Zobrazeno 1 - 10
of 419
pro vyhledávání: '"Chen, Zhenghua"'
Autor:
Zhou, Fei, Wang, Peng, Zhang, Lei, Chen, Zhenghua, Wei, Wei, Ding, Chen, Lin, Guosheng, Zhang, Yanning
Meta-learning offers a promising avenue for few-shot learning (FSL), enabling models to glean a generalizable feature embedding through episodic training on synthetic FSL tasks in a source domain. Yet, in practical scenarios where the target task div
Externí odkaz:
http://arxiv.org/abs/2411.01432
Source-Free Unsupervised Domain Adaptation (SFUDA) has gained popularity for its ability to adapt pretrained models to target domains without accessing source domains, ensuring source data privacy. While SFUDA is well-developed in visual tasks, its a
Externí odkaz:
http://arxiv.org/abs/2409.19635
Remaining Useful Life (RUL) prediction is a critical aspect of Prognostics and Health Management (PHM), aimed at predicting the future state of a system to enable timely maintenance and prevent unexpected failures. While existing deep learning method
Externí odkaz:
http://arxiv.org/abs/2409.19629
Unsupervised Domain Adaptation (UDA) has emerged as a key solution in data-driven fault diagnosis, addressing domain shift where models underperform in changing environments. However, under the realm of continually changing environments, UDA tends to
Externí odkaz:
http://arxiv.org/abs/2407.17117
Process mining, as a high-level field in data mining, plays a crucial role in enhancing operational efficiency and decision-making across organizations. In this survey paper, we delve into the growing significance and ongoing trends in the field of p
Externí odkaz:
http://arxiv.org/abs/2407.11280
Limited by the scale and diversity of time series data, the neural networks trained on time series data often overfit and show unsatisfacotry performances. In comparison, large language models (LLMs) recently exhibit impressive generalization in dive
Externí odkaz:
http://arxiv.org/abs/2406.08765
Autor:
Ragab, Mohamed, Gong, Peiliang, Eldele, Emadeldeen, Zhang, Wenyu, Wu, Min, Foo, Chuan-Sheng, Zhang, Daoqiang, Li, Xiaoli, Chen, Zhenghua
Source-free domain adaptation (SFDA) aims to adapt a model pre-trained on a labeled source domain to an unlabeled target domain without access to source data, preserving the source domain's privacy. While SFDA is prevalent in computer vision, it rema
Externí odkaz:
http://arxiv.org/abs/2406.02635
Autor:
Wang, Ziyan, Ragab, Mohamed, Yang, Wenmian, Wu, Min, Pan, Sinno Jialin, Zhang, Jie, Chen, Zhenghua
Unsupervised domain adaptation (UDA) has achieved remarkable success in fault diagnosis, bringing significant benefits to diverse industrial applications. While most UDA methods focus on cross-working condition scenarios where the source and target d
Externí odkaz:
http://arxiv.org/abs/2405.17493
Autor:
Geng, Xue, Wang, Zhe, Chen, Chunyun, Xu, Qing, Xu, Kaixin, Jin, Chao, Gupta, Manas, Yang, Xulei, Chen, Zhenghua, Aly, Mohamed M. Sabry, Lin, Jie, Wu, Min, Li, Xiaoli
Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) tasks. However, deploying them brings significant challenges due to the huge cost of memory, energy, and computation. To address these challenges, researchers have
Externí odkaz:
http://arxiv.org/abs/2405.06038
Predicting Remaining Useful Life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time series sensory data from such systems, deep lea
Externí odkaz:
http://arxiv.org/abs/2405.04336