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Proceedings Papers

A New Framework for Efficient Sequential Sampling-Based RBDO Using Space Mapping

Jeong Woo Park, Ikjin Lee

Proc. ASME. IDETC-CIE2022, Volume 3B: 48th Design Automation Conference (DAC), V03BT03A003, August 14–17, 2022

Paper No: DETC2022-89776

DOI: https://doi.org/10.1115/DETC2022-89776

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Topics: Accuracy and precision, Event history analysis, Failure, Metamodeling, Probability, Reliability-based optimization, Sampling methods, Sensitivity analysis, Simulation

Journal Articles

A New Framework for Efficient Sequential Sampling-Based RBDO Using Space Mapping

Jeong Woo Park, Ikjin Lee

J. Mech. Des. March 2023, 145(3): 031702.

Paper No: MD-22-1354

DOI: https://doi.org/10.1115/1.4055547

Published Online: October 31, 2022

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Topics: Design, Optimization, Reliability, Reliability-based optimization, Probability, Failure

Journal Articles

Deep Generative Tread Pattern Design Framework for Efficient Conceptual Design

Mingyu Lee, Youngseo Park, Hwisang Jo, Kibum Kim, Seungkyu Lee, Ikjin Lee

J. Mech. Des. July 2022, 144(7): 071703.

Paper No: MD-21-1462

DOI: https://doi.org/10.1115/1.4053469

Published Online: February 15, 2022

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Topics: Design, Performance evaluation, Tires, Conceptual design, Optimization, Generators

Proceedings Papers

Shared Autonomous Vehicle System Design for Battery Electric Vehicle (BEV) and Fuel Cell Electric Vehicle (FCEV)

Ungki Lee, Sunghyun Jeon, Ikjin Lee

Proc. ASME. IDETC-CIE2021, Volume 3B: 47th Design Automation Conference (DAC), V03BT03A029, August 17–19, 2021

Paper No: DETC2021-67734

DOI: https://doi.org/10.1115/DETC2021-67734

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Topics: Autonomous vehicles, Batteries, Design, Electric vehicles, Fuel cell vehicles, Optimization, Proton exchange membrane fuel cells

Journal Articles

Confidence-Based Design Optimization for a More Conservative Optimum Under Surrogate Model Uncertainty Caused by Gaussian Process

Yongsu Jung, Kyeonghwan Kang, Hyunkyoo Cho, Ikjin Lee

J. Mech. Des. September 2021, 143(9): 091701.

Paper No: MD-20-1678

DOI: https://doi.org/10.1115/1.4049883

Published Online: February 11, 2021

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Topics: Design, Optimization, Reliability, Uncertainty, Modeling

Proceedings Papers

Sampling-Based Reliability Analysis Using Deep Feedforward Neural Network (DFNN)

Ungki Lee, Ikjin Lee

Proc. ASME. IDETC-CIE2020, Volume 5: 24th Reliability, Stress Analysis, and Failure Prevention Conference (RSAFP), V005T05A006, August 17–19, 2020

Paper No: DETC2020-22275

DOI: https://doi.org/10.1115/DETC2020-22275

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Topics: Artificial neural networks, Event history analysis, Feedforward control, Design, Trains, Dimensions, Shapes, Analytical methods, Density, Polynomials

Proceedings Papers

Confidence-Based Design Optimization Under Data Uncertainty Using Most Probable Point-Based Approach

Yongsu Jung, Hyunkyoo Cho, Ikjin Lee

Proc. ASME. IDETC-CIE2019, Volume 2B: 45th Design Automation Conference, V02BT03A042, August 18–21, 2019

Paper No: DETC2019-97718

DOI: https://doi.org/10.1115/DETC2019-97718

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Topics: Design, Optimization, Reliability-based optimization, Uncertainty

Proceedings Papers

Determination of the Number of Input Data Through Bi-Objective Confidence-Based Design Optimization

Yongsu Jung, Hyunkyoo Cho, Zunyi Duan, Ikjin Lee

Proc. ASME. IDETC-CIE2019, Volume 2B: 45th Design Automation Conference, V02BT03A043, August 18–21, 2019

Paper No: DETC2019-97721

DOI: https://doi.org/10.1115/DETC2019-97721

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Topics: Design, Optimization, Reliability-based optimization, Uncertainty

Journal Articles

Deep Generative Design: Integration of Topology Optimization and Generative Models

Sangeun Oh, Yongsu Jung, Seongsin Kim, Ikjin Lee, Namwoo Kang

J. Mech. Des. November 2019, 141(11): 111405.

Paper No: MD-19-1151

DOI: https://doi.org/10.1115/1.4044229

Published Online: September 16, 2019

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Topics: Design, Optimization, Topology

Proceedings Papers

Design Automation by Integrating Generative Adversarial Networks and Topology Optimization

Sangeun Oh, Yongsu Jung, Ikjin Lee, Namwoo Kang

Proc. ASME. IDETC-CIE2018, Volume 2A: 44th Design Automation Conference, V02AT03A008, August 26–29, 2018

Paper No: DETC2018-85506

DOI: https://doi.org/10.1115/DETC2018-85506

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Topics: Design automation, Optimization, Topology, Design, Automobiles, Machinery, Product design, Wheels

Proceedings Papers

Most Probable Point-Based Approximated Dimension Reduction Method in Reliability-Based Design Optimization

Yongsu Jung, Hyunkyoo Cho, Ikjin Lee

Proc. ASME. IDETC-CIE2018, Volume 2B: 44th Design Automation Conference, V02BT03A062, August 26–29, 2018

Paper No: DETC2018-86382

DOI: https://doi.org/10.1115/DETC2018-86382

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Topics: Dimensions, Reliability-based optimization, Approximation, Event history analysis, Finite element analysis, Reliability, Design, Failure, Probability

Journal Articles

A Study on Computational Efficiency Improvement of Novel SORM Using the Convolution Integration

Jeong Woo Park, Ikjin Lee

J. Mech. Des. February 2018, 140(2): 024501.

Paper No: MD-17-1293

DOI: https://doi.org/10.1115/1.4038563

Published Online: December 11, 2017

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Topics: Failure, Probability, Reliability, Errors, Dimensions

Proceedings Papers

Reliability-Based Design Optimization (RBDO) for Electric Vehicle Market Systems

Ungki Lee, Namwoo Kang, Ikjin Lee

Proc. ASME. IDETC-CIE2017, Volume 2A: 43rd Design Automation Conference, V02AT03A038, August 6–9, 2017

Paper No: DETC2017-68045

DOI: https://doi.org/10.1115/DETC2017-68045

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Topics: Electric vehicles, Reliability-based optimization, Design, Uncertainty, Batteries, Battery life, Engineering systems and industry applications, Failure, Preferences, Product design

Proceedings Papers

Design Sensitivity Method for Sampling-Based RBDO With Fixed COV

Hyunkyoo Cho, K. K. Choi, Ikjin Lee, David Lamb

Proc. ASME. IDETC-CIE2015, Volume 2B: 41st Design Automation Conference, V02BT03A059, August 2–5, 2015

Paper No: DETC2015-47359

DOI: https://doi.org/10.1115/DETC2015-47359

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Topics: Design, Failure, Probability, Event history analysis, Optimization, Reliability-based optimization, Simulation

Journal Articles

Design Sensitivity Method for Sampling-Based RBDO With Varying Standard Deviation

Hyunkyoo Cho, K. K. Choi, Ikjin Lee, David Lamb

J. Mech. Des. January 2016, 138(1): 011405.

Paper No: MD-15-1333

DOI: https://doi.org/10.1115/1.4031829

Published Online: November 16, 2015

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Topics: Design, Failure, Probability, Optimization

Proceedings Papers

Inverse Reliability Analysis for Approximated Second-Order Reliability Method Using Hessian Update

Jongmin Lim, Byungchai Lee, Ikjin Lee

Proc. ASME. IDETC-CIE2014, Volume 2B: 40th Design Automation Conference, V02BT03A050, August 17–20, 2014

Paper No: DETC2014-34581

DOI: https://doi.org/10.1115/DETC2014-34581

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Topics: Event history analysis, Reliability, Failure, Probability, Approximation, Computation

Journal Articles

Reliability-Based Vehicle Safety Assessment and Design Optimization of Roadway Radius and Speed Limit in Windy Environments

Jaekwan Shin, Ikjin Lee

J. Mech. Des. August 2014, 136(8): 081006.

Paper No: MD-13-1361

DOI: https://doi.org/10.1115/1.4027512

Published Online: June 2, 2014

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Topics: Design, Optimization, Roads, Vehicles, Wind velocity, Reliability, Safety, Wind

Proceedings Papers

First-Order Reliability Analysis of Vehicle Safety in Highway Horizontal Curves

Jaekwan Shin, Ikjin Lee

Proc. ASME. IDETC-CIE2013, Volume 3B: 39th Design Automation Conference, V03BT03A054, August 4–7, 2013

Paper No: DETC2013-13139

DOI: https://doi.org/10.1115/DETC2013-13139

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Topics: Event history analysis, Highways, Reliability, Safety, Vehicles

Proceedings Papers

Sampling-Based Approach for Design Optimization in the Presence of Interval Variables

David Yoo, Ikjin Lee

Proc. ASME. IDETC-CIE2013, Volume 3B: 39th Design Automation Conference, V03BT03A055, August 4–7, 2013

Paper No: DETC2013-13151

DOI: https://doi.org/10.1115/DETC2013-13151

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Topics: Design, Optimization, Simulation, Failure, Probability, Approximation, Event history analysis

Proceedings Papers

Sampling-Based RBDO Using Probabilistic Sensitivity Analysis and Virtual Support Vector Machine

Hyeongjin Song, K. K. Choi, Ikjin Lee, Liang Zhao, David Lamb

Proc. ASME. IDETC-CIE2012, Volume 3: 38th Design Automation Conference, Parts A and B, 1213-1225, August 12–15, 2012

Paper No: DETC2012-70715

DOI: https://doi.org/10.1115/DETC2012-70715

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Topics: Sensitivity analysis, Support vector machines

Journal citation

NARROW

A New Framework for Efficient Sequential Sampling-Based RBDO Using Space Mapping

A New Framework for Efficient Sequential Sampling-Based RBDO Using Space Mapping

Deep Generative Tread Pattern Design Framework for Efficient Conceptual Design

Shared Autonomous Vehicle System Design for Battery Electric Vehicle (BEV) and Fuel Cell Electric Vehicle (FCEV)

Confidence-Based Design Optimization for a More Conservative Optimum Under Surrogate Model Uncertainty Caused by Gaussian Process

Sampling-Based Reliability Analysis Using Deep Feedforward Neural Network (DFNN)

Confidence-Based Design Optimization Under Data Uncertainty Using Most Probable Point-Based Approach

Determination of the Number of Input Data Through Bi-Objective Confidence-Based Design Optimization

Deep Generative Design: Integration of Topology Optimization and Generative Models

Design Automation by Integrating Generative Adversarial Networks and Topology Optimization

Most Probable Point-Based Approximated Dimension Reduction Method in Reliability-Based Design Optimization

A Study on Computational Efficiency Improvement of Novel SORM Using the Convolution Integration

Reliability-Based Design Optimization (RBDO) for Electric Vehicle Market Systems

Design Sensitivity Method for Sampling-Based RBDO With Fixed COV

Design Sensitivity Method for Sampling-Based RBDO With Varying Standard Deviation

Inverse Reliability Analysis for Approximated Second-Order Reliability Method Using Hessian Update

Reliability-Based Vehicle Safety Assessment and Design Optimization of Roadway Radius and Speed Limit in Windy Environments

First-Order Reliability Analysis of Vehicle Safety in Highway Horizontal Curves

Sampling-Based Approach for Design Optimization in the Presence of Interval Variables

Sampling-Based RBDO Using Probabilistic Sensitivity Analysis and Virtual Support Vector Machine

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