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Proceedings Papers

Automated Sub-Feature Labeling Using Prompt-Based Pretrained Language Model

Seyoung Park, Yilan Jiang, Harrison Kim

Proc. ASME. IDETC-CIE2024, Volume 3A: 50th Design Automation Conference (DAC), V03AT03A018, August 25–28, 2024

Publisher: American Society of Mechanical Engineers

Paper No: DETC2024-142891

https://doi.org/10.1115/DETC2024-142891

View Paper titled, Automated Sub-Feature Labeling Using Prompt-Based Pretrained Language Model

Topics: Batteries, Product design

Journal Articles

Importance-Induced Customer Segmentation Using Explainable Machine Learning

Seyoung Park, Yilan Jiang, Harrison Kim

Publisher: ASME

J. Mech. Des. April 2025, 147(4): 044501.

Paper No: MD-24-1118

https://doi.org/10.1115/1.4066746

Published Online: October 24, 2024

View Article titled, Importance-Induced Customer Segmentation Using Explainable Machine Learning

Topics: Image segmentation, Construction, Machine learning

Journal Articles

An Automated Data-Driven Approach for Product Design Strategies to Respond to Market Disruption Following COVID-19

Seyoung Park, Kangcheng Lin, Junegak Joung, Harrison Kim

Publisher: ASME

J. Mech. Des. March 2025, 147(3): 031402.

Paper No: MD-24-1210

https://doi.org/10.1115/1.4066684

Published Online: October 18, 2024

View Article titled, An Automated Data-Driven Approach for Product Design Strategies to Respond to Market Disruption Following COVID-19

Topics: Batteries, Laptop computers, Pandemics, Preferences, Product design

Proceedings Papers

Investigating the Effect of a Brand Factor in Product Design Based on a Data-Driven Approach Using Online Reviews

Seyoung Park, Harrison Kim

Proc. ASME. IDETC-CIE2023, Volume 3A: 49th Design Automation Conference (DAC), V03AT03A019, August 20–23, 2023

Publisher: American Society of Mechanical Engineers

Paper No: DETC2023-114966

https://doi.org/10.1115/DETC2023-114966

View Paper titled, Investigating the Effect of a Brand Factor in Product Design Based on a Data-Driven Approach Using Online Reviews

Topics: Bills of materials, Data-driven design, Equilibrium (Physics), Product design

Journal Articles

Analysis of Brand Effects in Data-Driven Design Based on Online Reviews

Seyoung Park, Harrison Kim

Publisher: ASME

J. Mech. Des. December 2023, 145(12): 121704.

Paper No: MD-23-1262

https://doi.org/10.1115/1.4063288

Published Online: October 6, 2023

View Article titled, Analysis of Brand Effects in Data-Driven Design Based on Online Reviews

Topics: Data-driven design

Journal Articles

Finding Social Networks Among Online Reviewers for Customer Segmentation

Seyoung Park, Harrison M. Kim

Publisher: ASME

J. Mech. Des. December 2022, 144(12): 121703.

Paper No: MD-22-1222

https://doi.org/10.1115/1.4055624

Published Online: October 10, 2022

View Article titled, Finding Social Networks Among Online Reviewers for Customer Segmentation

Topics: Image segmentation, Design, Construction

Journal Articles

Phrase Embedding and Clustering for Sub-Feature Extraction From Online Data

Seyoung Park, Harrison M. Kim

Publisher: ASME

J. Mech. Des. May 2022, 144(5): 054501.

Paper No: MD-21-1257

https://doi.org/10.1115/1.4052904

Published Online: December 6, 2021

View Article titled, Phrase Embedding and Clustering for Sub-Feature Extraction From Online Data

Topics: Batteries, Design, Feature extraction, Fingerprints, Noise (Sound), Preferences, Product design, Security, Storage, Natural language processing

Proceedings Papers

Data-Driven Customer Segmentation Based On Online Review Analysis and Customer Network Construction

Seyoung Park, Harrison M. Kim

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A015, August 17–19, 2021

Publisher: American Society of Mechanical Engineers

Paper No: DETC2021-70036

https://doi.org/10.1115/DETC2021-70036

View Paper titled, Data-Driven Customer Segmentation Based On Online Review Analysis and Customer Network Construction

Topics: Construction, Image segmentation, Preferences, Design, Product design

Proceedings Papers

Optimal Modular Remanufactured Product Configuration and Harvesting Planning for End-of-Life Products

Jinju Kim, Seyoung Park, Harrison M. Kim

Proc. ASME. IDETC-CIE2021, Volume 5: 26th Design for Manufacturing and the Life Cycle Conference (DFMLC), V005T05A021, August 17–19, 2021

Publisher: American Society of Mechanical Engineers

Paper No: DETC2021-69964

https://doi.org/10.1115/DETC2021-69964

View Paper titled, Optimal Modular Remanufactured Product Configuration and Harvesting Planning for End-of-Life Products

Topics: Design, Manufacturing, Product design

Journal Articles

Optimal Modular Remanufactured Product Configuration and Harvesting Planning for End-of-Life Products

Jinju Kim, Seyoung Park, Harrison M. Kim

Publisher: ASME

J. Mech. Des. April 2022, 144(4): 042001.

Paper No: MD-21-1352

https://doi.org/10.1115/1.4052389

Published Online: November 9, 2021

View Article titled, Optimal Modular Remanufactured Product Configuration and Harvesting Planning for End-of-Life Products

Topics: Design, Product design, Manufacturing, Optimization

Journal Articles

Analysis of Dynamic Changes in Customer Sentiment on Product Features After the Outbreak of COVID-19 Based on Online Reviews

Jinju Kim, Seyoung Park, Harrison M. Kim

Publisher: ASME

J. Mech. Des. February 2022, 144(2): 024501.

Paper No: MD-21-1175

https://doi.org/10.1115/1.4052789

Published Online: November 9, 2021

View Article titled, Analysis of Dynamic Changes in Customer Sentiment on Product Features After the Outbreak of COVID-19 Based on Online Reviews

Topics: Batteries, Data collection, Design, Feature extraction, Preferences, Profitability, Product design

Proceedings Papers

Improving the Accuracy and Diversity of Feature Extraction From Online Reviews Using Keyword Embedding and Two Clustering Methods

Seyoung Park, Harrison M. Kim

Proc. ASME. IDETC-CIE2020, Volume 11A: 46th Design Automation Conference (DAC), V11AT11A020, August 17–19, 2020

Publisher: American Society of Mechanical Engineers

Paper No: DETC2020-22642

https://doi.org/10.1115/DETC2020-22642

View Paper titled, Improving the Accuracy and Diversity of Feature Extraction From Online Reviews Using Keyword Embedding and Two Clustering Methods

Topics: Feature extraction, Filters, Noise (Sound), Preferences, Product design, Product development

Journal citation

NARROW

Automated Sub-Feature Labeling Using Prompt-Based Pretrained Language Model

Importance-Induced Customer Segmentation Using Explainable Machine Learning

An Automated Data-Driven Approach for Product Design Strategies to Respond to Market Disruption Following COVID-19

Investigating the Effect of a Brand Factor in Product Design Based on a Data-Driven Approach Using Online Reviews

Analysis of Brand Effects in Data-Driven Design Based on Online Reviews

Finding Social Networks Among Online Reviewers for Customer Segmentation

Phrase Embedding and Clustering for Sub-Feature Extraction From Online Data

Data-Driven Customer Segmentation Based On Online Review Analysis and Customer Network Construction

Optimal Modular Remanufactured Product Configuration and Harvesting Planning for End-of-Life Products

Optimal Modular Remanufactured Product Configuration and Harvesting Planning for End-of-Life Products

Analysis of Dynamic Changes in Customer Sentiment on Product Features After the Outbreak of COVID-19 Based on Online Reviews

Improving the Accuracy and Diversity of Feature Extraction From Online Reviews Using Keyword Embedding and Two Clustering Methods

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