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이익진 교수

Lee, Ikjin

KAIST 기계공학과 · 의사결정과학

연구실 소개

이 교수의 연구실은 신뢰성 기반 최적화(RBDO) 및 비정상적 불확실성 하에서의 설계 최적화를 핵심으로 하며, 특히 첫째도 신뢰도 분석의 정확도를 향상시키기 위한 고차수 신뢰도 방법(SORM) 개발과, 둘째로 설계 변수에 대한 민감도 분석 및 최적화 후처리 기법을 통해 기존의 근사 기반 방법의 오차를 보정하는 데 초점을 맞추고 있습니다. 차용된 차량 역학 모델과 함께 Monte Carlo 기반의 정확한 대체모델을 활용한 신뢰도 및 민감도 분석 기법도 개발 중이며, 실제 도로 설계 및 차량 안정성 분석에 적용 가능한 실용적 응용을 추구하고 있습니다.

신뢰성 기반 최적화SORM민감도 분석차량 역학대체모델

연구 현황

논문 수
193
총 인용 수
4,897
최근 5년 논문
54
주요 분야
의사결정과학

연구 성과 추이

표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.

5개년 연도별 논문 게재 수
54총합
2022
2023
2024
2025
2026
5개년 연도별 피인용 수
742총합
20222023202420252026

주요 논문

15
1
논문|인용수 193·2007
Dimension reduction method for reliability-based robust design optimization
Ikjin Lee, Kyung K. Choi, Liu Du, David Gorsich
SJR Q1Computers & Structures
Statistics, Probability and UncertaintyDecision Sciences
2
논문|인용수 183·2008
Inverse analysis method using MPP-based dimension reduction for reliability-based design optimization of nonlinear and multi-dimensional systems
Ikjin Lee, Kyung K. Choi, Liu Du, David Gorsich
SJR Q1Computer Methods in Applied Mechanics and Engineering
Statistics, Probability and UncertaintyDecision Sciences
3
논문|인용수 159·2011
Sampling-based RBDO using the stochastic sensitivity analysis and Dynamic Kriging method
Ikjin Lee, Kyung K. Choi, Liang Zhao
SJR Q1Structural and Multidisciplinary Optimization
Statistics, Probability and UncertaintyDecision Sciences
4
논문|인용수 88·2012
A Novel Second-Order Reliability Method (SORM) Using Noncentral or Generalized Chi-Squared Distributions
Ikjin Lee, Yoojeong Noh, David Yoo
SJR Q1Journal of Mechanical Design

This paper proposes a novel second-order reliability method (SORM) using noncentral or general chi-squared distribution to improve the accuracy of reliability analysis in existing SORM. Conventional SORM contains three types of errors: (1) error due to approximating a general nonlinear limit state function by a quadratic function at most probable point in standard normal U-space, (2) error due to approximating the quadratic function in U-space by a parabolic surface, and (3) error due to calcula

Statistics, Probability and UncertaintyDecision Sciences
5
논문|인용수 85·2019
Bio-inspired bimaterial composites patterned using three-dimensional printing
Kwonhwan Ko, Suyeong Jin, Sang Eon Lee, Ikjin Lee, Jung‐Wuk Hong
SJR Q1Composites Part B Engineering
BiomaterialsMaterials Science
6
논문|인용수 80·2022
A survey of machine learning techniques in structural and multidisciplinary optimization
Palaniappan Ramu, Pugazhenthi Thananjayan, Erdem Acar, Gamze Bayrak, Jeong‐Woo Park, Ikjin Lee
SJR Q1Structural and Multidisciplinary Optimization
Statistics, Probability and UncertaintyDecision Sciences
7
논문|인용수 79·2011
Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems With Correlated Random Variables
Ikjin Lee, Kyung K. Choi, Yoojeong Noh, Liang Zhao, David Gorsich
SJR Q1Journal of Mechanical Design

This study presents a methodology for computing stochastic sensitivities with respect to the design variables, which are the mean values of the input correlated random variables. Assuming that an accurate surrogate model is available, the proposed method calculates the component reliability, system reliability, or statistical moments and their sensitivities by applying Monte Carlo simulation to the accurate surrogate model. Since the surrogate model is used, the computational cost for the stocha

Statistics, Probability and UncertaintyDecision Sciences
8
논문|인용수 69·2020
Robust design optimization (RDO) of thermoelectric generator system using non-dominated sorting genetic algorithm II (NSGA-II)
Ungki Lee, Su-Dong Park, Ikjin Lee
SJR Q1Energy
Materials ChemistryMaterials Science
9
논문|인용수 66·2009
Sensitivity analyses of FORM‐based and DRM‐based performance measure approach (PMA) for reliability‐based design optimization (RBDO)
Ikjin Lee, Kyung K. Choi, David Gorsich
SJR Q1International Journal for Numerical Methods in Engineering

Abstract In gradient‐based design optimization, the sensitivities of the constraint with respect to the design variables are required. In reliability‐based design optimization (RBDO), the probabilistic constraint is evaluated at the most probable point (MPP), and thus the sensitivities of the probabilistic constraints at MPP are required. This paper presents the rigorous analytic derivation of the sensitivities of the probabilistic constraint at MPP for both first‐order reliability method (FORM)

Statistics, Probability and UncertaintyDecision Sciences
10
논문|인용수 59·2009
System reliability-based design optimization using the MPP-based dimension reduction method
Ikjin Lee, Kyung K. Choi, David Gorsich
SJR Q1Structural and Multidisciplinary Optimization
Statistics, Probability and UncertaintyDecision Sciences
11
논문|인용수 57·2014
Reliability analysis and reliability-based design optimization of roadway horizontal curves using a first-order reliability method
Jaekwan Shin, Ikjin Lee
SJR Q2Engineering Optimization

This article presents reliability analysis and reliability-based optimization of roadway minimum radius design based on vehicle dynamics, mainly focusing on exit ramps and interchanges. The performance functions are formulated as failure modes of vehicle rollover and sideslip. To accurately describe the failure modes, analytical models for rollover and sideslip are derived considering nonlinear characteristics of vehicle behaviour using the commercial software TruckSim. The probability of an acc

Statistics, Probability and UncertaintyDecision Sciences
12
논문|인용수 50·2015
Post optimization for accurate and efficient reliability‐based design optimization using second‐order reliability method based on importance sampling and its stochastic sensitivity analysis
Jongmin Lim, Byung-Chai Lee, Ikjin Lee
SJR Q1International Journal for Numerical Methods in Engineering

Summary In this study, a post optimization technique for a correction of inaccurate optimum obtained using first‐order reliability method (FORM) is proposed for accurate reliability‐based design optimization (RBDO). In the proposed method, RBDO using FORM is first performed, and then the proposed second‐order reliability method (SORM) is performed at the optimum obtained using FORM for more accurate reliability assessment and its sensitivity analysis. In the proposed SORM, the Hessian of a perfo

Statistics, Probability and UncertaintyDecision Sciences
13
논문|인용수 49·2011
Reliability-based design optimization with confidence level under input model uncertainty due to limited test data
Yoojeong Noh, Kyung K. Choi, Ikjin Lee, David Gorsich, David Lamb
SJR Q1Structural and Multidisciplinary Optimization
Statistics, Probability and UncertaintyDecision Sciences
14
논문|인용수 44·2019
Modified screening-based Kriging method with cross validation and application to engineering design
Kyeonghwan Kang, Caiyan Qin, Bong Jae Lee, Ikjin Lee
SJR Q1Applied Mathematical Modelling
Computational Theory and MathematicsComputer Science
15
논문|인용수 40·2022
Expected system improvement (ESI): A new learning function for system reliability analysis
Seonghyeok Yang, Hwisang Jo, Kyung‐Eun Lee, Ikjin Lee
SJR Q1Reliability Engineering & System Safety
Statistics, Probability and UncertaintyDecision Sciences

대표 연구 분야

Statistics, Probability and UncertaintyComputational Theory and MathematicsCivil and Structural EngineeringAutomotive EngineeringMechanical EngineeringRenewable Energy, Sustainability and the Environment

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