• Smart Structures and Systems
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• 제19권5호
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• pp.499-512
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• 2017

Traditional structural dynamic analysis and Structural Health Monitoring (SHM) of large scale concrete civil structures rely on manufactured embedding transducers to obtain structural dynamic properties. However, the embedding of manufactured transducers is very expensive and low efficiency for signal acquisition. In dynamic structural analysis and SHM areas, piezoelectric transducers are more and more popular due to the advantages like quick response, low cost and adaptability to different sizes. In this paper, the applicable feasibility assessment of the designed "artificial" piezoelectric transducers called Concrete Piezoelectric Smart Module (CPSM) in dynamic structural analysis is performed via three major experiments. Experimental Modal Analysis (EMA) based on Ibrahim Time Domain (ITD) Method is applied to experimentally extract modal parameters. Numerical modal analysis by finite element method (FEM) modeling is also performed for comparison. First ten order modal parameters are identified by EMA using CPSMs, PCBs and FEM modeling. Comparisons are made between CPSMs and PCBs, between FEM and CPSMs extracted modal parameters. Results show that Power Spectral Density by CPSMs and PCBs are similar, CPSMs acquired signal amplitudes can be used to predict concrete compressive strength. Modal parameter (natural frequencies) identified from CPSMs acquired signal and PCBs acquired signal are different in a very small range (~3%), and extracted natural frequencies from CPSMs acquired signal and FEM results are in an allowable small range (~5%) as well. Therefore, CPSMs are applicable for signal acquisition of dynamic responses and can be used in dynamic modal analysis, structural health monitoring and related areas.

• 한국정밀공학회지
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• 제27권7호
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• pp.13-20
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• 2010

Due to the recent quantum leaps forward in bio-, nano-, and information-technologies, the precisionization and miniaturization of mechanical and electrical components are in high demand. The allowable margin for vibration limits for such equipments is becoming stricter. In order to meet this demand, understandings on the characteristics of vibration isolation systems are highly required. Among the components comprising the vibration isolation system, air spring has become a focal point. In order to develop a complex defect tester for COG bonding of display panels, a vibration isolation system composed of air springs for mounting is considered in this study. The dynamic characteristics of the air spring are investigated, which is the most essential ingredient for reducing the vibration problem of the tester to the lowest level. Uncoupled dynamic parameters of the air spring are identified through MTS experiments, followed by suggestion of a model-based approach to obtain the remaining coupled dynamic parameters. Finally, the dynamic behaviors of the air spring are estimated and discussed.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2014년도 추계학술대회
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• pp.114-116
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• 2014

The interpolation of the missing AIS dynamic data can be used for predicting the lost data of the ship's state which is able to product the valuable information for analyzing and investigating the maritime accidents. The previous research proposed some interpolating methods however there exists some problem, firstly, the interpolated parameters such as COG, SOG, HDG weren't described sufficiently and accurately as in AIS message, secondly, each method is only suitable to some kinds of given AIS data, finally at heavy wind and current area, the parameters of AIS dynamic change quickly in short time, therefore, the modelling of the variation of ship's dynamic based on the physical characteristic is very difficult, in these cases the time-series and numerical method are usually better. This research proposes the other method through numerical analysis which can be suitable for many different kinds of the lost data, parameters are interpolated sufficiently, beside that this model is appropriate to all variation in short time interval. All the given AIS dynamic are regarded as the functions to time, then curves are established for fitting all data. Experiments are carried out to evaluate the performance of this approach, the interpolation results show this approach can be applied well in practice.

• 대한조선학회논문집
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• 제47권6호
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• pp.808-812
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• 2010

This paper studies the finite element modeling and analytical parameters for the numerical evaluation of dynamic stiffness of large foundation for shipboard equipments such as marine diesel engine. For the purpose, numerical method and procedure to evaluate the dynamic stiffness are established based on the impact test method, which are applied for the dynamic stiffness evaluation of a real diesel generator foundation of ship. Numerical investigations compared with the measured data are carried out to evaluate the effects of modeling ranges of ship substructure, finite element sizes, lower support structures and damping coefficients. From the results, modeling and analytical parameters for proper evaluation of dynamic stiffness of large foundation of shipboard equipment are suggested.

• 한국자기공명학회논문지
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• 제9권2호
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• pp.93-102
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• 2005

Purpose: To develop an advanced non-linear curve fitting (NLCF) algorithm for dynamic susceptibility contrast study of brain. Materials and Methods: The first pass effects give rise to spuriously high estimates of $K^{trans}$ in voxels with large vascular components. An explicit threshold value has been used to reject voxels. Results: By using this non-linear curve fitting algorithm, the blood perfusion and the volume estimation were accurately evaluated in T2*-weighted dynamic contrast enhanced (DCE)-MR images. From the recalculated each parameters, perfusion weighted image were outlined by using modified non-linear curve fitting algorithm. This results were improved estimation of T2*-weighted dynamic series. Conclusion: The present study demonstrated an improvement of an estimation of kinetic parameters from dynamic contrast-enhanced (DCE) T2*-weighted magnetic resonance imaging data, using contrast agents. The advanced kinetic models include the relation of volume transfer constant $K^{trans}\;(min^{-1})$ and the volume of extravascular extracellular space (EES) per unit volume of tissue $\nu_e$.

• International Journal of Railway
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• 제4권2호
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• pp.50-55
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• 2011

The dynamic stability of railway vehicle has long been one of the important issues in railway safety. The dynamic simulator has been used as a tool for investigating the dynamic stability of railway vehicles and wheel/rail interfaces. In particular, small scale simulators have been widely used in laboratory studies instead of full scale roller rigs which can be quite costly and rather inconvenient for testing out the effect of diverse design parameters. But techniques for design of a small scale simulator for the fundamental study about the dynamic characteristics of the wheel-rail systems and the bogie systems have not been well developed in Korea. Therefore, a research on the development of a small scale simulator for investigating bogie dynamics needs to be undertaken. The present paper investigates design of a small-scaled derailment simulator and the design of a small scale bogie. The simulator developed can be used to investigate the effect of diverse parameters such as attack angle, wheelbase and cant on dynamic behavior of the bogie and key dynamic performance parameters such as derailment coefficient and critical speed.

• Structural Engineering and Mechanics
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• 제75권4호
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• pp.519-527
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• 2020

Dynamic analysis of a vehicle-track coupling system is important to structural design, damage detection and condition assessment of the structural system. Deterministic analysis of the vehicle-track coupling system has been extensively studied in the past, however, the structural parameters of the coupling system have uncertainties in engineering practices. It is essential to treat the parameters of the vehicle-track coupling system with consideration of uncertainties. In this paper, a method for predicting the bounds of the vehicle-track coupling system responses with uncertain parameters is presented. The uncertain system parameters are modeled as fuzzy variables instead of conventional random variables with known probability distributions. Then, the dynamic response functions of the coupling system are transformed into a component function based on the high dimensional representation approximation. The Lagrange interpolation method is used to approximate the component function. Finally, the bounds of the system's dynamic responses can be predicted by using Monte Carlo method for the interpolation polynomials of the Lagrange interpolation function. A numerical example is introduced to illustrate the ability of the proposed method to predict the bounds of the system's dynamic responses, and the results are compared with the direct Monte Carlo method. The results show that the proposed method is effective and efficient to predict the bounds of the system's dynamic responses with fuzzy variables.

• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.543-548
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• 2007

We present a dynamic Bayesian network (DBN) model of a generalized class of nonstationary birth-death processes. The model includes birth and death rate parameters that are randomly selected from a known discrete set of values. We present an on-line algorithm to obtain optimal estimates of the parameters. We provide a simulation of real-time characterization of load traffic estimation using our DBN approach.

• Korean Journal of Geomatics
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• 제3권1호
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• pp.23-32
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• 2003

Rigorous sensor and dynamic modeling techniques are required if spatial information is to be accurately extracted from video imagery. First, a mathematical model for an uncalibrated video camera and a description of a bundle adjustment with added parameters, for purposes of general block triangulation, is presented. This is followed by the application of invariance-based techniques, with constraints, to derive initial approximations for the camera parameters. Finally, dynamic modeling using the Kalman Filter is discussed. The results of various experiments with real video imagery, which apply the developed techniques, are given.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.852-856
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• 2004

An induction motor is one of the most popular electrical apparatuses owing to its simple structure and robust construction. Parameter identification of the induction motor has long been researched either for a vector control technique or fault detection. Since vector control is a well-established technique for induction motor control, this paper concentrates on successive identification of physical parameters with on-load data for the purpose of condition monitoring and/or fault detection. For extracting six physical parameters from the on-load data in the framework of the induction motor state equation, unmeasured initial state values and profiles of load torque have to be estimated as well. However, the analytic optimization methods in general fail to estimate these auxiliary but significant parameters owing to the difficulty of obtaining their gradient information. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS) newly developed is applied to the identification of whole unknown parameters in the mathematical equations of an induction motor with normal operating data. Profiles of identified parameters appear to be reasonable and therefore the proposed approach is available for fault diagnosis of induction motors by monitoring physical parameters.