• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.108-115
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• 1994

Most pattern classifiers have been designed based on the ML (Maximum Likelihood) training algorithm which is simple and relatively powerful. The ML training is an efficient algorithm to individually estimate the model parameters of each class under the assumption that all class models in a classifier are statistically independent. That assumption, however, is not valid in many real situations, which degrades the performance of the classifier. In this paper, we propose a minimum-error-rate training algorithm based on the MAP (Maximum a Posteriori) approach. The algorithm regards the normalized outputs of the classifier as estimates of the a posteriori probability, and tries to maximize those estimates. According to Bayes decision theory, the proposed algorithm satisfies the condition of minimum-error-rate classificatin. We apply this algorithm to NPM (Neural Prediction Model) for speech recognition, and derive new disrminative training algorithms. Experimental results on ten Korean digits recognition have shown the reduction of 37.5% of the number of recognition errors.

• Bulletin of the Society of Naval Architects of Korea
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• v.23 no.2
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• pp.27-32
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• 1986

This paper describes an application of the adaptive finite element computations to a free surface flow problem in a canal. A-posteriori error estimates for the adaptive finite element computations are based on the dual extremum principles. Previously the dual extremum principles were applied to compute the upper and lower bounds of the added mass of two-dimensional cylinders in a canal[1,2]. However, the present method improves the convergence of the computed results by utilizing the local error estimates and by applying the adaptive meshes in the finite element computations. In a test result using triangular elements it is shown that the numerical error in the adaptive finite elements reduces quadratically compared with that in a uniform mesh subdivision.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.10
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• pp.2060-2066
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• 2002

The closed-loop state and input observer is a pole-placement type observer and estimates unknown state and input variables simultaneously. Pole-placement type observers may have poor transient performance with respect to ill-conditioning factors such as unknown initial estimates, round-off error, etc. For the robust transient performance, the effects of these ill-conditioning factors must be minimized in designing observers. In this paper, the transient performance of the closed-loop state and input observer is investigated quantitatively by considering the error bounds due to ill-conditioning factors. The performance indices are selected from these error bounds and are related to the observer robustness with respect to the ill -conditioning factors. The closed-loop state and input observer with small performance indices is considered as a well-conditioned observer from the transient perspective.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.3 s.345
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• pp.49-58
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• 2006

In this paper, a curve-fitting channel estimation method is proposed for orthogonal frequency division multiplexing (OFDM) system in a time-varying frequency-selective fading channel. The method can greatly improve channel state information (CSI) estimation accuracy by performing smoothing and interpolation through consecutive curve-fitting processes in both time domain and frequency domain. It first evaluates least-squares (LS) estimates using pilot symbols and then the estimates are approximated to a polynomial with proper degree in the LS error sense, starting from one preferred domain in which pilots we densely distributed. Smoothing, interpolation, and prediction are performed subsequently to obtain CSI estimates for data transmission. The channel estimation processes are completed by smoothing and interpolating CSI estimates in the other domain once again using the channel estimates obtained in one domain. The performance of proposed method is influenced heavily on the time variation and frequency selectivity of channel and pilot arrangement. Hence, a proper degree of polynomial and an optimum approximation interval according to various system and channel conditions are required for curve-fitting. From extensive simulation results in various channel environments, we see that the proposed method performs better than the conventional methods including the optimal Wiener filtering method, in terms of the mean square error (MSE) and bit error rate (BER).

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.75-81
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• 2010

Adaptive array antenna system estimates target signal, as making a null pattern toward the interference signal. However, receiving a coherent interference signal at adaptive array antenna system, as target signal is regarded as interference signal, the performance of system rapidly decreases. The main beam does not direct an incidence direction of target signal due to sensitivity in directivity error. This paper proposes beamforming algorithm which combines high order limit condition and directivity error modify method. And it minimizes degree of freedom and decreases interference signal and directivity error using proposed algorithm. This paper compares and analyzes the performance of proposed algorithm and general algorithm using simulation. In this paper, the proposed algorithm correctly estimates an incident angle, but general algorithm occurs an error of about $0.8^{\circ}$.

• Journal of the Korean Data and Information Science Society
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• v.19 no.1
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• pp.25-36
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• 2008

Error Correcting Output Codes (ECOC) can improve generalization performance when applied to multicategory classification problem. In this study we propose a new criterion to select hyperparameters included in ECOC scheme. Instead of margins of a data we propose to use the probability of misclassification error since it makes the criterion simple. Using this we obtain an upper bound of leave-one-out error of OVA(one vs all) method. Our experiments from real and synthetic data indicate that the bound leads to good estimates of parameters.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.82-82
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• 2000

The way which estimates a position from Navigation is Dead-reckoning Navigation and Radio Navigation. Generally dead-reckoning navigation uses Gyro and odometer as sensor, but these have problems which are an integrating noise and a noise-sensitivity. Gps which is used by radio-navigation has a Troposthetic error and MultiPath ewer and so on. For minimizing a Troposthetic error and a Multipath error, this paper suggests to an algorithm which use vanishing point on CCD camera.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2002.09a
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• pp.16.1-16
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• 2002

• Bulletin of the Korean Mathematical Society
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• v.39 no.4
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• pp.705-714
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• 2002

We consider a jump-diffusion model generated by a Levy process for an asset price. We present an error estimate for the option prices between the jump-diffusion model and the Black-scholes model when the former converges weakly to the latter.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.667-676
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• 1999

We investigate the effects of omission of initial observations in each individuals in the panel data regression model when the disturbances follow a serially correlated one way error components. We show that the first transformed observation can have a relative large hat matrix diagonal component and a large influence on parameter estimates when the correlation coefficient is large in absolute value.