• Smart Structures and Systems
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• v.14 no.6
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• pp.1055-1079
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• 2014

Substructure shake table testing is a class of real-time hybrid simulation (RTHS). It combines shake table tests of substructures with real-time computational simulation of the remaining part of the structure to assess dynamic response of the entire structure. Unlike in the conventional hybrid simulation, substructure shake table testing imposes acceleration compatibilities at substructure boundaries. However, acceleration tracking of shake tables is extremely challenging, and it is not possible to produce perfect acceleration tracking without time delay. If responses of the experimental substructure have high correlation with ground accelerations, response errors are inevitably induced by the erroneous input acceleration. Feeding the erroneous responses into the RTHS procedure will deteriorate the simulation results. This study presents a set of techniques to enable reliable substructure shake table testing. The developed techniques include compensation techniques for errors induced by imperfect input acceleration of shake tables, model-based actuator delay compensation with state observer, and force correction to eliminate process and measurement noises. These techniques are experimentally investigated through RTHS using a uni-axial shake table and three-story steel frame structure at the Johns Hopkins University. The simulation results showed that substructure shake table testing with the developed compensation techniques provides an accurate and reliable means to simulate the dynamic responses of the entire structure under earthquake excitations.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2432-2434
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.3
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• pp.19-32
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• 2000

This paper suggests the variable structure controller with a new variable boundary layer for the accurate tracking control of the variable structure systems. Up to now, variable structure controller (VSC) applying the variable boundary layer did not remove chattering from an arbitrary initial state of the system trajectory because VSC has the limited initial state according to the fixed sliding surface. But, by using the linear time-varying sliding surfaces, the scheme has the robustness against chattering from all states. The suggested method can be applied to the second-order nonlinear systems with parameter uncertainty and extraneous disturbances, and has better tracking performance than the conventional method. To demonstrate the advantages of the proposed algorithm, it is applied to a two-link manipulator.

• Smart Structures and Systems
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• v.4 no.6
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• pp.795-807
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• 2008

A challenging problem in structural damage detection based on vibration data is the requirement of a large number of sensors and the numerical difficulty in obtaining reasonably accurate results when the system is large. To address this issue, the substructure identification approach may be used. Due to practical limitations, the response data are not available at all degrees of freedom of the structure and the external excitations may not be measured (or available). In this paper, an adaptive damage tracking technique, referred to as the sequential nonlinear least-square estimation with unknown inputs and unknown outputs (SNLSE-UI-UO) and the sub-structure approach are used to identify damages at critical locations (hot spots) of the complex structure. In our approach, only a limited number of response data are needed and the external excitations may not be measured, thus significantly reducing the number of sensors required and the corresponding computational efforts. The accuracy of the proposed approach is illustrated using a long-span truss with finite-element formulation and an 8-story nonlinear base-isolated building. Simulation results demonstrate that the proposed approach is capable of tracking the local structural damages without the global information of the entire structure, and it is suitable for local structural health monitoring.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.641-643
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• 2000

CATV Network Management system of Korea is used for mainly monitor forward broadcasting signal because of the difficulty of tracking, measuring and control reverse path nosie. Thereby Purpose of this Structure is removing return Path noise of CATV Network for maintaining two way Netowrk Service of the Highest quality.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.68-68
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• 2000

This paper presents a fuzzy modified PID controller that uses linear fuzzy inference method. In this structure, the proportional and derivative gains vary with the output of the system under control. 2-input PD type fuzzy controller is designed to obtain the varying gains. The proposed fuzzy PID structure maintains the same performance as the general-purpose linear PID controller, and enhances the tracking performance over a wide range of input. Numerical simulations and experimental results show the effectiveness of the fuzzy PID controller in comparison with the conventional PID controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.1
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• pp.1-10
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• 2005

This paper proposes a new and simple input prediction method for robust servo system. A robust tracking control system for optical disk drives was proposed recently based on both Coprime Factorization (CF) and Zero Phase Error Tracking (ZPET) control. The CF control system can be designed simply and systematically. Moreover, this system has not only stability but also robustness to parameter uncertainties and disturbance rejection capability. Since optical disk tracking servo system can detect only tracking error, it was proposed that the reference input signal for ZPET could be estimated from tracking errors. In this paper, we propose a new control structure for the ZPET controller. It requires less memory than the previously proposed method for the reference signal generation. Numerical simulation results show that the proposed method is effective.

• Journal of Electrical Engineering and Technology
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• v.6 no.3
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• pp.391-396
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• 2011

The tracking pattern formed on the dielectric surface due to a surface electrical discharge exhibits fractal structure. In order to quantitatively investigate the fractal characteristics of the surface tracking pattern, the dielectric breakdown model has been employed to numerically generate the surface tracking pattern. In dielectric breakdown model, the pattern growth is determined stochastically by a probability function depending on the local electric potential difference. For the computation of the electric potential for all points of the lattice, a two-dimensional discrete Laplace equation is solved by mean of the successive over-relaxation method combined to the Gauss-Seidel method. The box counting method has been used to calculate the fractal dimensions of the simulated patterns with various exponent $\eta$ and breakdown voltage $\phi_b$. As a result of the simulation, it is found that the fractal nature of the surface tracking pattern depends strongly on $\eta$ and $\phi_b$.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.247-256
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• 2022

Visual tracking of selected target objects is fundamental challenging problems in computer vision. Object tracking localize the region of target object with bounding box in the video. We propose a Siam-FPN based custom fully CNN to solve visual tracking problems by regressing the target area in an end-to-end manner. A method of preserving the feature information flow using a feature map connection structure was applied. In this way, information is preserved and emphasized across the network. To regress object region and to classify object, the region proposal network was connected with the Siamese network. The performance of the tracking algorithm was evaluated using the OTB-100 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.621 in Success Plot and 0.838 in Precision Plot were achieved.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.20-28
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• 2015

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.