Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time,recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through a pseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets of measurements involving various load cases, we expedite the speed of thePD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time, recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through apseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets ofmeasurements involving various load cases, we expedite the speed of the PD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small. Copyright (C) 2009 John Wiley & Sons, Ltd.

Determination of amines using 2-(11H-benzo[a]carbazol-11-yl) ethyl chloroformate (BCEC-Cl) as labeling reagent by HPLC with fluorescence detection and identification with APCI/MS

A pre-column derivatization method for the sensitive determination of amines using a labeling reagent 2-(11H-benzo[a]-carbazol-11-yl) ethyl chloroformate (BCEC-Cl) followed by high-performance, liquid chromatography with fluorescence detection has been developed. Identification of derivatives was carried out by LC/APCI/MS in positive-ion mode. The chromophore of 1,2-benzo-3,4-dihydrocarbazole-9-ethyl chloroformate (BCEOC-Cl) reagent was replaced by 2-(11H-benzo[a]-carbazol-11-yl) ethyl functional group, which resulted in a sensitive fluorescence derivatizing reagent BCEC-Cl. BCEC-Cl could easily and quickly label amines. Derivatives were stable enough to be efficiently analyzed by HPLC and showed an intense protonated molecular ion corresponding m/z [M+ H](+) under APCI/MS in positive-ion mode. The collision-induced dissociation of the protonated molecular ion formed characteristic fragment ions at m/z 261.8 and m/z 243.8 corresponding to the cleavages of CH2O-CO and CH2-OCO bonds. Studies on derivatization demonstrated excellent derivative yields over the pH 9.0-10.0. Maximal yields close to 100% were observed with three- to four-fold molar reagent excess. In addition, the detection responses for BCEC-derivatives were compared to those obtained using 1,2-benzo-3,4-dihydrocarbazole-9-ethyl chloroformate (BCEOC-Cl) and 9-fluorenyl methylchloroformate, (FMOC-Cl) as labeling reagents. The ratios I-BCEC/I-BCEOC = 1.94-2.17 and I-BCEC/I-FMOC = 1.04-2.19 for fluorescent (FL) responses (here, I was relative fluorescence intensity). Separation of the derivatized amines had been optimized on reversed-phase Eclipse XDB-C-8 column. Detection limits calculated from 0.50 pmol injection, at a signal-to-noise ratio of 3, were 1.77-14.4 fmol. The relative standard deviations for within-day determination (n = 11) were 1.84-2.89% for the tested amines. The mean intra- and inter-assay precision for all amines levels were < 3.64% and 2.52%, respectively. The mean recoveries ranged from 96.6% to 107.1% with their standard deviations in the range of 0.8-2.7. Excellent linear responses were observed with coefficients of > 0.9996. (C) 2006 Elsevier B.V. All rights reserved.

Determination of long-chain fatty acids in bryophyte plants extracts by HPLC with fluorescence detection and identification with MS

A sensitive method for the determination of long-chain fatty acids (LCFAs) (>C20) using 1-[2-(p-toluenesulfonate)-ethyl]-2-phenylimidazole-[4.5-f]-9,10-phenanthrene (TSPP) as tagging reagent with fluorescence detection and identification with post-column APCI/MS has been developed. The LCFAs in bryophyte plant samples were obtained based on distillation extraction with 1: 1 (v/v) chloroform/methanol as extracting solvent. TSPP could easily and quickly label LCFAs at 90 degrees C in the presence of K2CO3 catalyst in DMF. Eleven free LCFAs from the extracts of bryophyte plants were sensitively determined. Maximal labeling yields close to 100% were observed with a five-fold excess of molar reagent. Separation of the derivatized fatty acids exhibited a good baseline resolution in combination with a gradient elution on a reversed-phase Eclipse XDB-C-8 column. Calculated detection limits from 1.0 pmol injection, at a signal-to-noise ratio of 3, were 26.19-76.67 fmol. Excellent linear responses were observed with coefficients of >0.9996. Good compositional data were obtained from the analysis of the extracted LCFAs containing as little as 0.2 g of bryophyte plant samples. Therefore, the facile TSPP derivatization coupled with HPLC/APCI/MS analysis allowed the development of a highly sensitive method for the quantitation of trace levels of LCFAs from biological and natural environmental samples. (c) 2006 Elsevier B.V. All rights reserved.

2-(2-Phenyl-1H-phenanthro-[9,10-d]imidazole-1-yl)-acetic acid (PPIA) and its application for determination of amines by high performance liquid chromatography with fluorescence detection and identification with mass spectroscopy/atmospheric pressure chemical ionization

A simple, sensitive, and mild method for the determination of amino compounds based on a condensation reaction with 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride (EDC-HCI) as the dehydrant with fluorescence detection has been developed. Amines were derivatized to their acidamides with labeling reagent 2-(2-phenyl-1H-phenanthro-[9,10-d]imidazole-1-yl)-acetic acid (PPIA). Studies on derivatization conditions indicated that the coupling reaction proceeded rapidly and smoothly in the presence of a base catalyst in acetonitrile to give the corresponding sensitively fluorescent derivatives with an excitation maximum at lambda(ex) 260nm and an emission maximum at lambda(em) 380nm. The labeled derivatives exhibited high stability and were enough to be efficiently analyzed by high-performance liquid chromatography. Identification of derivatives was carried out by online post-column mass spectrometry (LC/APCI-MS/MS) and showed an intense protonated molecular ion corresponding m/z [MH](+) under APCI in positive-ion mode. At the same time, the fluorescence properties of derivatives in various solvents or at different temperature were investigated. The method, in conjunction with a gradient elution, offered a baseline resolution of the common amine derivatives on a reversed-phase Eclipse XDB-C-8 column. LC separation for the derivatized amines showed good reproducibility with acetonitrile-water as mobile phase. Detection limits calculated from 0.78 pmol injection, at a signal-to-noise ratio of 3, were 3.1-18.2 fmol. The mean intra- and inter-assay precision for all amine levels were < 3.85% and 2.11%, respectively. Excellent linear responses were observed with coefficients of > 0.9996. The established method for the determination of aliphatic amines from real wastewater and biological samples was satisfactory. (c) 2006 Elsevier B.V. All rights reserved.

Development of a sensitive fluorescent derivatization reagent 1,2-benzo-3,4-dihydrocarbazole-9-ethyl chloroformate (BCEOC) and its application for determination of amino acids from seeds and bryophyte plants using high-performance liquid chromatography with fluorescence detection and identification with electrospray ionization mass spectrometry

A pre-column derivatization method for the sensitive determination of amino acids and peptides using the tagging reagent 1,2-benzo-3,4dihydrocarbazole-9-ethyl chloroformate (BCEOC) followed by high-performance liquid chromatography with fluorescence detection has been developed. Identification of derivatives was carried out by liquid chromatography/electrospray ionization mass spectrometry (LC/ESI-MS/MS). The chromophore of 2-(9-carbazole)-ethyl chloroformate (CEOC) reagent was replaced by 1,2-benzo-3,4-dihydrocarbazole functional group, which resulted in a sensitive fluorescence derivatizing reagent BCEOC. BCEOC can easily and quickly label peptides and amino acids. Derivatives are stable enough to be efficiently analyzed by high-performance liquid chromatography. The derivatives showed an intense protonated molecular ion corresponding m/z (M + H)(+) under electrospray ionization (ESI) positive-ion mode with an exception being Tyr detected at negative mode. The collision-induced dissociation of protonated molecular ion formed a product at m/z 246.2 corresponding to the cleavage of C-O bond of BCEOC molecule. Studies on derivatization demonstrate excellent derivative yields over the pH 9.0-10.0. Maximal yields close to 100% are observed with a 3-4-fold molar reagent excess. Derivatives exhibit strong fluorescence and extracted detzvatization solution with n-hexane/ethyl acetate (10:1, v/v) allows for the direct injection with no significant interference from the major fluorescent reagent degradation by-products, such as 1,2-benzo-3,4-dihydrocarbazole-9-ethanol (BDC-OH) (a major by-product), mono- 1,2-benzo-3,4-dihydrocarbazole-9-ethyl carbonate (BCEOC-OH) and bis-(1,2-benzo-3,4-dihydrocarbazole-9-ethyl) carbonate (BCEOC)(2). In addition, the detection responses for BCEOC derivatives are compared to those obtained with previously synthesized 2-(9-carbazole)-ethyl chloroformate (CEOC) in our laboratory. The ratios AC(BCEOC)/AC(CEOC) = 2.05-6.51 for fluorescence responses are observed (here, AC is relative fluorescence response). Separation of the derivatized peptides and amino acids had been optimized on Hypersil BDS C-18 column. Detection limits were calculated from 1.0 pmol injection at a signal-to-noise ratio of 3, and were 6.3 (Lys)-177.6 (His) fmol. The mean interday accuracy ranged from 92 to 106% for fluorescence detection with mean %CV < 7.5. The mean interday precision for all standards was < 10% of the expected concentration. Excellent linear responses were observed with coefficients of > 0.9999. Good compositional data could be obtained from the analysis of derivatized protein hydrolysates containing as little as 50.5 ng of sample. Therefore, the facile BCEOC derivatization coupled with mass spectrometry allowed the development of a highly sensitive and specific method for the quantitative analysis of trace levels of amino acids and peptides from biological and natural environmental samples. (c) 2005 Elsevier B.V. All rights reserved.

Modelling 3D camera movement for vibration characterisation and multiple object identification with application to lighting assessment

Utilising cameras as a means to survey the surrounding environment is becoming increasingly popular in a number of different research areas and applications. Central to using camera sensors as input to a vision system, is the need to be able to manipulate and process the information captured in these images. One such application, is the use of cameras to monitor the quality of airport landing lighting at aerodromes where a camera is placed inside an aircraft and used to record images of the lighting pattern during the landing phase of a flight. The images are processed to determine a performance metric. This requires the development of custom software for the localisation and identification of luminaires within the image data. However, because of the necessity to keep airport operations functioning as efficiently as possible, it is difficult to collect enough image data to develop, test and validate any developed software. In this paper, we present a technique to model a virtual landing lighting pattern. A mathematical model is postulated which represents the glide path of the aircraft including random deviations from the expected path. A morphological method has been developed to localise and track the luminaires under different operating conditions. © 2011 IEEE.

A novel toxicity fingerprinting method for pollutant identification with lux-marked biosensors

A novel technique is described for the identification and quantification of environmental pollutants based on toxicity fingerprinting with a metabolic lux-marked bacterial biosensor. This method involved characterizing the toxicity-based responses of the biosensor to seven calibration pollutants as acute temporal-dose response fingerprints. An algorithm is described to allow comparisons of responses of an unknown pollutant to be made against the calibration data. This is based on predicting pollutant concentration at each of six different time points over the course of a 5-min assay. If the prediction is consistent between the unknown pollutant and a calibration pollutant at the 95% test level, this is considered to be a positive identification. All seven calibration pollutants could be successfully distinguished from each other with this technique. Environmental samples, individually spiked with single concentrations of pollutants, were compared in this way against the calibration pollutants. An 83% identification success was achieved, with no false positives at the 95% test level. This is a simple and rapid technique that potentially can be applied to monitoring of industrial wastewater or as a screening tool for regulators.

Robust Multimodal Person Identification With Limited Training Data

This paper presents a novel method of audio-visual feature-level fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there are limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new multimodal feature representation and a modified cosine similarity are introduced to combine and compare bimodal features with limited training data, as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal dataset created from the SPIDRE speaker recognition database and AR face recognition database with variable noise corruption of speech and occlusion in the face images. The system's speaker identification performance on the SPIDRE database, and facial identification performance on the AR database, is comparable with the literature. Combining both modalities using the new method of multimodal fusion leads to significantly improved accuracy over the unimodal systems, even when both modalities have been corrupted. The new method also shows improved identification accuracy compared with the bimodal systems based on multicondition model training or missing-feature decoding alone.

Parkinsonian tremor identification with multiple local field potential feature classification

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.