• ETRI Journal
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• v.27 no.4
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• pp.377-384
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• 2005

We investigate near-infrared light (NIR) propagation in a model of an adult head using an extensive Monte Carlo (MC) simulation. The adult head model is a four-layered slab which consists of a surface layer, a cerebrospinal fluid layer, a gray-matter layer, and a white-matter layer. We study the effects of a refractive index mismatch on the model, calculating the intensity of detected light, mean flight time, and partial mean flight time of each layer for various refractive indices of the cerebrospinal fluid layer as functions of source-detector spacing. The Monte Carlo simulation shows that the refractive index mismatch presents very rich results including rapidly decaying intensity of detected light and a peak and cross-over in the partial mean flight time with source-detector spacing. We also investigate spatial sensitivity profiles at various source-detector spacings, discussing the index mismatch effect on the model.

• Journal of the Korean Solar Energy Society
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• v.29 no.1
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• pp.58-63
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• 2009

In this paper, the power loss due to PV module mismatch in PV string is analyzed and a mismatch compensation method is proposed to improve the efficiency of PV system. The analysis of mismatch loss using PV model simulation reveals that the mismatch module may decrease the total efficiency because the MPPT function of power conditioner make the PV system operate at the local maximum point. The mismatch loss can be severe if the maximum power point current of mismatch module is less than that of string. The proposed compensation method which is simply implemented with a buck type converter shows the possibility to remove the mismatch loss. The effectiveness of the analysis and compensation method is verified by a prototype experiment.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.856-859
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• 1997

In this paper we treat the application of fault detection method in DC motor having both model mismatch and noise problems. A fault detection method presented by Kwon et al. (1994) for SISO systems has been here experimented. The model mismatch includes here linearization error as well as undermodelling. Comparisons are made with the real plant, DC motor. The experimental result of robust fault detection method is shown to have good performance via with the alternative fault detection method which do not account noise.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.143-150
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• 1994

Wall shear rate or stress is believed to be a major hemodynamic variable influencing atherosclerosis and artery-graft anastomic intimal hyperplasia. The purpose of this study is to verify the effects of radial wall motion, artery-graft compliance and diameter mismatch, and impedance phase angle on the wall shear rate distribution near an end-to-end artery-graft anastomosis model. The results show that radial wall motion of the elastic artery model lowers the mean wall shear rates under pulsatile flow condition by 15 to 20 % comparing to those under steady flow condition at the same mean flow rate. Impedance phase angle seems to have small effects on the mean and amplitude of the wall shear rate distribution. In order to study the effects of compliance and diameter mismatch on the wall shear rates, two models are studied-Model I has 6% and Model I has 6% and Model II has 11% smaller graft diameter. Divergent geometry caused by diameter mismatch near the distal sites reduces the mean wall shear rates significantly, and this low shear region is believed to be prone to intimal hyperplasia.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1419-1422
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• 1997

This paper deals with the Appliation of robust fault detection problem in uncertain linear systems, having both model mismatch and noise. A robust fault detection method presented by Kwon et al.(1994) for SISO uncertain systems. Here we experimented this method to the diesel engine systems described by difference ARMA models. The model mismatch includes here linearization error as well as undermodeling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.710-713
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• 1996

This paper deals with the fault detection problem in uncertain linear multivariable systems having both model mismatch and noise. A robust detection presented by Kwon et al.(1994) for SISO systems has been here extended to the multivariable systems are derived. The model mismatch includes here linearization error as well as undermodelling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.4
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• pp.206-208
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• 2014

In this letter, IQ mismatch of a CA-supporting terminal for LTE-A is analyzed and each of the analog blocks is modeled mathematically. Based on the model, a new digital calibration algorithm is proposed and verified using simulation results.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.7
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• pp.729-739
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• 1990

This paper deals with the fault detection problem in uncertain linear/non-linear systems having both undermodelling and noise. A robust fault detection method is presented which accounts for the effects of noise, model mismatch and nonlinearities. The basic idea is to embed the unmodelled dynamics in a stochastic process and to use the nominal model with a predetermined fixed denominator. This allows the input /output relationship to be represented as a linear function of the system parameters and also facilitate the quatification of the effect of noise, model mismatch and linearization errors on parameter estimation by the Bayesian method. Comparisons are made via simulations with traditional fault detection methods which do not account for model mismatch or linearization errors. The new method suggested in this paper is shown to have a marked improvement over traditional methods on a number of simulations, which is a consequence of the fact that the new method explicitly for the effects of undermodelling and linearization errors.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.83-85
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• 2007

A new approach on identifying a first-order plus time-delay (FOPTD) model using finite--duration pulse inputs has been presented recently [1]. The identification methods are very simple because it is enough to observe only two extremes and the time when they occur in the transient response to pulse input. However, when there is mismatch between actual system and FOPTD model. how sensitive the methods are has not been studied. In this paper, we investigate robustness issue of those identification algorithms in the presence of the model structure mismatch and uncertainties. Through an example we will demonstrate it.

• Phonetics and Speech Sciences
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• v.9 no.2
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• pp.85-94
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• 2017

Conventional speaker verification systems using time delay neural network, identity vector and probabilistic linear discriminant analysis (TDNN-Ivector-PLDA) are known to be very effective for verifying long-duration speech utterances. However, when test utterances are of short duration, duration mismatch between enrollment and test utterances significantly degrades the performance of TDNN-Ivector-PLDA systems. To compensate for the I-vector mismatch between long and short utterances, this paper proposes to use probabilistic linear discriminant analysis (PLDA) model adaptation with augmented data. A PLDA model is trained on vast amount of speech data, most of which have long duration. Then, the PLDA model is adapted with the I-vectors obtained from short-utterance data which are augmented by using vocal tract length perturbation (VTLP). In computer experiments using the NIST SRE 2008 database, the proposed method is shown to achieve significantly better performance than the conventional TDNN-Ivector-PLDA systems when there exists duration mismatch between enrollment and test utterances.