Zobrazeno 1 - 10
of 1 842
pro vyhledávání: '"liu, yingjie"'
Autor:
Tian, Jin, Wei, Baichun, Yang, Chifu, Luo, Suo, Feng, Jiadong, Li, Ping, Chen, Changbing, Liu, Yingjie, Zhu, Haiqi, Yi, Chunzhi
Objective: Shoulder exoskeletons can effectively assist with overhead work. However, their impacts on muscle synergy remain unclear. The objective is to systematically investigate the effects of the shoulder exoskeleton on muscle synergies during ove
Externí odkaz:
http://arxiv.org/abs/2411.15504
Objective: Overhead tasks are a primary inducement to work-related musculoskeletal disorders. Aiming to reduce shoulder physical loads, passive shoulder exoskeletons are increasingly prevalent in the industry due to their lightweight, affordability,
Externí odkaz:
http://arxiv.org/abs/2411.13770
Face Forgery Detection (FFD), or Deepfake detection, aims to determine whether a digital face is real or fake. Due to different face synthesis algorithms with diverse forgery patterns, FFD models often overfit specific patterns in training datasets,
Externí odkaz:
http://arxiv.org/abs/2409.16945
Autor:
Wu, Zhiqiang, Sun, Licheng, Liu, Yingjie, Yang, Jian, Dong, Hanlin, Lin, Shing-Ho J., Tang, Xuan, Mi, Jinpeng, Jin, Bo, Wei, Xian
Group Equivariant Convolution (GConv) can effectively handle rotational symmetry data. They assume uniform and strict rotational symmetry across all features, as the transformations under the specific group. However, real-world data rarely conforms t
Externí odkaz:
http://arxiv.org/abs/2408.12454
Autor:
Wu, Zhiqiang, Liu, Yingjie, Dong, Hanlin, Tang, Xuan, Yang, Jian, Jin, Bo, Chen, Mingsong, Wei, Xian
Introducing Group Equivariant Convolution (GConv) empowers models to explore symmetries hidden in visual data, improving their performance. However, in real-world scenarios, objects or scenes often exhibit perturbations of a symmetric system, specifi
Externí odkaz:
http://arxiv.org/abs/2408.11760
Although Federated Learning (FL) enables collaborative learning in Artificial Intelligence of Things (AIoT) design, it fails to work on low-memory AIoT devices due to its heavy memory usage. To address this problem, various federated pruning methods
Externí odkaz:
http://arxiv.org/abs/2405.04765
In this paper, a deep learning method for solving an improved one-dimensional Poisson-Nernst-Planck ion channel (PNPic) model, called the PNPic deep learning solver, is presented. In particular, it combines a novel local neural network scheme with an
Externí odkaz:
http://arxiv.org/abs/2401.17513
In recent years, surrogate models based on deep neural networks (DNN) have been widely used to solve partial differential equations, which were traditionally handled by means of numerical simulations. This kind of surrogate models, however, focuses o
Externí odkaz:
http://arxiv.org/abs/2310.15299
We propose an effective and robust algorithm for identifying partial differential equations (PDEs) with space-time varying coefficients from a single trajectory of noisy observations. Identifying unknown differential equations from noisy observations
Externí odkaz:
http://arxiv.org/abs/2304.05543
Although Deep Learning (DL) has achieved success in complex Artificial Intelligence (AI) tasks, it suffers from various notorious problems (e.g., feature redundancy, and vanishing or exploding gradients), since updating parameters in Euclidean space
Externí odkaz:
http://arxiv.org/abs/2302.08210