• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.290-292
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• 1993

This paper investigates the identification of discrete time nonlinear system using neural networks with two hidden layers. A New learning method of both NNI and NNC is proposed. For control of the dynamical system we use two neural networks, one for identification and the other for control, and proposed NN control system is based on a framework of MRC. We define a closed loop error. In the proposed learning method, the identification error and the closed loop error are utilized to train the NNI, whareas the control error and the closed loop error are used to train the NNC, The simulation results show that the identification and control schemes suggested are practically feasible and effective.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.523-525
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• 1998

In this paper, a robust control scheme for a class of SISO nonlinear dynamical system is proposed by using output-feedback linearization method. The presented control scheme is based on the VSS control theory concept. In this control scheme, we assume that the nonlinear dynamical system is minimum phase, i.e., the relative degree of the system is r < n and zero dynamics is stable. We also assume that the states of zero dynamics are not accessible. It is shown that the global asymptotically stability is guaranted under the proposed control scheme. The feasibility of the proposed control scheme is verified through a computer simulation.

• Honam Mathematical Journal
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• v.44 no.3
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• pp.402-418
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• 2022

Noise is a fundamental factor to increased validity and regularity of spike propagation and neuronal firing in the nervous system. In this paper, we examine the stochastic version of the Izhikevich-FitzHugh neuron dynamical model. This approach is based on techniques presented by Luo and Guo, which provide a general framework for the bifurcation and stability analysis of two dimensional stochastic dynamical system as an Itô averaging diffusion system. By using largest lyapunov exponent, local and global stability of the stochastic system at the equilibrium point are investigated. We focus on the two kinds of stochastic bifurcations: the P-bifurcation and the D-bifurcations. By use of polar coordinate, Taylor expansion and stochastic averaging method, it is shown that there exists choices of diffusion and drift parameters such that these bifurcations occurs. Finally, numerical simulations in various viewpoints, including phase portrait, evolution in time and probability density, are presented to show the effects of the diffusion and drift coefficients that illustrate our theoretical results.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.46.3-47
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• 2018

We present a general N-body exasolar system simulator in anticipation of upcoming searches for exoplanets and even exomoons by next generation telescopes such as James Webb Space Telescope. For habitable zones, traditionally defined by temperature, we here address the essential problem of dynamical stability of planetary orbits. Illustrative examples are presented on P-type orbits in stellar binary systems, that should be fairly common as in Kepler 16b. Specific attention is paid to reduced orbital lifetimes of exoplanets in the habitable zone by the stellar binary, that is propoesed by Maurice van Putten (2017). Especially, we focused on a classic work of complex three-body problem that is well known by Dvorak(1986). We charge his elliptic restricted three-body problem to extend unrestricted three-body problem to look into dynamical motions in view of circumbinary planet, furthermore, we suggest that opposite angular orientation of the planet is relative to the stability of orbits. In here, counter-rotation case is relatively more faster than co-rotation case for being stable. As a result, we find that various initial conditions and thresholds to approach dynamical stability and unstability with unexpectable isolated islands over enormous parameter space. Even, superkeplerian effect of binary is important to habitability of the exoplanet and we can verify that superfaster binary doesn't effect on th planet and increases survivality of planet around the binary.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.10-29
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• 2023

Grit or perseverance as a factor for student success and life has gained increasing attention. Statistical methods have been the norm in analyzing various aspects of grit, but they do not address the transient and dynamic behavior well. We, for the first time, developed two linear dynamical models that specifically address the feedback structure of a child's desire to achieve a high grade point average (GPA) and the necessary effort that will increase stress between parents and a child. We call the dynamical model as GSES (Goal, Status, Effort & Stress). The two dynamical models incorporate the positive (i.e., achieving a high GPA) and the negative sides (i.e., effort and elevated stress and thus unhappiness) for being gritty or perseverant. Different types of parenting style and a child's characteristics were simulated whether parents and a child are empathetic or stubborn to their expectations and stress (i.e., willing or unwilling to change). Simulations show that when both parents and a child are empathetic to each other's expectation and stress, the most stable situations with minimal stress and effort occur. When a stubborn parent's and a stubborn child were studied together, this resulted in the highest elevation of stress and effort. Stubborn parents and a complying or empathetic child resulted in considerably high stress to a child. Interference from parents may unexpectedly result in a situation in which a child's stress is seriously elevated. The GSES model shows the U-shaped happiness curve (i.e., reciprocal of stress) caused by the increasing and then decreasing goal

• Atmosphere
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• v.25 no.1
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• pp.155-167
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• 2015

A dynamical seasonal prediction system for boreal winter utilizing cryospheric information was developed. Using the Community Atmospheric Model, version3, (CAM3) as a modeling system, newly developed snow depth initialization method and sea ice concentration treatment were implemented to the seasonal prediction system. Daily snow depth analysis field was scaled in order to prevent climate drift problem before initializing model's snow fields and distributed to the model snow-depth layers. To maximize predictability gain from land surface, we applied one-month-long training procedure to the prediction system, which adjusts soil moisture and soil temperature to the imposed snow depth. The sea ice concentration over the Arctic region for prediction period was prescribed with an anomaly-persistent method that considers seasonality of sea ice. Ensemble hindcast experiments starting at 1st of November for the period 1999~2000 were performed and the predictability gain from the imposed cryospheric informations were tested. Large potential predictability gain from the snow information was obtained over large part of high-latitude and of mid-latitude land as a result of strengthened land-atmosphere interaction in the modeling system. Large-scale atmospheric circulation responses associated with the sea ice concentration anomalies were main contributor to the predictability gain.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.50-56
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• 2010

This paper presents a new design algorithm for piece-removing dynamical system, based on 6-Sigma DMADOV technique using ARIZ and Brainstorming. Our design target is the piece-removing system installed on a mobile platform of bead-grinding equipment. The 6-Sigma DMADOV technique guides us design process according to 6 steps, i.e., Define - Measure - Analyze - Design - Optimize - Verify. A Design strategy to reduce the weight of piece-removing dynamical system will be explored by using ARIZ, i.e.,(the abbreviation of Algorithm for Inventive Problem Solving in Russian). The ARIZ will result in a final solution that the height and angle control parts for a cutting tool should be replaced by a kinematical approach, rather than complicated mechatronic approach(using motors). The Optimize step is composed of two sub-steps: (i) Generating process for obtaining several ideas of piece-removing system by using Brainstorming technique, satisfying the final solution derived from the Design step using ARIZ, and (ii) Optimizing process for selecting the most optimal idea of piece-removing system by using Pugh's matrix from the viewpoints of weight, cost and accuracy. The laststep of Verify has shown that the final design obtained by the 6-Sigma DMADOV technique with ARIZ & Brainstormingcan improve an initial design with design requirements satisfied. In this paper, we have shown that ARIZ and Brainstorming can be cooperatively merged into 6-Sigma DMADOV to give us both a formulatedproblem-solving approach and diverse candidate solutions(or ideas) without trial-and-error efforts.

• 한국초전도학회:학술대회논문집
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• v.13
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• pp.12-12
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• 2003

• Journal of Astronomy and Space Sciences
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• v.12 no.1
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• pp.1-13
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• 1995

We carried out photometric and spectroscopic observatious of the blue compact dwarf galaxy Haro 6 in the Virgo Cluster of Galaxies. The long-slit spectroscopy was employed at three position angles, $\Phi$=$0^{\circ}$, $\Phi$=$30^{\circ}$, and $\Phi$=$120^{\circ}$with CCD camera mounted on the Cassegrain Spectrograph. Based on the mean intrinsic axial ratio < $q_0$ >=0.3, we derived inclination I of the system as $44^{\circ}$using our composite V-band CCD image. Careful analysis on the velocity field of the system shows an asymptotically fiat rotation curve with the maximum rotational velocity $V(r)_{max}$ reaches about 12km/sec. The calculation of the dynamical mass of Haro 6 with a simple mass model is briefly discussed with emphasis on the mass to luminosity ratio. From the IRAS Point Source Cataloque, we derived dust-to-gas ratio which indicates relatively low dust content, thus tempting us to conjecture the youth of the system.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.