• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2000.10a
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• pp.79-83
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• 2000

A sliding mode fuzzy control (SMFC) with disturbance estimator is applied to design a controller for the third generation benchmark problem on an wind-excited building. A distinctive feature in vibration control of large civil infrastructure is the existence of large disturbances, such as wind, earthquake, and sea wave forces. Those disturbances govern the behavior of the structure, however, they cannot be precisely measured, especially for the case of wind-induced vibration control. Since the structural accelerations are measured only at a limited number of locations without the measurement of the wind forces, the structure of the conventional control may have the feed-back loop only. General structure of the SMFC is composed of a compensation part and a convergent part. The compensation part prevents the system diverge, and the convergent part makes the system converge to the sliding surface. The compensation part uses not only the structural response measurement but also the disturbance measurement, so the SMFC has a feed-back loop and a feed-forward loop. To realize the virtual feed-forward loop for the wind-induced vibration control, disturbance estimation filter is introduced. the structure of the filter is constructed based on an auto regressive model for the stochastic wind force. This filter estimates the wind force at each time instance based on the measured structural responses and the stochastic information of the wind force. For the verification of the proposed algorithm, a numerical simulation is carried out on the benchmark problem of a wind-excited building. The results indicate that the present control algorithm is very efficient for reducing the wind-induced vibration and that the performance indices improve as the filter for wind force estimation is employed.

• Journal of the Korean Society of Systems Engineering
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• v.9 no.2
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• pp.37-53
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• 2013

With rapid growth of the world plant market, an increasing attention is paid on the plant engineering. Up to present time, Korean plant engineering technology has been concerned with the down stream part of the plant engineering, so called EPC, considered as a lower value chain compared with the up stream, composed of FEED(Front End Engineering Design) and PMC(Project Management Consultancy). In other words, a key issue for Korean plant industry is how to catch up the FEED technology, currently occupied by the advanced countries. In this paper, we propose an SE(Systems Engineering) approach, conventionally applied for aerospace and defense industry, to the FEED for plant engineering. Specifically, we proposed a new SE process composed of: 1) SPA matrix for reverse systems engineering, and 2) PPA matrix for forward systems engineering. To illustrate the proposed method, a case study for an environmental plant is performed.

• Proceedings of the Membrane Society of Korea Conference
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• 2004.05a
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• pp.179-182
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• 2004

This study reports the extraction behavior of $\alpha$-lactalbumin using bis(2-ethylhexyl) sulfosuccinate sodium (AOT) reverse micelles. Forward extraction of $\alpha$-lactalbumin in the reverse micellar organic phase from aqueous feed solutions was strongly dependent on the AOT concentration and the complete forward extraction of 0.03 mM $\alpha$-lactalbumin was successfully achieved at an AOT concentration of ca. 100 mM. A similar dependency of the forward extraction on the AOT concentration was obtained in isooctane, n-hexane, and n-octane systems. In the backward extraction from the micellar organic phase, the recovery of the protein as high as ca. 90% was obtained with pH control and/or salt addition.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.154-161
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Proceedings of the KWS Conference
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• 2004.05a
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• pp.144-145
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• 2004

Submerged arc welding technique using metal powder as supplementary filler metal has been studied. Forward feeding was used, and the deposit rate was increased by 50%. Good bead appearance, soundness and mechanical properties were obtained with feed rate of 120g/min..

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12A
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• pp.933-940
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• 2009

This paper proposes a robust detection scheme for quasi-orthogonal space-time block code over time-selective fading channels. The proposed detector performs interference cancellation and decision feedback equalization to remove the inter-antenna interference and inter-symbol interference when the channel varies from symbol to symbol. Cholesky factorization is used on the channel Gram matrix after performing interference cancellation to obtain feed forward equalizer and feedback equalizer. It is shown by simulations that the proposed detection scheme outperforms the conventional detection schemes and the exiting detection schemes to time-selectivity.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.1
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• pp.39-47
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• 2014

In this study, 2 degree of freedom PID controller is added to the conventional feed-forward controller for the purpose of improving its limitations such as set-point of tracking performance and disturbance suppression performance in the conventional PID controller. And the controller parameters optimization as a Real Coded Genetic Algorithm (RCGA) is used. Simulation and experiments verify the performance of the controller.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.518-523
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• 2015

This paper proposes a gait phase classifier using a Recurrent Neural Network (RNN). Walking is a type of dynamic system, and as such it seems that the classifier made by using a general feed forward neural network structure is not appropriate. It is known that an RNN is suitable to model a dynamic system. Because the proposed RNN is simple, we use a back propagation algorithm to train the weights of the network. The input data of the RNN is the lower body's joint angles and angular velocities which are acquired by using the lower limb exoskeleton robot, ROBIN-H1. The classifier categorizes a gait cycle as two phases, swing and stance. In the experiment for performance verification, we compared the proposed method and general feed forward neural network based method and showed that the proposed method is superior.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1233-1250
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• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.