• Journal of Drive and Control
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• v.19 no.4
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• pp.85-90
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• 2022

In this paper, a DC converter to improve the performance of Power LED system is discussed. The mathematical model of PWM converter power stage using 3-Terminal PWM cell is introduced for power LED system. A controller for DC converter system is used as a self-tunning regulator with a recursive least-squares algorithm. Minimum variance control method is used as a control law. Experiment results verified that proposed control system could improve the performance of Power LED system.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.6
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• pp.671-676
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• 2011

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.575-589
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• 2010

Exponential L$\acute{e}$evy models have become popular in modeling price processes recently in mathematical finance. Although it is a relatively simple extension of the geometric Brownian motion, it makes the market incomplete so that the option price is not uniquely determined. As a trial to find an appropriate price for an option, we suppose a situation where a hedger wants to initially invest as little as possible, but wants to have the expected squared loss at the end not exceeding a certain constant. For this, we assume that the underlying price process follows a variance-gamma model and it converges to a geometric Brownian motion as its quadratic variation converges to a constant. In the limit, we use the mean-variance approach to find the asymptotic minimum investment with the expected squared loss bounded. Some numerical results are also provided.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.521-529
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• 2015

Statistical process control techniques have been greatly implemented in industries for improving product quality and saving production costs. As a primary tool among these techniques, control charts are widely used to detect the occurrence of assignable causes. In most works on the control charts it considered the problem of monitoring the mean and variance, and the quality characteristic of interest is normally distributed. In some situations monitoring of the minimum and maximum values is more important and the quality characteristic of interest is the Weibull distribution rather than a normal distribution. In this paper, we consider the statistical design of minimum and maximum control charts when the distribution of the quality characteristic of interest is Weibull. The proposed minimum and maximum control charts are applied to the wind data. The results of the application show that the proposed method is more effective than traditional methods.

• International Journal of Quality Innovation
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• v.1 no.1
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• pp.32-37
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• 2000

In an industrial process, the proper objective is to find the optimal operating conditions with minimum process variability around the target. Vining and Myers(1990) suggest to use the separate model for the mean response and the process varian linear predictor ${\tau}_i={\log}\;{\sigma}^2_i$ is unknown and should be estimated. Noting that the variance of $\hat{{\tau}_i}$ is heterogeneous, another appropriate D-optimality criterion $D_3$ based on the method of generalized least squares is proposed in this paper.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.39-46
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• 1994

In this paper a state reconstruction filter for linear discrete-time stochastic systems with unknown inputs and noises is presented. The suggested filter can estimate the system state vector and the unknown inputs simultaneously As an extension of the filter a fault diagnosis filter for linear discrete-time stochastic systems with unknown inputs and noises is presented for each filters the optimal gain determination methods which minimize the variance of the state reconstruction errorare presented. Finally the usability of the filtersis shown via numerical examples.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.62-68
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• 2002

It is well known that the MDVR beamforming outperforms the conventional delay-sum beamformer in the sense of noise rejection and bearing resolution. However, the MDVR method requires long observation time to achieve high frequency resolution. The STMV method uses the steered covariance matrix of sensor data, so it has an ability to form an adaptive weight vector from a single time-series snapshot. But it uses the same weight vector across all frequencies. In this paper, we propose an SSMV method. The basic idea of the SSMV method is to decompose a full frequency band into several subbands to acquire a weight vector for each subband, individually. Also the wrap may be divided into several subarrays in order to reduce a computational load and the bandwidth of each subband. Simulations using real sea trial data show that the proposed SSMV method has good performance with short observation time.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1327-1335
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• 2012

TPMS (Tire Pressure Monitoring System) is a safety assistant system to prevent the serious accident due to the damaged tire by the abnormal tire pressure. It is designed to transmit the measured data for pressure and temperature of tires from the sensor unit installed in each tire to signal processing unit installed in a vehicle. Based on the received information, a driver monitors the condition of tires using a display device, to maintain the optimum travelling condition. Since TPMS should employ the wireless communication technique, it may suffer from various interferences from external electrical or electronics devices. In order to suppress them, the beamforming techniques such as switching, minimum-variance distortionless-response (MVDR), and generalized sidelobe canceler (GSC) have been considered for TPMS. In this paper, we calculate computational complexities of three beamformers and suggest mathematical basis to compare their performance of the complexity.

• Structural Monitoring and Maintenance
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• v.6 no.4
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• pp.317-346
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• 2019

Performance assessment of pavements proves useful, in terms of handling the ride quality, controlling the travel time of vehicles and adequate maintenance of pavements. Roughness profiles provide a good measure of the deteriorating condition of the pavement. For the accurate estimates of pavement roughness from dynamic vehicle responses, vehicle parameters should be known accurately. Information on vehicle parameters is uncertain, due to the wear and tear over time. Hence, condition monitoring of pavement requires the identification of pavement roughness along with vehicle parameters. The present study proposes a scheme which estimates the roughness profile of the pavement with the use of accurate estimates of vehicle parameters computed in parallel. Pavement model used in this study is a two-layer Euler-Bernoulli beam resting on a nonlinear Pasternak foundation. The asphalt topping of the pavement in the top layer is modeled as viscoelastic, and the base course bottom layer is modeled as elastic. The viscoelastic response of the top layer is modeled with the help of the Burgers model. The vehicle model considered in this study is a half car model, fitted with accelerometers at specified points. The identification of the coupled system of vehicle-pavement interaction employs a coupled scheme of an unbiased minimum variance estimator and an optimization scheme. The partitioning of observed noisy quantities to be used in the two schemes is investigated in detail before the analysis. The unbiased minimum variance estimator (MVE) make use of a linear state-space formulation including roughness, to overcome the linearization difficulties as in conventional nonlinear filters. MVE gives estimates for the unknown input and fed into the optimization scheme to yield estimates of vehicle parameters. The issue of ill-posedness of the problem is dealt with by introducing a regularization equivalent term in the objective function, specifically where a large number of parameters are to be estimated. Effect of different objective functions is also studied. The outcome of this research is an overall measure of pavement condition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.683-690
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• 2018

In this paper, a new signal processing technique is proposed for calibrating gain, phase, delay offsets in array antenna based anti-jamming minimum variance distortionless response (MVDR) global-positioning-system (GPS) receivers. The proposed technique estimates gain, phase and delay offsets across the antennas, and compensates for the offsets based on the estimates. A pilot signal with good correlation characteristics is used for accurate estimation of the gain, phase and delay offsets. Based on the cross-correlation, the delay offset is first estimated and then gain/phase offsets are estimated. For fine delay offset estimation and compensation, an interpolation technique is used, and specifically, the discrete Fourier transform (DFT) is employed for the interpolation technique to reduce the computational complexity. The proposed technique is verified through computer simulation using MATLAB. According to the simulation results, the proposed technique can reduce the gain, phaes and delay offset to 0.01 dB, 0.05 degree, and 0.5 ns, respectively.