 and Dow Jones Industrial Average (DJIA). Our results from the CV-CAPM validate that both S&P500 and DJIA are well-diversified market indices with statistically insignificant variation in unsystematic risks during and after the 2007 financial crisis. Furthermore, the CV-CAPM provides the smallest root mean square errors and mean absolute deviations compared to the traditional CAPM.
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Madden, Ryan J. "Development of Robust Control Techniques towards Damage Identification." Cleveland State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=csu1460986638.

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Huang, Chao-Min. "Robust Design Framework for Automating Multi-component DNA Origami Structures with Experimental and MD coarse-grained Model Validation." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu159051496861178.

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Maillard, Guillaume. "Hold-out and Aggregated hold-out Aggregated Hold-Out Aggregated hold-out for sparse linear regression with a robust loss function." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM005.

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En statistiques, il est fréquent d'avoir à choisir entre plusieurs estimateurs (sélection d'estimateurs) ou à les combiner. Cela permet notamment d'adapter la complexité d'un modèle statistique en fonction des données (compromis biais-variance). Pour les problèmes de minimisation de risque, une méthode simple et générale, la validation ou hold-out, consiste à consacrer une partie de l'échantillon à l'estimation du risque des estimateurs, dans le but de choisir celui de risque minimal. Cette procédure nécessite de choisir arbitrairement un sous-échantillon "de validation". Afin de réduire l'influence de ce choix, il est possible d'agréger plusieurs estimateurs hold-out en les moyennant (Agrégation d'hold-out). Dans cette thèse, le hold-out et l'agrégation d'hold-out sont étudiés dans différents cadres. Dans un premier temps, les garanties théoriques sur le hold-out sont étendues à des cas où le risque n'est pas borné: les méthodes à noyaux et la régression linéaire parcimonieuse. Dans un deuxième temps, une étude précise du risque de ces méthodes est menée dans un cadre particulier: l'estimation de densité L² par des séries de Fourier. Il est démontré que l'agrégation de hold-out peut faire mieux que le meilleur des estimateurs qu'elle agrège, ce qui est impossible pour une méthode qui, comme le hold-out ou la validation croisée, sélectionne un seul estimateur
In statistics, it is often necessary to choose between different estimators (estimator selection) or to combine them (agregation). For risk-minimization problems, a simple method, called hold-out or validation, is to leave out some of the data, using it to estimate the risk of the estimators, in order to select the estimator with minimal risk. This method requires the statistician to arbitrarily select a subset of the data to form the "validation sample". The influence of this choice can be reduced by averaging several hold-out estimators (Aggregated hold-out, Agghoo). In this thesis, the hold-out and Agghoo are studied in various settings. First, theoretical guarantees for the hold-out (and Agghoo) are extended to two settings where the risk is unbounded: kernel methods and sparse linear regression. Secondly, a comprehensive analysis of the risk of both methods is carried out in a particular case: least-squares density estimation using Fourier series. It is proved that aggregated hold-out can perform better than the best estimator in the given collection, something that is clearly impossible for a procedure, such as hold-out or cross-validation, which selects only one estimator
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Corbier, Christophe. "Contribution à l’estimation robuste de modèles dynamiques : Application à la commande de systèmes dynamiques complexes." Thesis, Paris, ENSAM, 2012. http://www.theses.fr/2012ENAM0041/document.

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L'identification des systèmes dynamiques complexes reste une préoccupation lorsque les erreurs de prédictions contiennent des outliers d'innovation. Ils ont pour effet de détériorer le modèle estimé, si le critère d'estimation est mal choisi et mal adapté. Cela a pour conséquences de contaminer la distribution de ces erreurs, laquelle présente des queues épaisses et s'écarte de la distribution normale. Pour résoudre ce problème, il existe une classe d'estimateurs, dits robustes, moins sensibles aux outliers, qui traitent d'une manière plus « douce » la transition entre résidus de niveaux très différents. Les M-estimateurs de Huber font partie de cette classe. Ils sont associés à un mélange des normes L2 et L1, liés à un modèle de distribution gaussienne perturbée, dit gross error model. A partir de ce cadre formel, nous proposons dans cette thèse, un ensemble d'outils d'estimation et de validation de modèles paramétriques linéaires et pseudo-linéaires boîte-noires, avec extension de l'intervalle de bruit dans les petites valeurs de la constante d'accord de la norme de Huber. Nous présentons ainsi les propriétés de convergence du critère d'estimation et de l'estimateur robuste. Nous montrons que l'extension de l'intervalle de bruit réduit la sensibilité du biais de l'estimateur et améliore la robustesse aux points de levage. Pour un type de modèle pseudo-linéaire, il est présenté un nouveau contexte dit L-FTE, avec une nouvelle méthode de détermination de L, dans le but d'établir les linéarisations du gradient et du Hessien du critère d'estimation, ainsi que de la matrice de covariance asymptotique de l'estimateur. De ces relations, une version robuste du critère de validation FPE est établie et nous proposons un nouvel outil d'aide au choix de modèle estimé. Des expérimentations sur des processus simulés et réels sont présentées et analysées.L'identification des systèmes dynamiques complexes reste une préoccupation lorsque les erreurs de prédictions contiennent des outliers d'innovation. Ils ont pour effet de détériorer le modèle estimé, si le critère d'estimation est mal choisi et mal adapté. Cela a pour conséquences de contaminer la distribution de ces erreurs, laquelle présente des queues épaisses et s'écarte de la distribution normale. Pour résoudre ce problème, il existe une classe d'estimateurs, dits robustes, moins sensibles aux outliers, qui traitent d'une manière plus « douce » la transition entre résidus de niveaux très différents. Les M-estimateurs de Huber font partie de cette classe. Ils sont associés à un mélange des normes L2 et L1, liés à un modèle de distribution gaussienne perturbée, dit gross error model. A partir de ce cadre formel, nous proposons dans cette thèse, un ensemble d'outils d'estimation et de validation de modèles paramétriques linéaires et pseudo-linéaires boîte-noires, avec extension de l'intervalle de bruit dans les petites valeurs de la constante d'accord de la norme de Huber. Nous présentons ainsi les propriétés de convergence du critère d'estimation et de l'estimateur robuste. Nous montrons que l'extension de l'intervalle de bruit réduit la sensibilité du biais de l'estimateur et améliore la robustesse aux points de levage. Pour un type de modèle pseudo-linéaire, il est présenté un nouveau contexte dit L-FTE, avec une nouvelle méthode de détermination de L, dans le but d'établir les linéarisations du gradient et du Hessien du critère d'estimation, ainsi que de la matrice de covariance asymptotique de l'estimateur. De ces relations, une version robuste du critère de validation FPE est établie et nous proposons un nouvel outil d'aide au choix de modèle estimé. Des expérimentations sur des processus simulés et réels sont présentées et analysées
Complex dynamic systems identification remains a concern when prediction errors contain innovation outliers. They have the effect to damage the estimated model if the estimation criterion is badly chosen and badly adapted. The consequence is the contamination of the distribution of these errors; this distribution presents heavy tails and deviates of the normal distribution. To solve this problem, there is a robust estimator's class, less sensitive to the outliers, which treat the transition between residuals of very different levels in a softer way. The Huber's M-estimators belong to this class. They are associated to a mixed L2 - L1 norm, related to a disturbed Gaussian distribution model, namely gross error model. From this formal context, in this thesis we propose a set of estimation and validation tools of black-box linear and pseudo-linear models, with extension of the noise interval to low values of the tuning constant in the Huber's norm. We present the convergence properties of the robust estimation criterion and the robust estimator. We show that the extension of the noise interval reduces the sensitivity of the bias of the estimator and improves the robustness to the leverage points. Moreover, for a pseudo-linear model structure, we present a new context, named L-FTE, with a new method to determine L, in order to linearize the gradient and the Hessien of estimation criterion and the asymptotic covariance matrix of the estimator. From these expressions, a robust version of the FPE validation criterion is established and we propose a new decisional tool for the estimated model choice. Experiments on simulated and real systems are presented and analyzed
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Wroblewski, Adam C. "Model Identification, Updating, and Validation of an Active Magnetic Bearing High-Speed Machining Spindle for Precision Machining Operation." Cleveland State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=csu1318379242.

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Alvarado, Christiam Segundo Morales. "Estudo e implementação de métodos de validação de modelos matemáticos aplicados no desenvolvimento de sistemas de controle de processos industriais." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-05092017-092437/.

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A validação de modelos lineares é uma etapa importante em um projeto de Identificação de Sistemas, pois a escolha correta do modelo para representar a maior parte da dinâmica do processo, dentro de um número finito de técnicas de identificação e em torno de um ponto de operação, permite o sucesso no desenvolvimento de controladores preditivos e de controladores robustos. Por tal razão, o objetivo principal desta Tese é o desenvolvimento de um método de validação de modelos lineares, tendo como ferramentas de avaliação os métodos estatísticos, avaliações dinâmicas e análise da robustez do modelo. O componente principal do sistema de validação de modelos lineares proposto é o desenvolvimento de um sistema fuzzy para análise dos resultados obtidos pelas ferramentas utilizadas na etapa de validação. O projeto de Identificação de Sistemas é baseado em dados reais de operação de uma Planta-Piloto de Neutralização de pH, localizada no Laboratório de Controle de Processos Industriais da Escola Politécnica da USP. Para verificar o resultado da validação, todos os modelos são testados em um controlador preditivo do tipo QDMC (Quadratic Dynamic Matrix Control) para seguir uma trajetória de referência. Os critérios utilizados para avaliar o desempenho do controlador QDMC, para cada modelo utilizado, foram a velocidade de resposta do controlador e o índice da mínima variabilidade da variável de processo. Os resultados mostram que a confiabilidade do sistema de validação projetado para malhas com baixa e alta não-linearidade em um processo real, foram de 85,71% e 50%, respectivamente, com relação aos índices de desempenho obtidos pelo controlador QDMC.
Linear model validation is the most important stage in System Identification Project because, the model correct selection to represent the most of process dynamic allows the success in the development of predictive and robust controllers, within identification technique finite number and around the operation point. For this reason, the development of linear model validation methods is the main objective in this Thesis, taking as a tools of assessing the statistical, dynamic and robustness methods. Fuzzy system is the main component of model linear validation system proposed to analyze the results obtained by the tools used in validation stage. System Identification project is performed through operation real data of a pH neutralization pilot plant, located at the Industrial Process Control Laboratory, IPCL, of the Escola Politécnica of the University of São Paulo, Brazil. In order to verify the validation results, all modes are used in QDMC type predictive controller, to follow a set point tracking. The criterions used to assess the QDMC controller performance were the speed response and the process variable minimum variance index, for each model used. The results show that the validation system reliability were 85.71% and 50% projected for low and high non-linearity in a real process, respectively, linking to the performance indexes obtained by the QDMC controller.
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Books on the topic "Robust model validation"

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Lee, Herbert K. H., Matthew Taddy, Robert Gramacy, and Genetha Gray. Designing and analysing a circuit device experiment using treed Gaussian processes. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.28.

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This article describes a new circuit device, developed in collaboration with scientists at Sandia National Laboratories, based on treed Gaussian processes (TGP). The circuit devices under study are bipolar junction transistors, which are used to amplify electrical current. To aid with the design of the device, a computer model predicts its peak output as a function of the input dosage and a number of design parameters. The methodology also involves a novel sequential design procedure to generate data to fit the emulator. Both physical and computer simulation experiments are performed, and the results show that the TGP model can be useful for spatial data and semiparametric regression in the context of a computer experiment for designing a circuit device, for sequential design of (computer) experiments, sequential robust local optimization, validation, calibration, and sensitivity analysis.
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Book chapters on the topic "Robust model validation"

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Maugan, Fabien, Scott Cogan, Emmanuel Foltête, and Aurélien Hot. "Robust Modal Test Design Under Epistemic Model Uncertainties." In Model Validation and Uncertainty Quantification, Volume 3, 207–14. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0_22.

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Van Buren, Kendra, and François Hemez. "Robust-Optimal Design Using Multifidelity Models." In Model Validation and Uncertainty Quantification, Volume 3, 199–205. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0_21.

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Hemez, François. "Robust Estimation of Truncation-Induced Numerical Uncertainty." In Model Validation and Uncertainty Quantification, Volume 3, 223–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0_24.

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Hemez, François, and Kendra Van Buren. "Designing a Mechanical Latch for Robust Performance." In Model Validation and Uncertainty Quantification, Volume 3, 193–203. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5_19.

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Van Buren, Kendra L., and François M. Hemez. "Achieving Robust Design through Statistical Effect Screening." In Model Validation and Uncertainty Quantification, Volume 3, 145–60. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04552-8_14.

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Pereiro, D., S. Cogan, E. Sadoulet-Reboul, and F. Martinez. "Robust Model Calibration with Load Uncertainties." In Topics in Model Validation and Uncertainty Quantification, Volume 5, 89–97. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6564-5_10.

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Silva, Arinan Dourado Guerra, Aldemir Ap Cavalini, and Valder Steffen. "Model Based Robust Balancing Approach for Rotating Machines." In Model Validation and Uncertainty Quantification, Volume 3, 243–51. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5_24.

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Maugan, Fabien, Scott Cogan, Emmanuel Foltête, and Aurélien Hot. "Robust Sensor and Exciter Design for Linear Structures." In Model Validation and Uncertainty Quantification, Volume 3, 177–83. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5_17.

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Lépine, P., S. Cogan, E. Foltête, and M. O. Parent. "Robust Model Calibration Using Determinist and Stochastic Performance Metrics." In Model Validation and Uncertainty Quantification, Volume 3, 185–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5_18.

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Maugan, Fabien, Scott Cogan, Emmanuel Foltête, Fabrice Buffe, and Gaëtan Kerschen. "Robust Design of Notching Profiles Under Epistemic Model Uncertainties." In Model Validation and Uncertainty Quantification, Volume 3, 383–90. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04552-8_38.

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Conference papers on the topic "Robust model validation"

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Gevers, M. "Identification and validation for robust control: design issues." In IEE Seminar on Model Validation for Plant Control and Condition Monitoring. IEE, 2000. http://dx.doi.org/10.1049/ic:20000239.

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Barb, Florin, and Jan Mulder. "Robust Model Validation, Part I: Theoretical Aspects." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-5477.

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Carvalho, Vinícius, Arinan Dourado, Aldemir Ap Cavalini Jr, and Valder Steffen Jr. "Experimental Validation of Robust Model Based Balancing Approach." In 24th ABCM International Congress of Mechanical Engineering. ABCM, 2017. http://dx.doi.org/10.26678/abcm.cobem2017.cob17-2087.

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Barb, Florin, and Jan Mulder. "Robust Model Validation, Part II: A Robust Semi-Definite Optimization Based Solution." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-5555.

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Oomen, Tom, and Okko Bosgra. "Estimating disturbances and model uncertainty in model validation for robust control." In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4738592.

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Lim, K., G. Balas, and T. Anthony. "A minimum-norm model validation identification for robust control." In Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-3717.

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Madden, Ryan J., Jerzy T. Sawicki, and Alexander H. Pesch. "Model Validation for Identification of Damage Dynamics." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27341.

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Robust control techniques have allowed engineers to create more descriptive models by including uncertainty in the form of additive noise and plant perturbations. As a result, the complete model set is robust to any discrepancies between the mathematical model and actual system. Experimental unfalsification of the model set leads to the guarantee that the model and uncertainties are able to recreate all experimental data points. In this work, such a robust control relevant model validation technique is applied to structural health monitoring in order to 1) detect the presence of damage and 2) identify the damage dynamics when used in conjunction with model-based identification. Additionally, the robust control relevant model validation technique allows for a novel quality measure of the identified damage dynamics. Feasibility of the method is demonstrated experimentally on a rotordynamic crack detection test rig with the detection and identification of a change in structure. Further insight is gained from application of the method to seeded damage on a rotor levitated on active magnetic bearings in the form of a local reduction in stiffness.
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Barb, Florin, and Jan Mulder. "Robust Model Validation, Part III: The Aeroservoelastic System Case Study." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-5556.

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Uslu, Berk, Sunil K. Sinha, Shaoqing Ge, and Rahul Yadav. "A Validation and Verification Framework for Robust Drinking Water Pipeline Model Prediction Models." In Pipelines 2013. Reston, VA: American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784413012.116.

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Vazquez, Angel G. Alatorre, Alessandro Correa-Victorino, and Ali Charara. "Robust multi-model longitudinal tire-force estimation scheme: Experimental data validation." In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017. http://dx.doi.org/10.1109/itsc.2017.8317799.

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Reports on the topic "Robust model validation"

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Malej, Matt, and Fengyan Shi. Suppressing the pressure-source instability in modeling deep-draft vessels with low under-keel clearance in FUNWAVE-TVD. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40639.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) documents the development through verification and validation of three instability-suppressing mechanisms in FUNWAVE-TVD, a Boussinesq-type numerical wave model, when modeling deep-draft vessels with a low under-keel clearance (UKC). Many large commercial ports and channels (e.g., Houston Ship Channel, Galveston, US Army Corps of Engineers [USACE]) are traveled and affected by tens of thousands of commercial vessel passages per year. In a series of recent projects undertaken for the Galveston District (USACE), it was discovered that when deep-draft vessels are modeled using pressure-source mechanisms, they can suffer from model instabilities when low UKC is employed (e.g., vessel draft of 12 m¹ in a channel of 15 m or less of depth), rendering a simulation unstable and obsolete. As an increasingly large number of deep-draft vessels are put into service, this problem is becoming more severe. This presents an operational challenge when modeling large container-type vessels in busy shipping channels, as these often will come as close as 1 m to the bottom of the channel, or even touch the bottom. This behavior would subsequently exhibit a numerical discontinuity in a given model and could severely limit the sample size of modeled vessels. This CHETN outlines a robust approach to suppressing such instability without compromising the integrity of the far-field vessel wave/wake solution. The three methods developed in this study aim to suppress high-frequency spikes generated nearfield of a vessel. They are a shock-capturing method, a friction method, and a viscosity method, respectively. The tests show that the combined shock-capturing and friction method is the most effective method to suppress the local high-frequency noises, while not affecting the far-field solution. A strong test, in which the target draft is larger than the channel depth, shows that there are no high-frequency noises generated in the case of ship squat as long as the shock-capturing method is used.
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Dutra, Lauren M., Matthew C. Farrelly, Brian Bradfield, Jamie Ridenhour, and Jamie Guillory. Modeling the Probability of Fraud in Social Media in a National Cannabis Survey. RTI Press, September 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0046.2109.

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Cannabis legalization has spread rapidly in the United States. Although national surveys provide robust information on the prevalence of cannabis use, cannabis disorders, and related outcomes, information on knowledge, attitudes, and beliefs (KABs) about cannabis is lacking. To inform the relationship between cannabis legalization and cannabis-related KABs, RTI International launched the National Cannabis Climate Survey (NCCS) in 2016. The survey sampled US residents 18 years or older via mail (n = 2,102), mail-to-web (n = 1,046), and two social media data collections (n = 11,957). This report outlines two techniques that we used to problem-solve several challenges with the resulting data: (1) developing a model for detecting fraudulent cases in social media completes after standard fraud detection measures were insufficient and (2) designing a weighting scheme to pool multiple probability and nonprobability samples. We also describe our approach for validating the pooled dataset. The fraud prevention and detection processes, predictive model of fraud, and the methods used to weight the probability and nonprobability samples can be applied to current and future complex data collections and analysis of existing datasets.
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