• Structural Engineering and Mechanics
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• v.41 no.1
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• pp.25-41
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• 2012

Kriging surrogate model provides explicit functions to represent the relationships between the inputs and outputs of a linear or nonlinear system, which is a desirable advantage for response estimation and parameter identification in structural design and model updating problem. However, little research has been carried out in applying Kriging model to crack identification. In this work, a scheme for crack identification based on a Kriging surrogate model is proposed. A modified rectangular grid (MRG) is introduced to move some sample points lying on the boundary into the internal design region, which will provide more useful information for the construction of Kriging model. The initial Kriging model is then constructed by samples of varying crack parameters (locations and sizes) and their corresponding modal frequencies. For identifying crack parameters, a robust stochastic particle swarm optimization (SPSO) algorithm is used to find the global optimal solution beyond the constructed Kriging model. To improve the accuracy of surrogate model, the finite element (FE) analysis soft ANSYS is employed to deal with the re-meshing problem during surrogate model updating. Specially, a simple method for crack number identification is proposed by finding the maximum probability factor. Finally, numerical simulations and experimental research are performed to assess the effectiveness and noise immunity of this proposed scheme.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2115-2123
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• 2006

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.926-931
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• 2008

In order to design a high-performance controller with excellent positioning and tracking performance, an optimal tuning method based on the integrated design concept is studied. DOBs, feedforward controllers and CCC are applied to control the bi-axial linear servomechanism. To derive accurate dynamic models of mechanical subsystems equipped with linear servos for the integrated tuning, system identification processes are conducted through the sine sweeping. An optimal tuning problem with stability, robustness and overshoot constraints is formulated as a nonlinear constrained optimization problem. Optimal gains are obtained through the SQP method. Experimental results confirm that both tracking and contouring errors are significantly reduced by applying the proposed controller and integrated tuning method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.815-823
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• 1999

This study considers an implementation of artificial neural networks to the receding horizon optimal control and is applications to power systems. The Generalized Backpropagation-Through-Time (GBTT) algorithm is presented to deal with a quadratic cost function defined in a finite-time horizon. A decentralized approach is used to control the complex global system with simpler local controllers that need only local information. A Neural network based Receding horizon Optimal Control (NROC) 1aw is derived for the local nonlinear systems. The proposed NROC scheme is implemented with two artificial neural networks, Identification Neural Network (IDNN) and Optimal Control Neural Network (OCNN). The proposed NROC is applied to a power system to improve the damping of the low-frequency oscillation. The simulation results show that the NROC based power system stabilizer performs well with good damping for different loading conditions and fault types.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1392-1395
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• 1996

Nonlinear PID controllers have increasingly used in current industrial practice because it is robust and is easy to operate. Little guideline and tuning method, however, has been recommended for the nonlinear PID controllers while a lot of result is available for the linear PID controllers. Application guideline and tuning formulae are presented for error square type nonlinear controllers, which are most popular currently, are presented.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.24-28
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• 2008

In RFID System, when multiple tags respond simultaneously, a collision can occur. A method that solves this collision is referred to anti-collision algorithm. Among the existing anti-collision algorithms, static framed slot allocation algorithm is very simple. But when the number of tags is variable, its performance degrades because of the fixed frame size. This paper proposes an optimal frame size allocation scheme that determines the frame size. The proposed scheme is based on the number of collision slots at every frame. According to the simulation results, the tag identification time is faster that of SFSA.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.91-94
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• 1995

A newly developed discrete-time control design method for impact machines is proposed. It is composed of identification and control using neural networks, where the optimal controller with saturationn and no use of velocity measurements is obtained. By computer simulation, the proposed method is demonstrated to be effective: as the training progresses, the cost function becomes smaller, the proposed control is superior to PID control tuned with Ziegler-Nichols (Z-N) parameters; robust performance with respect to uncertainty, disturbances and working time is so good.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.5
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• pp.453-459
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• 1991

This paper presents a new algorithm to select optimal parameters and location of power system stabilizer (PSS). A new performance measure, which evaluates the share of a particular mode among state responses, is introduced. The gradient of the performance measure with respect to PSS parametes is derived in an explicit form, so optimal parameters of PSS can be obtained by the steepest descent method. The machine, with which it is most probable to reduce the performance measure, is identified as the optimum site for PSS application.

• Anatomy and Cell Biology
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• v.56 no.3
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• pp.293-298
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• 2023

The mentalis muscle is a paired muscle originating from the alveolar bone of the mandible. This muscle is the main target muscle for botulinum neurotoxin (BoNT) injection therapy, which aims to treat cobblestone chin caused by mentalis hyperactivity. However, a lack of knowledge on the anatomy of the mentalis muscle and the properties of BoNT can lead to side effects, such as mouth closure insufficiency and smile asymmetry due to ptosis of the lower lip after BoNT injection procedures. Therefore, we have reviewed the anatomical properties associated with BoNT injection into the mentalis muscle. An up-to-date understanding of the localization of the BoNT injection point according to mandibular anatomy leads to better injection localization into the mentalis muscle. Optimal injection sites have been provided for the mentalis muscle and a proper injection technique has been described. We have suggested optimal injection sites based on the external anatomical landmarks of the mandible. The aim of these guidelines is to maximize the effects of BoNT therapy by minimizing the deleterious effects, which can be very useful in clinical settings.

• Journal of the Korean Wood Science and Technology
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• v.45 no.6
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• pp.797-808
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• 2017

Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.