Zobrazeno 1 - 10
of 3 733
pro vyhledávání: '"Xu, Zhiwei"'
Wave energy harvesting is critical for advancing the development and utilization of marine resources. In this study, we present a novel multi-roller structure triboelectric nanogenerator (MR-TENG) designed specifically for efficient water wave energy
Externí odkaz:
http://arxiv.org/abs/2409.03601
Autor:
Xu, Zhiwei, Mao, Hangyu, Zhang, Nianmin, Xin, Xin, Ren, Pengjie, Li, Dapeng, Zhang, Bin, Fan, Guoliang, Chen, Zhumin, Wang, Changwei, Yin, Jiangjin
In partially observable multi-agent systems, agents typically only have access to local observations. This severely hinders their ability to make precise decisions, particularly during decentralized execution. To alleviate this problem and inspired b
Externí odkaz:
http://arxiv.org/abs/2408.09501
In this study, we present an innovative fusion of language models and query analysis techniques to unlock cognition in artificial intelligence. Our system seamlessly integrates a Chess engine with a language model, enabling it to predict moves and pr
Externí odkaz:
http://arxiv.org/abs/2408.04910
Transformer models have emerged as potent solutions to a wide array of multidisciplinary challenges. The deployment of Transformer architectures is significantly hindered by their extensive computational and memory requirements, necessitating the rel
Externí odkaz:
http://arxiv.org/abs/2407.02081
Autor:
Li, Dapeng, Dong, Hang, Wang, Lu, Qiao, Bo, Qin, Si, Lin, Qingwei, Zhang, Dongmei, Zhang, Qi, Xu, Zhiwei, Zhang, Bin, Fan, Guoliang
In recent years, multi-agent reinforcement learning algorithms have made significant advancements in diverse gaming environments, leading to increased interest in the broader application of such techniques. To address the prevalent challenge of parti
Externí odkaz:
http://arxiv.org/abs/2404.17780
Edge intelligence enables resource-demanding Deep Neural Network (DNN) inference without transferring original data, addressing concerns about data privacy in consumer Internet of Things (IoT) devices. For privacy-sensitive applications, deploying mo
Externí odkaz:
http://arxiv.org/abs/2403.12568
Autor:
Mao, Hangyu, Zhao, Rui, Li, Ziyue, Xu, Zhiwei, Chen, Hao, Chen, Yiqun, Zhang, Bin, Xiao, Zhen, Zhang, Junge, Yin, Jiangjin
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work studies the former. Specifically, the Perception and Decision-making Interleaving Transformer (PDiT) network is proposed, which
Externí odkaz:
http://arxiv.org/abs/2312.15863
The effective analysis of high-dimensional Electronic Health Record (EHR) data, with substantial potential for healthcare research, presents notable methodological challenges. Employing predictive modeling guided by a knowledge graph (KG), which enab
Externí odkaz:
http://arxiv.org/abs/2312.15611
Parameter sharing, as an important technique in multi-agent systems, can effectively solve the scalability issue in large-scale agent problems. However, the effectiveness of parameter sharing largely depends on the environment setting. When agents ha
Externí odkaz:
http://arxiv.org/abs/2312.09009
The coordination between agents in multi-agent systems has become a popular topic in many fields. To catch the inner relationship between agents, the graph structure is combined with existing methods and improves the results. But in large-scale tasks
Externí odkaz:
http://arxiv.org/abs/2312.04245