• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.97-102
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• 1997

A new method based on the alternating conditional expectation (ACE) algorithm is developed to calculate axial shape index (ASI) for the 3-level excore detector. The ACE algorithm, a type of non-parametric regression algorithms, yields an optimal relationship between a dependent variable and multiple independent variables. In this study, the simple correlation between ASI and excore detector signals is developed using the Younggwang nuclear power plant unit 3 (YGN-3) data without any preprocessing on the relationships between independent variables and dependent variable. The numerical results show that simple correlations exist between the three excore signals and ASI of the core. The accuracy of the new method is much better than those of the current CPC and COLSS algorithms.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1996.10a
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• pp.313-318
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• 1996

In this paper, a parametric study of the unbalance response and the stability is carried out to show the influence of seal parameters on the response of rotor. The seal parameters optimized are the seal clearance and the seal length. The minimum quantity of a Q factor in the critical speed and the maximum quantity of a logarithmic decreement in the operating speed, avoiding the reign of resonance, are the objective function. This paper describes a new approach to find a seal parameter of rotor system. The optimization method is used genetic algorithms, which are search algorithms based on the mechanics of natural selection and natural genetics. The results show the capability of this method and indicate that an optimal design of seals can improve the unbalance and the stability of rotor.

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.983-994
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• 2000

We consider the problem of determining the circle of best fit to a set of data points in the plane. In [1] and [2] several algorithms already have been given for fitting a circle in least squares sense of minimizing the geometric distances to the given data points. In this paper we present another new descent algorithm which computes a parametric represented circle in order to minimize the sum of the squares of the distances to the given points. For any choice of starting values our algorithm has the advantage of ensuring convergence to a local minimum. Numerical examples are given.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.21
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• pp.51-60
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• 1990

In this paper we deal with the miniaml cost network flow problem with uncertain arc capacity constraints. When the arc capacities are fuzzy with linear L-R type membership function, using parametric programming procedure, we reduced it to the deterministic minimal cost network flow problem which can be solved by various typical network flow algorithms. A modified Algorithm using the Out-of-kilter algorithm is developed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.475-481
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• 2019

The importance of performance-based design feedback is being emphasized when it comes to the potential impact that affects all the lifecycle of the building. However, the latency and disconnection of domain expert in the sector of AEC/FM remain current obstacles between design and performance feedback. It is hard to utilize performance feedback information for design exploration and support design decision making during the conceptual phase of design. Using parametric design, this paper proposes various design alternatives from a set of rules and constraints defined by algorithms for the geometric configurations of an Office Building. A Building Performance Analysis (BPA) was to developed using Autodesk® Revit® 2019 which integrates Autodesk® Green Building Studio® to predict its sufficient daylighting conditions of the LEED v4's Daylighting Autonomy (DA). The parametric-based performance feedback of this study outlines potential design improvements for further exploration in application to the early design process.

• Structural Engineering and Mechanics
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• v.86 no.3
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• pp.337-348
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• 2023

The optimization of reinforced concrete (RC) cantilever retaining walls is a complex problem and requires the use of advanced techniques like metaheuristic algorithms. For this purpose, an optimization model must first be developed, which involves mathematical complications, multidisciplinary knowledge, and programming skills. This task has proven to be too arduous and has halted the mainstream acceptance of optimization. Therefore, it is necessary to unravel the complications of optimization into an easily applicable form. Currently, the most commonly used method for designing retaining walls is by following the proportioning limits provided by the ACI handbook. However, these limits, derived manually, are not verified by any optimization technique. There is a need to validate or modify these limits, using optimization algorithms to consider them as optimal limits. Therefore, this study aims to propose updated proportioning limits for the economical design of a RC cantilever retaining wall through a comprehensive parametric investigation using the genetic algorithm (GA). Multiple simulations are run to examine various design parameters, and trends are drawn to determine effective ranges. The optimal limits are derived for 5 geometric and 3 reinforcement variables and validated by comparison with their predecessor, ACI's preliminary proportioning limits. The results indicate close proximity between the optimized and code-provided ranges; however, the use of optimal limits can lead to additional cost optimization. Modifications to achieve further optimization are also discussed. Besides the geometric variables, other design parameters not covered by the ACI building code, like reinforcement ratios, bar diameters, and material strengths, and their effects on cost optimization, are also discussed. The findings of this investigation can be used by experienced engineers to refine their designs, without delving into the complexities of optimization.

• The Korean Journal of Nuclear Medicine
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• v.37 no.5
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• pp.288-300
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• 2003

Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.

• Smart Structures and Systems
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• v.14 no.5
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1205-1211
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• 2008

This paper describes methods to estimate 3-D position of target with respect to reference frame through monocular image from unmanned aerial vehicle (UAV). 3-D position of target is used as information for surveillance, recognition and attack. In this paper. 3-D position of target is estimated to make guidance and control law, which can follow target, user interested. It is necessary that position of target is measured in image to solve 3-D position of target. In this paper, kalman filter is used to track and output position of target in image. Estimation of target's 3-D position is possible using result of image tracking and information of UAV and camera. To estimate this, two algorithms are used. One is methode from arithmetic derivation of dynamics between UAV, carmer, and target. The other is LPV (Linear Parametric Varying). These methods have been run on simulation, and compared in this paper.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.11 no.2
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• pp.31-39
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• 2015

Pipe wall-thinning by flow-accelerated corrosion (FAC) and various types of erosion is a significant and costly damage phenomenon in secondary piping systems of nuclear power plants (NPPs). Most NPPs have management programs to ensure pipe integrity due to wall-thinning that includes periodic measurements for pipe wall thicknesses using ultrasonic tests (UTs). Nevertheless, thinning evaluations are not easy because the amount of thickness reduction being measured is often quite small compared to the accuracy of the inspection technique. U.S. Electric Power Research Institute (EPRI) had proposed Total Point Method (TPM) as a thinning occurrence evaluation method, which is a very useful method for detecting locally thinned pipes or fittings. However, evaluation engineers have to discern manually the measurement data because there are no numerical algorithm for TPM. In this study, numerical algorithms were developed based on non-parametric and parametric statistical method.