Statistical Multi-Objective Structural Damage Identification based on Dynamic Parameters

Evolutionary algorithms are suitable to solve damage identification problems in a multi-objective context. However, the performance of these methods can deteriorate quickly with increasing noise intensities originating numerous uncertainties. In this paper, a statistic structural damage detection method formulated in a multi-objective context is proposed. The statistic analysis is implemented to take into account the uncertainties existing in the structural model and measured structural modal parameters. The presented method is verified by a number of simulated damage scenarios. The effects of noise and damage levels on damage detection are investigated.

A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).

Identification of Mechanical Parameters in Mathematical Model of Human Abdominal Fascia

Миглена Г. Кирилова-Донева - Едномерен експеримент на релаксация беше извършен с 14 образци от човешка пъпна фасция. Механичното поведение на фасцията по време на релаксация беше моделирано прилагайки нелинейната теория на Максвел-Гуревич-Рабинович. Параметрите на модела за изследваните образци бяха определени и стойностите им бяха сравнени в зависимост от посоката на натоварване на образците по време на експеримента. Установено бе, че стойностите на началния вискозитет ∗η0 и на параметъра ∗m, който се влияе от скоростта на деформация на материала се изменят в много широки граници не само за образци от различни донори, но и за образци от един донор. В резултат от прилагането на модела бе изчислено изменението на вискозитета и вискозната деформация на материала по време на релаксацията. Бе показано, че изменението на вискозитета и вискозната деформация зависи от посоката на натоварване на образците.

Modular continuous crystallizer: operation, experimental arrangement and identification of process parameters

Crystallization is employed in different industrial processes. The method and operation can differ depending on the nature of the substances involved. The aim of this study is to examine the effect of various operating conditions on the crystal properties in a chemical engineering design window with a focus on ultrasound assisted cooling crystallization. Batch to batch variations, minimal manufacturing steps and faster production times are factors which continuous crystallization seeks to resolve. Continuous processes scale-up is considered straightforward compared to batch processes owing to increase of processing time in the specific reactor. In cooling crystallization process, ultrasound can be used to control the crystal properties. Different model compounds were used to define the suitable process parameters for the modular crystallizer using equal operating conditions in each module. A final temperature of 20oC was employed in all experiments while the operating conditions differed. The studied process parameters and configuration of the crystallizer were manipulated to achieve a continuous operation without crystal clogging along the crystallization path. The results from the continuous experiment were compared with the batch crystallization results and analysed using the Malvern Morphologi G3 instrument to determine the crystal morphology and CSD. The modular crystallizer was operated successfully with three different residence times. At optimal process conditions, a longer residence time gives smaller crystals and narrower CSD. Based on the findings, at a constant initial solution concentration, the residence time had clear influence on crystal properties. The equal supersaturation criterion in each module offered better results compared to other cooling profiles. The combination of continuous crystallization and ultrasound has large potential to overcome clogging, obtain reproducible and narrow CSD, specific crystal morphologies and uniform particle sizes, and exclusion of milling stages in comparison to batch processes.

The Effect of Spatial Scale on Predicting Time Series: A Study on Epidemiological System Identification

A susceptible-infective-recovered (SIR) epidemiological model based on probabilistic cellular automaton (PCA) is employed for simulating the temporal evolution of the registered cases of chickenpox in Arizona, USA, between 1994 and 2004. At each time step, every individual is in one of the states S, I, or R. The parameters of this model are the probabilities of each individual (each cell forming the PCA lattice ) passing from a state to another state. Here, the values of these probabilities are identified by using a genetic algorithm. If nonrealistic values are allowed to the parameters, the predictions present better agreement with the historical series than if they are forced to present realistic values. A discussion about how the size of the PCA lattice affects the quality of the model predictions is presented. Copyright (C) 2009 L. H. A. Monteiro et al.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

Offline Detection, Identification, and Correction of Branch Parameter Errors Based on Several Measurement Snapshots

This paper proposes a three-stage offline approach to detect, identify, and correct series and shunt branch parameter errors. In Stage 1 the branches suspected of having parameter errors are identified through an Identification Index (II). The II of a branch is the ratio between the number of measurements adjacent to that branch, whose normalized residuals are higher than a specified threshold value, and the total number of measurements adjacent to that branch. Using several measurement snapshots, in Stage 2 the suspicious parameters are estimated, in a simultaneous multiple-state-and-parameter estimation, via an augmented state and parameter estimator which increases the V - theta state vector for the inclusion of suspicious parameters. Stage 3 enables the validation of the estimation obtained in Stage 2, and is performed via a conventional weighted least squares estimator. Several simulation results (with IEEE bus systems) have demonstrated the reliability of the proposed approach to deal with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. Finally the proposed approach is confirmed on tests performed on the Hydro-Quebec TransEnergie network.

A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification

This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder-Mead algorithm (Simplex method) is presented; consequently, the convergence of the PSOS becomes independent of the heuristic constants and its stability and confidence are enhanced. The formulated hybrid method performs better in different benchmark functions than the Simulated Annealing (SA) and the basic PSO (PSO(b)). Two damage identification problems, taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss and second, a cracked free-free beam, both modeled with finite elements. In these cases, the damage location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator) was identified by PSOS providing good results. (C) 2009 Elsevier Ltd. All rights reserved

On the analysis of notched concrete beams: From measurement with digital image correlation to identification with boundary element method of a cohesive model

A way of coupling digital image correlation (to measure displacement fields) and boundary element method (to compute displacements and tractions along a crack surface) is presented herein. It allows for the identification of Young`s modulus and fracture parameters associated with a cohesive model. This procedure is illustrated to analyze the latter for an ordinary concrete in a three-point bend test on a notched beam. In view of measurement uncertainties, the results are deemed trustworthy thanks to the fact that numerous measurement points are accessible and used as entries to the identification procedure. (C) 2010 Elsevier Ltd. All rights reserved.

Identification of Elastic, Dielectric, and Piezoelectric Constants in Piezoceramic Disks

Three-dimensional modeling of piezoelectric devices requires a precise knowledge of piezoelectric material parameters. The commonly used piezoelectric materials belong to the 6mm symmetry class, which have ten independent constants. In this work, a methodology to obtain precise material constants over a wide frequency band through finite element analysis of a piezoceramic disk is presented. Given an experimental electrical impedance curve and a first estimate for the piezoelectric material properties, the objective is to find the material properties that minimize the difference between the electrical impedance calculated by the finite element method and that obtained experimentally by an electrical impedance analyzer. The methodology consists of four basic steps: experimental measurement, identification of vibration modes and their sensitivity to material constants, a preliminary identification algorithm, and final refinement of the material constants using an optimization algorithm. The application of the methodology is exemplified using a hard lead zirconate titanate piezoceramic. The same methodology is applied to a soft piezoceramic. The errors in the identification of each parameter are statistically estimated in both cases, and are less than 0.6% for elastic constants, and less than 6.3% for dielectric and piezoelectric constants.

Valve friction and nonlinear process model closed-loop identification

Among several process variability sources, valve friction and inadequate controller tuning are supposed to be two of the most prevalent. Friction quantification methods can be applied to the development of model-based compensators or to diagnose valves that need repair, whereas accurate process models can be used in controller retuning. This paper extends existing methods that jointly estimate the friction and process parameters, so that a nonlinear structure is adopted to represent the process model. The developed estimation algorithm is tested with three different data sources: a simulated first order plus dead time process, a hybrid setup (composed of a real valve and a simulated pH neutralization process) and from three industrial datasets corresponding to real control loops. The results demonstrate that the friction is accurately quantified, as well as ""good"" process models are estimated in several situations. Furthermore, when a nonlinear process model is considered, the proposed extension presents significant advantages: (i) greater accuracy for friction quantification and (ii) reasonable estimates of the nonlinear steady-state characteristics of the process. (C) 2010 Elsevier Ltd. All rights reserved.

Hardware/firmware implementation of a soft sensor using an improved version of a fuzzy identification algorithm

This paper presents the design and implementation of an embedded soft sensor, i. e., a generic and autonomous hardware module, which can be applied to many complex plants, wherein a certain variable cannot be directly measured. It is implemented based on a fuzzy identification algorithm called ""Limited Rules"", employed to model continuous nonlinear processes. The fuzzy model has a Takagi-Sugeno-Kang structure and the premise parameters are defined based on the Fuzzy C-Means (FCM) clustering algorithm. The firmware contains the soft sensor and it runs online, estimating the target variable from other available variables. Tests have been performed using a simulated pH neutralization plant. The results of the embedded soft sensor have been considered satisfactory. A complete embedded inferential control system is also presented, including a soft sensor and a PID controller. (c) 2007, ISA. Published by Elsevier Ltd. All rights reserved.

In vitro characterization of rosiglitazone metabolites and determination of the kinetic parameters employing rat liver microsomal fraction

Rosiglitazone (RSG), a thiazolidinedione antidiabetic drug, is metabolized by CYP450 enzymes into two main metabolites: N-desmethyl rosiglitazone (N-Dm-R) and rho-hydroxy rosiglitazone (rho-OH-R). In humans, CYP2C8 appears to have a major role in RSG metabolism. On the other hand, the in vitro metabolism of RSG in animals has not been described in literature yet. Based on these concerns, the kinetic metabolism study of RSG using rat liver microsomal fraction is described for the first time. Maximum velocity (V (max)) values of 87.29 and 51.09 nmol/min/mg protein were observed for N-Dm-R and rho-OH-R, respectively. Michaelis-Menten constant (K (m)) values were of 58.12 and 78.52 mu M for N-Dm-R and rho-OH-R, respectively. Therefore, these results demonstrated that this in vitro metabolism model presents the capacity of forming higher levels of N-Dm-R than of rho-OH-R, which also happens in humans. Three other metabolites were identified employing mass spectrometry detection under positive electrospray ionization: ortho-hydroxy-rosiglitazone (omicron-OH-R) and two isomers of N-desmethyl hydroxy-rosiglitazone. These metabolites have also been observed in humans. The results observed in this study indicate that rats could be a satisfactory model for RSG metabolism.

Molecular dynamics, density functional, ADMET predictions, virtual screening, and molecular interaction field studies for identification and evaluation of novel potential CDK2 inhibitors in cancer therapy

In this work, we have used molecular dynamics, density functional theory, virtual screening, ADMET predictions, and molecular interaction field studies to design and propose eight novel potential inhibitors of CDK2. The eight molecules proposed showed interesting structural characteristics that are required for inhibiting the CDK2 activity and show potential as drug candidates for the treatment of cancer. The parameters related to the Rule of Five were calculated, and only one of the molecules violated more than one parameter. One of the proposals and one of the drug-like compounds selected by virtual screening indicated to be promising candidates for CDK2-based cancer therapy.

The systematic use of intraoperative vascular Doppler ultrasound during microsurgical subinguinal varicocelectomy improves precise identification and preservation of testicular blood supply

Objective: To evaluate the impact of systematic use of intraoperative Doppler ultrasound during microsurgical subinguinal varicocele repair. Design: Prospective clinical study. Setting: Andrology laboratory and male infertility section of the urology department of a tertiary care hospital. Patient(s): Two hundred and thirteen men with clinical varicocele. Intervention(s): Subinguinal microsurgical varicocele ligation using an intraoperative vascular Doppler flow detector. Main Outcome Measure(s): Number of veins ligated, lymphatic spared, arteries identified or accidentally ligated. Result(s): A statistically significant greater number of arteries were identified and preserved when intraoperative vascular Doppler was used. In addition, the average number of internal spermatic veins ligated was statistically significantly greater in the same group. Accidental artery ligation occurred in two cases (1.1%) in which the Doppler was not applied. There was no statistically significant difference in number of lymphatics spared between groups. Conclusion(s): Our findings showed that concomitant use of intraoperative vascular Doppler during microsurgical varicocelectomy allows more arterial branches to be preserved, and more internal spermatic veins are likely to be ligated. This device should be considered an attractive tool to improve surgical outcomes and safety. (Fertil Steril (R) 2010; 93: 2396-9. (C)2010 by American Society for Reproductive Medicine.)