• Smart Structures and Systems
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• 제31권2호
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• pp.131-140
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• 2023

As well-known, the extended Kalman filter (EKF) is a powerful tool for parameter identification with limited measurements. However, traditional EKF is not applicable when the external excitation is unknown. By using least-squares estimation (LSE) for force identification, an EKF with unknown input (EKF-UI) approach was recently proposed by the authors. In this approach, to ensure the influence matrix be of full column rank, the sensors have to be deployed at all the degrees-of-freedom (DOFs) corresponding to the unknown excitation, saying collocated measurements are required. However, it is not easy to guarantee that the sensors can be installed at all these locations. To circumvent this limitation, based on the idea of first-order-holder discretization (FOHD), an improved EKF with unknown input (IEKF-UI) approach is proposed in this study for the simultaneous identification of structural parameters and unknown excitation. By using projection matrix, an improved observation equation is obtained. Few displacement measurements are fused into the observation equation to avoid the so-called low-frequency drift. To avoid the ill-conditioning problem for force identification without collocated measurements, the idea of FOHD is employed. The recursive solution of the structural states and unknown loads is then analytically derived. The effectiveness of the proposed approach is validated via several numerical examples. Results show that the proposed approach is capable of satisfactorily identifying the parameters of linear and nonlinear structures and the unknown excitation applied to them.

• International Journal of Control, Automation, and Systems
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• 제6권5호
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• pp.755-766
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• 2008

This study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as well. Moreover, an online learning algorithm is demonstrated to automatically construct the RCFNN. The RCFNN initially contains no rules. The rules are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Structure learning is based on the measure of degree and parameter learning is based on the gradient descent algorithm. The simulation results from identifying dynamic systems demonstrate that the convergence speed of the proposed method exceeds that of conventional methods. Moreover, the number of adjustable parameters of the proposed method is less than the other recurrent methods.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.779-788
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• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• Wind and Structures
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• 제11권3호
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• pp.209-220
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• 2008

Flutter derivatives provide the basis of predicting the critical wind speed in flutter and buffeting analysis of long-span cable-supported bridges. Many studies have been performed on the methods and applications of identification of flutter derivatives of bridge decks under wind action. In fact, strong wind, especially typhoon, is always accompanied by heavy rain. Then, what is the effect of rain on flutter derivatives and flutter critical wind speed of bridges? Unfortunately, there have been no studies on this subject. This paper makes an initial study on this problem. Covariance-driven Stochastic Subspace Identification (SSI in short) which is capable of estimating the flutter derivatives of bridge decks from their steady random responses is presented first. An experimental set-up is specially designed and manufactured to produce the conditions of rain and wind. Wind tunnel tests of a quasi-streamlined thin plate model are conducted under conditions of only wind action and simultaneous wind-rain action, respectively. The flutter derivatives are then extracted by the SSI method, and comparisons are made between the flutter derivatives under the two different conditions. The comparison results tentatively indicate that rain has non-trivial effects on flutter derivatives, especially on and $H_2$ and $A_2$thus the flutter critical wind speeds of bridges.

• Journal of Communications and Networks
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• 제5권2호
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• pp.157-166
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• 2003

This paper deals with the problem of channel identification for Single Input Multiple Output (SIMO) slow fading channels using clustering algorithms. Due to the intrinsic memory of the discrete-time model of the channel, over short observation periods, the received data vectors of the SIMO model are spread in clusters because of the AWGN noise. Each cluster is practically centered around the ideal channel output labels without noise and the noisy received vectors are distributed according to a multivariate Gaussian distribution. Starting from the Markov SIMO channel model, simultaneous maximum ikelihood estimation of the input vector and the channel coefficients reduce to one of obtaining the values of this pair that minimizes the sum of the Euclidean norms between the received and the estimated output vectors. Viterbi algorithm can be used for this purpose provided the trellis diagram of the Markov model can be labeled with the noiseless channel outputs. The problem of identification of the ideal channel outputs, which is the focus of this paper, is then equivalent to designing a Vector Quantizer (VQ) from a training set corresponding to the observed noisy channel outputs. The Linde-Buzo-Gray (LBG)-type clustering algorithms [1] could be used to obtain the noiseless channel output labels from the noisy received vectors. One problem with the use of such algorithms for blind time-varying channel identification is the codebook initialization. This paper looks at two critical issues with regards to the use of VQ for channel identification. The first has to deal with the applicability of this technique in general; we present theoretical results for the conditions under which the technique may be applicable. The second aims at overcoming the codebook initialization problem by proposing a novel approach which attempts to make the first phase of the channel estimation faster than the classical codebook initialization methods. Sample simulation results are provided confirming the effectiveness of the proposed initialization technique.

• Journal of Microbiology and Biotechnology
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• 제14권4호
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• pp.737-744
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• 2004

A multiplex polymerase chain reaction (PCR) was developed for the simultaneous detection and differentiation among Vairimorpha spp. and Nosema spp. and identification of Vairimorpha necatrix from Lepidoptera insects. Three sets of primers were selected from different genomic sequences to specifically amplify an 831 bp amplicon within the SSU rRNA gene, specific for both Vairimorpha spp. and Nosema spp. (MSSR primer); a 542 bp amplicon within the SSU rRNA gene, specific for Vairimorpha spp. (VSSU primer); and a 476 bp amplicon within the actin gene, specific for Vairimorpha necatrix (VNAG primer). Using the primers in conjunction with multiplex PCR, it was possible to detect Vairimorpha spp. and Nosema spp. and to differentiate between them. The sensitivity of this PCR assay was approximately 10 spores per milliliter. It is proposed that the multiplex PCR is a sensitive, specific, and rapid tool that can serve as a useful differential diagnostic tool for detecting Vairimorpha spp. and Nosema spp. in Lepidoptera insect.

• Fisheries and Aquatic Sciences
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• 제16권4호
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• pp.273-277
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• 2013

Vibriosis caused by Vibrio anguillarum and edwardsiellosis caused by Edwardsiella tarda are septicemic diseases of many commercially important freshwater and marine fishes, and threaten the aquaculture industry in Korea. Early diagnosis and accurate identification of these two bacterial species could help to prevent these diseases and minimize the damage to cultured marine species. This study designed a duplex polymerase chain reaction (PCR) method for the simultaneous detection of two major fish pathogens: V. anguillarum and E. tarda. Each pair of oligonucleotide primers exclusively amplified the target groEL gene of the specific microorganism. Twenty-two Vibrio and ten non-Vibrio enteric species were used to check the specificity of the primers, which were found to be highly specific for the target species, even among closely related species. The detection limit was 400 pg for V. anguillarum and 4 ng for E. tarda when mixed purified DNA was used as the template. This assay showed high specificity and sensitivity in the simultaneous detection of V. anguillarum and E. tarda from artificially inoculated seawater and fish.

• The Plant Pathology Journal
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• 제32권6호
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• pp.575-579
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• 2016

Burkholderia glumae (bacterial grain rot), Xanthomonas oryzae pv. oryzae (bacterial leaf blight), and Acidovorax avenae subsp. avenae (bacterial brown stripe) are major seedborne pathogens of rice. Based on the 16S and 23S rDNA sequences for A. avenae subsp. avenae and B. glumae, and transposase A gene sequence for X. oryzae pv. oryzae, three sets of primers had been designed to produce 402 bp for B. glumae, 490 bp for X. oryzae, and 290 bp for A. avenae subsp. avenae with the $63^{\circ}C$ as an optimum annealing temperature. Samples collected from naturally infected fields were detected with two bacteria, B. glumae and A. avenae subsp. avenae but X. oryzae pv. oryzae was not detected. This assay can be used to identify pathogens directly from infected seeds, and will be an effective tool for the identification of the three pathogens in rice plants.

• Structural Engineering and Mechanics
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• 제56권5호
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• pp.871-897
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• 2015

This paper presents a novel method based on sensitivity of structural response for identifying both the system parameters and input excitation force of a bridge. This method, referred to as "Adjoint Variable Method", is a sensitivity-based finite element model updating method. The computational cost of sensitivity analyses is the main concern associated with damage detection by these methods. The main advantage of proposed method is inclusion of an analytical method to augment the accuracy and speed of the solution. The reliable performance of the method to precisely indentify the location and intensity of all types of predetermined single, multiple and random damages over the whole domain of moving vehicle speed is shown. A comparison study is also carried out to demonstrate the relative effectiveness and upgraded performance of the proposed method in comparison to the similar ordinary sensitivity analysis methods. Moreover, various sources of error including the effects of noise and primary errors on the numerical stability of the proposed method are discussed.

• Mass Spectrometry Letters
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• 제11권4호
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• pp.82-89
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• 2020

A systematic isolation and characterization study for Cassia auriculata (CA) seeds resulted in identifying antidiabetic compounds 1,3,8-trihydroxyanthraquinone and quercetin, quercetin-3-O-rutinoside, gallic acid, caffeic acid, ferulic acid, and ellagic acid. The ultra-high-performance liquid chromatography based triple quadrupole mass spectrometry methodology was developed and validated for simultaneous identification and confirmation of these compounds from CA seeds. Multiple reaction monitoring (MRM) based quantification method was developed with MRM optimizer software for MS1 and MS2 mass analysis. The method was optimized on precursor ions and product ions with the ion ratio of each compound. The calibration curves of seven bioactive analytes showed excellent linearity (r2 ≥ 0.99). The quantitation results found precise (RSD, < 10 %) with good recoveries (84.58 to 101.42%). The matrix effect and extraction recoveries were found within the range (91.66 to 102.11%) for the CA seeds. This is the first MS/MS-based methodology applied to quantifying seven antidiabetic compounds in CA seeds and its extract for quality control purposes.