• 한국해양학회지
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• v.26 no.2
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• pp.108-116
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• 1991

A production model was constructed by combining the production rate and biomass of Sargassum confusum measured at monthly intervals on the coast of Ohori, Korea, to estimate the algal production for a given period. The production for a certain period, e.g., for a year (P/SUB yr/), was calculated from the equation: P/SUB yr/ = .int.P/SUB t/$.$B/SUB t/dt, where pl and Bl are the production rate and biomass at time t. P/SUB l/ was considered as a function of temperature and light. Photosynthesis-Irradiance curves obtained from the in situ experiments were applied for P/SUB l/ Temperature and light intensity can be expressed as periodic functions of time (T, L=f(t)). Diurnal values of water temperature and light intensity at 3 m depth where S. confusum mainly found were substituted into the equation of P/SUB l/. Simulations using our models show that temperature was one of the most sensitive factors operating on the primary production. Thirty percent decrease of light intensity by cloud cover was estimated to decrease the annual production by 5%.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.535-538
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• 2001

Since 1990s, many literatures have shown that connectionist models, such as back propagation, recurrent network, and RBF (Radial Basis Function) outperform the traditional models, MA (Moving Average), AR (Auto Regressive), and ARIMA (Auto Regressive Integrated Moving Average) in time series prediction. Neural based approaches to time series prediction require the enough length of historical measurements to generate the enough number of training patterns. The more training patterns, the better the generalization of MLP is. The researches about the schemes of generating artificial training patterns and adding to the original ones have been progressed and gave me the motivation of developing VTG schemes in 1996. Virtual term is an estimated measurement, X(t+0.5) between X(t) and X(t+1), while the given measurements in the series are called actual terms. VTG (Virtual Tern Generation) is the process of estimating of X(t+0.5), and VTG schemes are the techniques for the estimation of virtual terms. In this paper, the alternative VTG schemes to the VTG schemes proposed in 1996 will be proposed and applied to multivariate time series prediction. The VTG schemes proposed in 1996 are called deterministic VTG schemes, while the alternative ones are called stochastic VTG schemes in this paper.

• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.2 no.2
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• pp.97-109
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• 1998

A comparison of the two dimensional heat loss, computed using the analytical method and the finite difference method in two models(i.e. one is a parabolic fin whose parabolic curves meet at the fin center line and the other is a transformed parabolic fin whose tip cuts vertically), is made assuming the analytical method is correct. For these methods, the root temperature and surrounding convection coefficients of these fins are assumed as constants. The results show that the relative errors of the heat loss between the two methods for the parabolic fin whose tip cuts vertically are smaller than those for the one whose tip does not cut. In case of Bi=0.01, the values of the heat loss obtained using a finite difference method are close to those values obtained using the analytical method for both models. The values of the heat loss from both models calculated by using the analytical method are almost the same for given range of non-dimensional fin length in case of Bi = 0.01 and 0.1.

• Korea-Australia Rheology Journal
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• v.14 no.4
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• pp.161-174
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• 2002

A new technique for numerical calculation of viscoelastic flow based on the combination of Neural Net-works (NN) and Brownian Dynamics simulation or Stochastic Simulation Technique (SST) is presented in this paper. This method uses a "universal approximator" based on neural network methodology in combination with the kinetic theory of polymeric liquid in which the stress is computed from the molecular configuration rather than from closed form constitutive equations. Thus the new method obviates not only the need for a rheological constitutive equation to describe the fluid (as in the original Calculation Of Non-Newtonian Flows: Finite Elements St Stochastic Simulation Techniques (CONNFFESSIT) idea) but also any kind of finite element-type discretisation of the domain and its boundary for numerical solution of the governing PDE's. As an illustration of the method, the time development of the planar Couette flow is studied for two molecular kinetic models with finite extensibility, namely the Finitely Extensible Nonlinear Elastic (FENE) and FENE-Peterlin (FENE-P) models.P) models.

• Radiation Oncology Journal
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• v.2 no.2
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• pp.281-285
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• 1984

The authors have intended to measure intrinsic dose distribution by Farmer dosimeter in irregularly shaped fields such as L.M and T shape models in order to determine dose inhomogeneity in those models. We made 2 off·axis points in each model and measured the depth dose at 1.5, 5 and 9cm below surface. The results showed $l\~3\%$ dose discrepancy between 2 points. We also measured the depth dose by geometric approximation and computer calculation in those models, and came to the conclusion that computer calculation using Clarkson's principle is simpler and the measurements are closer to the ideal data obtained by the experiment in three models of irregularly shaped fields than those of geometric approximation method.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1305-1325
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• 2008

As index option markets grow recently, many analysts and investors become interested in forecasting the volatility of KOSPI 200 Index to achieve portfolio's goal from the point of financial risk management and asset evaluation. To serve this purpose, we introduce NN and SVM integrated with other financial series models such as GARCH, EGARCH, and EWMA. Moreover, according to the empirical test, Integrating NN with GARCH or EWMA models improves prediction power in terms of the precision and the direction of the volatility of KOSPI 200 index. However, integrating SVM with financial series models doesn't improve greatly the prediction power. In summary, SVM-EGARCH was the best in terms of predicting the direction of the volatility and NN-GARCH was the best in terms of the prediction precision. We conclude with advantages of the integration process and the need for integrating models to enhance the prediction power.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.235-236
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• 2008

This paper is concerned with the stabilization problem of Markovian jump nonlinear systems with time-delay via Takagi-Sugeno (T-S) fuzzy control approach. The T-S fuzzy models are employed to represent nonlinear systems with Markovian jump parameters and time-delay. The purpose of this paper is to design a mode-independent fuzzy controller such that the closed-loop Markovian jump fuzzy system is stochastically stable. Based on a stochstic Lyapunov function, stabilization sufficient conditions using a mode-independent fuzzy controller are derived for the Markovian jump fuzzy system in terms of Linear Matrix Inequalities (LMIs). Finally, a simulation example is presented to illustrate the effectiveness of the proposed method.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.95-105
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• 2001

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control(AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model(SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied for the control of an uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control(CMFC).

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.390-392
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• 2009

This paper is concerned with the stabilization problem of nonlinear networked control systems with time-delay via Takagi-Sugeno(T-S) fuzzy control approach. The T-S fuzzy models are employed to represent nonlinear systems with Markovian jump parameters and time-delay. The purpose of this paper is to design a fuzzy controller such that the closed-loop Markovian jump fuzzy system is stochastically stable. Based on a stocastic Lyapunov function, stabilization sufficient conditions using a mode-independent fuzzy controller are derived for the Markovian jump fuzzy system in terms of Linear Matrix Inequalities(LMIs). Finally, a simulation example is presented to illustrate the effectiveness of the proposed method.