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Relevant bibliographies by topics / Bayesian Inference Damage Detection

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Academic literature on the topic 'Bayesian Inference Damage Detection'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Bayesian Inference Damage Detection"

1

Jiang, Xiaomo, and Sankaran Mahadevan. "Bayesian Probabilistic Inference for Nonparametric Damage Detection of Structures." Journal of Engineering Mechanics 134, no. 10 (October 2008): 820–31. http://dx.doi.org/10.1061/(asce)0733-9399(2008)134:10(820).

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Alkam, Feras, and Tom Lahmer. "Eigenfrequency-Based Bayesian Approach for Damage Identification in Catenary Poles." Infrastructures 6, no. 4 (April 13, 2021): 57. http://dx.doi.org/10.3390/infrastructures6040057.

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This study proposes an efficient Bayesian, frequency-based damage identification approach to identify damages in cantilever structures with an acceptable error rate, even at high noise levels. The catenary poles of electric high-speed train systems were selected as a realistic case study to cover the objectives of this study. Compared to other frequency-based damage detection approaches described in the literature, the proposed approach is efficiently able to detect damages in cantilever structures to higher levels of damage detection, namely identifying both the damage location and severity using a low-cost structural health monitoring (SHM) system with a limited number of sensors; for example, accelerometers. The integration of Bayesian inference, as a stochastic framework, in the proposed approach, makes it possible to utilize the benefit of data fusion in merging the informative data from multiple damage features, which increases the quality and accuracy of the results. The findings provide the decision-maker with the information required to manage the maintenance, repair, or replacement procedures.

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Smith, Reuel, Mohammad Modarres, and Enrique López Droguett. "A recursive Bayesian approach to small fatigue crack propagation and detection modeling." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 6 (March 15, 2018): 738–53. http://dx.doi.org/10.1177/1748006x18758721.

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Engineers have witnessed much advancement in the study of fatigue crack detection and propagation modeling. More recently, the use of certain damage precursors such as acoustic emission signals to assess the integrity of structures has been proposed for application to prognosis and health management of structures. However, due to uncertainties associated with small crack detection of damage precursors and crack size measurement errors of the detection technology used, applications of prognosis and health management assessments have been limited. In this article, a methodology is developed for the purpose of assessment of crack detection and propagation parameters and the minimization of uncertainties including detection and sizing errors associated with a series of known crack detection and propagation models that use acoustic emission as the precursor to fatigue cracking. The methodology is facilitated by the Bayesian inference of a joint-likelihood model which includes sizing and detection models. Examples where several dog-bone Al 7075T6 specimens are tested to produce fatigue crack initiation and propagation data and estimates based on remaining useful life support the effectiveness and usefulness of the proposed methodology.

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Prajapat, Kanta, and Samit Ray-Chaudhuri. "Detection of multiple damages employing best achievable eigenvectors under Bayesian inference." Journal of Sound and Vibration 422 (May 2018): 237–63. http://dx.doi.org/10.1016/j.jsv.2018.02.012.

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Hou, Yunyun, Ruiyu He, Jie Dong, Yangrui Yang, and Wei Ma. "IoT Anomaly Detection Based on Autoencoder and Bayesian Gaussian Mixture Model." Electronics 11, no. 20 (October 12, 2022): 3287. http://dx.doi.org/10.3390/electronics11203287.

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The Internet of Things (IoT) is increasingly providing industrial production objects to connect with the physical world and has been widely used in various fields. Although it has brought great industrial convenience, there are also potential security threats due to the vulnerabilities and malicious nodes in IoT. To correctly identify the traffic of malicious nodes in IoT and reduce the damage caused by malicious attacks on IoT devices, this paper proposes an autoencoder-based IoT malicious node detection method. The contributions of this paper are as follows: firstly, the high complexity multi-featured traffic data are processed and dimensionally reduced through the autoencoder to obtain the low-dimensional feature data. Then, the Bayesian Gaussian mixture model is adopted to cluster the data in a low-dimensional space to detect anomalies. Furthermore, the method of variational inference is used to estimate the parameters in the Bayesian Gaussian mixture model. To evaluate our model’s effectiveness, we used a public dataset for our experiments. As a result, in the experiment, the proposed method achieves a high accuracy rate of 99% distinguishing normal and abnormal traffic with three-dimension data reduced by the autoencoder, and it establishes our model’s better detection performance compared with previous K-means and Gaussian Mixture Model (GMM) solutions.

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Kernicky, Timothy, Matthew Whelan, and Ehab Al-Shaer. "Vibration-based damage detection with uncertainty quantification by structural identification using nonlinear constraint satisfaction with interval arithmetic." Structural Health Monitoring 18, no. 5-6 (October 25, 2018): 1569–89. http://dx.doi.org/10.1177/1475921718806476.

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Structural identification has received increased attention over recent years for performance-based structural assessment and health monitoring. Recently, an approach for formulating the finite element model updating problem as a constraint satisfaction problem has been developed. In contrast to widely used probabilistic model updating through Bayesian inference methods, the technique naturally accounts for measurement and modeling errors through the use of interval arithmetic to determine the set of all feasible solutions to the partially described and incompletely measured inverse eigenvalue problem. This article presents extensions of the constraint satisfaction approach permitting the application to larger multiple degree-of-freedom system models. To accommodate for the drastic increase in the dimensionality of the inverse problem, the extended methodology replaces computation of the complete set of solutions with an approach that contracts the initial search space to the interval hull, which encompasses the complete set of feasible solutions with a single interval vector solution. The capabilities are demonstrated using vibration data acquired through hybrid simulation of a 45-degree-of-freedom planar truss, where a two-bar specimen with bolted connections representing a single member of the truss serves as the experimental substructure. Structural identification is performed using data acquired with the undamaged experimental member as well as over a number of damage scenarios with progressively increased severity developed by exceeding a limit-state capacity of the member. Interval hull solutions obtained through application of the nonlinear constraint satisfaction methodology demonstrate the capability to correctly identify and quantify the extent of the damage in the truss while incorporating measurement uncertainties in the parameter identification.

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Mandal, Manisha, and Shyamapada Mandal. "Detection of transmission change points during unlock-3 and unlock-4 measures controlling COVID-19 in India." Journal of Drug Delivery and Therapeutics 11, no. 2 (March 15, 2021): 76–86. http://dx.doi.org/10.22270/jddt.v11i2.4600.

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Objective: To evaluate the efficiency of unlock-3 and unlock-4 measure related to COVID-19 transmission change points in India, for projecting the infected population, to help in prospective planning of suitable measures related to future interventions and lifting of restrictions so that the economic settings are not damaged beyond repair. Methods: The SIR model and Bayesian approach combined with Monte Carlo Markov algorithms were applied on the Indian COVID-19 daily new infected cases from 1 August 2020 to 30 September 2020. The effectiveness of unlock-3 and unlock-4 measure were quantified as the change in both effective transmission rates and the basic reproduction number (R0). Results: The study demonstrated that the COVID-19 epidemic declined after implementing unlock-4 measure and the identified change-points were consistent with the timelines of announced unlock-3 and unlock-4 measure, on 1 August 2020 and 1 September 2020, respectively. Conclusions: Changes in the transmission rates with 100% reduction as well as the R0 attaining 1 during unlock-3 and unlock-4 indicated that the measures adopted to control and mitigate the COVID-19 epidemic in India were effective in flattening and receding the epidemic curve. Keywords: COVID-19 in India, epidemiological parameters, unlock-3 and unlock-4, SIR model, Bayesian inference, Monte Carlo Markov sampling

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Sahu, Abhijeet, and Katherine Davis. "Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach." Sensors 22, no. 6 (March 9, 2022): 2100. http://dx.doi.org/10.3390/s22062100.

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False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

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Hospedales, Timothy, and Sethu Vijayakumar. "Multisensory Oddity Detection as Bayesian Inference." PLoS ONE 4, no. 1 (January 15, 2009): e4205. http://dx.doi.org/10.1371/journal.pone.0004205.

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Jiang, Xiaomo, Yong Yuan, and Xian Liu. "Bayesian inference method for stochastic damage accumulation modeling." Reliability Engineering & System Safety 111 (March 2013): 126–38. http://dx.doi.org/10.1016/j.ress.2012.11.006.

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Dissertations / Theses on the topic "Bayesian Inference Damage Detection"

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Goi, Yoshinao. "Bayesian Damage Detection for Vibration Based Bridge Health Monitoring." Kyoto University, 2018. http://hdl.handle.net/2433/232013.

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Lebre, Sophie. "Stochastic process analysis for Genomics and Dynamic Bayesian Networks inference." Phd thesis, Université d'Evry-Val d'Essonne, 2007. http://tel.archives-ouvertes.fr/tel-00260250.

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This thesis is dedicated to the development of statistical and computational methods for the analysis of DNA sequences and gene expression time series.

First we study a parsimonious Markov model called Mixture Transition Distribution (MTD) model which is a mixture of Markovian transitions. The overly high number of constraints on the parameters of this model hampers the formulation of an analytical expression of the Maximum Likelihood Estimate (MLE). We propose to approach the MLE thanks to an EM algorithm. After comparing the performance of this algorithm to results from the litterature, we use it to evaluate the relevance of MTD modeling for bacteria DNA coding sequences in comparison with standard Markovian modeling.

Then we propose two different approaches for genetic regulation network recovering. We model those genetic networks with Dynamic Bayesian Networks (DBNs) whose edges describe the dependency relationships between time-delayed genes expression. The aim is to estimate the topology of this graph despite the overly low number of repeated measurements compared with the number of observed genes.

To face this problem of dimension, we first assume that the dependency relationships are homogeneous, that is the graph topology is constant across time. Then we propose to approximate this graph by considering partial order dependencies. The concept of partial order dependence graphs, already introduced for static and non directed graphs, is adapted and characterized for DBNs using the theory of graphical models. From these results, we develop a deterministic procedure for DBNs inference.

Finally, we relax the homogeneity assumption by considering the succession of several homogeneous phases. We consider a multiple changepoint
regression model. Each changepoint indicates a change in the regression model parameters, which corresponds to the way an expression level depends on the others. Using reversible jump MCMC methods, we develop a stochastic algorithm which allows to simultaneously infer the changepoints location and the structure of the network within the phases delimited by the changepoints.

Validation of those two approaches is carried out on both simulated and real data analysis.

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Ko, Kyungduk. "Bayesian wavelet approaches for parameter estimation and change point detection in long memory processes." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/2804.

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The main goal of this research is to estimate the model parameters and to detect multiple change points in the long memory parameter of Gaussian ARFIMA(p, d, q) processes. Our approach is Bayesian and inference is done on wavelet domain. Long memory processes have been widely used in many scientiﬁc ﬁelds such as economics, ﬁnance and computer science. Wavelets have a strong connection with these processes. The ability of wavelets to simultaneously localize a process in time and scale domain results in representing many dense variance-covariance matrices of the process in a sparse form. A wavelet-based Bayesian estimation procedure for the parameters of Gaussian ARFIMA(p, d, q) process is proposed. This entails calculating the exact variance-covariance matrix of given ARFIMA(p, d, q) process and transforming them into wavelet domains using two dimensional discrete wavelet transform (DWT2). Metropolis algorithm is used for sampling the model parameters from the posterior distributions. Simulations with diﬀerent values of the parameters and of the sample size are performed. A real data application to the U.S. GNP data is also reported. Detection and estimation of multiple change points in the long memory parameter is also investigated. The reversible jump MCMC is used for posterior inference. Performances are evaluated on simulated data and on the Nile River dataset.

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Reichl, Johannes, and Sylvia Frühwirth-Schnatter. "A Censored Random Coefficients Model for the Detection of Zero Willingness to Pay." Springer, 2011. http://epub.wu.ac.at/3707/1/WU_epub_(2).pdf.

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In this paper we address the problem of negative estimates of willingness to pay. We find that there exist a number of goods and services, especially in the fields of marketing and environmental valuation, for which only zero or positive WTP is meaningful. For the valuation of these goods an econometric model for the analysis of repeated dichotomous choice data is proposed. Our model restricts the domain of the estimates of WTP to strictly positive values, while also allowing for the detection of zero WTP. The model is tested on a simulated and a real data set.

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Zhang, Hanze. "Bayesian inference on quantile regression-based mixed-effects joint models for longitudinal-survival data from AIDS studies." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7456.

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In HIV/AIDS studies, viral load (the number of copies of HIV-1 RNA) and CD4 cell counts are important biomarkers of the severity of viral infection, disease progression, and treatment evaluation. Recently, joint models, which have the capability on the bias reduction and estimates' efficiency improvement, have been developed to assess the longitudinal process, survival process, and the relationship between them simultaneously. However, the majority of the joint models are based on mean regression, which concentrates only on the mean effect of outcome variable conditional on certain covariates. In fact, in HIV/AIDS research, the mean effect may not always be of interest. Additionally, if obvious outliers or heavy tails exist, mean regression model may lead to non-robust results. Moreover, due to some data features, like left-censoring caused by the limit of detection (LOD), covariates with measurement errors and skewness, analysis of such complicated longitudinal and survival data still poses many challenges. Ignoring these data features may result in biased inference. Compared to the mean regression model, quantile regression (QR) model belongs to a robust model family, which can give a full scan of covariate effect at different quantiles of the response, and may be more robust to extreme values. Also, QR is more flexible, since the distribution of the outcome does not need to be strictly specified as certain parametric assumptions. These advantages make QR be receiving increasing attention in diverse areas. To the best of our knowledge, few study focuses on the QR-based joint models and applies to longitudinal-survival data with multiple features. Thus, in this dissertation research, we firstly developed three QR-based joint models via Bayesian inferential approach, including: (i) QR-based nonlinear mixed-effects joint models for longitudinal-survival data with multiple features; (ii) QR-based partially linear mixed-effects joint models for longitudinal data with multiple features; (iii) QR-based partially linear mixed-effects joint models for longitudinal-survival data with multiple features. The proposed joint models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also implemented to assess the performance of the proposed methods under different scenarios. Although this is a biostatistical methodology study, some interesting clinical findings are also discovered.

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Osborne, Michael A. "Bayesian Gaussian processes for sequential prediction, optimisation and quadrature." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:1418c926-6636-4d96-8bf6-5d94240f3d1f.

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We develop a family of Bayesian algorithms built around Gaussian processes for various problems posed by sensor networks. We firstly introduce an iterative Gaussian process for multi-sensor inference problems, and show how our algorithm is able to cope with data that may be noisy, missing, delayed and/or correlated. Our algorithm can also effectively manage data that features changepoints, such as sensor faults. Extensions to our algorithm allow us to tackle some of the decision problems faced in sensor networks, including observation scheduling. Along these lines, we also propose a general method of global optimisation, Gaussian process global optimisation (GPGO), and demonstrate how it may be used for sensor placement. Our algorithms operate within a complete Bayesian probabilistic framework. As such, we show how the hyperparameters of our system can be marginalised by use of Bayesian quadrature, a principled method of approximate integration. Similar techniques also allow us to produce full posterior distributions for any hyperparameters of interest, such as the location of changepoints. We frame the selection of the positions of the hyperparameter samples required by Bayesian quadrature as a decision problem, with the aim of minimising the uncertainty we possess about the values of the integrals we are approximating. Taking this approach, we have developed sampling for Bayesian quadrature (SBQ), a principled competitor to Monte Carlo methods. We conclude by testing our proposals on real weather sensor networks. We further benchmark GPGO on a wide range of canonical test problems, over which it achieves a significant improvement on its competitors. Finally, the efficacy of SBQ is demonstrated in the context of both prediction and optimisation.

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Suvorov, Anton. "Molecular Evolution of Odonata Opsins, Odonata Phylogenomics and Detection of False Positive Sequence Homology Using Machine Learning." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7320.

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My dissertation comprises three related topics of evolutionary and computational biology, which correspond to the three Chapters. Chapter 1 focuses on tempo and mode of evolution in visual genes, namely opsins, via duplication events and subsequent molecular adaptation in Odonata (dragonflies and damselflies). Gene duplication plays a central role in adaptation to novel environments by providing new genetic material for functional divergence and evolution of biological complexity. Odonata have the largest opsin repertoire of any insect currently known. In particular our results suggest that both the blue sensitive (BS) and long-wave sensitive (LWS) opsin classes were subjected to strong positive selection that greatly weakens after multiple duplication events, a pattern that is consistent with the permanent heterozygote model. Due to the immense interspecific variation and duplicability potential of opsin genes among odonates, they represent a unique model system to test hypotheses regarding opsin gene duplication and diversification at the molecular level. Chapter 2 primarily focuses on reconstruction of the phylogenetic backbone of Odonata using RNA-seq data. In order to reconstruct the evolutionary history of Odonata, we performed comprehensive phylotranscriptomic analyses of 83 species covering 75% of all extant odonate families. Using maximum likelihood, Bayesian, coalescent-based and alignment free tree inference frameworks we were able to test, refine and resolve previously controversial relationships within the order. In particular, we confirmed the monophyly of Zygoptera, recovered Gomphidae and Petaluridae as sister groups with high confidence and identified Calopterygoidea as monophyletic. Fossil calibration coupled with diversification analyses provided insight into key events that influenced the evolution of Odonata. Specifically, we determined that there was a possible mass extinction of ancient odonate diversity during the P-Tr crisis and a single odonate lineage persisted following this extinction event. Lastly, Chapter 3 focuses on identification of erroneously assigned sequence homology using the intelligent agents of machine learning techniques. Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to predicting functional gene annotations. We developed biologically informative features that can be extracted from multiple sequence alignments of putative homologous genes (orthologs and paralogs) and further utilized in context of guided experimentation to verify false positive outcomes.

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Zhang, Fan. "Statistical Methods for Characterizing Genomic Heterogeneity in Mixed Samples." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/419.

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"Recently, sequencing technologies have generated massive and heterogeneous data sets. However, interpretation of these data sets is a major barrier to understand genomic heterogeneity in complex diseases. In this dissertation, we develop a Bayesian statistical method for single nucleotide level analysis and a global optimization method for gene expression level analysis to characterize genomic heterogeneity in mixed samples. The detection of rare single nucleotide variants (SNVs) is important for understanding genetic heterogeneity using next-generation sequencing (NGS) data. Various computational algorithms have been proposed to detect variants at the single nucleotide level in mixed samples. Yet, the noise inherent in the biological processes involved in NGS technology necessitates the development of statistically accurate methods to identify true rare variants. At the single nucleotide level, we propose a Bayesian probabilistic model and a variational expectation maximization (EM) algorithm to estimate non-reference allele frequency (NRAF) and identify SNVs in heterogeneous cell populations. We demonstrate that our variational EM algorithm has comparable sensitivity and specificity compared with a Markov Chain Monte Carlo (MCMC) sampling inference algorithm, and is more computationally efficient on tests of relatively low coverage (27x and 298x) data. Furthermore, we show that our model with a variational EM inference algorithm has higher specificity than many state-of-the-art algorithms. In an analysis of a directed evolution longitudinal yeast data set, we are able to identify a time-series trend in non-reference allele frequency and detect novel variants that have not yet been reported. Our model also detects the emergence of a beneficial variant earlier than was previously shown, and a pair of concomitant variants. Characterization of heterogeneity in gene expression data is a critical challenge for personalized treatment and drug resistance due to intra-tumor heterogeneity. Mixed membership factorization has become popular for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. At the gene expression level, we derive a global optimization (GOP) algorithm that provides a guaranteed epsilon-global optimum for a sparse mixed membership matrix factorization problem for molecular subtype classification. We test the algorithm on simulated data and find the algorithm always bounds the global optimum across random initializations and explores multiple modes efficiently. The GOP algorithm is well-suited for parallel computations in the key optimization steps. "

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Asgrimsson, David Steinar. "Quantifying uncertainty in structural condition with Bayesian deep learning : A study on the Z-24 bridge benchmark." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251451.

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A machine learning approach to damage detection is presented for a bridge structural health monitoring system, validated on the renowned Z-24 bridge benchmark dataset where a sensor instrumented, threespan bridge was realistically damaged in stages. A Bayesian autoencoder neural network is trained to reconstruct raw sensor data sequences, with uncertainty bounds in prediction. The reconstruction error is then compared with a healthy-state error distribution and the sequence determined to come from a healthy state or not. Several realistic damage stages were successfully detected, making this a viable approach in a data-based monitoring system of an operational bridge. This is a fully operational, machine learning based bridge damage detection system, that is learned directly from raw sensor data.
En maskininlärningsmetod för strukturell skadedetektering av broar presenteras. Metoden valideras på det kända referensdataset Z-24, där en sensor-instrumenterad trespannsbro stegvist skadats. Ett Bayesianskt neuralt nätverk med autoenkoders tränas till att rekonstruera råa sensordatasekvenser, med osäkerhetsgränser i förutsägningen. Rekonstrueringsavvikelsen jämförs med avvikelsesfördelningen i oskadat tillstånd och sekvensen bedöms att komma från ett skadad eller icke skadat tillstånd. Flera realistiska stegvisa skadetillstånd upptäcktes, vilket gör metoden användbar i ett databaserat skadedetektionssystem för en bro i full storlek. Detta är ett lovande steg mot ett helt operativt databaserat skadedetektionssystem.

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Kennedy, Justin M. "Wave-induced marine craft motion estimation and control." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213481/1/Justin_Kennedy_Thesis.pdf.

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Marine craft at sea are affected by environmental disturbances including long-term ocean currents and relatively higher frequency wave disturbances. These disturbances impact on vessels resulting in wave-induced motion which reduces the performance of motion control systems and impacts on the safety of crew and cargo. This thesis investigates parameter estimation techniques for the online estimation of wave-induced motion models and platform control of marine craft in the presence of environmental disturbances.

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Book chapters on the topic "Bayesian Inference Damage Detection"

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Mohammadi-Ghazi, Reza, and Oral Buyukozturk. "Bayesian Inference for Damage Detection in Unsupervised Structural Health Monitoring." In Model Validation and Uncertainty Quantification, Volume 3, 283–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0_30.

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Pepi, Chiara, and Massimiliano Gioffré. "Vibration Based Bayesian Inference for Finite Element Model Parameters Estimation and Damage Detection." In Lecture Notes in Mechanical Engineering, 1591–607. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41057-5_129.

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Chowdhury, Ananda S., and Suchendra M. Bhandarkar. "Fracture Detection Using Bayesian Inference." In Computer Vision-Guided Virtual Craniofacial Surgery, 91–109. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-296-4_6.

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Hanson, Timothy, Sudipto Banerjee, Pei Li, and Alexander McBean. "Spatial Boundary Detection for Areal Counts." In Nonparametric Bayesian Inference in Biostatistics, 377–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19518-6_19.

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Bassetti, Federico, Fabrizio Leisen, Edoardo Airoldi, and Michele Guindani. "Species Sampling Priors for Modeling Dependence: An Application to the Detection of Chromosomal Aberrations." In Nonparametric Bayesian Inference in Biostatistics, 97–114. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19518-6_5.

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Eftekhar Azam, Saeed. "Recursive Bayesian Estimation of Partially Observed Dynamic Systems." In Online Damage Detection in Structural Systems, 7–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02559-9_2.

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Yang, Yuan, Zhongmin Cai, Weixuan Mao, and Zhihai Yang. "Identifying Intrusion Infections via Probabilistic Inference on Bayesian Network." In Detection of Intrusions and Malware, and Vulnerability Assessment, 307–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20550-2_16.

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Hegenderfer, Joshua, Sez Atamturktur, and Austin Gillen. "Damage Detection in Steel Structures Using Bayesian Calibration Techniques." In Topics in Modal Analysis II, Volume 6, 179–93. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2419-2_16.

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Ásgrímsson, Davíð Steinar, Ignacio González, Giampiero Salvi, and Raid Karoumi. "Bayesian Deep Learning for Vibration-Based Bridge Damage Detection." In Structural Integrity, 27–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81716-9_2.

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Dzunic, Zoran, Justin G. Chen, Hossein Mobahi, Oral Buyukozturk, and John W. Fisher. "A Bayesian State-Space Approach for Damage Detection and Classification." In Conference Proceedings of the Society for Experimental Mechanics Series, 171–83. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15248-6_18.

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Conference papers on the topic "Bayesian Inference Damage Detection"

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Zhou, K., Q. Shuai, and J. Tang. "Adaptive Damage Detection Using Tunable Piezoelectric Admittance Sensor and Intelligent Inference." In ASME 2014 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/smasis2014-7624.

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The piezoelectric impedance/admittance-based damage detection has been recognized to be sensitive to small-sized damage due to its high frequency measurement capability. Recently, a new class of admittance-based damage detection schemes has been proposed, in which the piezoelectric transducer is integrated with a tunable inductive circuitry. The present research focuses on exploiting the tunable nature of the piezoelectric admittance sensor for the effective identification of damage. In particular, we incorporate the Bayesian inference network into the damage detection process which can intelligently guide the accurate identification of damage location and severity by taking full advantage of the baseline model and measurement as well as the online measurement. As the tunable sensor can provide greatly enriched measurement information, the Bayesian inference can adequately utilize such information and furthermore directly and continuously update the structural model until the model prediction matches with the measurement results. This new approach takes into account the model uncertainty, measurement error, and incompleteness of measurements. Extensive numerical analyses and experimental studies are carried out on a panel structure for methodology demonstration and validation.

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Bellam Muralidhar, Nanda Kishore, and Dirk Lorenz. "A Model-Based Damage Identification using Guided Ultrasonic Wave Propagation in Fiber Metal Laminates." In VI ECCOMAS Young Investigators Conference. València: Editorial Universitat Politècnica de València, 2021. http://dx.doi.org/10.4995/yic2021.2021.12684.

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Abstract:

Fiber metal laminates (FML) are lightweight hybrid structural materials that combine the ductile properties of metal with high specific stiffness of fiber reinforced plastics. These advantages led to a dramatic increase in such materials for aeronautical structures over the last few years. One of the most common and vulnerable defects in FML is impact-related delamination, often invisible to the human eye. Guided ultrasonic waves (GUW) show high potential for monitoring structural integrity and damage detection in thin-walled structures by using the physical phenomena of wave propagation interacting with the defects [1]. The focus of this research project is on describing an inverse solution for the detection and characterization of defect in FML. Model-based damage analysis utilizes an accurate finite element model (FEM) of GUW interaction with the damage. The FEM is developed by project partners from mechanics at Helmut-Schmidt-University in Hamburg, Germany, and will be treated as a black-box for further analysis. A Bayesian approach (Markov chain Monte Carlo) is employed to characterize the damage and quantify its uncertainties. This inference problem in a stochastic framework requires a very large number of forward solves. Therefore, a profound investigation is carried out on different reduced-order modeling (ROM) methods in order to apply a suitable technique that significantly improves the computational efficiency. The proposed method is well illustrated on a simpler case study for the damage detection, localization and characterization using 2D elastic wave equation. The damage in this case is modeled as a reduction in the wave propagation velocity. The inference problem utilizes a parameterized projection-based ROM coupled with a surrogate model [2] instead of the underlying highdimensional model. This research is funded by the Deutsche Forschungsgemeinschaft Research Unit 3022 under grant LO1436/12-1.REFERENCES [1] R. Lammering, U. Gabbert, M. Sinapius, T. Schuster, P. Wierach (Eds)(2018) Lamb-Wave Based Structural Health Monitoring in Polymer Composites, Springer International Publishing. [2] Paul-Dubois-Taine A, Amsallem D. An adaptive and efficient greedy procedure for the optimal training of parametric reduced-order models. International Journal for Numerical Methods in Engineering 2014.

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Shuai, Q., K. Zhou, and J. Tang. "Structural damage identification using piezoelectric impedance and Bayesian inference." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Jerome P. Lynch. SPIE, 2015. http://dx.doi.org/10.1117/12.2084442.

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Najar, Fatma, Nuha Zamzami, and Nizar Bouguila. "Fake News Detection Using Bayesian Inference." In 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2019. http://dx.doi.org/10.1109/iri.2019.00066.

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Liu, Chen, Xuemei Bai, Gounou Charles Sobabe, Chenjie Zhang, Zhijun Wang, and Bin Guo. "Spectrum detection based on Bayesian inference." In 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2017. http://dx.doi.org/10.1109/cisp-bmei.2017.8302115.

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Hooi, Bryan, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, and Christos Faloutsos. "BIRDNEST: Bayesian Inference for Ratings-Fraud Detection." In Proceedings of the 2016 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2016. http://dx.doi.org/10.1137/1.9781611974348.56.

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Canillas, Remi, Omar Hasan, Laurent Sarrat, and Lionel Brunie. "Supplier Impersonation Fraud Detection using Bayesian Inference." In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2020. http://dx.doi.org/10.1109/bigcomp48618.2020.00-53.

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Shuai, Q., G. Liang, and J. Tang. "Piezoelectric admittance-based damage identification by Bayesian inference with pre-screening." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Gyuhae Park. SPIE, 2016. http://dx.doi.org/10.1117/12.2219159.

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Jin, Yuanwei. "Cognitive multi-antenna radar detection using Bayesian inference." In 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2012. http://dx.doi.org/10.1109/sam.2012.6250524.

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Gao, Hong-Yun, and Kin-Man Lam. "Salient object detection using octonion with Bayesian inference." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025666.

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