Search

Your search keyword '"Sun, Weidong"' showing total 1,629 results
Start Over Author "Sun, Weidong"
1,629 results on '"Sun, Weidong"'

6. Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification

Author

Zhang, Yuxiang, Li, Wei, Sun, Weidong, Tao, Ran, and Du, Qian

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing attention. It is necessary to train a model only on source domain (SD) and directly transferring the model to target domain (TD), when TD needs to be processed in real time and cannot be reused for training. Based on the idea of domain generalization, a Single-source Domain Expansion Network (SDEnet) is developed to ensure the reliability and effectiveness of domain extension. The method uses generative adversarial learning to train in SD and test in TD. A generator including semantic encoder and morph encoder is designed to generate the extended domain (ED) based on encoder-randomization-decoder architecture, where spatial and spectral randomization are specifically used to generate variable spatial and spectral information, and the morphological knowledge is implicitly applied as domain invariant information during domain expansion. Furthermore, the supervised contrastive learning is employed in the discriminator to learn class-wise domain invariant representation, which drives intra-class samples of SD and ED. Meanwhile, adversarial training is designed to optimize the generator to drive intra-class samples of SD and ED to be separated. Extensive experiments on two public HSI datasets and one additional multispectral image (MSI) dataset demonstrate the superiority of the proposed method when compared with state-of-the-art techniques.

Published
2022
Full Text
View/download PDF

24. Transcranial Low-level Laser Therapy: a Novel Treatment for Depression

Author

LIANG Xuemei, WANG Rui, ZHAO Yuhuan, XU Tianjiao, WANG Wei, SUN Weidong

Subjects
depressive disorder, low-level light therapy, low-level laser, mitochondrial, neurotransmitter, neuronal plasticity, neruroinflammation, review, therapy, Medicine
Abstract

As the second most serious disease worldwide, the etiology and pathogenesis of depression have not been clearly defined. Depression treatment faces a bottleneck, due to limitations of common therapies, such as side effects associated with drug therapy and high risk of recurrence after drug therapy, high requirements for clinicians to perform psychotherapy, and high cost of modalities and disorientation in physical therapy. Transcranial low-level laser, a new therapy has recently proven to be effective in scientific research and clinical antidepressant treatment, utilizes low-level laser non-invasively passing through the skull without causing damage to regulate biological tissues and protect neurons. Even though the molecular and cellular mechanisms underlying the therapy for depression are not yet clear, this non-drug therapy could be highly promising for depression treatment.

Published
2023
Full Text
View/download PDF

42. Category-Oriented Adversarial Data Augmentation via Statistic Similarity for Satellite Images

Author

Zhang, Huan, Leng, Wei, Han, Xiaolin, Sun, Weidong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yu, Shiqi, editor, Zhang, Zhaoxiang, editor, Yuen, Pong C., editor, Han, Junwei, editor, Tan, Tieniu, editor, Guo, Yike, editor, Lai, Jianhuang, editor, and Zhang, Jianguo, editor

Published
2022
Full Text
View/download PDF

43. Fast UAV Trajectory Optimization using Bilevel Optimization with Analytical Gradients

Author

Sun, Weidong, Tang, Gao, and Hauser, Kris

Subjects
Computer Science - Robotics
Abstract

We present an efficient optimization framework that solves trajectory optimization problems by decoupling state variables from timing variables, thereby decomposing a challenging nonlinear programming (NLP) problem into two easier subproblems. With timing fixed, the state variables can be optimized efficiently using convex optimization, and the timing variables can be optimized in a separate NLP, which forms a bilevel optimization problem. The challenge of obtaining the gradient of the timing variables is solved by sensitivity analysis of parametric NLPs. The exact analytic gradient is computed from the dual solution as a by-product, whereas existing finite-difference techniques require additional optimization. The bilevel optimization framework efficiently optimizes both timing and state variables which is demonstrated on generating trajectories for an unmanned aerial vehicle. Numerical experiments demonstrate that bilevel optimization converges significantly more reliably than a standard NLP solver, and analytical gradients outperform finite differences in terms of computation speed and accuracy. Physical experiments demonstrate its real-time applicability for reactive target tracking tasks.

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results