• 품질경영학회지
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• 제50권3호
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• pp.349-371
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• 2022

Purpose: Compared to the rapid growth rate of the domestic automotive LED industry so far, the predictive analysis method for demand forecasting or market outlook was insufficient. Accordingly, product characteristics are analyzed through the life trend of LEDs for automotive exterior lamps and the relative strengths of p and q using the Bass model. Also, future demands are predicted. Methods: We used sales data of a leading company in domestic market of automotive LEDs. Considering the autocorrelation error term of this data, parameters m, p, and q were estimated through the modified estimation method of OLS and the NLS(Nonlinear Least Squares) method, and the optimal method was selected by comparing prediction error performance such as RMSE. Future annual demands and cumulative demands were predicted through the growth curve obtained from Bass-NLS model. In addition, various nonlinear growth curve models were applied to the data to compare the Bass-NLS model with potential market demand, and an optimal model was derived. Results: From the analysis, the parameter estimation results by Bass-NLS obtained m=1338.13, p=0.0026, q=0.3003. If the current trend continues, domestic automotive LED market is predicted to reach its maximum peak in 2021 and the maximum demand is $102.23M. Potential market demand was $1338.13M. In the nonlinear growth curve model analysis, the Gompertz model was selected as the optimal model, and the potential market size was $2864.018M. Conclusion: It is expected that the Bass-NLS method will be applied to LED sales data for automotive to find out the characteristics of the relative strength of q/p of products and to be used to predict current demand and future cumulative demand.

• Computers and Concrete
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• 제14권1호
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• pp.19-40
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• 2014

Reinforced concrete deep beams are structural beams with low shear span-to-depth ratio, and hence in which the strain distribution is significantly nonlinear and the conventional beam theory is not applicable. A strut-and-tie model is considered one of the most rational and simplest methods available for shear strength prediction and design of deep beams. The strut-and-tie model approach describes the shear failure of a deep beam using diagonal strut and truss mechanism: The diagonal strut mechanism represents compression stress fields that develop in the concrete web between diagonal cracks of the concrete while the truss mechanism accounts for the contributions of the horizontal and vertical web reinforcements. Based on a database of 406 experimental observations, this paper proposes a new strut-and-tie-model for accurate prediction of shear strength of reinforced concrete deep beams, and further improves the model by correcting the bias and quantifying the scatter using a Bayesian parameter estimation method. Seven existing deterministic models from design codes and the literature are compared with the proposed method. Finally, a limit-state design formula and the corresponding reduction factor are developed for the proposed strut-andtie model.

• Transactions on Control, Automation and Systems Engineering
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• 제4권3호
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• pp.244-250
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• 2002

This paper proposed a modified observer based on Busawon's high gain observer using an appropriate time depended function, which can be chosen to make each estimated state converge faster to its real value. The stability of the modified observer is proved by using Lyapunov function. The modified nonlinear observer is applied to estimate the states in stirred tank bioreactor: out-put substrate concentration, output biomass concentration and the specific growth rate of the process. The convergences of the modified observer and Busawon's observer are compared trough simulation results. As the results, the modified observer converges faster to its real value than the well-known Busawon's observer.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1771-1776
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• 2003

In mobile robot navigation, one of the key problems is the pose estimation of the mobile robot. Although the odometry can be used to describe the motions of the mobile robots quite simple and accurately, the validities of the models are limited by a number of error sources contaminating the encoder outputs so that applying the conventional extended Kalman filter to these nominal model does not yield the satisfactory performance. As a remedy for this problem, we consider the uncertain nonlinear kinematic model of the mobile robot that contains the norm bounded uncertainties and also propose a new robust extended Kalman filter based on the Krein space approach. The proposed robust filter has the same recursive structure as the conventional extended Kalman filter and can hence be readily designed to effectively account for the uncertainties. The computer simulations will be given to verify the robustness against the parameter variation as well as the reliable performance of the proposed robust filter.

• 제어로봇시스템학회논문지
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• 제13권10호
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• pp.927-932
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• 2007

The sophisticated mathematical model is required for the design and the database construction of the advanced flight control system of UAV. In this paper, flight test of KARI's research UAV, often called DURUMI-II, is implemented for the data acquisition from the maneuver flight. The flight path reconstruction is implemented to ensure that the measured data is consistent and error free. The nonlinear system identification for the refined mathematical modeling is implemented with the verified measurements from the flight path reconstruction. The simulation with the identified results have a good validation when the simulated responses were compared to the flight tested data.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.141-144
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• 2003

A composite adaptive dual fuzzy controller combining the approximate mathematical model, linguistic model description, linguistic control rules and identification modeling error into a single adaptive fuzzy controller is developed for a nonlinear system. It ensures the system output tracks the desired reference value and excites the plant sufficiently for accelerating the parameter estimation process so that the control performances are greatly improved. Using the Lyapunov synthesis approach, proposed controller is analyzed and simulation results verify the effectiveness of the proposed control algorithm.

• Journal of applied mathematics & informatics
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• 제27권1_2호
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• pp.315-326
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• 2009

The unknown parameters in regression models are usually estimated by using various existing methods. There are several existing methods, such as the least squares method, which is the most common one, the least absolute deviation method, the regression quantile method, and the asymmetric least squares method. For the nonlinear time series regression models, which do not satisfy the general conditions, we will compare them in two ways: 1) a theoretical comparison in the asymptotic sense and 2) an empirical comparison using Monte Carlo simulation for a small sample size.

• Transactions on Control, Automation and Systems Engineering
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• 제2권3호
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• pp.149-156
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• 2000

Time delay observer (TDO), thanks to the time delay control (TDC) concept, requires little knowledge of a plant model, and hence is easy to design, robust to parameter variation and computationally efficient, yet can reconstruct states rather reliable for nonlinear plant. In this paper, we propose an improved version of TDO that solves two problems inherent in TDO as follows: TDO displays large reconstruction errors due to low-frequency uncertainty and has some restrictions on selecting its gains. By introducing a low pass filter and a state associated with it, we obtain an enhanced time delay observer (ETDO). This observer turns out to have smaller reconstruction errors than those of TDO and not to have any restriction on selecting its gains, thereby solving the problems. Through performance comparison by transfer function and simulation, we validate the analysis results of two observers (TDO and ETDO) and evaluate the performances. Finally, through experiments on BLDC motor system, the analysis results are clearly conformed.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권12호
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• pp.793-798
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• 2004

In this paper, the problem of adaptively identifying time-varying nonlinear systems is considered. For that purpose, the discrete time-varying Volterra series is employed as a system model, and the filtered weighted least squares (FWLS) algorithm, developed for adaptive identification of linear time-varying systems, is utilized for the adaptive identification of time-varying quadratic Volterra systems. To demonstrate the performance of the proposed approach, some simulation results are provided. Note that the FWLS algorithm, decomposing the conventional weighted basis function (WBF) algorithm into a cascade of two (i.e., estimation and filtering) procedures, leads to fast parameter tracking with low computational burden, and the proposed approach can be easily extended to the adaptive identification of time-varying higher-order Volterra systems.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제48권11호
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• pp.632-639
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• 1999

SRM(Switched Reluctance Motor) drives require the accurate position and speed information of the rotor. These informations are generally provided by a shaft encoder or resolver. High temperature, EMI, and dust may make detection performance deteriorate. Therefore, the elimination of the position and speed sensor is desirable. In this paper, a nonlinear adaptive observer using the MRAS(Model Reference Adaptive System) is proposed. The rotor speed and position are estimated by the adaptation law using the real and estimated currents. The stability of the adaptive observer is proved by Lyapunov stability theory. The proposed methods are implemented with TMS320C31 DSP. Experimental results prove that the observer has a good estimation performance of the rotor speed and position despite of the parameter variations and loads, and the speed control can be accomplished in the wide speed range.