• Journal of information and communication convergence engineering
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• v.1 no.1
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• pp.53-57
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• 2003

Chaos theory is a study researching the irregular, unpredictable behavior of deterministic and non-linear dynamical system. The interpretation using Chaos makes us evaluate characteristic existing in status space of system by tine series, so that the extraction of Chaos characteristic understanding and those characteristics enables us to do high precision interpretation. Therefore, This paper propose the new method which is adopted in extracting character features and recognizing characters using the Chaos Theory. Firstly, it gets features of mesh feature, projection feature and cross distance feature from input character images. And their feature is converted into time series data. Then using the modified Henon system suggested in this paper, it gets last features of character image after calculating Box-counting dimension, Natural Measure, information bit and information dimension which are meant fractal dimension. Finally, character recognition is performed by statistically finding out the each information bit showing the minimum difference against the normalized pattern database. An experimental result shows 99% character classification rates for 2,350 Korean characters (Hangul) using proposed method in this paper.

• Journal of Communications and Networks
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• v.6 no.3
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• pp.197-202
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• 2004

This work presents a stability analysis of the synchronous state for one-way master-slave time distribution networks with single star topology. Using bifurcation theory, the dynamical behavior of second-order phase-locked loops employed to extract the synchronous state in each node is analyzed in function of the constitutive parameters. Two usual inputs, the step and the ramp phase perturbations, are supposed to appear in the master node and, in each case, the existence and the stability of the synchronous state are studied. For parameter combinations resulting in non-hyperbolic synchronous states the linear approximation does not provide any information, even about the local behavior of the system. In this case, the center manifold theorem permits the construction of an equivalent vector field representing the asymptotic behavior of the original system in a local neighborhood of these points. Thus, the local stability can be determined.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2011.11a
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• pp.20-27
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• 2011

A system for fine-resolution long-range weather forecast is introduced in this study. The system is basically consisted of a global-scale coupled general circulation model (CGCM) and Weather Research and Forecast (WRF) regional model. The system makes use of a data assimilation method in order to reduce the initial shock or drift that occurs at the beginning of coupling due to imbalance between model dynamics and observed initial condition. The long-range predictions are produced in the system based on a non-linear ensemble method. At the same time, the model bias are eliminated by estimating the difference between hindcast model climate and observation. In this research, the predictability of the forecast system is studied, and it is illustrated that the system can be effectively used for the high resolution long-term weather prediction. Also, using the system, fine-resolution climatological data has been produced with high degree of accuracy. It is proved that the production of agrometeorological variables that are not intensively observed are also possible.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.117-127
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• 2007

Many real world control systems usually track several control objectives, simultaneously. At the moment, it is desirable to meet all specified goals using the controllers with simple structures like as proportional-integral (PI) and proportional-integral-derivative (PID) which are very useful in industry applications. Since in practice, these controllers are commonly tuned based on classical or trial-and-error approaches, they are incapable of obtaining good dynamical performance to capture all design objectives and specifications. This paper addresses a new method to bridge the gap between the power of optimal multiobjective control and PI/PID industrial controls. First the PI/PID control problem is reduced to a static output feedback control synthesis through the mixed $H_2/H_{\infty}$ control technique, and then the control parameters are easily carried out using an iterative linear matrix inequalities (ILMI) algorithm. Numerical examples on load-frequency control (LFC) and power system stabilizer (PSS) designs are given to illustrate the proposed methodology. The results are compared with genetic algorithm (GA) based multiobjective control and LMI based full order mixed $H_2/H_{\infty}$ control designs.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.44.2-44.2
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• 2019

Comets, one of the least-altered leftovers from the nascent solar system, have probably preserved the primitive structure inside, whereas their surfaces become modified from the initial states after repetitive orbital revolutions around the Sun. Resurfacing makes the surface drier and more consolidated than the bulk nuclei, creating inert refractory dust layer ("dust mantle"). Near-infrared (NIR; 1.25-2.25 m) polarimetry is theoretically expected to maximize contrast of the porosity between inner fresh and evolved dust particles, by harboring more dust constituents in the single wavelength than the optical; thus, intensifies electromagnetic interaction in dust aggregates. Despite such an advantage, only a few studies have been made in this approach mainly due to the limited accessibility of available facilities. Herein, we present our new multi-band NIR polarimetric study of near-Earth object 252P/LINEAR over 12 days near perihelion, together with the results of optical (0.48-0.80m) imaging observations and backward dynamical simulation of the comet. Based on the results, we will characterize the dust properties of the comet and discuss the possible environmental (temperature and UV radiation) effects that could produce the observed phenomena.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.2
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• pp.495-508
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• 1995

The self-excited vibrations of airfoil is related to the classical flutter problems, and it has been studied as a system with linear stiffness and small damping. However, since the actual aircraft wing and the many mechanical elements of airfoil type have various design variables and parameters, some of these could have strong nonlinearities, and the nonlinearities could be unexpectedly strong as the parameters vary. This abrupt chaotic behavior undergoes ordered routes, and the behaviors after these routes are uncontrollable and unexpectable since it is extremely sensitive to initial conditions. In order to study the chaotic behavior of the system, three parameters are considered, i.e., free-stream velocity, elastic distance and zero-lift angle. If the chaotic parameter region can be identified from the mathematically modeled nonlinear differential equation system, the designs which avoid chaotic regions could be suggested. In this study, by using recently developed dynamically system methods, and chaotic regions on the parameter plane will be found and the safe design variables will be suggested.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.261-265
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• 2014

This paper studies the problem of the sampled-data control for Lur'e system with nonlinearities. The nonlinearities are expressed as convex combinations of sector and slope bounds. It is assumed that the sampling periods are arbitrarily varying but bounded. By constructing a new augmented Lyapunov-Krasovskii functional which have an augmented quadratic form with states as well as the nonlinear function, the stabilizing sampled-data controller gains are obtained by solving a set of linear matrix inequalities. The effectiveness of the developed method is demonstrated by numerical simulations.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.279-286
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• 2004

An accumulated crack damage which propagates progressively with time was frequently observed on several engineering structures, This paper numerically demonstrates this damage process on large cooling tower shells under thermal and wind loads. Damage states under varying loads are investigated and the influence of this progressive damage process on the life-cycle of cooling towers discussed. The paper presents briefly some fundamentals of the geometrically and physically non-linear numerical analysis employed for reinforced concrete, especially concerning the models used for concrete, steel reinforcement and the bond between them. As a numerical example an existing cooling tower with noticeable meridian crack damage is analysed. The existing damage state of the cooling tower is determined by quasi-static analyses for temperature, hygric and cyclic wind leading. The change in the dynamical behaviour of the structure as mirrored in its natural frequencies and mode shapes is presented and discussed. Finally, the example shows that such damage processes develop progressively over the life-time of the structures.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.161-164
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• 1995

This paper introduces an identification model called the Dynamic Neural Network(DNN) with a multilayer neural network in the forward path and a linear dynamical system in the feedback path, and defines Dynamic BackPropagation(DBP) as a learning algorithm for it. This identification model uses the feedback of its own output as a learning signal, which is not affected by a noise added to the output terminal of the plant so, it can be considered as a parallel identification model, and when compared with a series-parallel model which does not use the concept of the feedback, the proposed identification scheme exhibits more robust performance.

• Journal of the Korean Institute of Navigation
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• v.21 no.1
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• pp.109-118
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• 1997

In this paper, applications of multilayer neural networks to control of flexible robot beam are considered. The multilayer nerual networks can be used to approximate any continuous function to a desired degree of accuracy and the weights are updated by Gradient Method. When a flexible beam is rotated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlled insuch a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible manipulators so that is arrested as soon as possbile at the end of rotation. Accurate control of lightweight beam during the large changes in configuration common to robotic tasks requires dynamic models that describe both rigid body motions, as well as the flexural vibrations. Therefore, a linear dynamic state-space model of for a single link flexible robot beam is derived and PD controller, LQP controller, and inverse dynamical neural networks controller are composed. The effectiveness the proposed control system is confirmed by computer simulation.