Zobrazeno 1 - 10
of 3 306
pro vyhledávání: '"Chen, LiMing"'
Log anomaly detection (LAD) is essential to ensure safe and stable operation of software systems. Although current LAD methods exhibit significant potential in addressing challenges posed by unstable log events and temporal sequence patterns, their l
Externí odkaz:
http://arxiv.org/abs/2410.16612
Limited by the complexity of basis function (B-spline) calculations, Kolmogorov-Arnold Networks (KAN) suffer from restricted parallel computing capability on GPUs. This paper proposes a novel ReLU-KAN implementation that inherits the core idea of KAN
Externí odkaz:
http://arxiv.org/abs/2406.02075
Inertial Measurement Unit (IMU) sensors are widely employed for Human Activity Recognition (HAR) due to their portability, energy efficiency, and growing research interest. However, a significant challenge for IMU-HAR models is achieving robust gener
Externí odkaz:
http://arxiv.org/abs/2406.18569
Autor:
Chen, Siyu, Yan, Wenchao, Zhu, Mingyang, Li, Yaojun, Hu, Xichen, Xu, Hao, Feng, Jie, Ge, Xulei, Wang, Wenzhao, Lu, Guangwei, Wei, Mingxuan, Lu, Lin, Huang, Xiaojun, Li, Boyuan, Yuan, Xiaohui, Liu, Feng, Chen, Min, Chen, Liming, Zhang, Jie
A dual-beam platform for all-optical electron-photon scattering, or Thomson/Compton scattering, with adjustable collision-angle and parameter tuning ability has been developed, which, in principle, can be used for the verification of strong-field qua
Externí odkaz:
http://arxiv.org/abs/2404.13922
Autor:
Li, Jing, Sun, Li, Wang, Yaogang, Guo, Lichuan, Li, Daiqing, Liu, Chang, Sun, Ning, Xu, Zheng, Li, Shu, Jiang, Yunwen, Wang, Yuan, Zhang, Shunming, Chen, Liming
Publikováno v:
JMIR mHealth and uHealth, Vol 8, Iss 3, p e15390 (2020)
BackgroundMobile-based interventions appear to be promising in ameliorating huge burdens experienced by patients with type 2 diabetes. However, it is unclear how effective mobile-based interventions are in glycemic management of patients with type 2
Externí odkaz:
https://doaj.org/article/1be1235726a348bfbaee7076c487ecc9
Autor:
Li, Shuangjian, Zhu, Tao, Duan, Furong, Chen, Liming, Ning, Huansheng, Nugent, Christopher, Wan, Yaping
Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception. However, achieving high efficiency and long sequence recognition remains a challenge. Despite the extensive investigation of temporal deep le
Externí odkaz:
http://arxiv.org/abs/2403.20183
Graph neural networks have achieved remarkable success in learning graph representations, especially graph Transformer, which has recently shown superior performance on various graph mining tasks. However, graph Transformer generally treats nodes as
Externí odkaz:
http://arxiv.org/abs/2403.16358
The massive generation of time-series data by largescale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a predominant use of the Channel D
Externí odkaz:
http://arxiv.org/abs/2403.09223
Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields such as healthcare and assisted living, where real-time understanding of ongo
Externí odkaz:
http://arxiv.org/abs/2403.08214
Accurate representations of 3D faces are of paramount importance in various computer vision and graphics applications. However, the challenges persist due to the limitations imposed by data discretization and model linearity, which hinder the precise
Externí odkaz:
http://arxiv.org/abs/2312.04028