• Proceedings of the KSME Conference
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• 2003.11a
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• pp.460-465
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• 2003

The modeling of PEM (Proton Exchange Membrane) fuel cell system consisting of fuel cell stack and BOP (Balance of Plant) is presented in this paper. The effects of temperature, pressure (air, hydrogen), and humidity on the fuel cell system performance were mainly investigated using thermo-dynamical and electro-chemical equations. To understand the power distribution characteristics of fuel cell system, the effects of operating temperature and air pressure on maximum power and system power were also demonstrated. Through this study, we can get the basic insight into the fuel cell stack and BOP component sizing and it can be used effectively for the optimization of the practical fuel cell systems in purpose.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1560-1565
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• 2003

Tapping mode atomic force microscopy (TM-AFM) utilizes the dynamic response of a resonating probe tip as it approaches and retracts from a sample to measure the topography and material properties of a nanostructure. We present recent results based on nonlinear dynamical systems theory, computational continuation techniques and detailed experiments that yield new perspectives and insight into AFM. A dynamic model including van der Waals and Derjaguin-Muller-Toporov (DMT) contact forces demonstrates that periodic solutions can be represented with respect to the approach distance and excitation frequency. Turning points on the surface lead to hysteretic amplitude jumps as the tip nears/retracts from the sample. Experiments are performed using a tapping mode tip on a graphite sample to verify the predictions.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.04a
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• pp.316-323
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• 1997

This paper compares input shaping techniques for controlling residual vibration of flexible structures. Input shaping generates vibration-reducing shaped commands through convolution of an impulse sequence with the desired command. Both feedforward and feedback control approaches with/without input shaper for uncertain dynamical systems are investigated to evaluate the control performances. The control objective is to achieve a fast settling time and robustness to plant uncertainty, to eliminate residual vibrations. It is shown by a series of simulation that a properly designed feedback controller with input shaper performs well, as compared with open-loop controller with input shaper.

• Coupled systems mechanics
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• v.7 no.2
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• pp.141-162
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• 2018

This work presents a novel model for analysis of the loading rate influence onto structure response. The model is based on the principles of nonlinear system dynamics, i.e., consists of a system of nonlinear differential equations. In contrast to classical linearized models, this one comprises mass and loading as integral parts of the model. Application of the Kelvin and the Maxwell material models relates the novel formulation to the existing material formulations. All the analysis is performed on a proprietary computer program based on Wolfram Mathematica. This work can be considered as an extended proof of concept for the application of the nonlinear solid model in material response to dynamic loading.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.379-386
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• 1996

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) and hidden Markov models (HMM's) hybrid classifier. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or vari- ous time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrasts the multilayer feedforward networks are suitable for static patterns. Ank a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of MLP and HMM algorithms that might lead to further improved EMG recognition systems.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.5
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• pp.299-307
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• 2003

The local structure of CMAC neural networks (NN) results in better and faster controllers for nonlinear dynamical systems. In this paper, we propose a CMAC NN-based disturbance observer and its corresponding controller for a flexible joint robot. The CMAC NN-based disturbance observer compensates for the parametric uncertainties and the external disturbances throughout the entire mechanical system. Finally, a simulation result is given to demonstrate the effectiveness of proposed design method's robust tracking performance.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2193-2196
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• 1998

Cellular automata are dynamical systems in which space and time are discrete, where each cell has a finite number of states and updates its states by interactive rules among the cell-neighborhood. From the characteristics of self-reproduction and self- organization, it is possible to create a neural network which has the specific patterns or structures dynamically. CAM-Brain is a kind of such neural network system which evolves its structure by adopting evolutionary computations like genetic algorithms (GA). In this paper, we suggest the evolution strategies for the structure of neural networks based on cellular automata.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2611-2613
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• 2000

This paper deals with a novel approach to unknown inputs observer(UIO) design for linear time-invariant dynamical systems using a fast Walsh transform and Walsh function's differential operation. Generally, UIO has a derivation of system outputs which is not available from the measurement directly. And it is an obstacle to estimate the unknown inputs properly when unexpected measurement noises are presented. Therefore, this paper propose an algebraic approach to eliminate such problems by using a Walsh function's differential operation.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2699-2701
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• 2000

This paper presents the Runge-Kutta neural networks(RKNN's) using the Runge-Kutta approximation method and the orthogonal function for control of unknown dynamical systems described by ordinary differencial equations in high accuracy. These subnetworks of RKNN's are based on orthogonal function. Computer simulations show the usefulness of the proposed scheme.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.375-379
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• 2015

Planetary nebula in elliptical galaxies pose a problem in dark matter theory. Using data from the Planetary Nebula Spectrograph (PN. S), Romanowsky et al. (2003) reported that less dark matter than expected was found within 5 to 6 effective radii of three elliptical galaxies. We attempt to explain similar observations of elliptical galaxies with MOdified Newtonian Dynamics (MOND). We collect 16 elliptical galaxies with planetary nebulae from the public web data of PN. S. We investigate the dynamical behavior by analyzing the line-of-sight velocity dispersion in the framework of MOND.