no code implementations • 19 Dec 2023 • Shuli Wang, Kun Gao, Lanfang Zhang, Yang Liu, Lei Chen
Specifically, based on a certain length of historical trajectory data, the situation-specific driving preferences of each driver are identified, where key driving behavior feature vectors are extracted to characterize heterogeneity in driving behavior among different drivers.
no code implementations • 15 Oct 2023 • Long Bai, Shilong Yao, Kun Gao, Yanjun Huang, Ruijie Tang, Hong Yan, Max Q. -H. Meng, Hongliang Ren
Considering that Coupled Dictionary Learning (CDL) method can obtain a reasonable linear mathematical relationship between resource images, we propose a novel CDL-based Synthetic Aperture Radar (SAR) and multispectral pseudo-color fusion method.
no code implementations • 11 Aug 2023 • Chao Yang, Lu Wang, Kun Gao, Shuang Li
Leveraging the temporal point process modeling and learning framework, the rule content and weights will be gradually optimized until the likelihood of the observational event sequences is optimal.
no code implementations • 28 Apr 2022 • Kun Gao, Katsumi Inoue, Yongzhi Cao, Hanpin Wang
We map the symbolic forward-chained format of LPs into NN constraint functions consisting of operations between subsymbolic vector representations of atoms.
no code implementations • 18 Feb 2022 • Jie Zhu, Said Easa, Kun Gao
This paper presents a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field.
no code implementations • 4 Jul 2020 • Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
no code implementations • 31 Jul 2017 • Yang Jiang, Zeyang Dou, Qun Hao, Jie Cao, Kun Gao, Xi Chen
In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks.