• Title/Summary/Keyword: Data estimation

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Notes on the Comparative Study of the Reliability Estimation for Standby System with Rayleigh Lifetime Distribution

  • Kim, Hee-Jae
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.1
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    • pp.239-250
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    • 2004
  • We shall propose maximum likelihood, Bayesian and generalized maximum likelihood estimation for the reliability of the two-unit hot standby system with Rayleigh lifetime distribution that switch is perfect. Each estimation will be compared numerically in terms of various mission times, parameter values and asymptotic relative efficiency through Monte Carlo simulation.

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Localization Error Recovery Based on Bias Estimation (바이어스추정을 기반으로 한 위치추정의 오차회복)

  • Kim, Yong-Shik;Lee, Jae-Hoon;Kim, Bong-Keun;Ohba, Kohtaro;Ohya, Akihisa
    • The Journal of Korea Robotics Society
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    • v.4 no.2
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    • pp.112-120
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    • 2009
  • In this paper, a localization error recoverymethod based on bias estimation is provided for outdoor localization of mobile robot using different-type sensors. In the previous data integration method with DGPS, it is difficult to localize mobile robot due to multi-path phenomena of DGPS. In this paper, fault data due to multi-path phenomena can be recovered by bias estimation. The proposed data integration method uses a Kalman filter based estimator taking into account a bias estimator and a free-bias estimator. A performance evaluation is shown through an outdoor experiment using mobile robot.

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A Study on Statistical Methods for the Light Weight Estimation of Ultra Large Container Ships (초대형 컨테이너선의 경하중량 추정을 위한 통계적 방법 연구)

  • Cho, Yong-Jin
    • Journal of Ocean Engineering and Technology
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    • v.23 no.3
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    • pp.14-19
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    • 2009
  • The present study developed a model to estimate the light weight of an ultra-large container ship. The weight estimation model utilized container ship data obtained from shipyards and the subdivided this weight data into appropriate weight groups. Parameters potentially affecting the group weight were selected and expanded based on experience for weight estimation, and a correlation analysis was performed by the SPSS program to determine the key parameters characterizing the group weight. A weight estimation model applying the multi-regression analysis was proposed to assess the weight of an ultra-large container ship at the preliminary design stage, and the results obtained by the suggested method showed good agreement with the shipyard data.

Parameter Estimation of Shallow Arch Using Quantum-Inspired Evolution Algorithm (양자진화 알고리즘을 이용한 얕은 아치의 파라미터 추정)

  • Shon, Sudeok;Ha, Junhong
    • Journal of Korean Association for Spatial Structures
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    • v.20 no.1
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    • pp.95-102
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    • 2020
  • The structural design of arch roofs or bridges requires the analysis of their unstable behaviors depending on certain parameters defined in the arch shape. Their maintenance should estimate the parameters from observed data. However, since the critical parameters exist in the equilibrium paths of the arch, and a small change in such the parameters causes a significant change in their behaviors. Thus, estimation to find the critical ones should be carried out using a global search algorithm. In this paper we study the parameter estimation for a shallow arch by a quantum-inspired evolution algorithm. A cost functional to estimate the system parameters included in the arch consists of the difference between the observed signal and the estimated signal of the arch system. The design variables are shape, external load and damping constant in the arch system. We provide theoretical and numerical examples for estimation of the parameters from both contaminated data and pure data.

M-quantile kernel regression for small area estimation (소지역 추정을 위한 M-분위수 커널회귀)

  • Shim, Joo-Yong;Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.4
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    • pp.749-756
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    • 2012
  • An approach widely used for small area estimation is based on linear mixed models. However, when the functional form of the relationship between the response and the input variables is not linear, it may lead to biased estimators of the small area parameters. In this paper we propose M-quantile kernel regression for small area mean estimation allowing nonlinearities in the relationship between the response and the input variables. Numerical studies are presented that show the sample properties of the proposed estimation method.

Simulation study on the estimation of multinomial proportions

  • Kim, Dae-Hak
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.2
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    • pp.411-417
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    • 2012
  • In this paper, we consider the estimation of multinomial proportions. Multinomial distribution is the most important multivaritate distribution. Estimation of multinomial parameters for multinomial distribution is widely applicable to many practical research areas including genetics. We investigated the properties of several frequency substitution estimates and derived the maximum likelihood estimate of multinomial proportions of Hardy Weinberg proportions. Phenotype and genotype frequencies of allele are used to the estimation of multinomial proportions. These estimates are then analyzed via numerical data. Small sample Monte Carlo simulation is conducted to compare considered estimates of multinomial proportions.

Weighted Estimation of Survival Curves for NBU Class Based on Censored Data

  • Lee, Sang-Bock
    • Journal of the Korean Data and Information Science Society
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    • v.7 no.1
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    • pp.59-68
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    • 1996
  • In this paper, we consider how to estimate New Better Than Used (NBU) survival curves from randomly right censored data. We propose several possible NBU estimators and study their properties. Numerical studies indicate that the proposed estimators are appropriate in practical use. Some useful examples are presented.

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Design of Lateral Force Estimation Model for Rough Terrain Mobile Robot and Improving Estimation Reliability on Friction Coefficient (야지 주행 로봇을 위한 횡 방향 힘 추정 모델의 설계 및 마찰계수 추정 신뢰도의 향상)

  • Kim, Jiyong;Lee, Jihong;Joo, Sang Hyun
    • The Journal of Korea Robotics Society
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    • v.13 no.3
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    • pp.174-181
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    • 2018
  • For a mobile robot that travels along a terrain consisting of various geology, information on tire force and friction coefficient between ground and wheel is an important factor. In order to estimate the lateral force between ground and wheel, a lot of information about the model and the surrounding environment of the vehicle is required in conventional method. Therefore, in this paper, we are going to estimate lateral force through simple model (Minimal Argument Lateral Slip Curve, MALSC) using only minimum data with high estimation accuracy and to improve estimation reliability of the friction coefficient by using the estimated lateral force data. Simulation is carried out to analyze the correlation between the longitudinal and transverse friction coefficients and slip angles to design the simplified lateral force estimation model by analysing simulation data and to apply it to the actual field environment. In order to verify the validity of the equation, estimation results are compared with the conventional method through simulation. Also, the results of the lateral force and friction coefficient estimation are compared from both the conventional method and the proposed model through the actual robot running experiments.

Estimation of geomechanical parameters of tunnel route using geostatistical methods

  • Aalianvari, Ali;Soltani-Mohammadi, Saeed;Rahemi, Zeynab
    • Geomechanics and Engineering
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    • v.14 no.5
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    • pp.453-458
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    • 2018
  • Geomechanical parameters are important factors for engineering projects during design, construction and support stages of tunnel and dam projects. Geostatistical estimation methods are known as one of the most significant approach at estimation of Geomechanical parameters. In this study, Azad dam headrace tunnel is chosen to estimate Geomechanical parameters such as Rock Quality Designation (RQD) and uniaxial compressive strength (UCS) by ordinary kriging as a geostatistical method. Also Rock Mass Rating (RMR) distribution is presented along the tunnel. Main aim in employment of geostatistical methods is estimation of points that unsampled by sampled points.To estimation of parameters, initially data are transformed to Gaussian distribution, next structural data analysis is completed, and then ordinary kriging is applied. At end, specified distribution maps for each parameter are presented. Results from the geostatistical estimation method and actual data have been compared. Results show that, the estimated parameters with this method are very close to the actual parameters. Regarding to the reduction of costs and time consuming, this method can use to geomechanical estimation.

Kalman Filter Estimation of the Servo Valve Effective Orifice Area for a Auxiliary Power Unit (보조 동력장치용 서보밸브 유효 오리피스 면적의 칼만필터 추정)

  • Zhang, J.F.;Kim, C.T.;Jeong, H.S.
    • Transactions of The Korea Fluid Power Systems Society
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    • v.4 no.4
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    • pp.1-7
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    • 2007
  • Flow rate is one of the important variables for precise motion control and detection of the faults and fluid loss in many hydraulic components and systems. But in many cases, it is not easy to measure it directly. The orifice area of a servo valve by which the fluid flows is one of key factors to monitor the flow rate. In this paper, we have constructed an estimation algorithm for the effective orifice area by using the model of a servo valve cylinder control system and Kalman filter algorithm. Without geometry information about the servo valve, it is shown that the effective orifice area can be estimated by using only displacement and pressure data corrupted with noise. And the effect of the biased sensor data and system parameter errors on the estimation results are discussed. The paper reveals that sensor calibration is important in accurate estimation and plausible parameter data such as oil bulk modulus and actuator volume are acceptable for the estimation without any error. The estimation algorithm can be used as an useful tool for detecting leakage, monitoring malfunction and/or degradation of the system performance.

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