Zobrazeno 1 - 10
of 327
pro vyhledávání: '"Meng, Fanlin"'
In the context of increasing demands for long-term multi-energy load forecasting in real-world applications, this paper introduces Patchformer, a novel model that integrates patch embedding with encoder-decoder Transformer-based architectures. To add
Externí odkaz:
http://arxiv.org/abs/2404.10458
Autor:
Cao, Yue, Li, Sifan, Lv, Chenchen, Wang, Di, Sun, Hongjian, Jiang, Jing, Meng, Fanlin, Xu, Lexi, Cheng, Xinzhou
In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon tran
Externí odkaz:
http://arxiv.org/abs/2305.15071
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumption, can help analyze electricity consumption behaviou
Externí odkaz:
http://arxiv.org/abs/2207.00041
Publikováno v:
In Water Research 15 March 2025 272
Autor:
Zhang, Baoyue, Yu, Jiang, Huang, Ruiping, Ye, Jianying, Meng, Fanlin, Xu, Zhaochu, Cui, Wenwen, Song, Jia, Wang, Siqi, Du, Yanjun, Lv, Qingzhi, Zhu, Wanling, Liu, Dan, Wang, Yongjun
Publikováno v:
In Materials Today Nano March 2025 29
Publikováno v:
In Environmental Impact Assessment Review January 2025 110
Upgrading effluent standards of wastewater treatment plants (WWTPs) and repairing sewerage systems leads to contradictions and synergies between water pollution control and climate change mitigation. This affects historical trajectories and character
Externí odkaz:
http://arxiv.org/abs/2202.11511
Autor:
Dai, Shuang, Meng, Fanlin
Online federated learning (OFL) and online transfer learning (OTL) are two collaborative paradigms for overcoming modern machine learning challenges such as data silos, streaming data, and data security. This survey explored OFL and OTL throughout th
Externí odkaz:
http://arxiv.org/abs/2202.03070
Domain adaptation solves image classification problems in the target domain by taking advantage of the labelled source data and unlabelled target data. Usually, the source and target domains share the same set of classes. As a special case, Open-Set
Externí odkaz:
http://arxiv.org/abs/2110.12635
Autor:
Yong, Han, Li, Jiani, Li, Bingjun, Meng, Fanlin, Wu, Xuehong, Jin, Tingxiang, Li, Yunquan, Wu, Yonggang
Publikováno v:
In International Journal of Thermal Sciences September 2024 203