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SRDI: Synergistic distance-based approaches for supervised statistical pattern recognition in structural health monitoring

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STRUCTURES 2025年 75卷

作者： Sakib, Nazmuz Rana, Shohel Bangladesh Univ Engn & Technol BUET Dept Civil Engn Dhaka 1000 Bangladesh

This paper introduces a method for identifying structural damage by statistically analyzing acceleration data collected from the structure. To overcome challenges associated with high-dimensional damage-sensitive features (HD-DSFs) in data-driven methods, this study proposes a novel approach that combines two distance-based metrics, Dynamic Time Warping (DTW) and Mahalanobis Distance (MD), strategically applied on the acceleration data directly. The primary contribution lies in introducing a method within supervised statistical pattern recognition that mitigates the need for resource-intensive techniques such as signal processing or machine learning based techniques. A state matrix is developed from the acceleration responses at the sensor positions. This approach can identify damage through low-dimensional feature samples represented as distance quantities. MD between the state matrices of the undamaged and damage state of the structure are calculated and averaged to formulate a training vector (TRV). Accumulating TRV for all the damage cases, the State representation matrix is developed. During the inspection phase, a testing vector (TSV) is formulated for the unknown state of the structure. A damage index termed SRDI is developed by computing the DTW distance between the state representation matrix and the TSV. Finally, the damage is identified where the SRDI is at its minimum value. Validation using experimental vibration data from a large-scale experimental structure and a progressive damage test of a real bridge structure confirms the effectiveness of the proposed method.

关键词： Structural health monitoring Vibration response Data-driven Dynamic time warping Mahalanobis distance Damage identification statistical pattern recognition

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statistical pattern-recognition USING EFFICIENT 2-DIMENSIONAL MOMENTS WITH APPLICATIONS TO CHARACTER-recognition

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pattern recognition 1993年第6期26卷 877-882页

作者： TSIRIKOLIAS, K MERTZIOS, BG Automatic Control Systems Laboratory Department of Electrical Engineering Democritus University of Thrace 67 100 Xanthi Greece

A new set of efficient two-dimensional (2D) moments is introduced, which are normalized with respect to standard deviation. They are invariant under rotation, translation and scale of the image, less sensitive to noise and appear to have better classification performance over the existing sets of moments. Application of the proposed technique to optical character recognition is given.

关键词： MOMENTS statistical pattern recognition CLASSIFICATION CLUSTERING statistical SIGNAL PROCESSING OCR

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statistical pattern recognition technique to enhance anomalies in magnetic surveys

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JOURNAL OF APPLIED GEOPHYSICS 1997年第2期37卷 55-66页

作者： Rosati, I Cardarelli, E UNIV ROMA LA SAPIENZA DIPARTIMENTO IDRAUL TRANSPORTI & STRADEI-00184 ROMEITALY

To classify and recognize objects or region of interest in an image, several methods in pattern recognition techniques were developed. Texture is one of the main characters used to classify and characterize an image whether it be an aerial photograph, a satellite image, a photomicrograph or, as in this study, a geophysical image. This paper describes the application of pattern recognition techniques to 2D geophysical data. The aim is to determine if they are useful to identify magnetic anomaly, especially with a very low signal/noise ratio. We utilized the method proposed by Haralick et al. (Haralick, R.M., Shanmugam, K. and Dinstein, I., 1973. Textural features for image classification. IEEE Trans. Syst. Man Cybern., 6-3: 610-621.) based on gray tone spatial dependence matrices;14 features can be calculated to define the spatial relationship of pixels values;we verify that some of these features mark the boundaries of regions having a common statistical character and that match well with magnetic anomalies present in the image. Two synthetic models were utilized to test the discrimination capacity of the PR technique. Synthetic magnetic anomalies were computed by using the method proposed by Talwani (Talwani, M., 1965. Computation with the help of a digital computer of magnetic anomalies caused by bodies of arbitrary shape. Geophysics, 30: 797-817.). The synthetic images were altered with a random noise up to three times higher than anomaly amplitude. Then gray-tone spatial dependence matrix was applied for image classification. The same method has been finally used with experimental data collected during two magnetic surveys carried out in the same area for archaeological purpose. The results indicate that the method can be utilized to interpret geophysical data. Thus it can allow the anomalies detection and consequently facilitates geophysical data interpretation. (C) 1997 Elsevier Science B.V.

关键词： statistical pattern recognition texture analysis processing magnetic data geophysical image

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statistical pattern recognition: A review

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2000年第1期22卷 4-37页

作者： Jain, AK Duin, RPW Mao, JC Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA Delft Univ Technol Dept Appl Phys NL-2600 GA Delft Netherlands IBM Corp Almaden Res Ctr San Jose CA 95120 USA

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning. selection of training and lest samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining. web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

关键词： statistical pattern recognition classification clustering feature extraction feature selection error estimation classifier combination neural networks

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statistical pattern recognition approach for long-time monitoring of the *** stadium by means of AR models and PCA

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ENGINEERING STRUCTURES 2017年 153卷 317-333页

作者： Datteo, Alessio Luca, Francescantonio Busca, Giorgio Politecn Milan Dept Mech Engn Via La Masa 34 I-20156 Milan Italy

In recent years, the interest for the automatic evaluation of the state of civil structures is increased. The development of Structural Health Monitoring is allowed by the low costs of the hardware and the improving of the computational capacity of computers that can analyze considerable amount of data in short time. A Structural Health Monitoring (SHM) system should continuously monitor structures, extracting and processing relevant information, to efficiently allocate the resources for maintenance and ensure the security of the structure. Considering the latest developments in this field, great attention has been paid to data-based approaches, especially to autoregressive models;these econometric models, born in the field of finance, are usually used to analyze the vibration time series provided by the sensors applied on the monitored structures. Indexes based on these autoregressive models can be used as features by which the structural integrity can be assessed. This work proposes the application of a multivariable analysis, Principal Component Analysis (PCA), to the set of the autoregressive model parameters estimated on the vibration responses of a real structure under operational conditions. This approach reduces a complex set of data to a lower dimension, by representing the behavior of the structure through the few variables. This work uses the principal components of the autoregressive model parameters as indicators that can effectively describe different operational levels and some important environmental effects. The strategy is applied for the first time on the data collected by the long-time monitoring system installed on the stands of the G. Meazza stadium in Milan. The results will show that this procedure is effective in representing the status of the structure and can be used in a structural health monitoring prospective.

关键词： statistical pattern recognition Autoregressive models Principal component analysis Structural health monitoring Environmental conditions Operational conditions

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statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2009年第7期23卷 2192-2204页

作者： Gul, Mustafa Catbas, F. Necati Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA

statistical pattern recognition methodologies have gained considerable attention for Structural Health Monitoring (SHM) applications to detect changes in a structure (e.g. damage). For most of such applications, outlier analysis of the damage sensitive features obtained from the SHM data is used to detect the changes in the structure. There are a number of different approaches used by different research groups and it is widely accepted that success of a certain methodology may depend on the structure and/or structural change to be identified. Therefore, it is very important that promising methodologies are verified by using different test structures and damage cases. The main objective of this study is to investigate statistical pattern recognition methods in the context of SHM using different laboratory structures. Time series modeling, i.e. auto-regressive models, is used in conjunction with Mahalanobis distance-based outlier detection algorithms to identify different types of structural changes on different test structures. Similar approaches were reported in the literature but here the methodology is modified by using random decrement functions to eliminate the effects of the exogenous input. Then a number of tests are conducted by using two different test structures in laboratory conditions in order to evaluate the results in a comparable fashion. The first test specimen is a simply supported steel beam where the second structure is a highly redundant steel grid structure. Various damage conditions are simulated by using these structures. The ambient vibration data is analyzed by using the methodology described and results are presented. Finally, the advantages and drawbacks of the methodology are discussed in the light of experimental results. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Structural Health Monitoring Damage detection Time series modeling statistical pattern recognition Outlier detection Dynamics

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statistical pattern recognition for Optimal Sensor Placement in Damage Detection Applications 10th

Statistical Pattern Recognition for Optimal Sensor Placement...

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10th European Workshop on Structural Health Monitoring (EWSHM)

作者： Liangou, Theodora Katsoudas, Anastasios Silionis, Nicholas Anyfantis, Konstantinos Natl Tech Univ Athens Sch Naval Architecture & Marine Engn Athens Greece

ISBN: (纸本)9783031072581;9783031072574

A Structural Health Monitoring (SHM) architecture involves the processing of sensor measurements and their translation to decisions about the structure's condition. Given a specific SHM approach, the sensor topology, the feature selection, and the employed detector are the main elements that control the detection performance. This work provides an exploratory analysis of the statistical response patterns that govern a structure subjected to variable loads and methodically arrives at an optimal sensor topology, that maximizes the detection performance. For demonstration purposes, a thin square plate subjected to probabilistically described loads is considered. The damage of interest corresponds to a uniform thickness loss, the detection of which is evaluated at different damage levels (from 1% to a 90% unrealistic upper bound). The damage is to be identified indirectly, through strain sensing. The problem is numerically approached (Finite Elements and Monte Carlo Simulations). The generalized Gaussian likelihood ratio test is employed for setting up the detector. The effect of the feature vector arrangement to the detection performance is assessed through estimations of the probability of detection and false alarm, under the Neyman-Pearson framework. The optimal feature vector has been derived through case-based informal (selective process) or formal (Genetic Algorithms) optimization.

关键词： Optimal sensor placement statistical pattern recognition Detection theory Genetic Algorithm Corrosion

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statistical pattern recognition FOR REAL-TIME IMAGE EDGE DETECTION ON FPGA 12

STATISTICAL PATTERN RECOGNITION FOR REAL-TIME IMAGE EDGE DET...

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12th IEEE International Conference on Signal Processing (ICSP)

作者： Liu, Ziyan Qi, Jia Feng, Liang Feng, Li Guizhou Univ Coll Elect & Informat Guiyang 550025 Peoples R China State Grid Chongqing Elect Power Co Chongqing 400014 Peoples R China

ISBN: (纸本)9781479921867

Image edge detection is a fundamental process in computer vision. Image edges represent the major fraction of information in an image. Traditional edge-detection techniques focus on the gradient calculation method. In this paper, for the first time, the statistical pattern recognition method is used to detect the edge after the real-time image was processed via the median filtering method and implemented on FPGA. In comparison to the Sobel algorithm, the proposed method has superior anti-noise capability.

关键词： statistical pattern recognition Median Filtering FPGA Real-Time Image Edge Detection

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Outliers in statistical pattern recognition and an application to automatic chromosome classification

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pattern recognition LETTERS 1997年第6期18卷 525-539页

作者： Ritter, G Gallegos, MT Fakultät für Mathematik und Informatik Universität Passau D-94 030 Passau Germany

We propose a heuristic method of parameter estimation in mixture models for data with outliers and design a Bayesian classifier for assignment of m objects to n greater than or equal to m classes under constraints. This method of outlier handling combined with the classifier is applied to the well-known problem of automatic, constrained classification of chromosomes into their biological classes. We show that it decreases the error rate relative to the classical, normal, model by more than 50%. When applied to the Edinburgh feature data of the large Copenhagen image data set Cpr our best classifier yields an error rate close to 1.3% relative to chromosomes;4 out of 5 cells are correctly classified. (C) 1997 Elsevier Science B.V.

关键词： outlier estimation mixture distributions trimming method Bayesian classification statistical pattern recognition automatic chromosome classification karyotyping diagnostic classification biomedical data model

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Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry

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MEDICAL ENGINEERING & PHYSICS 2009年第8期31卷 971-978页

作者： Victor Marcos, J. Hornero, Roberto Alvarez, Daniel del Campo, Felix Zamarron, Carlos Univ Valladolid Biomed Engn Grp ETS Ingenieros Telecomunicac E-47011 Valladolid Spain Hosp Rio Hortega Serv Neumol Valladolid Spain Hosp Clin Univ Serv Neumol Santiago De Compostela Spain

The aim of this study is to assess the utility of traditional statistical pattern recognition techniques to help in obstructive sleep apnoea (OSA) diagnosis Classifiers based on quadratic (QDA) and linear (LDA) discriminant analysis. K-nearest neighbours (KNN) and logistic regression (LR) were evaluated Spectral and nonlinear input features from oxygen saturation (SaO(2)) signals were applied. A total of 187 recordings from patients suspected of suffering from OSA were available. This initial dataset was divided into training set (74 subjects)and test set(113 subjects). Twelve classification algorithms were developed by applying QDA, LDA, KNN and LR with spectral features, nonlinear features and combination of both groups. The performance of each algorithm was measured on the test set by means of classification accuracy and receiver operating characteristic (ROC) analysis QDA, LDA and LR showed better classification capability than KNN The classifier based on LDA with spectral features provided the best diagnostic ability with an accuracy of 87.61% (91.05% sensitivity and 82.61% specificity) and an area under the ROC curve (AROC) of 0.925. The proposed statistical pattern recognition techniques could be applied as an OSA screening tool. (C) 2009 IPEM Published by Elsevier Ltd. All rights reserved.

关键词： Obstructive sleep apnoea Oxygen saturation statistical pattern recognition Discriminant analysis K-Nearest neighbours Logistic regression

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