Product innovation concept generation based on deep learning and Kansei engineering-Reference-Cited by-同舟云学术

Product innovation concept generation based on deep learning and Kansei engineering

Published:2021-06-03 Issue:10 Volume:32 Page:559-589
ISSN:0954-4828
Container-title:Journal of Engineering Design
language:en
Short-container-title:Journal of Engineering Design

Author:

Li Xiong¹²,Su Jianning³,Zhang Zhipeng³,Bai Ruisheng¹

Affiliation:

1. School of Mechanical & Electronical Engineering, Lanzhou University of Technology, Lanzhou, People’s Republic of China

2. School of Bailie Mechanical Engineering, Lanzhou City University, Lanzhou, People's Republic of China

3. School of Design Art, Lanzhou University of Technology, Lanzhou, People’s Republic of China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/09544828.2021.1928023

Reference68 articles.

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2. Arjovsky, M., S. Chintala, and L. Bottou. 2017. “Wasserstein Generative Adversarial Networks.” In Proceedings of the 34th International Conference on Machine Learning, 214-223 PMLR.

3. Brock, A., J. Donahue, and K. Simonyan. 2019. “Large Scale GAN Training for High Fidelity Natural Image Synthesis.” In. https://arxiv.org/abs/1809.11096v1: ArXiv e-Print.

4. A one-to-many conditional generative adversarial network framework for multiple image-to-image translations

5. Affective design using machine learning: a survey and its prospect of conjoining big data

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