Zobrazeno 1 - 10
of 1 511
pro vyhledávání: '"Wang, Che"'
Smart contracts are susceptible to being exploited by attackers, especially when facing real-world vulnerabilities. To mitigate this risk, developers often rely on third-party audit services to identify potential vulnerabilities before project deploy
Externí odkaz:
http://arxiv.org/abs/2409.09661
To enhance the intelligence degree in operation and maintenance, a novel method for fault detection in power grids is proposed. The proposed GNN-based approach first identifies fault nodes through a specialized feature extraction method coupled with
Externí odkaz:
http://arxiv.org/abs/2311.16522
In this paper, we propose a method for knowledge graph construction in power distribution networks. This method leverages entity features, which involve their semantic, phonetic, and syntactic characteristics, in both the knowledge graph of distribut
Externí odkaz:
http://arxiv.org/abs/2311.08724
Recently, it has been shown that for offline deep reinforcement learning (DRL), pre-training Decision Transformer with a large language corpus can improve downstream performance (Reid et al., 2022). A natural question to ask is whether this performan
Externí odkaz:
http://arxiv.org/abs/2310.00771
Autor:
Wang, Che-Hsuan1 (AUTHOR) mrgigi1218@gmail.com, Chung, Kou-Toung2 (AUTHOR) chungkoutoung@gmail.com, Su, Li-Yu3 (AUTHOR) julia10025@gmail.com, Wu, Wan-Jhen4 (AUTHOR) efgy78@gmail.com, Wang, Pei-Hwa5 (AUTHOR) demonwang@ntu.edu.tw, Lee, Ming-Chung6 (AUTHOR) mileslee@sunten.com.tw, Shen, Szu-Chuan1 (AUTHOR) scs@ntnu.edu.tw, Wu, Chung-Hsin1 (AUTHOR) megawu@ntnu.edu.tw
Publikováno v:
International Journal of Molecular Sciences. Oct2024, Vol. 25 Issue 20, p11034. 19p.
Autor:
He, Tairan, Zhang, Yuge, Ren, Kan, Liu, Minghuan, Wang, Che, Zhang, Weinan, Yang, Yuqing, Li, Dongsheng
A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative representations. Unfortunately, these handcrafted objectives rely hea
Externí odkaz:
http://arxiv.org/abs/2210.06041
In reinforcement learning, Monte Carlo algorithms update the Q function by averaging the episodic returns. In the Monte Carlo UCB (MC-UCB) algorithm, the action taken in each state is the action that maximizes the Q function plus a UCB exploration te
Externí odkaz:
http://arxiv.org/abs/2209.02864
Autor:
Lin, Jia-Sheng, Hsieh, Yi-Yen, Hsiao, Kai-Yuan, Yang, Yi-Chun, Wang, Che-Hung, Lu, Ming-Yen, Wu, Wen-Wei, Tuan, Hsing-Yu
Publikováno v:
In Chemical Engineering Journal 15 October 2024 498
Autor:
Chen, Jie, Ji, Chao, Liu, Silin, Wang, Jin, Wang, Che, Pan, Jue, Qiao, Jinyu, Liang, Yu, Cai, Mengjiao, Ma, Jinlu
Publikováno v:
In Cancer Pathogenesis and Therapy October 2024 2(4):299-313
Publikováno v:
In International Journal of Refrigeration September 2024 165:500-512