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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Robust fitting"

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Dunlap, Brett I. "Robust and variational fitting." Physical Chemistry Chemical Physics 2, no. 10 (2000): 2113–16. http://dx.doi.org/10.1039/b000027m.

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Elsaied, Hanan, and Roland Fried. "ROBUST FITTING OF INARCH MODELS." Journal of Time Series Analysis 35, no. 6 (June 27, 2014): 517–35. http://dx.doi.org/10.1111/jtsa.12079.

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3

Yu, Jieqi, Sanjeev R. Kulkarni, and H. Vincent Poor. "Robust ellipse and spheroid fitting." Pattern Recognition Letters 33, no. 5 (April 2012): 492–99. http://dx.doi.org/10.1016/j.patrec.2011.11.025.

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Domínguez-Soria, Víctor D., Gerald Geudtner, José Luis Morales, Patrizia Calaminici, and Andreas M. Köster. "Robust and efficient density fitting." Journal of Chemical Physics 131, no. 12 (September 28, 2009): 124102. http://dx.doi.org/10.1063/1.3216476.

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Ladrón de Guevara, I., J. Muñoz, O. D. de Cózar, and E. B. Blázquez. "Robust Fitting of Circle Arcs." Journal of Mathematical Imaging and Vision 40, no. 2 (December 22, 2010): 147–61. http://dx.doi.org/10.1007/s10851-010-0249-8.

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Chang, Chung, and R. Todd Ogden. "Robust fitting for neuroreceptor mapping." Statistics in Medicine 28, no. 6 (March 15, 2009): 1004–16. http://dx.doi.org/10.1002/sim.3510.

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Aigner, Martin, and Bert Jüttler. "Robust fitting of parametric curves." PAMM 7, no. 1 (December 2007): 1022201–2. http://dx.doi.org/10.1002/pamm.200700009.

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Tew, David P. "Communication: Quasi-robust local density fitting." Journal of Chemical Physics 148, no. 1 (January 7, 2018): 011102. http://dx.doi.org/10.1063/1.5013111.

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Welsh, A. H., and A. F. Ruckstuhl. "Robust fitting of the binomial model." Annals of Statistics 29, no. 4 (August 2001): 1117–36. http://dx.doi.org/10.1214/aos/1013699996.

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Vorobyov, S. A., Yue Rong, N. D. Sidiropoulos, and A. B. Gershman. "Robust iterative fitting of multilinear models." IEEE Transactions on Signal Processing 53, no. 8 (August 2005): 2678–89. http://dx.doi.org/10.1109/tsp.2005.850343.

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Дисертації з теми "Robust fitting"

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Xing, Yanru. "Robust mixture regression model fitting by Laplace distribution." Kansas State University, 2013. http://hdl.handle.net/2097/16534.

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Анотація:
Master of Science
Department of Statistics
Weixing Song
A robust estimation procedure for mixture linear regression models is proposed in this report by assuming the error terms follow a Laplace distribution. EM algorithm is imple- mented to conduct the estimation procedure of missing information based on the fact that the Laplace distribution is a scale mixture of normal and a latent distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies, together with the comparisons made with other existing procedures in this literature. A sensitivity study is also conducted based on a real data example to illustrate the application of the proposed method.
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Truong, Ha-Giang. "Robust fitting: Assisted by semantic analysis and reinforcement learning." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2567.

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Анотація:
Many computer vision applications require robust model estimation from a set of observed data. However, these data usually contain outliers, due to imperfect data acquisition or pre-processing steps, which can reduce the performance of conventional model-fitting methods. Robust fitting is thus critical to make the model estimation robust against outliers and reach stable performance. All of the contributions made in this thesis are for maximum consensus. In robust model fitting, maximum consensus is one of the most popular criteria, which aims to estimate the model that is consistent to as many observations as possible, i.e. obtain the highest consensus. The thesis makes contributions in two aspects of maximum consensus, one is non-learning based approaches and the other is learning based approaches. The first motivation for our work is the remarkable progress in semantic segmentation in recent years. Semantic segmentation is a useful process and is usually available for scene understanding, medical image analysis, and virtual reality. We propose novel methods, which make use of semantic segmentation, to improve the efficiency of two robust non-learning based algorithms. Another motivation for our contributions is the advances in reinforcement learning. In the thesis, a novel unsupervised learning framework is proposed to learn (without labelled data) to solve robust estimation directly. In particular, we formulate robust fitting problem as a special case of goal-oriented learning, and adopt the Reinforcement Learning framework as the basis of our approach. Our approach is agnostic to the input features and can be generalized to various practical applications.
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Wang, Hanzi. "Robust statistics for computer vision : model fitting, image segmentation and visual motion analysis." Monash University, Dept. of Electrical and Computer Systems Engineering, 2004. http://arrow.monash.edu.au/hdl/1959.1/5345.

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Yang, Li. "Robust fitting of mixture of factor analyzers using the trimmed likelihood estimator." Kansas State University, 2014. http://hdl.handle.net/2097/18118.

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Анотація:
Master of Science
Department of Statistics
Weixin Yao
Mixtures of factor analyzers have been popularly used to cluster the high dimensional data. However, the traditional estimation method is based on the normality assumptions of random terms and thus is sensitive to outliers. In this article, we introduce a robust estimation procedure of mixtures of factor analyzers using the trimmed likelihood estimator (TLE). We use a simulation study and a real data application to demonstrate the robustness of the trimmed estimation procedure and compare it with the traditional normality based maximum likelihood estimate.
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Mordini, Nicola. "Multicentre study for a robust protocol in single-voxel spectroscopy: quantification of MRS signals by time-domain fitting algorithms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7579/.

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Анотація:
Magnetic Resonance Spectroscopy (MRS) is an advanced clinical and research application which guarantees a specific biochemical and metabolic characterization of tissues by the detection and quantification of key metabolites for diagnosis and disease staging. The "Associazione Italiana di Fisica Medica (AIFM)" has promoted the activity of the "Interconfronto di spettroscopia in RM" working group. The purpose of the study is to compare and analyze results obtained by perfoming MRS on scanners of different manufacturing in order to compile a robust protocol for spectroscopic examinations in clinical routines. This thesis takes part into this project by using the GE Signa HDxt 1.5 T at the Pavillion no. 11 of the S.Orsola-Malpighi hospital in Bologna. The spectral analyses have been performed with the jMRUI package, which includes a wide range of preprocessing and quantification algorithms for signal analysis in the time domain. After the quality assurance on the scanner with standard and innovative methods, both spectra with and without suppression of the water peak have been acquired on the GE test phantom. The comparison of the ratios of the metabolite amplitudes over Creatine computed by the workstation software, which works on the frequencies, and jMRUI shows good agreement, suggesting that quantifications in both domains may lead to consistent results. The characterization of an in-house phantom provided by the working group has achieved its goal of assessing the solution content and the metabolite concentrations with good accuracy. The goodness of the experimental procedure and data analysis has been demonstrated by the correct estimation of the T2 of water, the observed biexponential relaxation curve of Creatine and the correct TE value at which the modulation by J coupling causes the Lactate doublet to be inverted in the spectrum. The work of this thesis has demonstrated that it is possible to perform measurements and establish protocols for data analysis, based on the physical principles of NMR, which are able to provide robust values for the spectral parameters of clinical use.
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Willersjö, Nyfelt Emil. "Comparison of the 1st and 2nd order Lee–Carter methods with the robust Hyndman–Ullah method for fitting and forecasting mortality rates." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48383.

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Анотація:
The 1st and 2nd order Lee–Carter methods were compared with the Hyndman–Ullah method in regards to goodness of fit and forecasting ability of mortality rates. Swedish population data was used from the Human Mortality Database. The robust estimation property of the Hyndman–Ullah method was also tested with inclusion of the Spanish flu and a hypothetical scenario of the COVID-19 pandemic. After having presented the three methods and making several comparisons between the methods, it is concluded that the Hyndman–Ullah method is overall superior among the three methods with the implementation of the chosen dataset. Its robust estimation of mortality shocks could also be confirmed.
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Relvas, Carlos Eduardo Martins. "Modelos parcialmente lineares com erros simétricos autoregressivos de primeira ordem." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-28052013-182956/.

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Анотація:
Neste trabalho, apresentamos os modelos simétricos parcialmente lineares AR(1), que generalizam os modelos parcialmente lineares para a presença de erros autocorrelacionados seguindo uma estrutura de autocorrelação AR(1) e erros seguindo uma distribuição simétrica ao invés da distribuição normal. Dentre as distribuições simétricas, podemos considerar distribuições com caudas mais pesadas do que a normal, controlando a curtose e ponderando as observações aberrantes no processo de estimação. A estimação dos parâmetros do modelo é realizada por meio do critério de verossimilhança penalizada, que utiliza as funções escore e a matriz de informação de Fisher, sendo todas essas quantidades derivadas neste trabalho. O número efetivo de graus de liberdade e resultados assintóticos também são apresentados, assim como procedimentos de diagnóstico, destacando-se a obtenção da curvatura normal de influência local sob diferentes esquemas de perturbação e análise de resíduos. Uma aplicação com dados reais é apresentada como ilustração.
In this master dissertation, we present the symmetric partially linear models with AR(1) errors that generalize the normal partially linear models to contain autocorrelated errors AR(1) following a symmetric distribution instead of the normal distribution. Among the symmetric distributions, we can consider heavier tails than the normal ones, controlling the kurtosis and down-weighting outlying observations in the estimation process. The parameter estimation is made through the penalized likelihood by using score functions and the expected Fisher information. We derive these functions in this work. The effective degrees of freedom and asymptotic results are also presented as well as the residual analysis, highlighting the normal curvature of local influence under different perturbation schemes. An application with real data is given for illustration.
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Messina, Carl J. "Labeled sampling consensus a novel algorithm for robustly fitting multiple structures using compressed sampling." Master's thesis, University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4983.

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Анотація:
The ability to robustly fit structures in datasets that contain outliers is a very important task in Image Processing, Pattern Recognition and Computer Vision. Random Sampling Consensus or RANSAC is a very popular method for this task, due to its ability to handle over 50% outliers. The problem with RANSAC is that it is only capable of finding a single structure. Therefore, if a dataset contains multiple structures, they must be found sequentially by finding the best fit, removing the points, and repeating the process. However, removing incorrect points from the dataset could prove disastrous. This thesis offers a novel approach to sampling consensus that extends its ability to discover multiple structures in a single iteration through the dataset. The process introduced is an unsupervised method, requiring no previous knowledge to the distribution of the input data. It uniquely assigns labels to different instances of similar structures. The algorithm is thus called Labeled Sampling Consensus or L-SAC. These unique instances will tend to cluster around one another allowing the individual structures to be extracted using simple clustering techniques. Since divisions instead of modes are analyzed, only a single instance of a structure need be recovered. This ability of L-SAC allows a novel sampling procedure to be presented "compressing" the required samples needed compared to traditional sampling schemes while ensuring all structures have been found. L-SAC is a flexible framework that can be applied to many problem domains.
ID: 030423298; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.E.E.)--University of Central Florida, 2011.; Includes bibliographical references (p. 70-72).
M.S.E.E.
Masters
Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering
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Laranjeira, Moreira Matheus. "Visual servoing on deformable objects : an application to tether shape control." Electronic Thesis or Diss., Toulon, 2019. http://www.theses.fr/2019TOUL0007.

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Анотація:
Cette thèse porte sur le problème du contrôle de la forme d'ombilicaux pour des robots sous-marins légers téléopérés (mini-ROVs), qui conviennent, grâce à leur petite taille et grande manoeuvrabilité, à l'exploration des eaux peu profondes et des espaces encombrés. La régulation de la forme de l'ombilical est cependant un tâche difficile, car ces robots n'ont pas une puissance de propulsion suffisante pour contrebalancer les forces de traînée du câble. Pour faire face à ce problème, nous avons introduit le concept de Cordée de mini-ROVs, dans lequel plusieurs robots sont reliés à l'ombilical et peuvent, ensemble, contrebalancer les perturbations extérieures et contrôler la forme du câble. Nous avons étudié l'utilisation des caméras embarquées pour réguler la forme d'une portion de l'ombilical reliant deux robots successifs, un leader et un suiveur. Seul le robot suiveur se chargera de la tâche de régulation de la forme du câble. Le leader est libéré pour explorer ses alentours. L'ombilical est supposé être légèrement pesant et donc modélisé par une chaînette. Les paramètres de forme du câble sont estimés en temps réel par une procédure d'optimisation non-linéaire qui adapte le modèle de chaînette aux points détectés dans les images des caméras. La régulation des paramètres de forme est obtenue grâce à une commande reliant le mouvement du robot à la variation de la forme de l'ombilical. L'asservissement visuel proposé s'est avéré capable de contrôler correctement la forme du câble en simulations et expériences réalisées en basin
This thesis addresses the problem of tether shape contrai for small remotely operated underwater vehicles (mini-ROVs), which are suitable, thanks to their small size and high maneuverability, for the exploration of shallow waters and cluttered spaces. The management of the tether is, however, a hard task, since these robots do not have enough propulsion power to counterbalance the drag forces acting on the tether cable. ln order to cape with this problem, we introduced the concept of a Chain of miniROVs, where several robots are linked to the tether cable and can, together, manage the external perturbations and contrai the shape of the cable. We investigated the use of the embedded cameras to regulate the shape of a portion of tether linking two successive robots, a leader and a follower. Only the follower robot deals with the tether shape regulation task. The leader is released to explore its surroundings. The tether linking bath robots is assumed to be negatively buoyant and is modeled by a catenary. The tether shape parameters are estimated in real-time by a nonlinear optimization procedure that fits the catenary model to the tether detected points in the image. The shape parameter regulation is thus achieved through a catenary-based contrai scheme relating the robot motion with the tether shape variation. The proposed visual servoing contrai scheme has proved to properly manage the tether shape in simulations and real experiments in pool
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Yu, Xinming. "Robust estimation for range image segmentation and fitting." Thesis, 1993. http://spectrum.library.concordia.ca/4144/1/NN84686.pdf.

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Анотація:
In the dissertation a new robust estimation technique for range image segmentation and fitting has been developed. The performance of the algorithm has been considerably improved by incorporating the genetic algorithm. The new robust estimation method randomly samples range image points and solves equations determined by these points for parameters of selected primitive type. From K samples we measure RESidual Consensus (RESC) to choose one set of sample points which determines an equation best fitting the largest homogeneous surface patch in the current processing region. The residual consensus is measured by a compressed histogram method which can be used at various noise levels. After obtaining surface parameters of the best fitting and the residuals of each point in the current processing region, a boundary list searching method is used to extract this surface patch out of the processing region and to avoid further computation. Since the RESC method can tolerate more than 80% of outliers, it is a substantial improvement over the least median squares method. The method segments range image into planar and quadratic surfaces, and works very well even in smoothly connected curve regions. A genetic algorithm is used to accelerate the random search. A large number of offline average performance experiments on GA are carried out to investigate different types of GAs and the influence of control parameters. A steady state GA works better than a generational replacement GA. The algorithms have been validated on the large set of synthetic and real range images.
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Книги з теми "Robust fitting"

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Weinberg, Jonathan M. Knowledge, Noise, and Curve-Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198724551.003.0016.

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The psychology of the ‘Gettier effect’ appears robust—but complicated. Contrary to initial reports, more recent and thorough work by several groups of researchers indicates strongly that it is in fact found widely across cultures. Nonetheless, I argue that the pattern of psychological results should not at all be taken to settle the epistemological questions about the nature of knowledge. For the Gettier effect occurs both intermittently and with sensitivity to epistemically irrelevant factors. In short, the effect is noisy. And good principles of model selection indicate that, the noisier one’s data, the more one should prefer simpler curves over those that may be more complicated yet hew closer to the data. While we should not endorse K=JTB at this time, nonetheless the question ‘Is knowledge really just justified true belief?’ ought to be treated as once again in play.
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Richardson, George, and Robert Fitton. Biographical Sketch of the Life of ... Robert Fitton. Creative Media Partners, LLC, 2018.

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Частини книг з теми "Robust fitting"

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Chin, Tat-Jun, David Suter, Shin-Fang Ch’ng, and James Quach. "Quantum Robust Fitting." In Computer Vision – ACCV 2020, 485–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69525-5_29.

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Frühwirth, Rudolf, and Are Strandlie. "Track Fitting." In Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors, 103–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65771-0_6.

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AbstractTrack fitting is an application of established statistical estimation procedures with well-known properties. For a long time, estimators based on the least-squares principle were—with some notable exceptions—the principal methods for track fitting. More recently, robust and adaptive methods have found their way into the reconstruction programs. The first section of the chapter presents least-squares regression, the extended Kalman filter, regression with breakpoints, general broken lines and the triplet fit. The following section discusses robust regression by the M-estimator, the deterministic annealing filter, and the Gaussian-sum filter for electron reconstruction. The next section deals with linearized fits of space points to circles and helices. The chapter concludes with a section on track quality and shows how to test the track hypothesis, how to detect outliers, and how to find kinks in a track.
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Frühwirth, Rudolf, and Are Strandlie. "Vertex Fitting." In Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors, 143–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65771-0_8.

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AbstractThe methods used for vertex fitting are closely related to the ones used in track fitting. The chapter describes least-squares estimators as well as robust and adaptive estimators. Furthermore, it is shown how the vertex fit can be extended to a kinematic fit by imposing additional constraints on the tracks participating in the fit.
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Ieng, Sio-Song, Jean-Philippe Tarel, and Pierre Charbonnier. "Evaluation of Robust Fitting Based Detection." In Lecture Notes in Computer Science, 341–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24671-8_27.

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Enqvist, Olof, Erik Ask, Fredrik Kahl, and Kalle Åström. "Robust Fitting for Multiple View Geometry." In Computer Vision – ECCV 2012, 738–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33718-5_53.

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Storer, Markus, Peter M. Roth, Martin Urschler, Horst Bischof, and Josef A. Birchbauer. "Efficient Robust Active Appearance Model Fitting." In Communications in Computer and Information Science, 229–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11840-1_17.

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Wang, Hanzi, and David Suter. "Robust Fitting by Adaptive-Scale Residual Consensus." In Lecture Notes in Computer Science, 107–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24672-5_9.

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Cruz Hernández, Heriberto, and Luis Gerardo de la Fraga. "A Multi-objective Robust Ellipse Fitting Algorithm." In NEO 2016, 141–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64063-1_6.

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Chai, Dengfeng, and Qunsheng Peng. "Image Feature Detection as Robust Model Fitting." In Computer Vision – ACCV 2006, 673–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612704_67.

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Li, Zhaoxi, Cai Meng, Dingzhe Li, and Limin Liu. "Robust Ellipse Fitting with an Auxiliary Normal." In Lecture Notes in Computer Science, 601–12. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87355-4_50.

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Тези доповідей конференцій з теми "Robust fitting"

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de la Fraga, Luis Gerardo, and Gustavo M. Lopez Dominguez. "Robust fitting of ellipses with heuristics." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586304.

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Yu, Jieqi, Sanjeev R. Kulkarni, and H. Vincent Poor. "Robust fitting of ellipses and spheroids." In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. IEEE, 2009. http://dx.doi.org/10.1109/acssc.2009.5470160.

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Guruswami, Venkatesan, and David Zuckerman. "Robust Fourier and Polynomial Curve Fitting." In 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2016. http://dx.doi.org/10.1109/focs.2016.75.

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"SIMULTANEOUS ROBUST FITTING OF MULTIPLE CURVES." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002040801750182.

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Attila Sarhegyi. "Robust Sine Wave Fitting in ADC Testing." In 2006 IEEE Instrumentation and Measurement Technology. IEEE, 2006. http://dx.doi.org/10.1109/imtc.2006.236674.

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Sarhegyi, Attila, and Istvan Kollar. "Robust Sine Wave Fitting in ADC Testing." In IEEE Instrumentation and Measurement Technology Conference. IEEE, 2006. http://dx.doi.org/10.1109/imtc.2006.328246.

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Arellano, Claudia, and Rozenn Dahyot. "Robust Bayesian fitting of 3D morphable model." In the 10th European Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2534008.2534013.

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Huang, Weiduo. "Robust Conicoid Fitting in Converting GPS Height." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5364106.

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9

Hanzi Wang. "Maximum kernel density estimator for robust fitting." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518377.

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10

Watson, G. A., Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Robust Solutions to Linear Data Fitting Problems." In Numerical Analysis and Applied Mathematics. AIP, 2007. http://dx.doi.org/10.1063/1.2790151.

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Звіти організацій з теми "Robust fitting"

1

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, September 2022. http://dx.doi.org/10.31979/mti.2022.2033.

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Анотація:
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.
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