• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.537-549
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• 1995

In this article, we propose a class of weighted estimators for the excess risk in additive risk model with a binary covariate. The proposed estimator is consistent and asymptotically normal. When the assumed model is inappropriate, however, the estimators with different weights converge to nonidentical constants. This fact enables us to develop a goodness-of-fit test for the excess assumption by comparing estimators with diffrent weights. It is shown that the proposed test converges in distribution to normal with mean zero and is consistent under the model misspecifications. Furthermore, the finite-sample properties of the proposed test procedure are investigated and two examples using real data are presented.

• 센서학회지
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• 제20권1호
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• pp.14-18
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• 2011

An analytical pinning-voltage model of a pinned photodiode has been proposed and derived. The pinning-voltage is calculated using doping profiles based on shallow- and exponential-junction approximations. Therefore, the derived pinning-voltage model is analytically expressed in terms of the process parameters of the implantation. Good agreement between the proposed model and simulated results has been obtained. Consequently, the proposed model can be used to predict the pinning-voltage and related performance of a pinned photodiode in a CMOS active pixel sensor.

• 한국시뮬레이션학회논문지
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• 제9권4호
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• pp.41-50
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• 2000

We have many simulation constraints to meet as a modeled system becomes large and complex. Real-time simulations are the examples in that they are constrained by certain non-function constraints (e.g., timing constraints). In this paper, an enhanced hierarchical modeling methodology is proposed to efficiently deal with constraint-simulations. The proposed modeling method enhances hierarchical modeling methods to provide multi-resolution model. A simulation model is composed by determining the optimal level of abstraction that is guaranteed to meet the given simulation constraints. Four modeling activities are defined in the proposed method: 1) Perform the logical architectural design activity to produce a multi-resolution model, 2) Organize abstraction information of the multi-resolution model with AT (Abstraction Tree) structure, 3) Formulate the given constraints based on U (Integer Programming) approach and embrace the constraints to AT, and 4) Compose a model based on the determined level of abstraction with which the multi-resolution model can satisfy all given simulation constraints. By systematically handling simulation constraints while minimizing the modeler's interventions, we provide an efficient modeling environment for constraint-simulations.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.613-614
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• 2006

In this paper, BSIM4's IIR(Intrinsic Input Resistance) model that has a difficulty to predict $Z_{11}$ exactly is investigated by analyzing S-parameter measurement. Then a BSIM4 macro model for 70nm RF MOSFETs is proposed. That model uses external effective gate resistance which is composed of R and parallel RC. Comparison between simulation results using proposed model and IIR model is shown. The proposed model shows a better agreement between measured and simulated results up to 20GHz.

• ETRI Journal
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• 제36권6호
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• pp.1016-1022
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• 2014

This paper presents a hybrid model solution for user motion recognition. The use of a single classifier in motion recognition models does not guarantee a high recognition rate. To enhance the motion recognition rate, a hybrid model consisting of decision trees and artificial neural networks is proposed. We define six user motions commonly performed in an indoor environment. To demonstrate the performance of the proposed model, we conduct a real field test with ten subjects (five males and five females). Experimental results show that the proposed model provides a more accurate recognition rate compared to that of other single classifiers.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.619-622
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• 2000

This research presents an analytical model to predict the instruction fetch rate on superscalar Processors. The proposed model is also able to analyze the performance relationship between cache miss and branch prediction miss. The proposed model takes into account various kind of architectural parameters such as branch instruction probability, cache miss rate, branch prediction miss rate, and etc.. To prove the correctness of the proposed model, we performed extensive simulations and compared the results with those of the analytical models. Simulation results showed that the pro-posed model can estimate the instruction fetch rate accurately within 10% error in most cases. The model is also able to show the effects of the cache miss and branch prediction miss on the performance of instruction fetch rate, which can provide an valuable information in designing a balanced system.

• 한국공간구조학회논문집
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• 제3권1호
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• pp.77-85
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• 2003

This paper presents a truss model that can predict the shear behavior of reinforced concrete (RC) beams subjected to the combined actions of shear and flexure. Unlike other truss models, the proposed truss model, TATM, takes into account the effect of the flexural moment on the shear strength of RC beams with different shear span-to-depth ratios. To check the successfulness of the proposed model experimentally obtained stress shear strain curves were compared to the predicted ones using the proposed truss model. Furthermore, the shear strengths of 170 RC test beams with variable shear span-to-depth ratios were compared to the shear strengths as given by the truss model reported in this paper.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.581-585
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2114-2116
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• 2001

Model reduction from high order systems to low order systems in frequency domain is considered four point (${\angle}$G(jw)=0, - ${\pi}/2$, ${\pi}$, and -3${\pi}$/2) instead of two point (${\angle}$G(jw) = - ${\pi}$/2,- ${\pi}$) of existing method in Nyquist curve. The Performances of reduced order model by proposed approach is similar to original model. In this paper, we proposed a new tuning algorithm for PID controller using model reduction in frequency domain. Simulations for some examples with varies dynamic characteristics are provided to show the effectiveness of the proposed tuning algorithm for PID controller using model reduction.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2669-2671
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• 2001

In this paper, we propose design a Multi-Fuzzy Inference model structure. In order to determine structure of the proposed Multi-Fuzzy Inference model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.