• Journal of Biomedical Engineering Research
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• v.12 no.3
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• pp.191-202
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• 1991

This paper describes ECG data compression algorithm using neural network. As a learning method, we use back error propagation algorithm. ECG data compression is performed using learning ability of neural network. CSE database, which is sampled 12bit digitized at 500samp1e/sec, is selected as a input signal. In order to reduce unit number of input layer, we modify sampling ratio 250samples/sec in QRS complex, 125samples/sec in P & T wave respectively. hs a input pattern of neural network, from 35 points backward to 45 points forward sample Points of R peak are used.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.215-228
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• 2003

Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, interaction effect identification, category merging and discretizing continuous variable, etc. CHAID(Chi-square Automatic Interaction Detector), is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. CHAID modeling selects a set of predictors and their interactions that optimally predict the dependent measure. In this paper we explore CHAID algorithm in view of accuracy and speed by sampling proportion.

• Journal of Korean Society for Quality Management
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• v.23 no.4
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• pp.74-89
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• 1995

In recent years, the safety of customers and the demand for rights to be protected from the risk have become stronger than ever day by day, and the function concerning product liability(PL) and quality assurance(QA) has been emphasized. Basically these functions can be obtained by inspection and there is the single rectifying sampling inspection for attribute (KSA-3105) as an existing method. But we can not say this method is good enough because of limitations in the range of applications and the approximate design of inspection methods which can not meet the rapidity and accuracy of quality information transfer according to the maturity of information period. Therefore, in this paper, a new algorithm is developed which can design the accurate inspection method by using the linear cost function that has not been considered in the existing inspection methods. Also in addition to this, a optimal rectifying sampling inspection plan, contributing to minimize the total costs, can be developed by programming the algorithm developed in this study and it can be applied to any field having many processes almost limitlessly.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1600-1604
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• 2017

A new Complex Image Method based on Genetic Algorithm (GA) is proposed to calculate the Green's functions of CPW (coplanar waveguide)-type microwave components and antennas. The closed-forms of the spectral-domain integrals are obtained by the GA, avoiding the conventional procedures of the tedious linear algebra and the sampling conditions sensitive to the complex-variable sampling paths adopted in the Prony's and GPOF methods. The proposed method is compared with the numerical Sommerfeld Integral, which results in good agreement.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.719-729
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• 2001

Overlap Maximization is a sampling technique to reduce survey costs and costs associated with the survey. It was first studied by Keyfitz(1951). Ernst(1998) presented a remarkable procedure for maximizing the overlap when the sampling units can be selected for two identical stratified designs simultaneously, But the approach involves mimicking the behaviour of nonlinear function by linear function and so it is less direct, even though the stratification problem for the overlap corresponds directly to the linear programming problem. furthermore, it uses the controlled selection algorithm that repeatedly needs zero-restricted controlled roundings, which are solutions of capacitated transportation problems. In this paper we suggest a comparatively simple procedure to use linear programming in order to maximize the overlap. We show how this procedure can be implemented practically.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.79-91
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• 2004

In this thesis, Bayesian parameter estimation procedure is discussed for the mean change model of multivariate normal random variates under the assumption of noninformative priors for all the parameters. Parameters are estimated by Gibbs sampling method. In Gibbs sampler, the change point parameter is generated by Metropolis-Hastings algorithm. We apply our methodology to numerical data to examine it.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.256-261
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• 2005

In this research, we propose a path-planning algorithm for an autonomous mobile robot using geographical information, under the condition that the robot moves in unknown environment. All image inputted by camera at every sampling time are analyzed and geographical elements are recognized, and the geographical information is embedded in environmental map. The geographical information was transformed into 1-dimensional evaluation value that expressed the difficulty of movement for the robot. The robot goes toward the goal searching for path that minimizes the evaluation value at every sampling time. Then, the path is updated by integrating the exploited information and the prediction on unexploited environment. We used a sensor fusion method for improving the mobile robot dead reckoning accuracy. The experiment results that confirm the effectiveness of the proposed algorithm on the robot's reaching the goal successfully using geographical information are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2562-2567
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• 2005

This paper suggests a new method discretization of nonlinear system using Taylor series expansion and zero-order hold assumption. This method is applied into the sampled-data representation of a nonlinear system with input time delay. Additionally, the delayed input is time varying and its amplitude is bounded. The maximum time-delayed input is assumed to be two sampling periods. Them mathematical expressions of the discretization method are presented and the ability of the algorithm is tested for some of the examples. And 'hybrid' discretization scheme that result from a combination of the ‘scaling and squaring' technique with the Taylor method are also proposed, especially under condition of very low sampling rates. The computer simulation proves the proposed algorithm discretized the nonlinear system with the variable time-delayed input accurately.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.849-853
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• 2014

In micro-scale electromechanical systems, the power to perform accurate position sensing often greatly exceeds the power needed to generate motion. This paper explores the implications of sampling rate and amplifier noise density selection on the performance of a system identification algorithm using a capacitive sensing circuit. Specific performance objectives are to minimize or limit convergence rate and power consumption to identify the dynamics of a rotary micro-stage. A rearrangement of the conventional recursive least-squares identification algorithm is performed to make operating cost an explicit function of sensor design parameters. It is observed that there is a strong dependence of convergence rate and error on the sampling rate, while energy dependence is driven by error that may be tolerated in the final identified parameters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.7
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• pp.651-662
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• 1991

An improved modular multiplication algorithm for RSA type public key cryptosystem and its application to a serial communication cricuit are presented. Correction on a published fast modular multiplication algorithm is proposed and verified thru simulation. Cryptosystem for RS 232C communication protocol isdesigned and prototyped for low speed data exchange between computers. The system adops the correct algoroithm and operates successfully using a small size key.