Zobrazeno 1 - 10
of 396
pro vyhledávání: '"Chen, Zhenghua"'
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
Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications. While Transformer-based models excel at capturing long-range dependencies, they face limitations in noise sen
Externí odkaz:
http://arxiv.org/abs/2404.08472
Knowledge distillation (KD) has become a widely used technique in the field of model compression, which aims to transfer knowledge from a large teacher model to a lightweight student model for efficient network development. In addition to the supervi
Externí odkaz:
http://arxiv.org/abs/2404.03693
Sourced from various sensors and organized chronologically, Multivariate Time-Series (MTS) data involves crucial spatial-temporal dependencies, e.g., correlations among sensors. To capture these dependencies, Graph Neural Networks (GNNs) have emerged
Externí odkaz:
http://arxiv.org/abs/2403.03645