activity
20192022
most citedForm + Function: Optimizing Aesthetic Product Design via Adaptive, Geometrized Preference Elicitation

12 citations · 15 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20221 cited

Adaptive Neural Network Ensemble Using Frequency Distribution

Ungki Lee, Namwoo Kang

Neural network (NN) ensembles can reduce large prediction variance of NN and improve prediction accuracy. For highly nonlinear problems with insufficient data set, the prediction a…

cs.LG20221 cited

Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes

Seongsin Kim, Minyoung Jwa, Soonwook Lee +2

The braking performance of the brake system is a target performance that must be considered for vehicle development. Apparent piston travel (APT) and drag torque are the most repre…

cs.HC20201 cited

A Study on Anxiety about Using Robo-taxis: HMI Design for Anxiety Factor Analysis and Anxiety Relief Based on Field Tests

Soyoung Yoo, Sunghee Lee, Seongsin Kim +3

Despite the approaching commercialization of robo-taxis, various anxiety factors concerning the safety of autonomous vehicles are expected to form a large barrier against consumers…

cs.HC201912 cited

Form + Function: Optimizing Aesthetic Product Design via Adaptive, Geometrized Preference Elicitation

Namwoo Kang, Yi Ren, Fred Feinberg +1

Visual design is critical to product success, and the subject of intensive marketing research effort. Yet visual elements, due to their holistic and interactive nature, do not lend…

cs.LG2019

Deep Generative Design: Integration of Topology Optimization and Generative Models

Sangeun Oh, Yongsu Jung, Seongsin Kim +2

Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative de…