• Wind and Structures
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• v.16 no.2
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• pp.157-178
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• 2013

Output-only modal parameter identification is based on the assumption that external forces on a linear structure are white noise. However, harmonic excitations are also often present in real structural vibrations. In particular, it has been realized that the use of forced acceleration responses without knowledge of external forces can pose a problem in the modal parameter identification, because an external force is imparted to its impulse acceleration response function. This paper provides a three-stage identification procedure as a solution to the problem of harmonic and white noise excitations in the acceleration responses of a linear dynamic system. This procedure combines the uses of the mode indicator function, the complex mode indication function, the enhanced frequency response function, an iterative rational fraction polynomial method and mode shape inspection for the correlation-related functions of the force-embedded acceleration responses. The procedure is verified via numerical simulation of a five-floor shear building and a two-dimensional frame and also applied to ambient vibration data of a large-span roof structure. Results show that the modal parameters of these dynamic systems can be satisfactorily identified under the requirement of wide separation between vibration modes and harmonic excitations.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.52.6-52
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• 2002

The method of identifying the plant models in this paper is the Observer Kalman filter identification (OKID) method. This method of system identification has several pertinent advantages. First, it assumes that the system in question is a discrete linear time-invariant (LTI) state-space system. Second, it requires only input and output data to formulate the model, no a priori knowledge of the system is needed. Third, the OKID method produces a psudo-Kalman state estimator, which is very useful for control applications. Last, the modal balanced realization of the system model means that tuncation errors will be small. Thus, even in the case of model order error the results of that error will...

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1412-1415
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• 1996

In this paper, a new identification method of the cascade control system is proposed which can overcome the weak points of Krishnaswamy and Rangaiah(1987)'s method. This new method consists of two steps. One is on-line process identification using the numerical integration to approximate the two process dynamics with a high order linear transfer function. The other is a model reduction technique to derive out low order transfer function(FOPTD or SOPTD) from the obtained high order linear transfer function to tune the controller using usual tuning rules. While the proposed method preserves the advantages of the Krishnaswamy and Rangaiah(1987)'s method, it has such a simplicity that it requires only measured input and output data and simple least-squares technique. Simulation results show that the proposed method can be a promising alternative in the identification of cascade control systems.

• Smart Structures and Systems
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• v.22 no.5
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• pp.631-641
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• 2018

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.

• Smart Structures and Systems
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• v.7 no.2
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• pp.153-167
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• 2011

The Subspace based System Identification Techniques (SSIT) have been very popular within the research circles in the last decade due to their proven superiority over the other existing system identification techniques. For operational (output only) modal analysis, the stochastic SSIT and for operational modal analysis in the presence of exogenous inputs, the combined deterministic stochastic SSIT have been used in the literature. This study compares the application of the two alternative techniques on a typical school building in Istanbul using 100 Monte Carlo simulations. The study clearly shows that the combined deterministic stochastic SSIT performs superior to the stochastic SSIT when the techniques are applied on noisy data from low to mid rise stiff structures.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.26.3-26
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• 2001

Van de Vusse reactor is known as a highly nonlinear chemical process and has been considered by a number of researchers as a benchmark problem for nonlinear chemical process. Various identification methods for nonlinear system are also verified by applying these methods to Van de Vusse reactor. From the point of view of identification, only the Volterra kernel of second order has been obtained until now. In this paper, the authors show that Volterra kernels of nonlinear Van de Vusse reactor of up to 3rd order are obtained by use of M-sequence correlation method. A pseudo-random M-sequence is applied to Van de Vusse reactor as an input and its output is measured. Taking the cross correlation function between the input and the output, we obtain up to 3rd order Volterra kernels, which is ...

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2012-2027
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• 2019

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.1
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• pp.10-15
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• 2002

It is presented to extended to an interaction matrix formulation to the problem of system and disturbance identification for a plant that is corrupted by both process and output disturbances. With only an assumed upper bound on the order of the system and an assumed upper bound on the number of disturbance frequencies, it is shown that both the disturbance-free model and disturbance effect can be recovered exactly from disturbance-corrupted input-output data without direct measurement of the periodic disturbances. The rich information returned by the identification can be used by an iterative learning or repetitive control system to eliminate unwanted periodic disturbances. Those can be helped to apply to the multiple dynamic systems for precision quality assurance.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.1023-1026
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• 2007

CBTC (Communication Based Train Control) is a railroad system, using wireless communication for data transmission between on-board units and ground units, that makes ground computer periodically collect position and speed data from each train and transmits the preceding train's distance data up to speed limit point so as to make on-board control unit control the optimum train speed. Due to the fact that wireless communication system for train control employs only limited frequency band and output frequency, a number of ground communication units should be installed at track-side; also a running train should carry out Handover function which maintains the communication by accessing a new communication channel of the adjacent on-board communication units in accordance with the output of received radio wave. Especially CBTC is largely dependent on Handoff function between ground communication units. Therefore, this paper provides the method of identification of hazard between ground communication units and on board units.

• Smart Structures and Systems
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• v.17 no.3
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• pp.491-522
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• 2016

A benchmark problem for modal identification of a cable-stayed bridge was proposed by a research team at Hong Kong Polytechnic University. By taking an instrumented cable-stayed bridge as a test bed, nineteen sets of vibration records with known/unknown excitations were provided to invited researchers. In this paper, the vibration responses of the bridge under a series of excitation conditions are examined to estimate the wind and traffic-induced variations of its dynamic characteristics. Firstly, two output-only experimental modal identification methods are selected. Secondly, the bridge and its monitoring system are described and the nineteen sets of vibration records are analyzed in time-domain and frequency-domain. Excitations sources of blind datasets are predicted based on the analysis of excitation conditions of known datasets. Thirdly, modal parameters are extracted by using the two selected output-only modal identification methods. The identified modal parameters are examined with respect to at least two different conditions such as traffic- and typhoon-induced loadings. Finally, the typhoon-induced effects on dynamic characteristics of the bridge are estimated by analyzing the relationship between the wind velocity and the modal parameters.