• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.50
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• pp.373-380
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• 1999

This paper summerizes the former 5 papers that studied computer programming for the estimation of the Weibull, Extreme value, Hazard, Normal and Log-normal parameters which have a close relation with the reliability of the various kinds of industrial products. Probability paper is very commonly used in estimating the parameters, however, it is very hard to neglect the errors in plotting the data, and especially in drawing the regression line. The main purpose of this paper is to reduce these errors and to help the engineers to use the parameters in improving the reliability of their prod- ucts. The following parts are included in the computer programming with the em- phases on significant digits and rounding of numerical values : $\bullet$ data input part for various cases $\bullet$ parameter estimation part $\bullet$ printing part for input data $\bullet$ printing part for the results $\bullet$ printing part for the graphic(probability paper). And the running results(monitor displays) of the program for a fictitious example of Weibull distribution is given for the interested ones.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.228-231
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• 2000

A hybrid fuzzy controller by means of the genetic algorithms is presented. The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PlD's output in steady state by a fuzzy variable. The HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance than any other controller. A auto-tuning algorithms is presented to automatically improve the performance of hybrid fuzzy controller using genetic algorithms. The algorithms estimates automatical Iy the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA three kinds of estimation modes are effectively utilized. The HFCs are applied to the second process with time-delay. Computer simulations are conducted at step input and the performances of systems are evaluated and also discussed in ITAE(Integral of the Time multiplied by the Absolute value of Error ) and other ways.

• Journal of Communications and Networks
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• v.14 no.2
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• pp.199-205
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• 2012

This work investigates the progressive linear precoder design for packet retransmissions in multi-input multi-output (MIMO) systems with multiple codewords and automatic repeat request (ARQ) bundling feedback. Assuming perfect channel state information, a novel progressive linear ARQ precoder is proposed in the perspective of minimizing the packet error rate. We devise the ARQ precoder by combining power loading and sub channel pairing between current retransmission and previous transmissions. Furthermore, we extend the design to the case that the channel estimation error exists. Numerical results show that the proposed scheme can improve the performance of MIMO ARQ systems significantly regardless of the channel estimation error.

• ETRI Journal
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• v.31 no.2
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• pp.162-172
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• 2009

A linear-prediction-based blind equalization algorithm for single-input single-output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second-order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single-input multiple-output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum-mean-square-error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.270-277
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• 1999

This paper deals with the maneuver detection and target tracking problem in uncertain parameter systems using a robust{{{{ { H}_{ } }}}} FIR filter to improve the unacceptable tracking performance due to the parametr uncertainty. The tracking filter used in the current paper is based on the robust{{{{ { H}_{ } }}}} FIR filter proposed by Kwon et al. [1,2] to estimate the state signal in uncertain systems with parameter uncertainty, and the basic scheme of the proposed method is the input estimation approach. Tracking performance of the maneuver detection and target tracking method proposed is compared with other techniques, Bogler allgorithm [4] and FIR tracking filter [2], via some simulations to examplify the good tracking performance of the proposed method over other techniques.

• Journal of Communications and Networks
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• v.9 no.3
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• pp.254-264
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• 2007

A promising approach to improve the performance of mobile location system is the use of antenna arrays in both transmitter and receiver sides. Using advanced array signal processing techniques, such multiple-input multiple-output (MIMO) communication systems can offer more mobile location information by exploiting the spatial properties of the multipath channel. In this paper, we propose a novel approach to determine the position of mobile terminal based on estimated multipath signal parameters using only one base station in MIMO communication systems. This approach intends to minimize the error occurring from the estimation of multiple paths and gives an optimal estimation of the position of mobile terminal by simultaneously calculating a set of nonlinear location equations. This solution breaks the bottleneck of conventional mobile location systems which have to require multilateration of at least three base stations.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.363-366
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• 2001

It is very difficult to maintain the performance of estimation models for the new breed of projects since the computing environment changes so rapidly in terms of programming languages, development tools, and methodologies. So, we propose to use the relevant cases for a neural network model, whose cost is the decreased number of cases. To balance the relevance and data availability, the qualitative input factors are used as criteria of data classification. With the data sets that have the same value for certain qualitative input factors, we can eliminate the factors from the model making reduced neural network models. So we need to seek the optimally reduced neural network model among them. To find the optimally case-selective neural network, we propose the search techniques and sensitivity analysis between data points and search space.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.121-127
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• 1997

Multilayer feedforward networks may be applied to identify the deterministic relationship between input and output data. When the results from the network require a high level of assurance, consideration of the stochastic relationship between the input and output data may be very important. Variance is one of the effective parameters to deal with the stochastic relationship. This paper presents a new algroithm for a multilayer feedforward network to learn the variance of dispersed data without preliminary calculation of variance. In this paper, the network with this learning algorithm is named as a variance learning neural network(VALEAN). Computer simulation examples are utilized for the demonstration and the evaluation of VALEAN.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1098_1099
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• 2009

In this paper, we present the estimation of wind turbine power generation using Cascade Architectures of Fuzzy Neural Networks(CAFNN). The proposed model uses the wind speed average, the standard deviation and the past output power as input data. The CAFNN identification process uses a 10-min average wind speed with its standard deviation. The method for rule-based fuzzy modeling uses Gaussian membership function. It has three fuzzy variables with three modifiable parameters. The CAFNN's configuration has three Logic Processors(LP) that are constructed cascade architecture and an effective optimization method uses two-level genetic algorithm. First, The CAFNN is trained with one-day average input variables. Once the CAFNN has been trained, test data are used without any update. The main advantage of using CAFNN is having simple structure of system with many input variables. Therefore, The proposed CAFNN technique is useful to predict the wind turbine(WT) power effectively and hence that information will be helpful to decide the control strategy for the WT system operation and application.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.3
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• pp.208-213
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• 2004

This paper is concerned with the theoretical estimation of the single-input single-output(SISO) positive position feedback(PPF) controller and the derivation of the stability conditions for the multi-input multi-output (MIMO) PPF controller. Although the stability condition for the SISO PPF controller was derived in the earlier works, the question regarding the performance estimation of the SISO PPF controller has never been studied theoretically. Hence, the SISO PPF controller for the single degree-of-freedom system was first investigated and then control parameters including gain, the filter frequency, and the damping factor of the PPF controller were analyzed in detail thus providing the design methodology for the SISO PPF controller. In the case of real structure. there are infinite number of natural modes so that some modes are to be controlled by a limited number of actuator and sensor. Based on the theoretical results on the SISO PPF controller, the stability condition for the multi-input multi-output PPF controller was derived when only the few number of modes are to be controlled. The control spillover problem is also discussed in detail.