• Atmosphere
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• v.33 no.4
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• pp.387-398
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• 2023

The double Fourier series (DFS) spectral dynamical core is evaluated for the two idealized test cases in comparison with the spherical harmonics (SPH) spectral dynamical core. A new approach in calculating the meridional expansion coefficients of DFS, which was recently developed to alleviate a computational error but only applied to the 2D spherical shallow water equation, is also tested. In the 3D deformational tracer transport test, the difference is not conspicuous between SPH and DFS simulations, with a slight outperformance of the new DFS approach in terms of undershooting problem. In the baroclinic wave development test, the DFS-simulated wave pattern is quantitatively similar to the SPH-simulated one at high resolutions, but with a substantially lower computational cost. The new DFS approach does not offer a salient advantage compared to the original DFS while computation cost slightly increases. This result suggests that the current DFS spectral method can be a practical and alternative dynamical core for high-resolution global modeling.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.388-388
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• 2000

The complexity of nonlinear systems makes it difficult to ascertain their behavior using classical methods of analysis. Many efforts have been focused on the advanced algorithms and techniques that hold the promise of improving real-time optimal control while at the same time providing higher accuracy. In this paper, a fuzzy cell mapping method of real-time optimal control far nonlinear dynamical systems is proposed. This approach combines fuzzy logic with cell mapping techniques in order to find the optimal input level and optimal time interval in the finite set which change the state of a system to achieve a desired obiective. In order to illustrate this method, we analyze the behavior of an inverted pendulum using fuzzy cell mapping.

• Atmosphere
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• v.33 no.2
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• pp.73-104
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• 2023

Over the past 60 years, Korean numerical weather prediction (NWP) has advanced rapidly with the collaborative effort between the science community and the operational modelling center. With an improved scientific understanding and the growth of information technology infrastructure, Korea is able to provide reliable and seamless weather forecast service, which can predict beyond a 10 days period. The application of NWP has expanded to support decision making in weather-sensitive sectors of society, exploiting both storm-scale high-impact weather forecasts in a very short range, and sub-seasonal climate predictions in an extended range. This article gives an approximate chronological account of the NWP over three periods separated by breakpoints in 1990 and 2005, in terms of dynamical core, physics, data assimilation, operational system, and forecast application. Challenges for future development of NWP are briefly discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.667-672
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• 2003

In this paper, we propose a systematic method for intelligent digital redesign of a fuzzy-model-based controller for continuous-time nonlinear dynamical systems which may also contain uncertainties. The continuous-time uncertain TS fuzzy model is first constructed to represent the uncertain nonlinear systems. An extended parallel distributed compensation(EPDC) technique is then used to design a fuzzy-model-based controller for both stabilization and tracking. The designed continuous-time controller is then converted to an equivalent discrete-time controller by using an integrated intelligent digital redesign method. This new design technique provides a systematic and effective framework for integration of the fuzzy-model-based control theory and the advanced digital redesign technique for nonlinear dynamical systems with uncertainties. Finally, The single link flexible-joint robot arm is used as an illustrative example to show the effectiveness and the feasibility of the developed design method.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.236-242
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• 1992

Multi-layered neural networks are applied to the identification and control of nonlinear dynamical system. Traditional adaptive control techniques can only deal with linear systems or some special nonlinear systems. A scheme for combining multi-layered neural networks with model reference network techniques has the capability to learn the nonlinearity and shows the great potential for adaptive control. In many interesting cases the system can be described by a nonlinear model in which the control input appears linearly. In this paper the identification of linear and nonlinear part are performed simultaneously. The projection algorithm and the new estimation method which uses the delta rule of neural network are compared throughout the simulation. The simulation results show that the identification and adaptive control schemes suggested are practically feasible and effective.

• The Journal of Korea Robotics Society
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• v.2 no.2
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• pp.152-160
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• 2007

In this paper, two schemes are introduced for dynamic simulation of underwater robotic systems. One is principle of dynamical balance, which is an easy and powerful tool for formulating dynamic equations of composite systems such as underwater vehicle-manipulator system. In the dynamic modeling, this principle gives us the closed-form of dynamic equations on matrix Lie group. The other is geometric integration algorithm, called 4-th order explicit Munthe-Kaas method. By this method, the derived differential equations can be integrated preserving geometric structure. Adopting these two schemes, dynamic simulation of underwater vehicle- manipulator system can be conducted more easily and more reliably.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.804-806
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• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.

• Journal of the Chungcheong Mathematical Society
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• v.33 no.1
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• pp.173-179
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• 2020

Let X be a compact space and Λ a finite index set. We deal with dynamical properties of iterated function systems on X. For an iterated function system 𝓕 on X, we prove that 𝓕 is c-expansive if and only if 𝓕^k is also c-expansive for each k ∈ ℕ. Furthermore we prove that the c-expansiveness of 𝓕 is equivalent to the original expansiveness of the shift map of it.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.177-182
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• 2003

The robust output tracking control problem of general nonlinear MIMO systems is discussed. The robustness against parameter uncertainties is considered. In this paper, we proposed the robust output tracking control scheme for a class of MIMO nonlinear dynamical systems using output feedback linearization method. The presented control scheme is based on the VSS. We assume that the nonlinear dynamical system is minimum phase, the relative degree of the system is r$_{1}$＋r$_{2}$＋…r$_{m}$$\leq$ n and zero dynamics is stable. It is shown that the outputs of the closed-loop system asymptotically track given output trajectories despite the uncertainties while maintaining the boundedness of all signals inside the loop. And we verified that the proposed control scheme is then applied to the control of a two degree of freedom (DOF) robotic manipulator with payload.d.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.939-942
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• 1996

This paper presents a stable learning algorithm for diagonal recurrent neural network(DRNN). DRNN is applied to a problem of controlling nonlinear dynamical systems. A architecture of DRNN is a modified model of the Recurrent Neural Network(RNN) with one hidden layer, and the hidden layer is comprised of self-recurrent neurons. DRNN has considerably fewer weights than RNN. Since there is no interlinks amongs in the hidden layer. DRNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. To guarantee convergence and for faster learning, an adaptive learning rate is developed by using Lyapunov function. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed algorithm is demonstrated by computer simulation.