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Journal articles on the topic 'Bayesian Inference Damage Detection'

Author: Grafiati

Published: 10 March 2023

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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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2

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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5

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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8

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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9

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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10

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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11

Li, Kang, Xian-ming Shi, Juan Li, Mei Zhao, and Chunhua Zeng. "Bayesian Estimation of Ammunition Demand Based on Multinomial Distribution." Discrete Dynamics in Nature and Society 2021 (April 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/5575335.

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In view of the small sample size of combat ammunition trial data and the difficulty of forecasting the demand for combat ammunition, a Bayesian inference method based on multinomial distribution is proposed. Firstly, considering the different damage grades of ammunition hitting targets, the damage results are approximated as multinomial distribution, and a Bayesian inference model of ammunition demand based on multinomial distribution is established, which provides a theoretical basis for forecasting the ammunition demand of multigrade damage under the condition of small samples. Secondly, the conjugate Dirichlet distribution of multinomial distribution is selected as a prior distribution, and Dempster–Shafer evidence theory (D-S theory) is introduced to fuse multisource previous information. Bayesian inference is made through the Markov chain Monte Carlo method based on Gibbs sampling, and ammunition demand at different damage grades is obtained by referring to cumulative damage probability. The study result shows that the Bayesian inference method based on multinomial distribution is highly maneuverable and can be used to predict ammunition demand of different damage grades under the condition of small samples.

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12

Merson, Alexander I., Jens Jasche, Filipe B. Abdalla, Ofer Lahav, Benjamin Wandelt, D. Heath Jones, and Matthew Colless. "Halo detection via large-scale Bayesian inference." Monthly Notices of the Royal Astronomical Society 460, no. 2 (April 22, 2016): 1340–55. http://dx.doi.org/10.1093/mnras/stw948.

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13

Halkias, Xanadu C., and Daniel P. W. Ellis. "Call detection and extraction using Bayesian inference." Applied Acoustics 67, no. 11-12 (November 2006): 1164–74. http://dx.doi.org/10.1016/j.apacoust.2006.05.006.

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14

Mørup, Morten, and Mikkel N. Schmidt. "Bayesian Community Detection." Neural Computation 24, no. 9 (September 2012): 2434–56. http://dx.doi.org/10.1162/neco_a_00314.

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Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled.

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15

Fu, Xiang Ping, Bin Peng, and Zheng Ji. "Damage Identification for Masonry Materials Based on Bayesian Inference." Applied Mechanics and Materials 405-408 (September 2013): 2498–502. http://dx.doi.org/10.4028/www.scientific.net/amm.405-408.2498.

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The basic frequency of masonry specimens can be obtained by dynamic tests with ambient or artificial excitation. The elastic modulus of masonry structures, as well as the damage factors, can then be determined by training their finite element models and make the calculated frequencies agree with the measured ones. Using 530 groups of dynamic test data, the damage factors of four masonry specimens were identified. The Bayesian inferences of the highly diverse measured results were conducted through a Markov Chain Monte Carlo (MCMC) sampling method, and the location of the damage was identified. The methodology was applicable, and can be used in the damage identification for other materials or structures.

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16

Salkovic, Edin, Mostafa M. Abbas, Samir Brahim Belhaouari, Khaoula Errafii, and Halima Bensmail. "OutPyR: Bayesian inference for RNA-Seq outlier detection." Journal of Computational Science 47 (November 2020): 101245. http://dx.doi.org/10.1016/j.jocs.2020.101245.

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17

Cole, W. G. "Three Graphic Representations to Aid Bayesian Inference." Methods of Information in Medicine 27, no. 03 (July 1988): 125–32. http://dx.doi.org/10.1055/s-0038-1635532.

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SummaryVisual representation may help physicians and patients interpret laboratory results, for example by aiding Bayesian reasoning. This paper is concerned with the psychological and formal properties of such visual representations. One popular way to present laboratory results is via signal detection curves. These curves represent many parameters of a laboratory test including parameters, such as distribution variance, that are not typically known. Such curves can be seriously misleading.Two alternative representations are suggested. Probability maps represent only the three laboratory test parameters most likely to be known: sensitivity, specificity and prevalence, and thus avoid the problems of the richer signal detection curves. Probability maps, however, do not remind the user of why there are false positives and false negatives nor of the nature of the criterion for positivity. Detection bars, a third type of representation, are a compromise between signal detection curves and probability maps.

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18

Liu, Haobang, Xianming Shi, Xiaojuan Chen, Yuan Li, Mei Zhao, and Yongchao Jiang. "Bayesian Inference of Ammunition Consumption Based on Normal-Inverse Gamma Distribution." Discrete Dynamics in Nature and Society 2022 (April 14, 2022): 1–12. http://dx.doi.org/10.1155/2022/6365712.

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To address the problems of high cost of new ammunition experiment, few data of field test and low accuracy of consumption prediction, this article proposes a Bayesian estimation method of ammunition consumption based on normal-inverse gamma distribution, and estimates the hyperparameters in the prior distribution through the prior information from the consumption of ammunition under different damage degrees of point targets, based on the normal distribution phenomenon of ammunition consumption at each damage degree. It is to establish a Bayesian estimation model for ammunition consumption under different damage degrees according to field test data based on Bayesian formula and solve for its posterior distribution. The example proves that the estimation results of ammunition consumption for point target with different damage degrees based on this method is more scientific and reasonable according to various prior information.

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19

Ding, Yang, Jing-liang Dong, Tong-lin Yang, Zhong-ping Wang, Shuang-xi Zhou, Yong-qi Wei, and An-ming She. "Damage Evaluation of Bridge Hanger Based on Bayesian Inference: Analytical Model." Advances in Materials Science and Engineering 2021 (May 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/9947727.

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With the increase of the long-span bridge, the damage of the long-span bridge hanger has attracted more and more attention. Nowadays, the probability statistics method based on Bayes’ theorem is widely used for evaluating the damage of bridge, that is, Bayesian inference. In this study, the damage evaluation model of bridge hanger is established based on Bayesian inference. For the damage evaluation model, the analytical expressions for calculating the weights by finite mixture (FM) method are derived. In order to solve the complex analytical expressions in damage evaluation model, the Metropolis-Hastings (MH) sampling of Markov chain Monte Carlo (MCMC) method was used. Three case studies are adopted to demonstrate the effect of the initial value and the applicability of the proposed model. The result suggests that the proposed model can evaluate the damage of the bridge hanger.

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20

Lee, Michael D. "BayesSDT: Software for Bayesian inference with signal detection theory." Behavior Research Methods 40, no. 2 (May 2008): 450–56. http://dx.doi.org/10.3758/brm.40.2.450.

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21

Coughlan, James M., and A. L. Yuille. "Manhattan World: Orientation and Outlier Detection by Bayesian Inference." Neural Computation 15, no. 5 (May 1, 2003): 1063–88. http://dx.doi.org/10.1162/089976603765202668.

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This letter argues that many visual scenes are based on a “Manhattan” three-dimensional grid that imposes regularities on the image statistics. We construct a Bayesian model that implements this assumption and estimates the viewer orientation relative to the Manhattan grid. For many images, these estimates are good approximations to the viewer orientation (as estimated manually by the authors). These estimates also make it easy to detect outlier structures that are unaligned to the grid. To determine the applicability of the Manhattan world model, we implement a null hypothesis model that assumes that the image statistics are independent of any three-dimensional scene structure. We then use the log-likelihood ratio test to determine whether an image satisfies the Manhattan world assumption. Our results show that if an image is estimated to be Manhattan, then the Bayesian model's estimates of viewer direction are almost always accurate (according to our manual estimates), and vice versa.

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Singh, Archana K., Hideki Asoh, Yuji Takeda, and Steven Phillips. "Statistical Detection of EEG Synchrony Using Empirical Bayesian Inference." PLOS ONE 10, no. 3 (March 30, 2015): e0121795. http://dx.doi.org/10.1371/journal.pone.0121795.

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23

Huang, Yong, Changsong Shao, Biao Wu, James L. Beck, and Hui Li. "State-of-the-art review on Bayesian inference in structural system identification and damage assessment." Advances in Structural Engineering 22, no. 6 (November 23, 2018): 1329–51. http://dx.doi.org/10.1177/1369433218811540.

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Bayesian inference provides a powerful approach to system identification and damage assessment for structures. The application of Bayesian method is motivated by the fact that inverse problems in structural engineering, including structural health monitoring, are typically ill-conditioned and ill-posed when using noisy incomplete data because of various sources of modeling uncertainties. One should not just search for a single “optimal” value for the vector of model parameters but rather attempt to describe the whole family of plausible model parameters based on measured data using a Bayesian probabilistic framework. In this article, the fundamental principles of Bayesian analysis and computation are summarized; then a review is given of recent state-of-the-art practices of Bayesian inference in system identification and damage assessment for civil infrastructure. Discussions of the benefits and deficiencies of these approaches, as well as potentially useful avenues for future studies, are also provided. Our focus is on meeting challenges that arise from system identification and damage assessment for the civil infrastructure but our presented theories also have a considerably broader applicability for inverse problems in science and technology.

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24

Kullaa, Jyrki. "Robust damage detection using Bayesian virtual sensors." Mechanical Systems and Signal Processing 135 (January 2020): 106384. http://dx.doi.org/10.1016/j.ymssp.2019.106384.

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25

Jiang, Xiaomo, and Sankaran Mahadevan. "Bayesian wavelet methodology for structural damage detection." Structural Control and Health Monitoring 15, no. 7 (November 2008): 974–91. http://dx.doi.org/10.1002/stc.230.

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26

Graves, T., R. B. Gramacy, C. L. E. Franzke, and N. W. Watkins. "Efficient Bayesian inference for ARFIMA processes." Nonlinear Processes in Geophysics Discussions 2, no. 2 (March 27, 2015): 573–618. http://dx.doi.org/10.5194/npgd-2-573-2015.

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Abstract. Many geophysical quantities, like atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long-range dependence (LRD). LRD means that these quantities experience non-trivial temporal memory, which potentially enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LRD. In this paper we present a modern and systematic approach to the inference of LRD. Rather than Mandelbrot's fractional Gaussian noise, we use the more flexible Autoregressive Fractional Integrated Moving Average (ARFIMA) model which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LRD, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g. short memory effects) can be integrated over in order to focus on long memory parameters, and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data, with favorable comparison to the standard estimators.

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27

Song, Shengli, Bin Xu, and Jian Yang. "Ship Detection in Polarimetric SAR Images via Variational Bayesian Inference." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 6 (June 2017): 2819–29. http://dx.doi.org/10.1109/jstars.2017.2687473.

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28

Sadough, S. M. S., and M. Modarresi. "Improved iterative joint detection and estimation through variational Bayesian inference." AEU - International Journal of Electronics and Communications 66, no. 5 (May 2012): 380–83. http://dx.doi.org/10.1016/j.aeue.2011.09.004.

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29

Zhang, Xiaoxu, Ying-Chang Liang, and Jun Fang. "Novel Bayesian Inference Algorithms for Multiuser Detection in M2M Communications." IEEE Transactions on Vehicular Technology 66, no. 9 (September 2017): 7833–48. http://dx.doi.org/10.1109/tvt.2017.2692776.

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Biao Chen, P. K. Varshney, and J. H. Michels. "Adaptive CFAR detection for clutter-edge heterogeneity using Bayesian inference." IEEE Transactions on Aerospace and Electronic Systems 39, no. 4 (October 2003): 1462–70. http://dx.doi.org/10.1109/taes.2003.1261145.

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31

Bronstein, Samuel, Stefan Engblom, and Robin Marin. "Bayesian inference in epidemics: linear noise analysis." Mathematical Biosciences and Engineering 20, no. 2 (2022): 4128–52. http://dx.doi.org/10.3934/mbe.2023193.

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<abstract><p>This paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model's convergence with increasing amounts of data under measurement limitations. Depending on how weakly informative the disease measurements are, we offer a kind of 'best case' as well as a 'worst case' analysis where, in the former case, we assume that the prevalence is directly accessible, while in the latter that only a binary signal corresponding to a prevalence detection threshold is available. Both cases are studied under an assumed so-called linear noise approximation as to the true dynamics. Numerical experiments test the sharpness of our results when confronted with more realistic situations for which analytical results are unavailable.</p></abstract>

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32

Jesus, Andre, Peter Brommer, Robert Westgate, Ki Koo, James Brownjohn, and Irwanda Laory. "Modular Bayesian damage detection for complex civil infrastructure." Journal of Civil Structural Health Monitoring 9, no. 2 (February 7, 2019): 201–15. http://dx.doi.org/10.1007/s13349-018-00321-8.

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33

Sohn, Hoon, and Kincho H. Law. "A Bayesian probabilistic approach for structure damage detection." Earthquake Engineering & Structural Dynamics 26, no. 12 (December 1997): 1259–81. http://dx.doi.org/10.1002/(sici)1096-9845(199712)26:12<1259::aid-eqe709>3.0.co;2-3.

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An, Dawn, Joo-Ho Choi, and Nam H. Kim. "Identification of correlated damage parameters under noise and bias using Bayesian inference." Structural Health Monitoring: An International Journal 11, no. 3 (October 12, 2011): 293–303. http://dx.doi.org/10.1177/1475921711424520.

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35

Fontanazza, C. M., G. Freni, and V. Notaro. "Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas." Water Science and Technology 66, no. 8 (October 1, 2012): 1669–77. http://dx.doi.org/10.2166/wst.2012.359.

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Flood damage in urbanized watersheds may be assessed by combining the flood depth–damage curves and the outputs of urban flood models. The complexity of the physical processes that must be simulated and the limited amount of data available for model calibration may lead to high uncertainty in the model results and consequently in damage estimation. Moreover depth–damage functions are usually affected by significant uncertainty related to the collected data and to the simplified structure of the regression law that is used. The present paper carries out the analysis of the uncertainty connected to the flood damage estimate obtained combining the use of hydraulic models and depth–damage curves. A Bayesian inference analysis was proposed along with a probabilistic approach for the parameters estimating. The analysis demonstrated that the Bayesian approach is very effective considering that the available databases are usually short.

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Badeli, Vahid, Sascha Ranftl, Gian Marco Melito, Alice Reinbacher-Köstinger, Wolfgang Von Der Linden, Katrin Ellermann, and Oszkar Biro. "Bayesian inference of multi-sensors impedance cardiography for detection of aortic dissection." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 41, no. 3 (December 21, 2021): 824–39. http://dx.doi.org/10.1108/compel-03-2021-0072.

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Purpose This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients. Design/methodology/approach A 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification. Findings The Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection. Originality/value This paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.

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Cai, Chenning, Gang Yan, and Jianfei Tang. "Detection of fatigue cracks under environmental effects using Bayesian statistical inference." International Journal of Applied Electromagnetics and Mechanics 52, no. 3-4 (December 29, 2016): 1015–21. http://dx.doi.org/10.3233/jae-162189.

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Wan, Qian, Jun Fang, Yinsen Huang, Huiping Duan, and Hongbin Li. "A Variational Bayesian Inference-Inspired Unrolled Deep Network for MIMO Detection." IEEE Transactions on Signal Processing 70 (2022): 423–37. http://dx.doi.org/10.1109/tsp.2022.3140926.

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Wang, Shigang, Min Wang, Shuyuan Yang, and Kai Zhang. "Salient Region Detection via Discriminative Dictionary Learning and Joint Bayesian Inference." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 5 (May 2018): 1116–29. http://dx.doi.org/10.1109/tcsvt.2016.2642341.

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Peters, Gareth W., Ido Nevat, Scott A. Sisson, Yanan Fan, and Jinhong Yuan. "Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference." IEEE Transactions on Signal Processing 58, no. 10 (October 2010): 5206–18. http://dx.doi.org/10.1109/tsp.2010.2052457.

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HANAKUMA, YOSHITOMO, JUNZOU YAMAMOTO, and EIJI NAKANISHI. "Detection of Abnormal Signals Without Trend Ingredient and Bayesian Statistical Inference." KAGAKU KOGAKU RONBUNSHU 24, no. 5 (1998): 803–5. http://dx.doi.org/10.1252/kakoronbunshu.24.803.

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Mukhopadhyay, Saurabh, Brian Waterhouse, and Alan Hartford. "Bayesian detection of potential risk using inference on blinded safety data." Pharmaceutical Statistics 17, no. 6 (August 30, 2018): 823–34. http://dx.doi.org/10.1002/pst.1898.

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Mongwe, Wilson Tsakane, Rendani Mbuvha, and Tshilidzi Marwala. "Bayesian inference of local government audit outcomes." PLOS ONE 16, no. 12 (December 14, 2021): e0261245. http://dx.doi.org/10.1371/journal.pone.0261245.

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The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with great success. This work presents the first application of a Bayesian inference approach to the problem of predicting the audit outcomes of financial statements of local government entities using financial ratios. Bayesian logistic regression (BLR) with automatic relevance determination (BLR-ARD) is applied to predict audit outcomes. The benefit of using BLR-ARD, instead of BLR without ARD, is that it allows one to automatically determine which input features are the most relevant for the task at hand, which is a critical aspect to consider when designing decision support systems. This work presents the first implementation of BLR-ARD trained with Separable Shadow Hamiltonian Hybrid Monte Carlo, No-U-Turn sampler, Metropolis Adjusted Langevin Algorithm and Metropolis-Hasting algorithms. Unlike the Gibbs sampling procedure that is typically employed in sampling from ARD models, in this work we jointly sample the parameters and the hyperparameters by putting a log normal prior on the hyperparameters. The analysis also shows that the repairs and maintenance as a percentage of total assets ratio, current ratio, debt to total operating revenue, net operating surplus margin and capital cost to total operating expenditure ratio are the important features when predicting local government audit outcomes using financial ratios. These results could be of use for auditors as focusing on these ratios could potentially speed up the detection of fraudulent behaviour in municipal entities, and improve the speed and quality of the overall audit.

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Lee, Jennifer Laura, and Wei Ji Ma. "Point-estimating observer models for latent cause detection." PLOS Computational Biology 17, no. 10 (October 29, 2021): e1009159. http://dx.doi.org/10.1371/journal.pcbi.1009159.

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The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several “point-estimating” observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by “committing” to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data.

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Eltouny, Kareem A., and Xiao Liang. "Bayesian‐optimized unsupervised learning approach for structural damage detection." Computer-Aided Civil and Infrastructure Engineering 36, no. 10 (May 7, 2021): 1249–69. http://dx.doi.org/10.1111/mice.12680.

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Zwaenepoel, Arthur, and Yves Van de Peer. "Model-Based Detection of Whole-Genome Duplications in a Phylogeny." Molecular Biology and Evolution 37, no. 9 (May 2, 2020): 2734–46. http://dx.doi.org/10.1093/molbev/msaa111.

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Abstract Ancient whole-genome duplications (WGDs) leave signatures in comparative genomic data sets that can be harnessed to detect these events of presumed evolutionary importance. Current statistical approaches for the detection of ancient WGDs in a phylogenetic context have two main drawbacks. The first is that unwarranted restrictive assumptions on the “background” gene duplication and loss rates make inferences unreliable in the face of model violations. The second is that most methods can only be used to examine a limited set of a priori selected WGD hypotheses and cannot be used to discover WGDs in a phylogeny. In this study, we develop an approach for WGD inference using gene count data that seeks to overcome both issues. We employ a phylogenetic birth–death model that includes WGD in a flexible hierarchical Bayesian approach and use reversible-jump Markov chain Monte Carlo to perform Bayesian inference of branch-specific duplication, loss, and WGD retention rates across the space of WGD configurations. We evaluate the proposed method using simulations, apply it to data sets from flowering plants, and discuss the statistical intricacies of model-based WGD inference.

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Yuan, Ming, Yun Liu, Donghuang Yan, and Yongming Liu. "Probabilistic fatigue life prediction for concrete bridges using Bayesian inference." Advances in Structural Engineering 22, no. 3 (September 17, 2018): 765–78. http://dx.doi.org/10.1177/1369433218799545.

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A probabilistic fatigue life prediction framework for concrete bridges is proposed in this study that considers the stress history from the construction stage to the operation stage. The proposed fatigue analysis framework combines the fatigue crack growth-based material life prediction model and a nonlinear structural analysis method. A reliability analysis is proposed using the developed probabilistic model to consider various uncertainties associated with the fatigue damage. A Bayesian network is established to predict the fatigue life of a concrete bridge according to the proposed framework. The proposed methodology is demonstrated using an experimental example for fatigue life prediction of a concrete box-girder. Comparison with experimental data of fatigue life shows a satisfactory accuracy using the proposed methodology, and the ratio of the posterior predicted mean (updating time n = 8) to the test value decreases to 33%–1% in the current investigation.

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Guo, Huiyong, and Meng Li. "Experimental Research on Damage Detection Based on Time Domain Data and Bayesian Fusion." Journal of Physics: Conference Series 2381, no. 1 (December 1, 2022): 012057. http://dx.doi.org/10.1088/1742-6596/2381/1/012057.

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Abstract Damages such as cracks often occur in engineering structures, and such damages often have the characteristics of time-domain variable stiffness. To effectively identify this kind of damage, a damage detection method based on time domain data and Bayesian fusion is presented in this paper. First, a hybrid model of the AR/ARCH model is used to identify structural nonlinear damage, and a damage indicator is also used: the second-order-variance indicator (SOVI). Although most of the nonlinear damage information is extracted by the indicator SOVI and ARCH model, some nonlinear information is still filtered out by the AR model part. Therefore, a linear cepstral metric indicator (CMI) based on the AR model is introduced to extract the remained nonlinear damage information, and further, the Bayesian fusion theory is applied to combine the results of SOVI and CMI to obtain complete nonlinear damage information and achieve better identification results. Finally, a three-story frame experimental model is used to demonstrate the effectiveness of the Bayesian fusion. Experimental results show that the Bayesian fusion method is superior to SOVI and CMI.

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Prajapat, Kanta, and Samit Ray-Chaudhuri. "Damage Detection in Railway Truss Bridges Employing Data Sensitivity under Bayesian Framework: A Numerical Investigation." Shock and Vibration 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6423039.

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In general, for a structure it is quite difficult to get information about all of its modes through its dynamic response under ambient or external excitation. Therefore, it is vital to exhaustively use the available information in the acquired modal data to detect any damage in the structures. Further, in a Bayesian algorithm, it can be quite beneficial if a damage localization algorithm is first used to localize damage in the structure. In this way, the number of unknown parameters in the Bayesian algorithm can be reduced significantly and thus, the efficiency of Bayesian algorithm can be enhanced. This study exploits a mode shape and its derivative based approach to localize damage in truss type structures. For damage quantification purpose, a parameter sensitivity based prediction error variance approach in Bayesian model updating is employed, which allows extracting maximum information available in the modal data. This work employs the sensitivity based Bayesian algorithm to determine the posterior confidence in truss type railway bridges. Results of the study show that the proposed approach can efficiently detect and quantify damage in railway truss bridges.

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Tashman, Zaid, Christoph Gorder, Sonali Parthasarathy, Mohamad M. Nasr-Azadani, and Rachel Webre. "Anomaly Detection System for Water Networks in Northern Ethiopia Using Bayesian Inference." Sustainability 12, no. 7 (April 5, 2020): 2897. http://dx.doi.org/10.3390/su12072897.

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For billions of people living in remote and rural communities in the developing countries, small water systems are the only source of clean drinking water. Due to the rural nature of such water systems, site visits may occur infrequently. This means broken water systems can remain in a malfunctioning state for months, forcing communities to return to drinking unsafe water. In this work, we present a novel two-level anomaly detection system aimed to detect malfunctioning remote sensored water hand-pumps, allowing for a proactive approach to pump maintenance. To detect anomalies, we need a model of normal water usage behavior first. We train a multilevel probabilistic model of normal usage using approximate variational Bayesian inference to obtain a conditional probability distribution over the hourly water usage data. We then use this conditional distribution to construct a level-1 scoring function for each hourly water observation and a level-2 scoring function for each pump. Probabilistic models and Bayesian inference collectively were chosen for their ability to capture the high temporal variability in the water usage data at the individual pump level as well as their ability to estimate interpretable model parameters. Experimental results in this work have demonstrated that the pump scoring function is able to detect malfunctioning sensors as well as a change in water usage behavior allowing for a more responsive and proactive pump system maintenance.

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