• Title/Summary/Keyword: Additive Model Error

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Adaptive filter Design for INS/GPS (INS/GPS를 위한 적응필터 구성)

  • Yu Myeong-Jong
    • Journal of Institute of Control, Robotics and Systems
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    • v.11 no.8
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    • pp.717-725
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    • 2005
  • The adaptive filter is proposed for the INS/GPS. The proposed filter can estimate the variance of the process noise using the residual of the filter. To verify the efficiency of the adaptive filter, it is applied to the loosely-coupled INS/CPS that employs the additive quaternion error model. Simulation results demonstrate that the proposed filter is more effective in estimating the attitude error than EKF.

Kernel Regression Estimation for Permutation Fixed Design Additive Models

  • Baek, Jangsun;Wehrly, Thomas E.
    • Journal of the Korean Statistical Society
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    • v.25 no.4
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    • pp.499-514
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    • 1996
  • Consider an additive regression model of Y on X = (X$_1$,X$_2$,. . .,$X_p$), Y = $sum_{j=1}^pf_j(X_j) + $\varepsilon$$, where $f_j$s are smooth functions to be estimated and $\varepsilon$ is a random error. If $X_j$s are fixed design points, we call it the fixed design additive model. Since the response variable Y is observed at fixed p-dimensional design points, the behavior of the nonparametric regression estimator depends on the design. We propose a fixed design called permutation fixed design, and fit the regression function by the kernel method. The estimator in the permutation fixed design achieves the univariate optimal rate of convergence in mean squared error for any p $\geq$ 2.

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Adaptive Filter Design for Radar Aided SDINS (레이다 보정형 스트랩다운 관성항법시스템을 위한 적응필터 구성)

  • 유명종;박찬주;김현백
    • Journal of Institute of Control, Robotics and Systems
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    • v.9 no.6
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    • pp.420-424
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    • 2003
  • A new adaptive filter is proposed for an aided strapdown inertial navigation system(SDINS). The proposed filter can be used to effectively estimate the time-varying variance of the measurement noise. Then, the in-flight alignment for the radar aided SDINS is designed using the additive quatermion error model. Simulation results show that the proposed adaptive filter effectively improves the performance of the radar aided SDINS.

Comparison of Regression Models for Estimating Ventilation Rate of Mechanically Ventilated Swine Farm (강제환기식 돈사의 환기량 추정을 위한 회귀모델의 비교)

  • Jo, Gwanggon;Ha, Taehwan;Yoon, Sanghoo;Jang, Yuna;Jung, Minwoong
    • Journal of The Korean Society of Agricultural Engineers
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    • v.62 no.1
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    • pp.61-70
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    • 2020
  • To estimate the ventilation volume of mechanically ventilated swine farms, various regression models were applied, and errors were compared to select the regression model that can best simulate actual data. Linear regression, linear spline, polynomial regression (degrees 2 and 3), logistic curve, generalized additive model (GAM), and gompertz curve were compared. Overfitting models were excluded even when the error rate was small. The evaluation criteria were root mean square error (RMSE) and mean absolute percentage error (MAPE). The evaluation results indicated that degree 3 exhibited the lowest error rate; however, an overestimation contradiction was observed in a certain section. The logistic curve was the most stable and superior to all the models. In the estimation of ventilation volume by all of the models, the estimated ventilation volume of the logistic curve was the smallest except for the model with a large error rate and the overestimated model.

An Improved Secondary Path Modeling Method by Modified Kuo Model

  • Park, Byoung-Uk;Kim, Hack-Yoon
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.1E
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    • pp.33-42
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    • 2003
  • Kuo et al proposed an on-line method for an adaptive prediction error filter for improving secondary path modeling performance in the modeling method of the secondary path. This method have some disadvantages, namely having to use additive noise with the result that noise control performance is not good since it is focused on the estimated performance of the secondary path. In this paper, we proposes a modified Kuo model using gain control parameter and delay. It uses a reference signal for additive noise to improve the problems in the existing Kuo model.

Further Results on Piecewise Constant Hazard Functions in Aalen's Additive Risk Model

  • Uhm, Dai-Ho;Jun, Sung-Hae
    • The Korean Journal of Applied Statistics
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    • v.25 no.3
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    • pp.403-413
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    • 2012
  • The modifications suggested in Uhm et al. (2011) are studied using a partly parametric version of Aalen's additive risk model. A follow-up time period is partitioned into intervals, and hazard functions are estimated as a piecewise constant in each interval. A maximum likelihood estimator by iteratively reweighted least squares and variance estimates are suggested based on the model as well as evaluated by simulations using mean square error and a coverage probability, respectively. In conclusion the modifications are needed when there are a small number of uncensored deaths in an interval to estimate the piecewise constant hazard function.

The performance estimation of Channel coding schemes in Wideband Code Division Multiple Access System with fading channel (페이딩 환경의 W-CDMA에서 채널부호화 방식의 성능평가)

  • 이종목;심용걸
    • Proceedings of the IEEK Conference
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    • 2000.11a
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    • pp.165-168
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    • 2000
  • The bit error rate(BER)of the data passed through Wideband-Code Division Multiple Access (W-CDMA) system with turbo-codes structure is presented. The performance of turbo-codes under W-CDMA system is estimated for various users and iteration numbers of decoding. The channel model is Additive White Gaussian Noise(AWGN) and Rayleigh fading channel. When iteration number increases, bit error probability of turbo-codes decreases. and when the number of users increase, bit error probability of turbo-codes increases.

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Trends in Materials Modeling and Computation for Metal Additive Manufacturing

  • Seoyeon Jeon;Hyunjoo Choi
    • Journal of Powder Materials
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    • v.31 no.3
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    • pp.213-219
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    • 2024
  • Additive Manufacturing (AM) is a process that fabricates products by manufacturing materials according to a three-dimensional model. It has recently gained attention due to its environmental advantages, including reduced energy consumption and high material utilization rates. However, controlling defects such as melting issues and residual stress, which can occur during metal additive manufacturing, poses a challenge. The trial-and-error verification of these defects is both time-consuming and costly. Consequently, efforts have been made to develop phenomenological models that understand the influence of process variables on defects, and mechanical/ electrical/thermal properties of geometrically complex products. This paper introduces modeling techniques that can simulate the powder additive manufacturing process. The focus is on representative metal additive manufacturing processes such as Powder Bed Fusion (PBF), Direct Energy Deposition (DED), and Binder Jetting (BJ) method. To calculate thermal-stress history and the resulting deformations, modeling techniques based on Finite Element Method (FEM) are generally utilized. For simulating the movements and packing behavior of powders during powder classification, modeling techniques based on Discrete Element Method (DEM) are employed. Additionally, to simulate sintering and microstructural changes, techniques such as Monte Carlo (MC), Molecular Dynamics (MD), and Phase Field Modeling (PFM) are predominantly used.

Error Intensity Function Models for ML Estimation of Signal Parameter, Part II : Applications to Gaussian and Impulsive Noise Environments (신호 파라미터의 ML추정 기법에 대한 에러 밀도 함수모델에 관한 연구 II : 가우시안 및 임펄스 잡음 환경에의 적용)

  • Kim, Joong Kyu
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.32B no.1
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    • pp.85-95
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    • 1995
  • The error intensity models for the ML estimation of a signal parameter have been developed in a companion paper [1]. While the methods described in [1] are applicable to any estimation problem with continuous parameters, our main application in this paper is the time delay estimation, and comparisons among the models derived in [1] (i.e. LC, LM, and ALM models)have been made. We first consider the case where only additive Gaussian noise is involved, and then the shot noise environment where coherent impulsive noise is also involved in addition to the Gaussian noise. We compare the models in terms of the probability of error, MSE(Mean Squared Error), and the computational complexity, which are the most important performance criteria in the analysis of parameter estimation. In conclusion, the ALM model turned out to be the most adequate model of all from the viewpoints of the criteria mentioned above.

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SOME PROPERTIES OF SIMEX ESTIMATOR IN PARTIALLY LINEAR MEASUREMENT ERROR MODEL

  • Meeseon Jeong;Kim, Choongrak
    • Journal of the Korean Statistical Society
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    • v.32 no.1
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    • pp.85-92
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    • 2003
  • We consider the partially linear model E(Y) : X$^{t}$ $\beta$+η(Z) when the X's are measured with additive error. The semiparametric likelihood estimation ignoring the measurement error gives inconsistent estimator for both $\beta$ and η(.). In this paper we suggest the SIMEX estimator for f to correct the bias induced by measurement error, and explore its properties. We show that the rational linear extrapolant is proper in extrapolation step in the sense that the SIMEX method under this extrapolant gives consistent estimator It is also shown that the SIMEX estimator is asymptotically equivalent to the semiparametric version of the usual parametric correction for attenuation suggested by Liang et al. (1999) A simulation study is given to compare two variance estimating methods for SIMEX estimator.