Gaussian process NARX model for damage detection in composite aircraft structures

2021 ◽  
pp. 1-10
Author(s):  
Samuel da Silva ◽  
Luis G G Villani ◽  
Marc Rebillat ◽  
Nazih Mechbal
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
False Alarms ◽  
Confidence Bounds ◽  
Model Uncertainties ◽  
Bottom Part ◽  
Number Of False Alarms ◽  
Simulation Errors ◽  
Nonlinear Autoregressive

Abstract This paper demonstrates the Gaussian process regression model's applicability combined with a nonlinear autoregressive exogenous (NARX) framework using experimental data measured with PZTs' patches bonded in a composite aeronautical structure for concerning a novel SHM strategy. A stiffened carbon-epoxy plate regarding a healthy condition and simulated damage on the center of the bottom part of the stiffener is utilized. Comparing the performance in terms of simulation errors is made to observe if the identified models can represent and predict the waveform with confidence bounds considering the confounding effect produced by noise or possible temperature variations assuming a dataset preprocessed using principal component analysis. The results of the GP-NARX identified model have attested correct classification with a reduced number of false alarms, even with model uncertainties propagation regarding healthy and damaged conditions.

Gaussian Process Regression – A tool for improved climate index reconstructions

2021 ◽  
Author(s):  
Marlene Klockmann ◽  
Eduardo Zorita
Keyword(s):  
Climate Variability ◽  
Gaussian Process ◽  
Climate Model ◽  
Model Simulation ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
Climate Index ◽  
Test Environment ◽  
Past Climate ◽  
Noisy Input

<p><span>We present a flexible non-linear framework of Gaussian Process Regression (GPR) for the reconstruction of past climate indexes such as the Atlantic Multidecadal Variability (AMV). These reconstructions are needed because the historical observation period is too short to provide a long-term perspective on climate variability. Climate indexes can be reconstructed from proxy data (e.g. tree rings) with the help of statistical models. Previous reconstructions of climate indexes mostly used some form of linear regression methods, which are known to underestimate the true amplitude of variability and perform poorly if noisy input data is used. </span></p><p><span>We implement the machine-learning method GPR for climate index reconstruction with the goal of preserving the amplitude of past climate variability. To test the framework in a controlled environment, we create pseudo-proxies from a coupled climate model simulation of the past 2000 years. In our test environment, the GPR strongly improves the reconstruction of the AMV with respect to a multi-linear Principal Component Regression. The amplitude of reconstructed variability is very close to the true variability even if non-climatic noise is added to the pseudo-proxies. In addition, the framework can directly take into account known proxy uncertainties and fit data-sets with a variable number of records in time. Thus, the GPR framework seems to be a highly suitable tool for robust and improved climate index reconstructions.</span></p>

scholarly journals False Alarms Rate Reduction Using Filtered Monitoring Indices

2017 ◽  
Vol 2 (1) ◽  
pp. 40-50
Author(s):  
M. AMMICHE ◽  
A. KOUADRI
Keyword(s):  
Fuzzy Logic ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Principal Component ◽  
Real Data ◽  
False Alarms ◽  
Detection Accuracy ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Number Of False Alarms

False alarms are the major problem in fault detection when using multivariate statistical process monitoring such as principal component analysis (PCA), they affect the detection accuracy and lead to make wrong decisions about the process operation status. In this work, filtering the monitoring indices is proposed to enhance the detection by reducing the number of false alarms. The filters that were used are: Standard Median Filter (SMF), Improved Median Filter (IMF) and fuzzy logic based filter. Signal to Noise Ratio (SNR), False Alarms Rate (FAR) and the detection time of the fault were used as criteria to compare their performance and their filtering action influence on monitoring. The algorithms were applied to cement rotary kiln data; real data, to remove spikes and outliers on the monitoring indices of PCA, and then, the filtered signals were used to supervise the system. The results, in which the fuzzy logic based filter showed a satisfactory performance, are presented and discussed.

scholarly journals Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index

2020 ◽  
Vol 12 (18) ◽  
pp. 2952
Author(s):  
Aisha Javed ◽  
Sejung Jung ◽  
Won Hee Lee ◽  
Youkyung Han
Keyword(s):  
Change Detection ◽  
Accuracy Assessment ◽  
Principal Component ◽  
False Alarms ◽  
Complex Objects ◽  
Number Of False Alarms ◽  
Object Based ◽  
Change Vector ◽  
New Buildings ◽  
Very High

Change detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. However, with increasing interest in very-high-resolution (VHR) imagery and determining changes in small and complex objects such as buildings or roads, traditional methods showed limitations, for example, the large number of false alarms or noise in the results. Thus, researchers have focused on extending PBCD to OBCD. In this study, we proposed a method for detecting the newly built-up areas by extending PBCD results into an OBCD result through the Dempster–Shafer (D–S) theory. To this end, the morphological building index (MBI) was used to extract built-up areas in multitemporal VHR imagery. Then, three PBCD algorithms, change vector analysis, principal component analysis, and iteratively reweighted multivariate alteration detection, were applied to the MBI images. For the final CD result, the three binary change images were fused with the segmented image using the D–S theory. The results obtained from the proposed method were compared with those of PBCD, OBCD, and OBCD results generated by fusing the three binary change images using the major voting technique. Based on the accuracy assessment, the proposed method produced the highest F1-score and kappa values compared with other CD results. The proposed method can be used for detecting new buildings in built-up areas as well as changes related to demolished buildings with a low rate of false alarms and missed detections compared with other existing CD methods.

Estimation of Alpha-Element Abundance Ratios Based on Gaussian Process Regression

2014 ◽  
Vol 543-547 ◽  
pp. 1685-1688
Author(s):  
Yu De Bu ◽  
Jing Chang Pan ◽  
Jie Wang
Keyword(s):  
Principal Component Analysis ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Sloan Digital Sky Survey ◽  
Estimation Accuracy ◽  
Element Abundance ◽  
Sky Survey ◽  
Data Release

In this paper, we present a method to estimate the [α/Fe ]using the spectra from ninth data release of Sloan Digital Sky Survey (SDSS). We first use principal component analysis (PCA) to reduce the dimension of the spectra, and then use Gaussian process regression (GPR) to estimate the [α/Fe ]ratios. The results show that GPR is accurate and efficient in estimating the [α/Fe]ratios. Further analysis shows that using PCA can improve the estimation accuracy of GPR.

scholarly journals Soft sensor based on Gaussian process regression and its application in erythromycin fermentation process

2016 ◽  
Vol 22 (2) ◽  
pp. 127-135 ◽  
Author(s):  
Congli Mei ◽  
Ming Yang ◽  
Dongxin Shu ◽  
Hui Jiang ◽  
Guohai Liu ◽  
...  
Keyword(s):  
Gaussian Process ◽  
High Performance ◽  
Fermentation Process ◽  
Low Cost ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
Soft Sensor ◽  
Support Vector ◽  
Soft Sensors ◽  
Uncertainty Measurement

Erythromycin fermentation process is a typical microbial fermentation process. Soft sensors can be used to estimate biomass of Erythromycin fermentation process for their relative low cost, simple development, and ability to predict difficult-to-measure variables. However, traditional soft sensors, e.g. artificial neural network (ANN) soft sensors, support vector machine (SVM) soft sensors, etc., cannot represent the uncertainty (measurement precision) of outputs. That results in difficulties in practice. Gaussian process regression (GPR) provides a novel framework to solve regression problems. The output uncertainty of a GPR model follows Gaussian distribution, expressed in terms of mean and variance. The mean represents the predicted output. The variance can be viewed as the measure of confidence in the predicted output that distinguishes the GPR from NN and SVM soft sensor models. We proposed a systematic approach based on GPR and principal component analysis (PCA) to establish a soft sensor to estimate biomass of Erythromycin fermentation process. Simulations on industrial data from an Erythromycin fermentation process show the proposed GPR soft sensor has high performance of modeling the uncertainty of estimates.

scholarly journals Gaussian Process Regression for foreground removal in HI intensity mapping experiments

2021 ◽  
Author(s):  
Paula S Soares ◽  
Catherine A Watkinson ◽  
Steven Cunnington ◽  
Alkistis Pourtsidou
Keyword(s):  
Power Spectrum ◽  
Gaussian Process ◽  
Low Frequency ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
Frequency Range ◽  
Intensity Mapping ◽  
Removal Technique ◽  
First Time ◽  
Foreground Removal

Abstract We apply for the first time Gaussian Process Regression (GPR) as a foreground removal technique in the context of single-dish, low redshift H i intensity mapping, and present an open-source python toolkit for doing so. We use MeerKAT and SKA1-MID-like simulations of 21cm foregrounds (including polarisation leakage), H i cosmological signal and instrumental noise. We find that it is possible to use GPR as a foreground removal technique in this context, and that it is better suited in some cases to recover the H i power spectrum than Principal Component Analysis (PCA), especially on small scales. GPR is especially good at recovering the radial power spectrum, outperforming PCA when considering the full bandwidth of our data. Both methods are worse at recovering the transverse power spectrum, since they rely on frequency-only covariance information. When halving our data along frequency, we find that GPR performs better in the low frequency range, where foregrounds are brighter. It performs worse than PCA when frequency channels are missing, to emulate RFI flagging. We conclude that GPR is an excellent foreground removal option for the case of single-dish, low redshift H i intensity mapping in the absence of missing frequency channels. Our python toolkit gpr4im and the data used in this analysis are publicly available on GitHub.

scholarly journals DETECTION OF METHANE PLUMES IN HYPERSPECTRAL IMAGES FROM SENTINEL-5P BY COUPLING ANOMALY DETECTION AND PATTERN RECOGNITION

2021 ◽  
Vol V-3-2021 ◽  
pp. 81-87
Author(s):  
E. Ouerghi ◽  
T. Ehret ◽  
C. de Franchis ◽  
G. Facciolo ◽  
T. Lauvaux ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Second Step ◽  
False Alarms ◽  
Infrared Wavelength ◽  
Local Maxima ◽  
Methane Absorption ◽  
Number Of False Alarms ◽  
The Impact

Abstract. Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting large methane leaks using hyperspectral data from the Sentinel-5P satellite. For that we exploit the fine spectral sampling of Sentinel-5P data to detect methane absorption features visible in the shortwave infrared wavelength range (SWIR). Our method involves three separate steps: i) background subtraction, ii) detection of local maxima in the negative logarithmic spectrum of each pixel and iii) anomaly detection in the background-free image. In the first step, we remove the impact of the albedo using albedo maps and the impact of the atmosphere by using a principal component analysis (PCA) over a time series of past observations. In the second step, we count for each pixel the number of local maxima that correspond to a subset of local maxima in the methane absorption spectrum. This counting method allows us to set up a statistical a contrario test that controls the false alarm rate of our detections. In the last step we use an anomaly detector to isolate potential methane plumes and we intersect those potential plumes with what have been detected in the second step. This process strongly reduces the number of false alarms. We validate our method by comparing the detected plumes against a dataset of plumes manually annotated on the Sentinel-5P L2 methane product.

DETECTION AND IDENTIFICATION OF ANOMALIES IN WIRELESS MESH NETWORKS USING PRINCIPAL COMPONENT ANALYSIS (PCA)

2009 ◽  
Vol 10 (04) ◽  
pp. 517-534 ◽  
Author(s):  
ZAINAB R. ZAIDI ◽  
SARA HAKAMI ◽  
TIM MOORS ◽  
BJORN LANDFELDT
Keyword(s):  
Wireless Networks ◽  
Anomaly Detection ◽  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Denial Of Service ◽  
Principal Component ◽  
False Alarms ◽  
Identification Scheme ◽  
Number Of False Alarms ◽  
Wireless Mesh

Anomaly detection is becoming a powerful and necessary component as wireless networks gain popularity. In this paper, we evaluate the efficacy of PCA based anomaly detection for wireless mesh networks (WMN). PCA based method [1] was originally developed for wired networks. Our experiments show that it is possible to detect different types of anomalies, such as Denial-of-service (DoS) attack, port scan attack [1], etc., in an interference prone wireless environment. However, the PCA based method is found to be very sensitive to small changes in flows causing non-negligible number of false alarms. This problem prompted us to develop an anomaly identification scheme which automatically identifies the flow(s) causing the detected anomaly and their contributions in terms of number of packets. Our results show that the identification scheme is able to differentiate false alarms from real anomalies and pinpoint the culprit(s) in case of a real fault or threat. Moreover, we also found that the threshold value used in [1] for distinguishing normal and abnormal traffic conditions is based on assumption of normally distributed traffic which is not accurate for current network traffic which is mostly self-similar in nature. Adjusting the threshold also reduced the number of false alarms considerably. The experiments were performed over an 8 node mesh testbed deployed in a suburban area, under different realistic traffic scenarios. Our identification scheme facilitates the use of PCA based method for real-time anomaly detection in wireless networks as it can filter the false alarms locally at the monitoring nodes without excessive computational overhead.

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