Zobrazeno 1 - 10
of 867
pro vyhledávání: '"Zhao Junbo"'
Autor:
Long Haibo, Zhao Yunsong, Zhao Junbo, Yuan Xiaoyi, Fan Hao, Luo Yushi, Li Wei, An Zibing, Mao Shengcheng, Liu Gang, Han Xiaodong
Publikováno v:
National Science Open, Vol 3 (2023)
This study presents a design strategy to enhance the high-temperature creep resistance of Ni-based superalloys. This strategy focuses on two principles: (1) minimizing the dimensions of γ/γ′ interfaces and γ channels by reducing the size of the
Externí odkaz:
https://doaj.org/article/3f7f469ad9234cb0ae5d4d6c60e542f9
Autor:
Su, Tong, Zhao, Junbo
Existing machine learning-based surrogate modeling methods for transient stability constrained-optimal power flow (TSC-OPF) lack certifications in the presence of unseen disturbances or uncertainties. This may lead to divergence of TSC-OPF or insecur
Externí odkaz:
http://arxiv.org/abs/2411.08329
Autor:
Su, Aofeng, Wang, Aowen, Ye, Chao, Zhou, Chen, Zhang, Ga, Chen, Gang, Zhu, Guangcheng, Wang, Haobo, Xu, Haokai, Chen, Hao, Li, Haoze, Lan, Haoxuan, Tian, Jiaming, Yuan, Jing, Zhao, Junbo, Zhou, Junlin, Shou, Kaizhe, Zha, Liangyu, Long, Lin, Li, Liyao, Wu, Pengzuo, Zhang, Qi, Huang, Qingyi, Yang, Saisai, Zhang, Tao, Ye, Wentao, Zhu, Wufang, Hu, Xiaomeng, Gu, Xijun, Sun, Xinjie, Li, Xiang, Yang, Yuhang, Xiao, Zhiqing
The emergence of models like GPTs, Claude, LLaMA, and Qwen has reshaped AI applications, presenting vast new opportunities across industries. Yet, the integration of tabular data remains notably underdeveloped, despite its foundational role in numero
Externí odkaz:
http://arxiv.org/abs/2411.02059
This study investigates the potential of Large Language Models (LLMs) for reconstructing and constructing the physical world solely based on textual knowledge. It explores the impact of model performance on spatial understanding abilities. To enhance
Externí odkaz:
http://arxiv.org/abs/2410.17529
Autor:
Wu, Siwei, Peng, Zhongyuan, Du, Xinrun, Zheng, Tuney, Liu, Minghao, Wu, Jialong, Ma, Jiachen, Li, Yizhi, Yang, Jian, Zhou, Wangchunshu, Lin, Qunshu, Zhao, Junbo, Zhang, Zhaoxiang, Huang, Wenhao, Zhang, Ge, Lin, Chenghua, Liu, J. H.
Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, merely increasing the number of model parameters yields diminishing perfo
Externí odkaz:
http://arxiv.org/abs/2410.13639
Quantization plays an important role in the physical layer (PHY) disaggregation which is fundamental to the Open Radio Access Network (O-RAN) architecture, since digitized signals must be transmitted over fronthaul connections. In this paper we explo
Externí odkaz:
http://arxiv.org/abs/2408.13205
With the increasing integration of inverter-based resources into the power grid, there has been a notable reduction in system inertia, potentially compromising frequency stability. To assess the suitability of existing area inertia estimation techniq
Externí odkaz:
http://arxiv.org/abs/2408.00511
Due to the availability of more comprehensive measurement data in modern power systems, there has been significant interest in developing and applying reinforcement learning (RL) methods for operation and control. Conventional RL training is based on
Externí odkaz:
http://arxiv.org/abs/2407.00304
Autor:
Xiao, Ruixuan, Ma, Wentao, Wang, Ke, Wu, Yuchuan, Zhao, Junbo, Wang, Haobo, Huang, Fei, Li, Yongbin
LLM-based agents have emerged as promising tools, which are crafted to fulfill complex tasks by iterative planning and action. However, these agents are susceptible to undesired planning hallucinations when lacking specific knowledge for expertise-in
Externí odkaz:
http://arxiv.org/abs/2406.14884
Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world dat
Externí odkaz:
http://arxiv.org/abs/2406.15126