• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.54-67
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• 2003

In this paper, a novel estimation technique for a robust adaptive control scheme is presented for a class of uncertain nonlinear systems with a general set of uncertainty. For a class of introduced more extended semi-strict feedback forms which generalize the systems studied in recent years, a novel estimation technique is proposed to estimate the states of the fully nonlinear unmodeled dynamics without stringent conditions. With the introduction of powerful functions, the estimation error can be tuned to a desired small region around the origin via the estimator parameters. In addition, with some effective functions, a modified adaptive backstepping for dynamic uncertainties is presented to drive the output to an arbitrarily small region around the origin by an appropriate choice of the design parameters. With our proposed schemes, we can remove or relax the assumptions of the existing results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.491-497
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• 2018

Most of the applications for distribution system operation highly rely on the voltage and current managements from the field devices. Voltage from the remote controlled switch contains unacceptably large measurement error due to the nonlinear characteristics of the bushing potential transformer. This paper proposes a new voltage magnitude estimation method by calculating voltage drop using current measurement, line impedance and loads deployment data. Contract demand power and pole transformer capacity managed by NDIS are used as a key element to improve accuracy of the proposed method. Various case studies using Matlab simulation have been performed to verify feasibility of the propose voltage estimation method.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.7
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• pp.640-645
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• 2013

This paper presents a step length estimation algorithm for Pedestrian Dead Reckoning using linear calibrated ZUPT (zero velocity update) with a foot mounted IMU. The IMU consists of 3 axis accelerometer, gyro and magnetometer. Attitude of IMU is estimated using an inertial navigation algorithm. To increase accuracy of step length estimation algorithm, we propose a stance detection algorithm and an enhanced ZUPT. The enhanced ZUPT calculates firefighter's step length considering velocity error caused by sensor bias during one step. This algorithm also works efficiently at various motions, such as crawling, sideways and stair stepping. Through experiments, the step length estimation performance of the proposed algorithm is verified.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.75-86
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• 1999

An air-data estimator for generic air-breathing hypersonic vehicles (AHSVs) is developed and demonstrated with an example vehicle configuration. The AHSV air-data estimation strategy emphasized improvement of the angle of attack estimate accuracy to a degree necessitated by the stringent operational requirements of the air-breathing propulsion. the resulting estimation problem involves highly nonlinear diffusion process (propagation); consequently, significant distortion of a posteriori conditional density is suspected. A simulation based statistical analysis tool is developed to characterize the nonlinear diffusion process. The statistical analysis results indicate that the diffusion process preserves the symmetry and unimodality of initial probability density shape state variables, and provide the basis for applicability of an Extended Kalman Filter (EKF). An EKF is designed for the AHSV air-data system and the air data estimation capabilities are demonstrated.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.891-901
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• 1997

The objective of this research is to investigate the problem of goodness of fit testing based on nonparametric density estimation with a data-driven smoothing parameter. The small and large smaple properties of the proposed test statistic $Z_{mn}$ are investigated with the minimizer $\widehat{m}$ of the estimated mean integrated squared error by the Diggle and Hall (1986) method.

• Structural Engineering and Mechanics
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• v.82 no.3
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• pp.271-282
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• 2022

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2897-2904
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• 2023

The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method is a widely used method in human reliability analysis (HRA). Performance shaping factors (PSFs) refer to the factors that may influence human performance and are used to adjust nominal human error probabilities (HEPs) in SPAR-H. However, the PSFs are assumed to be independent, which is unrealistic and can lead to unreasonable estimation of HEPs. In this paper, a new method is proposed to handle the dependencies among PSFs in SPAR-H to obtain more reasonable results. Firstly, the dependencies among PSFs are analyzed by using decision-making trial and evaluation laboratory (DEMATEL) method. Then, PSFs are assigned different weights according to their dependent relationships. Finally, multipliers of PSFs are modified based on the relative weights of PSFs. A case study is illustrated that the proposed method is effective in handling the dependent PSFs in SPAR-H, where the duplicate calculations of the dependent part can be reduced. The proposed method can deal with a more general situation that PSFs are dependent, and can provide more reasonable results.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2006.05a
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• pp.103-106
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• 2006

This study proposes a method of generating steel house shop drawing in an automated design method, reducing construction manpower and period. With one hour fire-resistant approval code, reflecting work ability and efficiency, steel-framed house market is expected to extend from one or two story house to multi-purpose facilities up to four story height. More models have been constructed in this system than the first appearance of fire-resistant approval in Korea in 1997. Also, cost estimation of components such as frame walls, roof trusses and floors is obtained with shop drawings. Also, the lack of suppliers of steel framed house shop drawing and unstandardized drawing method get constructors have difficulty in understanding its design. In steel framed house industry, shop drawings are essential part in building and constructing framework and they have major effects on construction deadlines and expenses. By exploring method of shop drawing automation, this study aims to optimize work flow with a standardized drawing method. The proposed system can be applied to manufacturing automation in domestic industry of factory-built panelizing method in the near future.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.109-118
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• 2008

We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.43-50
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• 2007

Reheating furnace is an essential facility of a rod mill plant where a billet is heated to the required rolling temperature so that it can be milled to produce wire. Although it is very important to obtain information on billet temperatures, it is not feasible during furnace operation. Consequently, a billet temperature profile should be estimated. Moreover, this estimation should be done within an appropriate time interval for an on-line application. In this paper, a billet heat transfer model based on 2D FEM(Finite Element Method) with spatially distributed emission factors is proposed for an on-line billet temperature estimation and also a measurement is carried out for two extremely different furnace operation patterns. Finally, the difference between the model outputs and the measurements is minimized by using a new optimization algorithm named uDEAS(Univariate Dynamic Encoding Algorithm for Searches) with multi-step tuning strategy. The obtained emission factors are applied to a simulation for the data which are not used in the model tuning for validation.