1 code implementation • 30 Aug 2022 • Haoyu Zuo, Qianzhi Jing, Tianqi Song, Huiting Liu, Lingyun Sun, Peter Childs, Liuqing Chen
However, existing semantic networks for design innovation is built on data source restricted to technological and scientific information.
no code implementations • 26 Aug 2021 • Haoyu Zuo, Yuan Yin, Peter Childs
This paper builds a patent-based knowledge graph, patent-KG, to represent the knowledge facts in patents for engineering design.
no code implementations • 29 Mar 2021 • Pan Wang, Zhifeng Gong, Shuo Wang, Hao Dong, Jialu Fan, Ling Li, Peter Childs, Yike Guo
To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal.
no code implementations • 28 Mar 2021 • Pan Wang, Danlin Peng, Simiao Yu, Chao Wu, Peter Childs, Yike Guo, Ling Li
A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG) signals, recorded while subjects looked at 50 categories of images.
no code implementations • 10 Feb 2021 • Pan Wang, Rui Zhou, Shuo Wang, Ling Li, Wenjia Bai, Jialu Fan, Chunlin Li, Peter Childs, Yike Guo
For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment.