• Journal of the korean society of oceanography
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• v.39 no.4
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• pp.222-233
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• 2004

Using nitrogen as the limiting nutrient, the default version of a microplankton-detritus model linked chlorophyll concentration to the autotroph nitrogen. However, phosphorus dynamics were added to simulate the results of a microcosm experiment. Using standard parameter values with a single value of microheterotroph fraction in the microplankton taken from the observed range, the best simulation successfully captured the main features of the time-courses of chlorophyll and particulate organic carbon, nitrogen and phosphorus, with root-mean-square error equivalent to 29% of particulate concentration. A standard version of microbiological model assumes complete internal cycling of nutrient elements; adding a term for ammonium and phosphate excretion by microheterotrophs did not significantly improve predictions. Relaxing the requirement for constant microheterotroph fraction resulted in an autotroph-heterotroph model AH, with dynamics resembling those of a Lotka-Volterra predator-prey system. AH fitted the microcosm data worse than did MP, justifying the suppression of Lotka-Volterra dynamics in MP. The paper concludes with a discussion of possible reasons for the success of the simple bulk dynamics of MP in simulating microplankton behaviour.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.882-885
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• 2003

In this paper, nonlinear echo cancellation using a correlation LMS (CLMS) algorithm is proposed to cancel the undesired nonlinear echo signals generated in the hybrid system of the telephone network. In the telephone network, the echo signals may result the degradation of the network performance. Furthermore, digital to analog converter (DAC) and analog to digital converter (ADC) may be the source of the nonlinear distortion in the hybrid system. The adaptive filtering technique based on the nonlinear Volterra filter has been the general technique to cancel such a nonlinear echo signals in the telephone network. But in the presence of the double-talk situation, the error signal for tap adaptations will be greatly larger, and the near-end signal can cause any fluctuation of tap coefficients, and they may diverge greatly. To solve a such problem, the correlation LMS (CLMS) algorithm can be applied as the nonlinear adaptive echo cancellation algorithm. The CLMS algorithm utilizes the fact that the far-end signal is not correlated with a near-end signal. Accordingly, the residual error for the tap adaptation is relatively small, when compared to that of the conventional normalized LMS algorithm. To demonstrate the performance of the proposed algorithm, the DAC of hybrid system of the telephone network is considered. The simulation results show that the proposed algorithm can cancel the nonlinear echo signals effectively and show robustness under the double-talk situations.

• The Pure and Applied Mathematics
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• v.17 no.3
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• pp.211-229
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• 2010

Various types of predator-prey systems are studied in terms of the stabilities of their steady-states. Necessary conditions for the existences of non-negative constant steady-states for those systems are obtained. The linearized stabilities of the non-negative constant steady-states for the predator-prey system with monotone response functions are analyzed. The predator-prey system with non-monotone response functions are also investigated for the linearized stabilities of the positive constant steady-states.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.813-826
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• 2015

This research deals with an estimation method for kinetic reaction model. The kinetic reaction model is a model to explain spread or changing process based on interaction between species on the Biochemical area. This model can be applied to a model for disease spreading as well as a model for system Biology. In the search, we assumed that the spread of species is stochastic and we construct the reaction model based on stochastic movement. We utilized Gillespie algorithm in order to construct likelihood function. We introduced a Bayesian estimation method using Markov chain Monte Carlo methods that produces more stable results. We applied the Bayesian estimation method to the Lotka-Volterra model and gene transcription model and had more stable estimation results.

• The Journal of the Acoustical Society of Korea
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• v.7 no.4
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• pp.98-109
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• 1988

This paper introduces an adaptive nonlinear filtering algorithm that uses the sequential regression(SER) method to update the second order Volterra filter coefficients in a recursive way. Conventionally, the SER method has been used to invert large matrices which result from direct application of Wiener filter theory to the Volterra filter. However, the algorithm proposed in this paper uses the SER approach to update the least squares solution which is derived for Gaussian input signals. In such an algorithm, the size of the matrix to be inverted is smaller than that of conventional approaches, and hence the proposed method is computationally simpler than conventional nonlinear system identification techniques. Simulation results are presented to demonstrate the performance of the proposed algorithm.

• Journal of applied mathematics & informatics
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• v.31 no.1_2
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• pp.277-284
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• 2013

In this paper, we investigate $h$-stability of the nonlinear perturbed difference system via $n_{\infty}$-similarity.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.149-158
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• 2006

In this paper, we consider the Lotka- Volterra predator-prey system, in which constant quantity of the prey is harvested in regular pulses. The ultimate behavior of the solutions starting from different regions is mainly studied. Further, some examples are given to illustrate our results.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.245-250
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• 1998

This thesis is concerned with the development of useful engineering techniques to detect and analyze nonlinearities in mechanical systems. The methods developed are based on the concepts of higher order spectra, in particular the bispectrum and trispectrum, and the Volterra series. The study of higher order statistics has been dominated by work on the bispectrum. The bispectrum can be viewed as a decomposition of the third moment(skewness) of a signal over frequency and as such is blind to symmetric nonlinearities.

• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.293-304
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• 2007

In this paper, we present a Krylov subspace based projection method for reduced-order modeling of large scale bilinear multi-input multi-output (MIMO) systems. The reduced-order bilinear system is constructed in such a way that it can match a desired number of moments of multi-variable transfer functions corresponding to the kernels of Volterra series representation of the original system. Numerical examples report the effectiveness of this method.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2E
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• pp.50-59
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• 1996

In this paper, an adaptive precompensator, which can reduce the distortion of a Wiener system effectively, is proposed. The previous techniques for adaptive precompensation, based on the Volterra series modeling to compensate the distortion of a nonlinear system, are not suitable for real-time implementation due to high computational burden and slow convergence burden and slow convergence rate. This paper presents an adaptive precompensation technique for the class of nonlinear subsystem, referred to as Wiener system. An adaptive algorithm for adjusting the parameters of a precompensator, structured by a hammerstein model, is derived using the stochastic gradient method. Also, an adaptive precompensatin technique which can effectively reduce nonlinear distortion in μ-law type of saturation characteristics is proposed. The validity of the proposed algorithm is confirmed through simulation by applying it to known Wiener systmes and a typical loudspeaker model.