Academic literature on the topic 'Initial values estimation'
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Journal articles on the topic "Initial values estimation"
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Wang, Zhenyu, Hana Sheikh, Kyongbum Lee, and Christos Georgakis. "Sequential Parameter Estimation for Mammalian Cell Model Based on In Silico Design of Experiments." Processes 6, no. 8 (2018): 100. http://dx.doi.org/10.3390/pr6080100.
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Due to the complicated metabolism of mammalian cells, the corresponding dynamic mathematical models usually consist of large sets of differential and algebraic equations with a large number of parameters to be estimated. On the other hand, the measured data for estimating the model parameters are limited. Consequently, the parameter estimates may converge to a local minimum far from the optimal ones, especially when the initial guesses of the parameter values are poor. The methodology presented in this paper provides a systematic way for estimating parameters sequentially that generates better initial guesses for parameter estimation and improves the accuracy of the obtained metabolic model. The model parameters are first classified into four subsets of decreasing importance, based on the sensitivity of the model’s predictions on the parameters’ assumed values. The parameters in the most sensitive subset, typically a small fraction of the total, are estimated first. When estimating the remaining parameters with next most sensitive subset, the subsets of parameters with higher sensitivities are estimated again using their previously obtained optimal values as the initial guesses. The power of this sequential estimation approach is illustrated through a case study on the estimation of parameters in a dynamic model of CHO cell metabolism in fed-batch culture. We show that the sequential parameter estimation approach improves model accuracy and that using limited data to estimate low-sensitivity parameters can worsen model performance.
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Wu, Yunlong, Qing Zhang, and Shuxuan Zhang. "Fast Cylindrical Fitting Method Using Point Cloud’s Normals Estimation." Mathematical Problems in Engineering 2018 (December 12, 2018): 1–7. http://dx.doi.org/10.1155/2018/8904653.
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Cylindrical fitting is an essential step in Large Process Pipeline’s measurement process, and precision of initial values of cylindrical fitting is a key element in getting a correct fitting result. In order to get well initial values, covariance matrixes of all points in cylinder’s three-dimensional laser scanning point cloud should be firstly established to estimate normals of all points, and then cylinder’s axis vector can be calculated by using least squares method. Secondly, remaining parameters’ initial values of the cylinder can be got by coordinate transformation. Finally, Levenberg-Marquardt algorithm is used in iterative optimization process to get fitting result by using the above values as initial values. Experiments demonstrate that this method can get precise initial values of cylindrical fitting and improve the accuracy and speed of cylindrical fitting.
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Awoda, Murtadha, and Ramzy Ali. "Parameter Estimation of a Permanent Magnetic DC Motor." Iraqi Journal for Electrical and Electronic Engineering 15, no. 1 (2019): 28–36. http://dx.doi.org/10.37917/ijeee.15.1.3.
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The identification of system parameters plays an essential role in system modeling and control. This paper presents a parameter estimation for a permanent magnetic DC motor using the simulink design optimization method. The parameter estimation may be represented as an optimization problem. Firstly, the initial values of the DC motor parameters are extracted using the dynamic model through measuring the values of voltage, current, and speed of the motor. Then, these values are used as an initial value for simulink design optimization. The experimentally inputoutput data can be collected using a suggested microcontroller based circuit that will be used later for estimating the DC motor parameters by building a simulink model. Two optimization algorithms are used, the pattern search and the nonlinear least square. The results show that the nonlinear least square algorithm gives a more accurate result that almost approaches to the actual measured speed response of the motor.
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Simonov, Andrey, Jean-Pierre Dubé, Günter Hitsch, and Peter Rossi. "State-Dependent Demand Estimation with Initial Conditions Correction." Journal of Marketing Research 57, no. 5 (2020): 789–809. http://dx.doi.org/10.1177/0022243720941529.
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The authors analyze the initial conditions bias in the estimation of brand choice models with structural state dependence. Using a combination of Monte Carlo simulations and empirical case studies of shopping panels, they show that popular, simple solutions that misspecify the initial conditions are likely to lead to bias even in relatively long panel data sets. The magnitude of the bias in the state dependence parameter can be as large as a factor of 2–2.5. The authors propose a solution to the initial conditions problem that samples the initial states as auxiliary variables in a Markov chain Monte Carlo procedure. The approach assumes that the joint distribution of prices and consumer choices is in equilibrium, which is plausible for the mature consumer packaged goods products commonly used in empirical applications. In Monte Carlo simulations, the approach recovers the true parameter values even in relatively short panels. Finally, the authors propose a diagnostic tool that uses common, biased approaches to bound the values of the state dependence and construct a computationally light test for state dependence.
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Karim, Muhammad Ahsar, Agus Yodi Gunawan, Mochamad Apri, and Kuntjoro Adji Sidarto. "SOLVING A PARAMETER ESTIMATION PROBLEM OF GOODWIN MODEL WITH FUZZY INITIAL VALUES." Far East Journal of Mathematical Sciences (FJMS) 107, no. 2 (2018): 321–38. http://dx.doi.org/10.17654/ms107020321.
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Mulry, Mary H., Stephen Kaputa, and Katherine J. Thompson. "Setting M-Estimation Parameters for Detection and Treatment of Influential Values." Journal of Official Statistics 34, no. 2 (2018): 483–501. http://dx.doi.org/10.2478/jos-2018-0022.
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Abstract Recent research on the use of M-estimation methodology for detecting and treating verified influential values in economic surveys found that initial parameter settings affect effectiveness. In this article, we explore the basic question of how to develop initial settings for the M-estimation parameters. The economic populations that we studied are highly skewed and are consequently highly stratified. While we investigated settings for several parameters, the most challenging problem was to develop an “automatic” data-driven method for setting the initial value of the tuning constant φ, the parameter with the greatest influence on performance of the algorithm. Of all the methods that we considered, we found that methods defined in terms of the accuracy of published estimates can be implemented on a large scale and yielded the best performance. We illustrate the methodology with an empirical analysis of 36 consecutive months of data from 19 industries in the Monthly Wholesale Trade Survey.
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Johansen, Søren, and Morten Ørregaard Nielsen. "THE ROLE OF INITIAL VALUES IN CONDITIONAL SUM-OF-SQUARES ESTIMATION OF NONSTATIONARY FRACTIONAL TIME SERIES MODELS." Econometric Theory 32, no. 5 (2015): 1095–139. http://dx.doi.org/10.1017/s0266466615000110.
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In this paper, we analyze the influence of observed and unobserved initial values on the bias of the conditional maximum likelihood or conditional sum-of-squares (CSS, or least squares) estimator of the fractional parameter,d, in a nonstationary fractional time series model. The CSS estimator is popular in empirical work due, at least in part, to its simplicity and its feasibility, even in very complicated nonstationary models.We consider a process,Xt, for which data exist from some point in time, which we call –N0+ 1, but we only start observing it at a later time,t= 1. The parameter (d,μ,σ2) is estimated by CSS based on the model${\rm{\Delta }}_0^d \left( {X_t - \mu } \right) = \varepsilon _t ,t = N + 1, \ldots ,N + T$, conditional onX1,...,XN. We derive an expression for the second-order bias of$\hat d$as a function of the initial values,Xt,t= –N0+ 1,...,N, and we investigate the effect on the bias of setting aside the firstNobservations as initial values. We compare$\hat d$with an estimator,$\hat d_c $, derived similarly but by choosingμ=C. We find, both theoretically and using a data set on voting behavior, that in many cases, the estimation of the parameterμpicks up the effect of the initial values even for the choiceN= 0.IfN0= 0, we show that the second-order bias can be completely eliminated by a simple bias correction. If, on the other hand,N0> 0, it can only be partly eliminated because the second-order bias term due to the initial values can only be diminished by increasingN.
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Journal, Baghdad Science. "A Comparison of the Methods for Estimation of Reliability Function for Burr-XII Distribution by Using Simulation." Baghdad Science Journal 10, no. 1 (2013): 85–96. http://dx.doi.org/10.21123/bsj.10.1.85-96.
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This deals with estimation of Reliability function and one shape parameter (?) of two- parameters Burr – XII , when ?(shape parameter is known) (?=0.5,1,1.5) and also the initial values of (?=1), while different sample shze n= 10, 20, 30, 50) bare used. The results depend on empirical study through simulation experiments are applied to compare the four methods of estimation, as well as computing the reliability function . The results of Mean square error indicates that Jacknif estimator is better than other three estimators , for all sample size and parameter values
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Panić, Branislav, Jernej Klemenc, and Marko Nagode. "Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation." Mathematics 8, no. 3 (2020): 373. http://dx.doi.org/10.3390/math8030373.
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A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.
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Zaika, Yury V. "Interval estimates of functionals in time-delay systems with uncertainty." International Journal of Mathematics and Mathematical Sciences 2003, no. 56 (2003): 3573–90. http://dx.doi.org/10.1155/s0161171203203264.
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We consider a linear dynamical system with delay and uncertainty in initial data and movement and measurement equations. We present an algorithm of estimating an interval of possible values of functionals on solutions. We construct suboptimal weight functions in integral observation operators to minimize a sure estimation.
Dissertations / Theses on the topic "Initial values estimation"
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Wang, Zhibo. "Estimations non-asymptotiques et robustes basées sur des fonctions modulatrices pour les systèmes d'ordre fractionnaire." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2023. http://www.theses.fr/2023ISAB0003.
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Cette thèse développe la méthode des fonctions modulatrices pour des estimations non-asymptotiques et robustes pour des pseudo-états des systèmes nonlinéaires d'ordre fractionnaire, des systèmes linéaires d'ordre fractionnaire avec des accélérations en sortie, et des systèmes à retards d'ordre fractionnaire. Les estimateurs conçus sont fournis en termes de formules intégrales algébriques, ce qui assure une convergence non-asymptotique. Comme une caractéristique essentielle des algorithmes d'estimation conçus, les mesures de sorties bruitées ne sont impliquées que dans les termes intégraux, ce qui confère aux estimateurs une robustesse contre les bruits. Premièrement, pour les systèmes nonlinéaires d'ordre fractionnaire et partiellement inconnu, l'estimation de la dérivée fractionnaire du pseudo-état est abordée via la méthode des fonctions modulatrices. Grâce à la loi de l'indice additif des dérivées fractionnaires, l'estimation est décomposée en une estimation des dérivées fractionnaires de la sortie et une estimation des valeurs initiales fractionnaires. Pendant ce temps, la partie inconnue est estimée via une stratégie innovante de fenêtre glissante. Deuxièmement, pour les systèmes linéaires d'ordre fractionnaire avec des accélérations comme sortie, l'estimation de l'intégrale fractionnaire de l'accélération est d'abord considérée pour les systèmes mécaniques de vibration d'ordre fractionnaire, où seules des mesures d'accélération bruitées sont disponibles. Basée sur des approches numériques existantes qui traitent des intégrales fractionnaires, notre attention se limite principalement à l'estimation des valeurs initiales inconnues en utilisant la méthode des fonctions modulatrices. Sur cette base, le résultat est ensuite généralisé aux systèmes linéaires plus généraux d'ordre fractionnaire. En particulier, le comportement des dérivées fractionnaires à zéro est étudié pour des fonctions absolument continues, ce qui est assez différent de celui de l'ordre entier. Troisièment, pour les systèmes à retards d'ordre fractionnaire, l'estimation du pseudo-état est étudiée en concevant un système dynamique auxiliaire d'ordre fractionnaire, qui fournit un cadre plus général pour générer les fonctions modulatrices requises. Avec l'introduction de l'opérateur de retard et du changement de coordonnées généralisé bicausal, l'estimation du pseudo-état du système considéré peut être réduite à celle de la forme normale correspondante. Contrairement aux travaux précédents le schéma présenté permet une estimation directe du pseudo-état plutôt que d'estimer les dérivées fractionnaires de la sortie et un ensemble de valeurs initiales fractionnaires. De plus, l'efficacité et la robustesse des estimateurs proposés sont vérifiées par des simulations numériques dans cette thèse. Enfin, un résumé de ce travail et un aperçu des travaux futurs sont tirés<br>This thesis develops the modulating functions method for non-asymptotic and robust estimations for fractional-order nonlinear systems, fractional-order linear systems with accelerations as output, and fractional-order time-delay systems. The designed estimators are provided in terms of algebraic integral formulas, which ensure non-asymptotic convergence. As an essential feature of the designed estimation algorithms, noisy output measurements are only involved in integral terms, which endows the estimators with robustness against corrupting noises. First, for fractional-order nonlinear systems which are partially unknown, fractional derivative estimation of the pseudo-state is addressed via the modulating functions method. Thanks to the additive index law of fractional derivatives, the estimation is decomposed into the fractional derivatives estimation of the output and the fractional initial values estimation. Meanwhile, the unknown part is fitted via an innovative sliding window strategy. Second, for fractional-order linear systems with accelerations as output, fractional integral estimation of the acceleration is firstly considered for fractional-order mechanical vibration systems, where only noisy acceleration measurements are available. Based on the existing numerical approaches addressing the proper fractional integrals of accelerations, our attention is primarily restricted to estimating the unknown initial values using the modulating functions method. On this basis, the result is further generalized to more general fractional-order linear systems. In particular, the behaviour of fractional derivatives at zero is studied for absolutely continuous functions, which is quite different from that of integer order. Third, for fractional-order time-delay systems, pseudo-state estimation is studied by designing a fractional-order auxiliary modulating dynamical system, which provides a more general framework for generating the required modulating functions. With the introduction of the delay operator and the bicausal generalized change of coordinates, the pseudo-state estimation of the considered system can be reduced to that of the corresponding observer normal form. In contrast to the previous work, the presented scheme enables direct estimation for the pseudo-state rather than estimating the fractional derivatives of the output and a bunch of fractional initial values. In addition, the efficiency and robustness of the proposed estimators are verified by numerical simulations in this thesis. Finally, a summary of this work and an insight into future work were drawn
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Sigstam, Kibret Negussie. "Optimization and estimation of solutions of Riccati equations /." Uppsala : Matematiska institutionen, Univ. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4288.
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Rana, Muhammad Sohel. "Analysis and Implementation of Numerical Methods for Solving Ordinary Differential Equations." TopSCHOLAR®, 2017. https://digitalcommons.wku.edu/theses/2053.
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Numerical methods to solve initial value problems of differential equations progressed quite a bit in the last century. We give a brief summary of how useful numerical methods are for ordinary differential equations of first and higher order. In this thesis both computational and theoretical discussion of the application of numerical methods on differential equations takes place. The thesis consists of an investigation of various categories of numerical methods for the solution of ordinary differential equations including the numerical solution of ordinary differential equations from a number of practical fields such as equations arising in population dynamics and astrophysics. It includes discussion what are the advantages and disadvantages of implicit methods over explicit methods, the accuracy and stability of methods and how the order of various methods can be approximated numerically. Also, semidiscretization of some partial differential equations and stiff systems which may arise from these semidiscretizations are examined.
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Ayvazyan, Vigen. "Etude de champs de température séparables avec une double décomposition en valeurs singulières : quelques applications à la caractérisation des propriétés thermophysiques des matérieux et au contrôle non destructif." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14671/document.
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La thermographie infrarouge est une méthode largement employée pour la caractérisation des propriétés thermophysiques des matériaux. L’avènement des diodes laser pratiques, peu onéreuses et aux multiples caractéristiques, étendent les possibilités métrologiques des caméras infrarouges et mettent à disposition un ensemble de nouveaux outils puissants pour la caractérisation thermique et le contrôle non desturctif. Cependant, un lot de nouvelles difficultés doit être surmonté, comme le traitement d’une grande quantité de données bruitées et la faible sensibilité de ces données aux paramètres recherchés. Cela oblige de revisiter les méthodes de traitement du signal existantes, d’adopter de nouveaux outils mathématiques sophistiqués pour la compression de données et le traitement d’informations pertinentes. Les nouvelles stratégies consistent à utiliser des transformations orthogonales du signal comme outils de compression préalable de données, de réduction et maîtrise du bruit de mesure. L’analyse de sensibilité, basée sur l’étude locale des corrélations entre les dérivées partielles du signal expérimental, complète ces nouvelles approches. L'analogie avec la théorie dans l'espace de Fourier a permis d'apporter de nouveaux éléments de réponse pour mieux cerner la «physique» des approches modales.La réponse au point source impulsionnel a été revisitée de manière numérique et expérimentale. En utilisant la séparabilité des champs de température nous avons proposé une nouvelle méthode d'inversion basée sur une double décomposition en valeurs singulières du signal expérimental. Cette méthode par rapport aux précédentes, permet de tenir compte de la diffusion bi ou tridimensionnelle et offre ainsi une meilleure exploitation du contenu spatial des images infrarouges. Des exemples numériques et expérimentaux nous ont permis de valider dans une première approche cette nouvelle méthode d'estimation pour la caractérisation de diffusivités thermiques longitudinales. Des applications dans le domaine du contrôle non destructif des matériaux sont également proposées. Une ancienne problématique qui consiste à retrouver les champs de température initiaux à partir de données bruitées a été abordée sous un nouveau jour. La nécessité de connaitre les diffusivités thermiques du matériau orthotrope et la prise en compte des transferts souvent tridimensionnels sont complexes à gérer. L'application de la double décomposition en valeurs singulières a permis d'obtenir des résultats intéressants compte tenu de la simplicité de la méthode. En effet, les méthodes modales sont basées sur des approches statistiques de traitement d'une grande quantité de données, censément plus robustes quant au bruit de mesure, comme cela a pu être observé<br>Infrared thermography is a widely used method for characterization of thermophysical properties of materials. The advent of the laser diodes, which are handy, inexpensive, with a broad spectrum of characteristics, extend metrological possibilities of infrared cameras and provide a combination of new powerful tools for thermal characterization and non destructive evaluation. However, this new dynamic has also brought numerous difficulties that must be overcome, such as high volume noisy data processing and low sensitivity to estimated parameters of such data. This requires revisiting the existing methods of signal processing, adopting new sophisticated mathematical tools for data compression and processing of relevant information.New strategies consist in using orthogonal transforms of the signal as a prior data compression tools, which allow noise reduction and control over it. Correlation analysis, based on the local cerrelation study between partial derivatives of the experimental signal, completes these new strategies. A theoretical analogy in Fourier space has been performed in order to better understand the «physical» meaning of modal approaches.The response to the instantaneous point source of heat, has been revisited both numerically and experimentally. By using separable temperature fields, a new inversion technique based on a double singular value decomposition of experimental signal has been introduced. In comparison with previous methods, it takes into account two or three-dimensional heat diffusion and therefore offers a better exploitation of the spatial content of infrared images. Numerical and experimental examples have allowed us to validate in the first approach our new estimation method of longitudinal thermal diffusivities. Non destructive testing applications based on the new technique have also been introduced.An old issue, which consists in determining the initial temperature field from noisy data, has been approached in a new light. The necessity to know the thermal diffusivities of an orthotropic medium and the need to take into account often three-dimensional heat transfer, are complicated issues. The implementation of the double singular value decomposition allowed us to achieve interesting results according to its ease of use. Indeed, modal approaches are statistical methods based on high volume data processing, supposedly robust as to the measurement noise
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LI, JIAN-YU, and 李健瑜. "Exploring the Effect of Initial and Boundary Values on Parameter Estimation of Latent Class Model." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/12762969756160022837.
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碩士<br>國立臺北大學<br>統計學系<br>95<br>Latent Class Model is applied in the area of psychology and science of behaviors. After obtaining the original data, researchers will classify the respondents and analyze mutual relationship among them to get statistical inferences. This paper mainly emphasizes on district data and applies EM(Expectation Maximization) algorithm to estimate the proportions of groups from the latent classes of the respondents.
The simulated data used in parameter estimation is generated by employing Monte Carlo simulation. The empirical experiments were then carried on a total of 160 combinations based on the number of latent classes, the number of questions, the sample size and the initial value approaches. Each experiment is repeated 1000 times then applied EM (Expectation Maximization) algorithm. The algorithm is combined with E-step and M-step. With estimated values and theoretical values to induce its statistical inference including the bias and the Mean Square Error of the underlying parameters.
The empirical results show the negative correlation between sample size and bias. Also, as the number of latent classes increases, the estimation of the underlying parameters gets poor. As for the initial value approaches used herein, the first and the second one demonstrated their superiority. Besides parameter estimation, the multiple regression on five related variables shows significant relationship.
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Schoeman, Daniel Frederik. "Vooruitberamingsmodelle in die telekommunikasie-omgewing." 1996. http://hdl.handle.net/10500/17681.
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Po-YangHsieh and 謝博揚. "Estimation of Initial Value for Inverse Heat Conduction Problem." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/08380831412189290050.
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Jiang, Yue Ling, and 江岳玲. "Estimations of Initial Values for Solving Quadratic Eigenvalue Problems by Using Newton's Method." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/11174348077360920308.
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Chia-YangHsu and 許佳揚. "Estimation of Initial Value for Inverse Heat Conduction Problem in Curvilinear Coordinate System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/93269675576366026587.
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碩士<br>國立成功大學<br>機械工程學系碩博士班<br>100<br>The study discusses the heat conduction problem when the initial value is unknown. Using the polynomial expansion method to express unknown initial value, at a particular point, we take several measured temperature value at different times to solve the coefficients of the expansion in order to get the initial value.
The study investigates into the accuracy of estimation value analyzed at different measurement point in cylindrical coordinate, the effects of how long the time we analysis, estimates the initial value using the measurement value at different time, the results of the central difference method as a simulation temperature value and the impact of estimation of initial value with measurement error. The results show that, in short time, we can get a good estimation in a particular measurement points that the temperature change larger with time whether or not the measurement error; on the other hand, in long time, we can also get good results for the initial value is a smooth curve. If the initial value is not a smooth curve or changes rapidly, we would not get great results, and we use several polynomial expansions to improve our estimation. As a result, the inverse algorithm of the study presents a good accuracy.
Books on the topic "Initial values estimation"
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Voronin, Evgeniy, Aleksandr Chibunichev, and Yuriy Blohinov. Reliability of solving inverse problems of analytical photogrammetry. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/2010462.
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The monograph is devoted to computational aspects of photogrammetric reconstruction of narrow-angle bundles of projecting beams that existed during the survey. Methods of improving the conditionality of systems of linear equations, ensuring the convergence of iterative refinement of their roots, increasing the stability of calculations in finite precision machine arithmetic are considered. The main efforts are focused on solving the problem of establishing reliable measurement weights within the framework of the least squares method. The criteria for the reliability of the weights are determined. Algorithms have been developed for matching the initial values of the measurement weights, adjusting the weights during equalization, and identifying insignificant parameters of mathematical measurement models. A new method for evaluating the accuracy of the equalization results has been developed.
 For specialists engaged in the processing of remote sensing data of the Earth and mathematical processing of the results of heterogeneous measurements using weighted methods of statistical estimation of the parameters of functional dependencies.
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Schelbert, Heinrich R. Image-Based Measurements of Myocardial Blood Flow. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0024.
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Image-based measurements of myocardial blood flow afford the assessment of coronary circulatory function. They reflect functional consequences of coronary stenoses, diffuse epicardial vessel disease and microvascular dysfunction and structural changes and thus provide a measure of the total ischemic burden. Measured flows contain therefore clinically important predictive information. Fundamental to flow measurements are the tissue tracer kinetics, their description through tracer kinetic models, high spatial and temporal resolution imaging devices and accurate extraction of radiotracer tissue concentrations from dynamically acquired images for estimating true flows from the tissue time activity curves. A large body of literature on measurements of myocardial blood flow exists for defining in humans normal values for flow at baseline and during hyperemic stress as well as for the myocardial flow reserve. The role of PET for flow measurements has been well established; initial results with modern SPECT devices are encouraging. Responses of myocardial blood flow to specific challenges like pharmacologic vasodilation and to sympathetic stimulation can uncover functional consequences of focal epicardial coronary stenoses, of conduit vessel disturbances and disease and impairments of microvascular function. Apart from risk stratification, flow measurements may allow detection of early preclinical disease, influence treatment strategies and identify therapy responses.
Book chapters on the topic "Initial values estimation"
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Ntalaperas, Dimitris, Iosif Angelidis, Giorgos Vafeiadis, and Danai Vergeti. "A Decision-Support System for the Digitization of Circular Supply Chains." In New Business Models for the Reuse of Secondary Resources from WEEEs. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74886-9_8.
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AbstractAs it has been already explained, it is very important for circular economies to minimize the wasted resources, as well as maximize the utilization value of the existing ones. To that end, experts can evaluate the materials and give an accurate estimation for both aspects. In that case, one might wonder, why is a decision support system employing machine learning necessary? While a fully automated machine learning model rarely surpasses a human’s ability in such tasks, there are several advantages in employing one. For starters, human experts will be more expensive to employ, rather than use an algorithm. One could claim that research towards developing an efficient and fully automated decision support system would end up costing more than employing actual human experts. In this instance, it is paramount to think long-term. Investing in this kind of research will create systems which are reusable, extensible, and scalable. This aspect alone more than remedies the initial costs. It is also important to observe that, if the number of wastes to be processed is more than the human experts can process in a timely fashion, they will not be able to provide their services, even if employment costs were not a concern. On the contrary, a machine learning model is perfectly capable of scaling to humongous amounts of data, conducting fast data processing and decision making. For power plants with particularly fast processing needs, an automated decision support system is an important asset. Moreover, a decision support system can predict the future based on past observations. While not always entirely spot on, it can give a future estimation about aspects such as energy required, amounts of wastes produced etc. in the future. Therefore, processing plants can plan of time and adapt to specific needs. A human expert can provide this as well to some degree, but on a much smaller scale. Especially in time series forecasting, it is interesting to note that, even if a decision support model does not predict exact values, it is highly likely to predict trends of the value increasing or decreasing in certain ranges. In the next sections, we are going to describe the four machine learning models that were developed and which compose the Decision Support System of FENIX. Section 8.1 describes how we predict the quality of the extracted materials based on features such as temperature, extruder speed, etc. Section 8.2 describes the process of extracting heuristic rules based on existing results. Section 8.3 describes how FENIX provides time-series forecasting to predict the future of a variable based on past observations. Finally, Sect. 8.4 describes the process of classifying materials based on images.
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Kwoh, Chee Keong, and Duncan Fyfe Gillies. "Estimating the initial values of unobservable variables in visual probabilistic networks." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60268-2_313.
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Wilson, John, Thècle Alix, Elise Gruhier, et al. "Initial Proposal for a General Systems Engineering Methodology to Early Design Phase Cost/Value Estimation." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70566-4_62.
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AbstractWe propose that a systems engineering methodology may be applied in an effective interactive design environment for lifecycle cost estimation and value optimization in the context of a manufacturing enterprise. In order to optimize a product design for value, engineering and manufacturing businesses need to be able to estimate accurately product lifecycle costs during the early design phases of its development, because this is when the majority of these costs are determined. Systems engineering defines realizing value as meeting stakeholder requirements and emphasizes formalizing these in order to link coherently the individual estimated costs of a design to the needs it fulfils. Furthermore, formalized requirement and design parameters are suitable for modelling and simulation, and we envision a systems model implemented within existing knowledge-based engineering tools embedded in a design environment. The results of this model may support design decisions, as well as reinforce systems engineering analyses in evaluating processes for value chain simulations.
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Zhang, Shuchun, Yongbin Chou, Xin Wei, and Zongquan Deng. "Modified PSO Optimizer for Arrays Pattern Optimization by Efficient Estimations of the Optimum Particle Initial Values." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6571-2_163.
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Ma, Cuiling. "Credibility of Estimating the Hysteretic Energy Demands of Concentrically Braced Steel Frames." In Advances in Frontier Research on Engineering Structures. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8657-4_16.
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AbstractEarthquake happens frequently in China, and seismic design is of high importance for buildings. The energy-based seismic design method with clear conception has attracted attention of more and more researchers. One of the initial questions is how to determine the energy requirement. In this paper, the finite element software is used to analyze the time history of the concentrically braced steel frames designed according to the current codes and standards. The analysis shows that the cumulative demand obtained by time history analysis is similar to the estimated value. The hysteretic energy dissipation of the concentrically braced steel frames is of small top and big bottom along the height of the structure, and the energy dissipation of the floors below the height of H/3 (H is the height of the structure) is larger. Under rare earthquakes, the yield mode of structures designed according to the elastic design method of small earthquakes is uncontrollable, so it is necessary to study the design method based on energy behavior.
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Philp, Tom J., Adrian J. Champion, Kevin I. Hodges, et al. "Identifying Limitations when Deriving Probabilistic Views of North Atlantic Hurricane Hazard from Counterfactual Ensemble NWP Re-forecasts." In Hurricane Risk in a Changing Climate. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08568-0_10.
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AbstractDownward counterfactual analysis – or quantitatively estimating how our observed history could have been worse – is increasingly being used by the re/insurance industry to identify, quantify, and mitigate against as-yet-unrealised “grey-swan” catastrophic events. While useful for informing site-specific adaptation strategies, the extraction of probabilistic information remains intangible from such downside-only focused analytics. We hypothesise that combined upward and downward counterfactual analysis (i.e., how history could have been either better or worse) may allow us to obtain probabilistic information from counterfactual research if it can be applied objectively and without bias.Here we test this concept of objective counterfactual analysis by investigating how initial-condition-driven track variability of events in our North Atlantic Hurricane (NAHU) record may affect present-day probabilistic views of US landfall risk. To do this, we create 10,000 counterfactual NAHU histories from NCEP GEFS v2 initial-condition ensemble reforecast data for the period 1985-2016 and compare the statistics of these counterfactual histories to a model-based version of our single observational history.While the methodology presented herein attempts to produce the histories as objectively as possible, there is clear – and, ultimately, intuitively understandable – systematic underprediction of US NAHU landfall frequency in the counterfactual histories. This limits the ability to use the data in real-world applications at present. However, even with this systematic under-prediction, it is interesting to note both the magnitude of volatility and spatial variability in hurricane landfalls in single cities and wider regions along the US coastline, which speaks to the potential value of objective counterfactual analysis once methods have evolved.
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Zhu, Yang, and Miroslav Krstic. "Basic Idea of Adaptive Control for Single-Input Systems." In Delay-Adaptive Linear Control. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691202549.003.0003.
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This chapter provides a variety of adaptive predictor control techniques to deal with different uncertainty collections from four basic uncertainties. These include delay, parameter, ODE state, and PDE state. In the presence of a discrete actuator delay that is long and unknown, but when the actuator state is available for measurement, a global adaptive stabilization result is obtainable. In contrast, the problem where the delay value is unknown, and where the actuator state is not measurable at the same time, is not solvable globally, since the problem is not linearly parameterized in the unknown delay. In this case, a local stabilization is feasible, with restrictions on the initial conditions such that not only do the initial values of the ODE and actuator state have to be small, but also the initial value of the delay estimation error has to be small (the delay value is allowed to be large but the initial value of its estimate has to be close to the true value of the delay).
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Yoshizawa, Itsuro, and Osamu Takahashi. "Perspective Chapter: Building Damage Estimation and Renovation Proposal System Using Constant Microtremor Measurement for Construction Risk Management." In Risk Management in Construction - Recent Advances [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.112457.
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In this chapter, the authors propose a system that measures microtremors of the ground and buildings and instantaneously calculates building damage estimation and emergency risk assessment. In the conventional damage estimation and emergency risk assessment due to microtremors, resonance judgments are mainly based on the initial state due to microtremors of the ground, and buildings are often seen. However, there is a possibility that the ground and the building will resonate continuously as the ground and the building are cracked and damaged by disturbances such as earthquakes and winds. In addition, the resonance causes great damage to the building. In the system proposed this time, damage estimation and emergency risk assessment are performed not only in the initial state but also during the duration. In addition, as a renovation proposal, a system is proposed that predicts the damage and emergency risk of the building during renovation with earthquake resistance, seismic isolation, and damping. In this system, the building hazard levels are classified into four categories based on two methods: one method involves calculating the response spectrum and transfer function of the building and ground using continuous microtremor measurements to determine the resonance performance, and the other method utilizes the PML (Probable Maximum Loss) values.
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Mohamed, Majeed. "Online Estimation of Terminal Airspace Sector Capacity from ATC Workload." In Air Traffic Management and Control. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.100274.
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Neural Partial Differentiation (NPD) approach is applied to estimate terminal airspace sector capacity in real-time from the ATC (Air Traffic Controller) dynamical neural model with permissible safe separation and affordable workload. A neural model of a multi-input-single-output (MISO) ATC dynamical system is primarily established and used to estimate parameters from the experimental data using NPD. Since the relative standard deviations of these estimated parameters are lesser, the predicted neural model response is well matched with the intervention of ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters that are unknown in practice.
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Kuroda, Masahiro. "Fast Computation of the EM Algorithm for Mixture Models." In Computational Statistics and Applications. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101249.
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Mixture models become increasingly popular due to their modeling flexibility and are applied to the clustering and classification of heterogeneous data. The EM algorithm is largely used for the maximum likelihood estimation of mixture models because the algorithm is stable in convergence and simple in implementation. Despite such advantages, it is pointed out that the EM algorithm is local and has slow convergence as the main drawback. To avoid the local convergence of the EM algorithm, multiple runs from several different initial values are usually used. Then the algorithm may take a large number of iterations and long computation time to find the maximum likelihood estimates. The speedup of computation of the EM algorithm is available for these problems. We give the algorithms to accelerate the convergence of the EM algorithm and apply them to mixture model estimation. Numerical experiments examine the performance of the acceleration algorithms in terms of the number of iterations and computation time.
Conference papers on the topic "Initial values estimation"
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Wang, Zhi-Bo, Da-Yan Liu, Driss Boutat, Yang Tian, and Hao-Ran Liu. "Modulating Functions Based Fast and Robust Estimation for a Class of Fractional Order Vibration Systems." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-67447.
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Abstract This paper aims to fast and robustly estimate the fractional integrals and derivatives of positions from noisy accelerations for a class of fractional order vibration systems defined by the Caputo fractional derivative. The main idea is to convert the original issue into the estimation of the fractional integrals of accelerations and the ones of the unknown initial conditions, on the basis of the additive index law. Being proper integrals, the fractional integrals of accelerations can be estimated via a numerical method. Consequently, solving the original problem boils down to estimating the unknown initial values. To this end, the modulating functions method is adopted. By constructing appropriate modulating functions, the unknown initial values are exactly given in terms of algebraic integral formulas in different situations. Finally, two illustrations are presented to verify the correctness and robustness of the proposed estimators.
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Peng, Lvbin, Bo Hu, and Kaigui Xie. "Initial Values Estimation and Solution Finding for Inverse Problem of Reliability Evaluation Using Deep Neural Network." In 2020 5th Asia Conference on Power and Electrical Engineering (ACPEE). IEEE, 2020. http://dx.doi.org/10.1109/acpee48638.2020.9136424.
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An, Lee-Sak, Yeun Chul Park, and Ho-Kyung Kim. "Reliability-Based Fatigue Evaluation Considering Various Initial Conditions of Defects." In IABSE Congress, New York, New York 2019: The Evolving Metropolis. International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/newyork.2019.1772.
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<p>This study presents the reliability-based fatigue evaluation procedure for deteriorated steel members of a bridge with estimation in initial conditions of defects. For this purpose, deterioration scenarios and limit states are defined when fatigue crack or pitting corrosion are found. Since the initial conditions of the defects, such as the initial crack depth or the time for corrosion nucleation have large uncertainties, the initial conditions are estimated using the inspection results. A numerical example is presented to confirm the applicability of proposed method for typical steel structural members. The initial crack depth and the time for corrosion nucleation estimated by the proposed methods are similar to those proposed in previous studies, depending on the size of the defect. It is also possible to estimate more reasonable initial conditions of defects compared to the fixed distribution parameters or deterministic values which have been presented in previous studies. Therefore, it is expected that the proposed evaluation procedure could facilitate precise fatigue reliability analysis.</p>
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Mariūnas, Mečislovas, Julius Griškevičius, and Gintaras Jonaitis. "Estimation of the parameters of barbell’ lifting law of motion." In Biomdlore. VGTU Technika, 2016. http://dx.doi.org/10.3846/biomdlore.2016.23.
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Characteristic points of the law of motion were determined based on the results of experimental study on the velocities and accelerations of the lifted barbell. Relationship between the height of the athlete, lifted weight, maximal velocity and acceleration was estimated analytically. It was shown, that at the initial time moment the athlete must apply approximately 1,5 times larger force than lifted weight. The values of the parameters of the barbell’ law of motion and lifting force approximate law of change were estimated.
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Alves de Sousa Neto, José Nilo, and Marcelo Ladeira. "Value Estimation of Properties Administered by the Brazilian Army Using Machine Learning and Spatial Components." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227798.
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The valuation of an institution’s patrimony represents a necessary condition for an efficient management of its assets. The execution and analysis of real estate appraisal reports are essential to the achievement of some strategic objectives of the Brazilian Army, but they are also quite costly in terms of time, labor and financial resources. Sometimes, great effort is required for the aforementioned steps to take place and the market value finally obtained is inconsistent with what was initially imagined by the authorities, causing the technical study carried out to not be effectively used in negotiations by the organization. This work proposes the development of predictive models capable of building estimates of real estate values, so that the formal requests of the managers that imply the stages of execution and analysis of appraisal reports can occur with this information as an initial input. Counting on linear and nonlinear approaches and on machine learning techniques, the models have a reasonable level of assertiveness and national geographic coverage when generate estimated market values of Union real estate assets. Intrinsic and extrinsic variables to the properties were considered, including tests of aggregation of spatial components on some of them. As the interpretability of the proposed solution is an important requirement in both linear and nonlinear approaches, the Shapley value was adopted as a tool to support the guarantee of explainability and a PLS-SEM conceptual model was built to select attributes in a reasoned manner. These two considerations associated with modeling of real estate prices at a national level represent an innovation of this work in relation to the scientific literature analyzed.
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Ehrich, Fredric F. "Component Minimum Life Estimation Based on Life Testing Suspended Prior to Failure." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0163.
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Abstract One possible approach to life extension of aerospace components is to take those parts or subsystems that have run the limited initial life estimate and been removed from service, and to operate them in simulated service for periods of time beyond the initial life estimate. It is often the case that such testing must be suspended before the part reaches the point of actual failure. A need therefore exists to use such suspended test data to infer a life limit. The standard deviation of the estimated life can generally be reliably quantified by applying results from laboratory material tests and information on the variability of the unit’s dimensions. The derived life estimate must therefore be consistent with the known standard deviation. The problem is structured as a variational one, that is, a problem of finding the values of a set of variables — the hypothetical duration of the individual suspended tests if they had been continued to failure — subject to the constraints that the period of continuation must be equal to or greater that zero, and that the standard deviation of the average value of the life of the sample set be equal to the known, predetermined value. The problem is made determinate by applying the conservative requirement that calculated average life of the sample set be the lowest possible value. Although the problem is nonlinear and not amenable to traditional linear programming solution, a very simple pair of algorithms have been identified which permit exact solution for all ranges of objective standard deviation. Solutions to typical problems are given to illustrate the utility of the procedure.
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Ban, Zhe, Carlos Pfeiffer, and Bernt Lie. "Parameter and State Estimation for an Oil Production Model using Julia." In 63rd International Conference of Scandinavian Simulation Society, SIMS 2022, Trondheim, Norway, September 20-21, 2022. Linköping University Electronic Press, 2022. http://dx.doi.org/10.3384/ecp192013.
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Dynamic models of industrial processes play an instrumental role in the operation of such processes from smart sensors, data reconciliation, to advanced control. For good performance, a precise model is normally required. The issue of improving models has received considerable critical attention. In this work, we consider the estimation of model parameters and initial states of a gas lifting oil well model, followed by filtering of its states. By utilizing information from both first-principle model and data, the results are presented to show the estimated values and their uncertainties. Julia is the main programming language used in this study. This research study provided an opportunity to advance the understanding of the optimization and estimation for the oil well operation.
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Tian, Yuan, Marc Compere, and Sergey Drakunov. "A Quaternion-Based Sliding Mode Observer for Gyro-Bias Estimation and Attitude Reconstruction." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10464.
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Abstract Localization accuracy is one of the most important parts of Unmanned Vehicle Systems, Automated Vehicles, Robotics and Navigation. The 6-DOF Inertial Measurement Unit (IMU) is a commonly used device for inertial navigation and is composed of a 3-axis accelerometer and 3-axis gyroscope. The body-fixed IMU measurements are combined with initial values to produce a position and orientation estimate in the inertial frame with every new measurement. However, IMU performance is greatly degraded by bias, scale-factor, non-orthogonality, temperature, and noise. This paper develops a sliding mode observer specifically focused on gyroscope bias estimation to improve gyro measurement results. The work presented here improves the performance of tilt sensors equipped in a commercially available smartphones with accelerometers and gyroscopes. The algorithm uses quaternions to avoid the well-known Euler angle singularities also known as gimbal lock. The observed gyro-bias can be used to reconstruct an improved estimation of the real attitude. A sliding-mode observer was constructed, and A* Matrix stability criterion were used to guarantee observer error convergence in finite time. The algorithm was verified using both a simulated IMU model and experimental tests with a custom designed rotational platform. Simulation tests used a predefined gyros-bias to ensure the algorithm-estimated results converged to the correct value. Simulation results show the observer error quickly converges to zero and the gyro-bias estimation converged to the expected values. The results also show that the proposed method is very effective for reconstructing the real attitude using the observed gyro-bias. This study presents a fast, simple gyro-bias estimation method that can help reconstruct the real attitude with a simple formulation that eliminates complicated constraints.
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Gadsden, Andrew, and Saeid Habibi. "Target Tracking Using the Smooth Variable Structure Filter." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2632.
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This article discusses the application of the smooth variable structure filter (SVSF) on a target tracking problem. The SVSF is a relatively new predictor-corrector method used for state and parameter estimation. It is a sliding mode estimator, where gain switching is used to ensure that the estimates converge to true state values. An internal model of the system, either linear or nonlinear, is used to predict an a priori state estimate. A corrective term is then applied to calculate the a posteriori state estimate, and the estimation process is repeated iteratively. The results of applying this filter on a target tracking problem demonstrate its stability and robustness. Both of these attributes make using the SVSF advantageous over the well-known Kalman and extended Kalman filters. The performances of these algorithms are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, tracking accuracy and computational complexity.
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Noergaard, Christian, Lasse Schmidt, and Michael M. Bech. "A Simple and Robust Sliding Mode Velocity Observer for Moving Coil Actuators in Digital Hydraulic Valves." In BATH/ASME 2016 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/fpmc2016-1789.
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This paper focuses on estimating the velocity and position of fast switching digital hydraulic valves actuated by electromagnetic moving coil actuators, based on measurements of the coil current and voltage. The velocity is estimated by a simple first-order sliding mode observer architecture and the position is estimated by integrating the estimated velocity. The binary operation of digi-valves enables limiting and resetting the position estimate since the moving member is switched between the mechanical end-stops of the valve. This enables accurate tracking since drifting effects due to measurement noise and integration of errors in the velocity estimate may be circumvented. The proposed observer architecture is presented along with stability proofs and initial experimental results. To reveal the optimal observer performance, an optimization of the observer parameters is carried out. Subsequently, the found observer parameters are perturbed to assess the robustness of the observer to parameter estimation errors. The proposed observer demonstrates accurate tracking of the valve movement when using experimentally obtained data from a moving coil actuated digi-valve prototype and observer parameters estimates in the vicinity of the optimized parameter values.
Reports on the topic "Initial values estimation"
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Torrijos, Ivan Dario Pinerez, Tina Puntervold, Skule Strand, Panagiotis Aslanidis, Ingebret Fjelde, and Aleksandr Mamonov. Core restoration: A guide for improved wettability assessments. University of Stavanger, 2021. http://dx.doi.org/10.31265/usps.198.
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The initial wetting of a reservoir sets a limit for the EOR potential during water-based recovery operations and “Smart Water” injection. For this reason, an improved understanding of the factors influencing the wetting can help to control and better forecast oil production during water-based floods. To preserve and reproduce the original reservoir wettability is a challenging task and wrong cleaning and core restoration procedures can lead to incorrect wettability estimations and thus induce serious errors when evaluating the initial wettability of a reservoir system or its EOR potential by water-based methods. Thereby, there is a need to improve the chemical knowledge on interactions among the rock, brine and fluids present in reservoir systems. This will help to understanding the influence of the parameters affecting wettability during cleaning and core restoration processes. Understanding which are the main parameters influencing oil recovery processes is of great relevance. The objective of this document is to provide suggestions for added-value experiments, complementing and challenging the standard RCA and SCAL procedures, prior to performing experimental research in which wettability and wettability alteration processes are important. Lessons learned will be highlighted and new ideas to optimize core restoration protocols to preserve and closely reproduce wettability are put forward. These recommended practices target core restoration procedures after the core material has been received in the laboratory. The target audience for this document is engineers and scientists with an interest in core preparation for wettability studies.
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Rahmani, Mehran, Xintong Ji, and Sovann Reach Kiet. Damage Detection and Damage Localization in Bridges with Low-Density Instrumentations Using the Wave-Method: Application to a Shake-Table Tested Bridge. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2033.
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This study presents a major development to the wave method, a methodology used for structural identification and monitoring. The research team tested the method for use in structural damage detection and damage localization in bridges, the latter being a challenging task. The main goal was to assess capability of the improved method by applying it to a shake-table-tested prototype bridge with sparse instrumentation. The bridge was a 4-span reinforced concrete structure comprising two columns at each bent (6 columns total) and a flat slab. It was tested to failure using seven biaxial excitations at its base. Availability of a robust and verified method, which can work with sparse recording stations, can be valuable for detecting damage in bridges soon after an earthquake. The proposed method in this study includes estimating the shear (cS) and the longitudinal (cL) wave velocities by fitting an equivalent uniform Timoshenko beam model in impulse response functions of the recorded acceleration response. The identification algorithm is enhanced by adding the model’s damping ratio to the unknown parameters, as well as performing the identification for a range of initial values to avoid early convergence to a local minimum. Finally, the research team detect damage in the bridge columns by monitoring trends in the identified shear wave velocities from one damaging event to another. A comprehensive comparison between the reductions in shear wave velocities and the actual observed damages in the bridge columns is presented. The results revealed that the reduction of cS is generally consistent with the observed distribution and severity of damage during each biaxial motion. At bents 1 and 3, cS is consistently reduced with the progression of damage. The trends correctly detected the onset of damage at bent 1 during biaxial 3, and damage in bent 3 during biaxial 4. The most significant reduction was caused by the last two biaxial motions in bents 1 and 3, also consistent with the surveyed damage. In bent 2 (middle bent), the reduction trend in cS was relatively minor, correctly showing minor damage at this bent. Based on these findings, the team concluded that the enhanced wave method presented in this study was capable of detecting damage in the bridge and identifying the location of the most severe damage. The proposed methodology is a fast and inexpensive tool for real-time or near real-time damage detection and localization in similar bridges, especially those with sparsely deployed accelerometers.