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Welcome to
Smart Design Lab

Mechanical Systems Engineering / Sookmyung Women’s University

People

    Smart Design Lab의
    대학원생, 학부연구생, 석사/박사후연구원을 모집합니다!


    - 주요 연구 분야: 인공지능(딥러닝), 최적설계, 스마트카
    - 전공 무관 (기계, 컴퓨터, 전자, 산업공학 등)
    - 참고: 대학원생 등록금 전액 지원 및 생활비 지원
    - 문의: nwkang@sm.ac.kr


Principal Investigator

Namwoo Kang (강남우)

Academic Work & Industrial Experiences

Education

Memberships

Download CV as PDF format


Researchers

Sangeun Oh (오상은)

    Researcher, Mechanical Systems Engineering, Sookmyung Women’s University
    M.S. Industrial and Systems Engineering, University of Florida
    B.S. Economics, Kyung Hee University
    Research Interests: Deep Learning

Soyoung Yoo (유소영)

    Researcher, Mechanical Systems Engineering, Sookmyung Women’s University
    B.S. Electronic Engineering, Korea Polytechnic University
    Research Interests: Deep Learning, Smart car

Publications

Journal Papers under Review

    [3] Kang, N., Ren, Y., Feinberg, F. M., and Papalambros, P. Y. “Form + Function: Optimizing Aesthetic Product Design via Adaptive, Geometrized Preference Elicitation” (PDF)

    [2] Kang, N., Feinberg, F. M., and Papalambros, P. Y. “Designing Profitable Joint Product-Service Channels” (PDF)

    [1] Lee, U., Kang, N.*, and Lee, I. “Reliability-based Design for Market Systems (RBDMS): Case Study on Electric Vehicle Design” (*corresponding author) (PDF)

Journal Papers

    [11] Kang, N., Bayrak, A., and Papalambros, P. Y. (2018) "Robustness and Real Options for Vehicle Design and Investment Decisions under Gas Price and Regulatory Uncertainties”, Journal of Mechanical Design (accepted) (PDF)

    [10] Jung, Y., Kang, N., and Lee I. (2018) “Modified Augmented Lagrangian Coordination and Alternating Direction Method of Multipliers with Parallelization in Non-hierarchical Analytical Target Cascading", Structural and Multidisciplinary Optimization (in press) (PDF)

    [9] Kang, N., Burnap, A., Kim, K. H., Reed, M. P., and Papalambros, P. Y. (2017) “Influence of Automobile Seat Form and Comfort Rating on Willingness to Pay”, International Journal of Vehicle Design, 75(1/2/3/4), pp.75-90 (PDF)

    [8] Kang, N., Feinberg, F. M., and Papalambros, P. Y. (2017) “Autonomous Electric Vehicle Sharing System Design”, Journal of Mechanical Design, 139(1), 011402. (PDF)

    [7] Bayrak, A., Kang, N.*, and Papalambros, P. Y. (2016) “Decomposition Based Design Optimization of Hybrid Electric Powertrain Architectures: Simultaneous Configuration and Sizing Design”, Journal of Mechanical Design, 138(7), 071405 (*corresponding author) (PDF)

    [6] Kang, N., Ren, Y., Feinberg, F. M., and Papalambros, P. Y. (2016) “Public Investment and Electric Vehicle Design: A Model-based Market Analysis Framework with Application to a USA-China Comparison Study”, Design Science, Vol. 2, e6, doi:10.1017/dsj.2016.7. (PDF)

    [5] D’Souza, K., Bayrak, A. E., Kang, N., Wang, H., Altin, B., Barton, K., Hu, J., Papalambros, P. Y., Epureanu, B. I., and Gerth, R. (2016) “An Integrated Design Approach for Evaluating the Effectiveness and Cost of a Fleet”, Journal of Defense Modeling and Simulation, 13(4), pp. 381-397. (PDF)

    [4] Kang, N., Feinberg, F. M., and Papalambros, P. Y. (2015) “Integrated Decision Making in Electric Vehicle and Charging Station Location Network Design”, Journal of Mechanical Design, 137(6), 061402. (PDF)

    [3] Kang, N., Kokkolaras, M., Papalambros, P. Y., Park, J., Na, W., Yoo, S., and Featherman, D. (2014) “Optimal Design of Commercial Vehicle Systems Using Analytical Target Cascading”, Structural and Multidisciplinary Optimization, 50(6), pp. 1103-1114. (PDF)

    [2] Kang, N., Kokkolaras, M., and Papalambros, P. Y. (2014) “Solving Multiobjective Optimization Problem Using Quasi-separable MDO Formulations and Analytical Target Cascading”, Structural and Multidisciplinary Optimization, 50(5), pp. 849-859. (PDF)

    [1] Kang, N., Kim, J. and Park, Y. (2007) “Integration of marketing domain and R&D domain in NPD design process”, Industrial Management & Data Systems, 107(6), pp. 780-801. (PDF)

Conference Proceedings (International)

    [11] Oh, S., Jung, Y., Lee, I., and Kang, N.* (2018) “Design Automation by Integrating Generative Adversarial Networks and Topology Optimization”, Proceedings of the ASME 2018 International Design & Engineering Technical Conferences, Quebec City, Quebec, Canada, Aug 26-Aug 29, DETC2018-85506 (*corresponding author) (PDF)

    [10] Lee, U., Kang, N.*, and Lee, I. (2017) “Reliability-based Design Optimization (RBDO) for Electric Vehicle Market Systems”, Proceedings of the ASME 2017 International Design & Engineering Technical Conferences, Charlotte, Aug 6-Aug 9, DETC2017-68045 (*corresponding author) (PDF)

    [9] Jung, Y., Kang, N., and Lee I. (2017) “Convergence Strategy for Parallel Solving of Analytical Target Cascading with Augmented Lagrangian Coordination”, Proceedings to the 12th World Congress on Structural and Multidisciplinary Optimization, Braunschweig, Germany, June 5-June 9.(PDF)

    [8] Kang, N., Bayrak, A., and Papalambros, P. Y. (2016) “A Real Options Approach to Hybrid Electric Vehicle Architecture Design for Flexibility”, Proceedings of the ASME 2016 International Design & Engineering Technical Conferences. (PDF)

    [7] Kang, N., Feinberg, F. M., and Papalambros, P. Y. (2015) “Autonomous Electric Vehicle Sharing System Design”, Proceedings of the ASME 2015 International Design & Engineering Technical Conferences (Dow Distinguished Award for Interdisciplinary Sustainability). (PDF)

    [6] Bayrak, A., Kang, N.*, and Papalambros, P. Y. (2015) “Decomposition Based Design Optimization of Hybrid Electric Powertrain Architectures: Simultaneous Configuration and Sizing Design”, Proceedings of the ASME 2015 International Design & Engineering Technical Conferences (*corresponding author). (PDF)

    [5] Kang, N., Emmanoulopoulos, M., Ren, Y., Feinberg, F. M., and Papalambros, P. Y. (2015) “A Framework for Quantitative Analysis of Government Policy Influence on Electric Vehicle Market”, Proceedings of the 20th International Conference on Engineering Design. (PDF)

    [4] Kang, N., Feinberg, F. M., and Papalambros, P. Y. (2014) “Integrated Decision Making in Electric Vehicle and Charging Station Location Network Design”, Proceedings of the ASME 2014 International Design & Engineering Technical Conferences. (PDF)

    [3] Kang, N., Feinberg, F. M., and Papalambros, P. Y. (2013) “A Framework for Enterprise-driven Product Service Systems Design”, Proceedings of the 19th International Conference on Engineering Design. (PDF)

    [2] Kang, N., Kokkolaras, M., and Papalambros, P. Y. (2013) “Solving Multiobjective Optimization Problem Using Quasi-separable MDO Formulations and Analytical Target Cascading”, Proceedings of the 10th World Congress on Structural and Multidisciplinary Optimization. (PDF)

    [1] Kang, N., Kokkolaras, M., Papalambros, P. Y., Park, J., Na, W., Yoo, S., and Featherman, D. (2012) “Optimal Design of Commercial Vehicle Systems Using Analytical Target Cascading”, Proceedings of the 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference .(PDF)

Conference Proceedings (Korean)

    [5] Lee, U., Kang, N., and Lee, I. (2018) “Improving the Accuracy of Consumer Preference Estimation Using Economic Simulation Model”, 한국전산역학회

    [4] Lee, U., Kang, N.*, and Lee, I. (2017) “전기차 시장을 고려한 신뢰성 기반 최적 설계”, 대한기계학회, pp. 89-90 (*corresponding author)

    [3] Jung, Y., Kang, N., and Lee, I. (2017) “Augmented Lagrangian Coordination을 이용한 Analytical Target Cascading 에서의 Parallelization 도입 및 수렴전략 개발”, 대한기계학회, pp. 188-189.

    [2] Kim, J., Kang, N., and Park, Y. (2009) “컨조인트와 트리즈의 통합에 관한 연구”, 기술경영경제학회, pp. 627-647. (PDF)

    [1] Kang, N., Kim, J., and Park, Y. (2006) “신제품 개발 프로세스에서 마케팅 영역과 제조 영역의 통합적 설계 : Conjoint 분석과 Taguchi 방법의 순차적 결합”, 한국경영과학회, Vol. 2006, No. 5, pp. 365-372. (PDF)
Google Scholar Citations

Reserch

[1] Design by Machine Learning (Deep Learning)



(i) Design Automation by Deep Learning
Recent advances in deep learning enable machines to learn existing designs by themselves and to create new designs. Generative adversarial networks (GANs) are widely used to generate new images and data by unsupervised learning. This research proposes a design automation process by combining GANs and topology optimization. The suggested process has been applied to the wheel design of automobiles and has shown that an aesthetically superior and technically meaningful design can be automatically generated without human interventions.

(ii) Eliciting Styling Preference on Vehicle Design by Machine Learning
Although eliciting consumer preference is a core part in design, capturing preferences on form (styling) is still a challenge. In particular, we have been examining the trade-off between forms and functions in a vehicle. We elicit consumers’ preferences on product design using state-of-the-art Machine Learning and parameterized 3D vehicle shapes. This research estimates willingness-to-pay for specific design elements.

(iii) Forecasting Demand of Hybrid Trucks by Hierarchical Bayesian
The market for delivery services has grown substantially along with the development of e-commerce. In an effort to reduce pollution caused by delivery trucks due to low fuel efficiency and high emissions, the Korean government is considering converting the diesel-powered delivery trucks in the current market to hybrid trucks. The objective of this research is to predict truck drivers’ demand for converting to hybrid trucks depending on converting costs and increase in fuel efficiency by using Machine Learning.

Supported by KOTI(The Korea Transport Institute), University of Michigan Transportation Research Institute(UMTRI), Rackham Graduate Student Research Grant

Related Works:
  • Design Automation by Integrating Generative Adversarial Networks and Topology Optimization, IDETC, 2018. (PDF)
  • Form + Function: Optimizing Aesthetic Product Design via Adaptive, Geometrized Preference Elicitation, Marketing Science (under review). (PDF) (Online Experiment)
  • Influence of Automobile Seat Form and Comfort Rating on Willingness to Pay, International Journal of Vehicle Design, 2017. (PDF)

[2] Autonomous Vehicle Sharing System Design



(i) Sharing Economy of Autonomous Electric Vehicles
Car sharing services promise “green” transportation systems. Two vehicle technologies offer marketable, sustainable sharing: autonomous vehicles (AVs) eliminate customer requirements for car pick-up and return, and battery electric vehicles entail zero emissions. Designing an autonomous electric vehicle (AEV) fleet must account for (1) the relationships among fleet operations, (2) charging station (CS) operations, (3) electric powertrain performance, and (4) consumer demand. This research proposes a system design optimization framework integrating these four subsystem problems. This framework is used to examine AEV sharing system profitability and feasibility for a variety of market scenarios. The results provide practical insights for service system decision makers.

(ii) Autonomous Taxi Service Design
When it comes to autonomous vehicles, most manufactures focus on the technological side and not on the design of how to provide the service. When traditional taxis are replaced by autonomous ones, there will be substantial behavioral changes by customers. The service providers will only succeed if they develop an optimal UI/UX design that adjust to these changes. This research develops a guideline of autonomous taxi service from the customers’ perspective, and further provides design requirements of autonomous vehicle development for manufacturers.

Supported by CRC(Convergence Research Center), Kakao Mobility, NRF (National Research Foundation of Korea), RED&B(KAIST), Dow Distinguished Award for Interdisciplinary Sustainability, MCube Grant

Related Works:
  • Autonomous Electric Vehicle Sharing System Design, Journal of Mechanical Design, 2017. (PDF)

[3] Electric Vehicle Design for Market Systems



We have been working on electric vehicle (EV) design problems by integrating electric powertrain simulation, charging station location network, consumer demand, and public policy models, into a large decision making framework. This research compares the public investment impacts on EV markets in China and US. In addition, We are exploring how to make design decisions today by taking future long-term design evolution into account under engineering and marketing uncertainties. We adopt real option theory used in Financial Engineering and RBDO (Reliability-Based Design Optimization) to hedge design risks and create smooth design transitions.

Supported by Toyota Motor Corporation, Automotive Research Center (US Army), Graham Doctoral Fellowship

Related Works:
  • Reliability-based Design Optimization (RBDO) for Electric Vehicle Market Systems, IDETC, 2017. (PDF)
  • A Real Options Approach to Hybrid Electric Vehicle Architecture Design for Flexibility, Journal of Mechanical Design, 2018. (PDF)
  • Public Investment and Electric Vehicle Design: A Model-based Market Analysis Framework with Application to a USA-China Comparison Study, Design Science, 2016. (PDF)
  • An Integrated Design Approach for Evaluating the Effectiveness and Cost of a Fleet, Journal of Defense Modeling and Simulation, 2016. (PDF)
  • Designing Profitable Joint Service-Product Channels, Journal of Services Marketing (under review). (PDF)
  • Integrated Decision Making in Electric Vehicle and Charging Station Location Network Design, Journal of Mechanical Design, 2015. (PDF)
  • A Framework for Enterprise-driven Product Service Systems Design, ICED, 2013. (PDF)
  • Integrated Design Process of Conjoint Analysis and TRIZ, KSIME, 2009. (PDF)
  • Integration of Marketing Domain and R&D Domain in NPD Design Process, Industrial Management & Data Systems, 2007. (PDF)

[4] Multidisciplinary Design Optimization



An engineering system problem (e.g., vehicle design) may not be solvable due to the size and complexity of models. Analytical Target Cascading (ATC) method has been developed to address this issue by partitioning a system into subsystems and coordinating them to meet a system goal. We have been designing commercial vehicle systems and hybrid vehicle architectures using ATC. However, we realized that ATC with parallel solving approach often fails to achieve convergence or involves high computational cost. Therefore, we research on robust convergence strategies for complex industrial applications that should be solved in parallel.

Supported by Hyundai Motor Company, Altair University Fellowship

Related Works:
  • Modified Augmented Lagrangian Coordination and Alternating Direction Method of Multipliers with Parallelization in Non-hierarchical Analytical Target Cascading, Structural and Multidisciplinary Optimization, 2018. (PDF)
  • Decomposition Based Design Optimization of Hybrid Electric Powertrain Architectures: Simultaneous Configuration and Sizing Design, Journal of Mechanical Design, 2016. (PDF)
  • Solving Multiobjective Optimization Problem Using Quasi-separable MDO Formulations and Analytical Target Cascading, Structural and Multidisciplinary Optimization, 2014. (PDF)
  • Optimal Design of Commercial Vehicle Systems Using Analytical Target Cascading, Structural and Multidisciplinary Optimization, 2014. (PDF)

Teaching

Sookmyung Women’s University: 2018 - Present

  • Solid Mechanics (Undergraduate course)
  • Engineering Mathematics (Undergraduate course)

KAIST: 2016 - 2017

  • Advanced Multidisciplinary Capstone Design (Graduate course)
  • Multidisciplinary Capstone Design I (Undergraduate course)
  • Multidisciplinary Capstone Design II (Undergraduate course)
  • Design Thinking for Startup (Undergraduate and graduate course)
  • Startup Management Practice (Graduate course)
  • Entrepreneurship (Graduate course)

University of Michigan: 2012 - 2016

  • Design Optimization (Graduate course)
  • Analytical Product Design (Graduate course)
  • Product Design Process (Graduate course)

News

To be posted

Smart Design Lab