Structural Identification Method Research Articles

Identifying influential nodes for the networks with community structure

Identifying influential nodes in complex network with community structures attracts lots of attention recently. In this paper, by combining the community structure identification method and the closeness of the network, we present a method for identifying the influential nodes. Comparing with the Susceptible–Infected–Recovered (SIR) model results on synthetic and empirical networks, the accuracy results measured by kendall’s tau τ and spearman ρ indicate that our method outperforms the existing methods to identify influential nodes for networks. The largest improved ratios η for the kendall’s tau τ and spearman ρ reach 161.6% and 157.6%.

COMPARATOR IDENTIFICATION OF THE MULTI-FACTOR EVALUATION MODEL OF ALTERNATIVES USING THE BAGGING METHOD

Proponent one step before the solution of the problem to induce a mathematical model of a multi-factor assessment of alternatives, which is based on the ideas of the theory of comparator identification. Methods of structural and parametric identification are seen. The structure of the model is established within the framework of the Kolmogorov-Gabor polynomial, which allows the synthesis of both traditional additive and multiplicative, as well as models of a multi-factor assessment of alternatives to present folding. To increase the accuracy of the model, a victorious ensemble of models is proposed, aggregating that work on the basis of the running method. The results of a computer model have been introduced, which will confirm the effectiveness of the described approach. Propagation of ideas based on the prospect of advancing the effectiveness of the identification of models for expert assessment of alternatives.

Identification of coal structures using geophysical logging data in Qinshui Basin, China: Investigation by kernel Fisher discriminant analysis

Coal structure is closely related to the porosity and permeability of coal reservoirs, which not only affects the enrichment of coalbed methane (CBM), but also influences the hydraulic fracturing and efficient development of CBM. The accurate identification of the coal structure would be a critical issue in CBM exploration and development, and is always a challenge. Compared with traditional methods for identifying coal structure base on borehole cores or mining seam observation, geophysical logging has become the most economic and efficient technique. Several linear correlations have been established to describe the relationships between the coal structures and well logs. However, those correlations cannot accurately reflect nonlinear relations between them. Therefore, a reliable and efficient method to identify coal structure is needed. As a powerful nonlinear classifier, the kernel Fisher discriminant analysis (KFD) method has been widely used due to its strong generalization ability. In this paper, a new quantitative coal structure identification model was developed based on the KFD method by using geophysical logging data. The model was trained, tested and optimized using 178 logging data sets from 15 CBM wells in Qinshui Basin, China. The approach accounted for all the available well logging attributes, and the training data sizes and kernel parameters were analyzed to get the most appropriate model in practice. In addition, the built model was validated individually by employing logging data of a new CBM well. The results indicate that the KFD based identification model has high prediction accuracy, which can be used as a reliable method for coal structure identification. The KFD method exhibits strong capability during the modeling and generalization in the determination of nonlinear relationships, which provides an efficient way for the coal structure prediction in basic research of coal reservoirs.

Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution

Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLSE) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLSE method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure.

A low-cost data acquisition system for dynamic structural identification

This work aims at the evaluation of an alternative data acquisition system to identify dynamics properties of structures, using low-cost devices with on-board micro-electro-mechanical systems (MEMS) accelerometers. The proposed acquisition system, using the Arduino platform, specifically an Arduino/Genuino 101 board, presented good results in the identification of natural frequencies of the structural models analyzed. It showed a maximum difference when compared to a professional accelerometer of 2.41%. In addition, for one of the models, by using two Arduino boards it was also possible to identify the mode shapes, with acceptable difference of 4.30% on natural frequencies, when compared to professional accelerometers. Models with frequency range between approximately 1.00 and 37.83 Hz were investigated and the results were considered promising. Thus, since the natural frequencies of conventional civil structures are close to or within this range, the proposed system demonstrates the ability to acquire the acceleration time series required for dynamic structural identification methods.

A study of quartz crystal microbalance modified with polyvinyl acetate nanofiber to differentiate short-chain alcohol isomers

The detection of short-chain alcohols and their isomers in the atmosphere remains a challenge in solving environmental, occupational safety, and health issues. We report on the sensing of isomers of butanol by quartz crystal microbalance (QCM). QCM chips were coated with polyvinyl acetate (PVAc) nanofibers through the electrospinning technique. The produced PVAc nanofibers were confirmed using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and atomic force microscopy (AFM). Four isomers of butanol, i.e., butan-1-ol, 2-methylpropan-1-ol, butan-2-ol, and 2-methylpropan-2-ol, were used in this study. The results indicate that the structures of the isomers of butanol affect the sensitivity of the QCM PVAc nanofiber sensor. Butan-1-ol that has the lowest vapor pressure exhibits the highest frequency shift. Linear butanol molecules exhibit a higher sensitivity than that of branched butanol molecules because of the ease of intermolecular interaction with nanofibers. These findings provide additional information on the interaction between the analyte and the active layer in QCM-based gas sensing. Notably, the QCM PVAc nanofiber sensor is clearly able to differentiate four isomers of butanol, which may be used in developing a promising new method for structural isomer identification.

Structure and Parameter Identification of Ship's Multi-motion State Model

ABSTRACT Bai, J.; Mao, Z., and Pu, T., 2018. Structure and parameter identification of ship's multi-motion state model. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 892–896. Coconut Creek (Florida), ISSN 0749-0208. At present, the problem of poor accuracy of parameter identification exists in the method of ship's multi-motion model structure and parameter identification based on operational modal analysis. Therefore, this paper presents a structure and parameter identification method of ship's multi-motion model based on extended Calman filter. In this method, the ship motion equation is established in the fixed coordinate system, and to convert to the computation of motion variables, to obtain the ship motion state model, and realize the identification of ship motion state parameters by using the augmented state equation and extended Calman filter. The experimental results show that the Zig-zag experiment value...

Modeling of molecular and properties of anthracite base on structural accuracy identification methods

Molecular model construction is essential for the study of the energy conversion process, the combustion reaction mechanism and the clean utilization of coal. In this study, an anthracite molecular structure of GBW(E)110031 was constructed based on the structural accuracy identification methods, such as 13C nuclear magnetic resonance spectroscopy (13C NMR), attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and quantitative chemical analysis. The 13C NMR spectrum was registered to analyze the carbon skeleton structure of the anthracite and twelve characteristic parameters of coal structures. The aromaticity, hydrogen aromaticity, and average aromatic nuclear size of the anthracite were calculated. These data were utilized to construct 17 basic structural units of aromatic carbon. The vibration modes of the carbon skeleton, several association forms of oxygen, and different types of functional group were confirmed based on the ATR-FTIR collected spectra. The content of functional groups and unsaturated aliphatic carbon alkenyl were determined by the calcium ion exchange method, the pyridine hydroxylamine hydrochloride oximation method and the bromine addition method. Based on these experimental data, a C202H104O21N2S2 anthracite molecular structure model was constructed. In the molecular structure model, benzene and naphthalene structures accounted for 70% of the mass of aromatic compounds, while aliphatic structures existed in the form of side chains and rings. Carbonyl group (CO) accounted for 85% of the oxygen atoms, while others existed in the form of carboxyl and hydroxyl groups. The nitrogen atoms presented in the structures of pyridine and pyrrole, and the sulfur atoms presented in the structures of thiophene. The theoretical analysis matched well with 13C NMR spectra interpretation, which was verified the reliability and rationality of the molecular structure model and analytical method.

Structure Identification of Continuous-Time Block-Oriented Nonlinear Systems in the Frequency Domain

Abstract A problem of structure identification of nonlinear dynamic systems on the set of continuous block-oriented models with nonlinear elements in the form of polynomial functions of finite degree and stable linear elements in the frequency domain is considered. Method of structure identification in steady state based on the observation of the system’s input and output variables at the input sinusoidal influences is proposed. The problem of the structure identification is considered according to the classical definition of identification of Zadeh. The structure definition criterion of the model is developed. The identification method is investigated by theoretical analysis and computer modelling.

Modal Parameters Identification Method Based on Symplectic Geometry Model Decomposition

This paper proposes a novel method of structural system modal identification, where the iterative method is introduced in symplectic geometric model decomposition (SGMD). The proposed method can decompose the measured response into finite symplectic geometric components and identify the modal parameters of time‐invariant structures and the instantaneous frequency of time‐variant system through each symplectic geometric component. To obtain the shape information of the structural model, the SGCs of the same frequency at different measuring points are subjected to singular value decomposition (SVD). Both simulated data verification and measured data verification were used to verify if the method proposed in this article is effective for time‐invariant system and time‐variant system identification. For the simulated data, we study on a structural system which is set up with time‐variant stiffness and time‐invariant system. The measured vibration data of beam structure and time‐variant wheel‐rail coupling system were also tested and varied. Compared with the results of empirical model decomposition, the proposed method is capable of identifying instantaneous frequencies with better accuracy.

Estimation and use materials of the airily-depressed surveys on the coal mines of Ukraine

Need to improve the methodological and technical means of obtaining, adjusting and updating the results of the airly-depressed surveys (ADS). With this purpose, identified issues that do not respond modern tasks of the ADS concerning the control of the aerological condition of coal mines. Methods of structural and parametric identification of the mine ventilation network are developed. It is proposed to replace obsolete control devices of aerodynamic parameters more modern, developed by Institute of Geotechnical Mechanics named by N. Poljakov of National Academy of Sciences of Ukraine, devices: anemometer portable mine APR-2, micromanometer differential mine MDR-6, the converter difference of pressure PRD-1. It is proposed a new approach to actualization of information, consisting in the joint of measurements by stationary and portable control devices. Methods of structural-parametric identification and measuring instruments of the aerodynamic parameters of a new technical level offered for the first time. Use of the proposed methods and technical means processing the results of the ADS will improve the quality of aerodynamic information received.

AUV control system and sonar target identification

Objectives In order to meet the requirements of underwater search and rescue, exploration, target detection and tracking, Methods we carry out a target identification test for the developed T-SEA Ⅰ AUV, through which a typical frame is selected for the target detection and identification from the acquired moving target images by virtue of the front-view sonar; describe the system composition and working principles of T-SEA Ⅰ AUV; then propose a framework for a control system in which laptop and surface control box act as a surface control unit, PC104 industrial control board acts as the main control unit of an autopilot and the PC104 camera board processes the camera images separately; use three-frame difference method to detected the sonar target, and extract the target characteristics according to a fast ellipse detection algorithm based on Hough transform. Results The lake test shows that the AUV runs reliably and normally and meets the requirements of underwater work; the sonar target identification is good. Conclusions This AUV's control system structure and target identification method can provide a reference for the development of medium and large-scale AUVs and the target identifications in other fields such as video, infrared, etc.

Analysis of chemical components in herbal formula Qi Bai Granule by UPLC-ESI-Q-TOF-MS

Qi Bai Granule (QBG), a traditional Chinese medicine formula for the treatment of idiopathic thrombocytopenic purpura, is composed of seven herbs. It is necessary to learn its chemical composition for quality control. In this study, a method for rapid separation and structural identification of the constituents in QBG was established by UPLC-ESI-Q-TOF-MS in negative and positive ion mode. As a result, 112 compounds, such as triterpenoids, flavonoids and monoterpenes were detected. Based on the retention times, accurate masses, fragment ions, related literatures, and/or authentic standards, 107 compounds were unambiguously identified or tentatively characterized. Additionally, 20% monarch, 50% minister, 5% assistant and 24% guide drugs of 112 compounds were detected, which on the whole was consistent with the compatibility of QBG. The results would provide a scientific basis for the quality control, quantitative analysis and further study in vivo or vitro of QBG.

Toward Automated Identification of Glycan Branching Patterns Using Multistage Mass Spectrometry with Intelligent Precursor Selection.

Glycans play important roles in a variety of biological processes. Their activities are closely related to the fine details of their structures. Unlike the simple linear chains of proteins, branching is a unique feature of glycan structures, making their identification extremely challenging. Multistage mass spectrometry (MS n) has become the primary method for glycan structural identification. The major difficulty for MS n is the selection of fragment ions as precursors for the next stage of scanning. Widely used strategies are either manual selection by experienced experts, which requires considerable expertise and time, or simply selecting the most intense peaks by which the product-ion spectrum generated may not be structurally informative and therefore fail to make the assignment. We here report a glycan "intelligent precursor selection" strategy (GIPS) to guide MS n experiments. Our approach consists of two key elements, an empirical model to calculate candidate glycan's probability and a statistical model to calculate fragment ion's distinguishing power in order to select the structurally most informative peak as the precursor for next-stage scanning. Using 15 glycan standards, including three pairs with isomeric sequences and eight variously fucosylated oligosaccharides on linear or branched hexasaccharide backbones isolated from a human milk oligosaccharide fraction by HPLC, we demonstrate its successful application to branching pattern analysis with improved efficiency and sensitivity and also the potential for automated operation.

Interactions between the premixed flame front and the three-dimensional Taylor–Green vortex

We study interactions between the premixed flame front and evolving vortices using the vortex-surface field (VSF) and direct numerical simulation of a hydrogen/air premixed flame propagating in a three-dimensional Taylor–Green (TG) flow. The VSF, a Lagrangian-based structure identification method, characterizes the evolution of a vortex surface consisting of vortex lines across the flame front. Compared to the VSF evolution in non-reacting TG flows, the flame has an impact on the evolutionary geometry of vortex surfaces. We find that the vortex surfaces merge into bulky structures without rolling-up of vortex tubes and the transition of the TG flow is suppressed in the burnt region. The vortex surfaces near the flame are expanded with a slight increase of vorticity magnitude owing to the flame-generated positive dilation. The VSF deformation in the burnt region, compared to that in the unburnt region during flow transition, is significantly suppressed near impermeable planes and slightly amplified near the flame front. The effects of flame-induced motion on flame dynamics and geometry are twofold. By comparing the wrinkling of isosurfaces of a progress variable in reacting TG flow and isosurfaces of a passive scalar in non-reacting TG flow, we find that the flame can increase its surface area and the scalar gradient by inducing an additional straining field near the flame front, whereas it can smooth the very sharp scalar structures in non-reacting TG flows under vortex straining near impermeable planes.

Sparse structural system identification method for nonlinear dynamic systems with hysteresis/inelastic behavior

Abstract A system identification technique suitable for single degree of freedom (SDOF) and multiple degree of freedom (MDOF) structural systems with either nonlinear elastic or inelastic/hysteretic behavior is proposed in this paper. The method is a parametric modeling technique based on sparse regularization. The proposed framework is capable of discovering the underlying governing equations of the system of interest from input-output data. We build on the work of Brunton et al. (2016) by including functions that allow the discovery of significant nonlinearities, and hystertic or inelastic behavior with permanent deformation. We also present model selection using sparse regularization and cross validation using Akaike criteria. We demonstrate through experimental validation that the technique presented in this paper is applicable to a significantly broader class of problems. The effectiveness of the proposed method is evaluated through numerical examples of a 2-story nonlinear or inelastic building with a adjustable stiffness device. We also present experimental validation using a unique nonlinear structural system that consists of a MDOF structural system and a nonlinear negative stiffness device (NSD) to illustrate the significant ability of the proposed framework. We successfully identify the following structural systems from experimental data: a SDOF yielding frame without NSD; a SDOF yielding frame with NSD; and a 3-DOF frame with NSD. The extracted sparse model also shows potential for generalization.

Insights into the dynamics of conical breakdown modes in coaxial swirling flow field

The main idea of this paper is to understand the fundamental vortex breakdown mechanisms in the coaxial swirling flow field. In particular, the interaction dynamics of the flow field is meticulously addressed with the help of high fidelity laser diagnostic tools. Time-resolved particle image velocimetry (PIV) (${\sim}1500~\text{frames}~\text{s}^{-1}$) is employed in $y{-}r$ and multiple $r{-}\unicode[STIX]{x1D703}$ planes to precisely delineate the flow dynamics. Experiments are carried out for three sets of co-annular flow Reynolds number $Re_{a}=4896$, 10 545, 17 546. Furthermore, for each $Re_{a}$ condition, the swirl number ‘$S_{G}$’ is varied independently from $0\leqslant S_{G}\leqslant 3$. The global evolution of flow field across various swirl numbers is presented using the time-averaged PIV data. Three distinct forms of vortex breakdown namely, pre-vortex breakdown (PVB), central toroidal recirculation zone (CTRZ; axisymmetric toroidal bubble type breakdown) and sudden conical breakdown are witnessed. Among these, the conical form of vortex breakdown is less explored in the literature. In this paper, much attention is therefore focused on exploring the governing mechanism of conical breakdown. It is should be interesting to note that, unlike other vortex breakdown modes, conical breakdown persists only for a very short band of $S_{G}$. For any small increase/decrease in $S_{G}$ beyond a certain threshold, the flow spontaneously reverts back to the CTRZ state. Energy ranked and frequency-resolved/ranked robust structure identification methods – proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) respectively – are implemented over instantaneous time-resolved PIV data sets to extract the dynamics of the coherent structures associated with each vortex breakdown mode. The dominant structures obtained from POD analysis suggest the dominance of the Kelvin–Helmholtz (KH) instability (axial $+$ azimuthal; accounts for ${\sim}80\,\%$ of total turbulent kinetic energy, TKE) for both PVB and CTRZ while the remaining energy is contributed by shedding modes. On the other hand, shedding modes contribute the majority of the TKE in conical breakdown. The frequency signatures quantified from POD temporal modes and DMD analysis reveal the occurrence of multiple dominant frequencies in the range of ${\sim}10{-}400~\text{Hz}$ with conical breakdown. This phenomenon may be a manifestation of high energy contribution by shedding eddies in the shear layer. Contrarily, with PVB and CTRZ, the dominant frequencies are observed in the range of ${\sim}20{-}40~\text{Hz}$ only. We have provided a detailed exposition of the mechanism through which conical breakdown occurs. In addition, the current work explores the hysteresis (path dependence) phenomena of conical breakdown as functions of the Reynolds and Rossby numbers. It has been observed that the conical mode is not reversible and highly dependent on the initial conditions.

The use of two staining methods for identification of spermatozoon structure in roosters

Many of the methods used to stain semen result in very pronounced coloring of the sperm, but unfortunately they do not distinguish their individual structures, which play a key role in the fertilization process. Hence the aim of this study was to identify sperm structures using two staining techniques in the semen of roosters from breeding flocks. The subject of the study was the sperm of roosters from a Ross 308 breeding flock. To capture the differences in the dimensions of sperm subjected to the effect of different chemical substances in dyes, microscope slides were stained by two techniques: with an AgNO3 solution and by a differential method (eosin-nigrosin test). Assessment was made of the degree of coloration and the number of details that could be identified in the morphological structure of the sperm. The use of AgNO3 allowed accurate identification of the acrosome, nucleus, and midpiece, which were visible in the slides stained with eosin-nigrosin, but only in dead spermatozoa. The AGNO3 staining technique used in this study reveals the cell nucleus within the head and can be an alternative method to analysis with a scanning electron microscope. This staining technique can be used to stain sperm structures that cannot be seen in other methods of slide preparation, which means that it can be considered for routine use in assessing the fertility of roosters in breeder flocks.

Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate

Structural parameters are the most important factors reflecting structural performance and conditions. As a result, their identification becomes the most essential aspect of the structural assessment and damage identification for the structural health monitoring. In this article, a structural parameter identification method based on Monte Carlo method and likelihood estimate is proposed. With which, parameters such as stiffness and damping are identified and studied. Identification effects subjected to three different conditions with no noise, with Gaussian noise, and with non-Gaussian noise are studied and compared. Considering the existence of damage, damage identification is also realized by the identification of the structural parameters. Both simulations and experiments are conducted to verify the proposed method. Results show that structural parameters, as well as the damages, can be well identified. Moreover, the proposed method is much robust to the noises. The proposed method may be prospective for the application of real structural health monitoring.

Structure identification and model selection in geographically weighted quantile regression models

Geographical weighted quantile regression (GWQR) is an important tool for exploring the spatial non-stationarity of the regression relationship and providing the entire description of the response distribution, which is useful in practice because of its robustness against outliers and flexibility in dealing with non-normal distributions. This paper proposes the GWQlasso method for structure identification and variable selection in GWQR models. The proposed method combines the local-linear estimation of the GWQR model and the adaptive group lasso, which can simultaneously identify spatially varying coefficients, non-zero constant coefficients and zero coefficients. The selection consistency and the oracle property of the proposal are studied. Moreover, the derived algorithm for the GWQlasso method and the selection of the tuning parameter by the BIC criterion are established. Simulations and real examples are used to illustrate the method.