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APPLIED INTELLIGENCE (2021)
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Han Sun et al.
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Kejie Jiang et al.
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Serkan Kiranyaz et al.
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Maria Valero et al.
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Ensemble learning-based structural health monitoring by Mahalanobis distance metrics
Hassan Sarmadi et al.
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FastEE: Fast Ensembles of Elastic Distances for time series classification
Chang Wei Tan et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2020)
Multi-taper method based substructure identification for shear structures
Dongyu Zhang et al.
JOURNAL OF VIBRATION AND CONTROL (2020)
A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects
Hassan Sarmadi et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
Dissimilarity-based representations for one-class classification on time series
Stefano Mauceri et al.
PATTERN RECOGNITION (2020)
Towards semi-supervised and probabilistic classification in structural health monitoring
L. A. Bull et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection
Alireza Entezami et al.
ADVANCES IN ENGINEERING SOFTWARE (2020)
A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications
Onur Avci et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
A Bayesian non-parametric clustering approach for semi-supervised Structural Health Monitoring
T. J. Rogers et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Parameterised time-frequency analysis methods and their engineering applications: A review of recent advances
Yang Yang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
A cointegration approach for heteroscedastic data based on a time series decomposition: An application to structural health monitoring
Haichen Shi et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Outlier ensembles: A robust method for damage detection and unsupervised feature extraction from high-dimensional data
L. A. Bull et al.
JOURNAL OF SOUND AND VIBRATION (2019)
Machine learning based novelty detection using modal analysis
Ali I. Ozdagli et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2019)
Automated real-time damage detection strategy using raw dynamic measurements
Rhara de Almeida Cardoso et al.
ENGINEERING STRUCTURES (2019)
Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring
Zhiyi Tang et al.
STRUCTURAL CONTROL & HEALTH MONITORING (2019)
Frequency domain decomposition-based multisensor data fusion for assessment of progressive damage in structures
Mehrisadat Makki Alamdari et al.
STRUCTURAL CONTROL & HEALTH MONITORING (2019)
Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods
Alireza Entezami et al.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2019)
A dissimilarity-based multiple instance learning approach for protein remote homology detection
Antonella Mensi et al.
PATTERN RECOGNITION LETTERS (2019)
Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection
Reza Mohammadi-Ghazi et al.
PATTERN RECOGNITION (2018)
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Anthony Bagnall et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2017)
Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
Osama Abdeljaber et al.
JOURNAL OF SOUND AND VIBRATION (2017)
Early Damage Detection Based on Pattern Recognition and Data Fusion
Joao Pedro Santos et al.
JOURNAL OF STRUCTURAL ENGINEERING (2017)
Bayesian characterization of buildings using seismic interferometry on ambient vibrations
Hao Sun et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
Early Damage Detection Based on Pattern Recognition and Data Fusion
Joao Pedro Santos et al.
JOURNAL OF STRUCTURAL ENGINEERING (2017)
Continuous Monitoring of High-Rise Buildings Using Seismic Interferometry
Aurelien Mordret et al.
BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA (2017)
A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges
Moises Silva et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2016)
Machine learning algorithms for damage detection: Kernel-based approaches
Adam Santos et al.
JOURNAL OF SOUND AND VIBRATION (2016)
Fault detection via recurrence time statistics and one-class classification
David Martinez-Rego et al.
PATTERN RECOGNITION LETTERS (2016)
The DTW-based representation space for seismic pattern classification
Mauricio Orozco-Alzate et al.
COMPUTERS & GEOSCIENCES (2015)
Structural modification assessment using supervised learning methods applied to vibration data
Vinicius Alves et al.
ENGINEERING STRUCTURES (2015)
Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles
Anthony Bagnall et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2015)
Time series classification with ensembles of elastic distance measures
Jason Lines et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2015)
Adaptable functional series TARMA models for non-stationary signal representation and their application to mechanical random vibration modeling
M. D. Spiridonakos et al.
SIGNAL PROCESSING (2014)
Experimental comparison of representation methods and distance measures for time series data
Xiaoyue Wang et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2013)
Toward Data-Driven Structural Health Monitoring: Application of Machine Learning and Signal Processing to Damage Detection
Yujie Ying et al.
JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2013)
Addressing Big Data Time Series
Thanawin Rakthanmanon et al.
ACM Transactions on Knowledge Discovery from Data (2013)
Three-way analysis of structural health monitoring data
Miguel A. Prada et al.
NEUROCOMPUTING (2012)
The dissimilarity space: Bridging structural and statistical pattern recognition
Robert P. W. Duin et al.
PATTERN RECOGNITION LETTERS (2012)
Pattern recognition of structural behaviors based on learning algorithms and symbolic data concepts
Alexandre Cury et al.
STRUCTURAL CONTROL & HEALTH MONITORING (2012)
Influence of the Autoregressive Model Order on Damage Detection
Eloi Figueiredo et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2011)
Classification of three-way data by the dissimilarity representation
Diana Porro-Munoz et al.
SIGNAL PROCESSING (2011)
Machine learning algorithms for damage detection under operational and environmental variability
Eloi Figueiredo et al.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2011)
Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studies
Israel Lopez et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2009)
Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications
Mustafa Gul et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2009)
Minimum spanning tree based one-class classifier
Piotr Juszczak et al.
NEUROCOMPUTING (2009)
Model-free data interpretation for continuous monitoring of complex structures
Daniele Posenato et al.
ADVANCED ENGINEERING INFORMATICS (2008)
Beyond Traditional Kernels: Classification in Two Dissimilarity-Based Representation Spaces
Elzbieta Pekalska et al.
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS (2008)
A review of nonlinear dynamics applications to structural health monitoring
Keith Worden et al.
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Analysis of local state space models for feature extraction in structural health monitoring
L. A. Overbey et al.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2007)
The application of machine learning to structural health monitoring
Keith Worden et al.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2007)
Time-series methods for fault detection and identification in vibrating structures
Spilios D. Fassois et al.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2007)
Use of chaotic excitation and attractor property analysis in structural health monitoring
JM Nichols et al.
PHYSICAL REVIEW E (2003)
Statistical damage classification under changing environmental and operational conditions
H Sohn et al.
JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES (2002)
Dissimilarity representations allow for building good classifiers
E Pekalska et al.
PATTERN RECOGNITION LETTERS (2002)
Statistical pattern recognition: A review
AK Jain et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2000)
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