Zobrazeno 1 - 10
of 435
pro vyhledávání: '"Wang, Qinwen"'
Anwendung von maschinellen Lernverfahren (ML) in der Produktionstechnik, in Zeiten der Industrie 4.0, stark angestiegen. Insbesondere die Datenverfügbarkeit ist an dieser Stelle elementar und für die erfolgreiche Umsetzung einer ML-Applikation Vora
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A88340
https://tud.qucosa.de/api/qucosa%3A88340/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A88340/attachment/ATT-0/
Due to the increasing computing power and corresponding algorithms, the use of machine learning (ML) in production technology has risen sharply in the age of Industry 4.0. Data availability in particular is fundamental at this point and a prerequisit
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A88341
https://tud.qucosa.de/api/qucosa%3A88341/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A88341/attachment/ATT-0/
Reducing scrap products and unnecessary rework has always been a goal of the manufacturing industry. With the increasing data availability and the developments in the field of artificial intelligence (AI) for industrial applications, machine learning
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A88293
https://tud.qucosa.de/api/qucosa%3A88293/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A88293/attachment/ATT-0/
Die Reduzierung von Ausschuss und unnötiger Nacharbeit ist ein elementares Ziel der Fertigungsindustrie. Mit der zunehmenden Datenverfügbarkeit und den Entwicklungen auf dem Gebiet der künstlichen Intelligenz (KI) für industrielle Anwendungen, wi
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A88292
https://tud.qucosa.de/api/qucosa%3A88292/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A88292/attachment/ATT-0/
Autor:
Liu, Enhong, Suarez, Joseph, You, Chenhui, Wu, Bo, Chen, Bingcheng, Hu, Jun, Chen, Jiaxin, Zhu, Xiaolong, Zhu, Clare, Togelius, Julian, Mohanty, Sharada, Hong, Weijun, Du, Rui, Zhang, Yibing, Wang, Qinwen, Li, Xinhang, Yuan, Zheng, Li, Xiang, Huang, Yuejia, Zhang, Kun, Yang, Hanhui, Tang, Shiqi, Isola, Phillip
In this paper, we present the results of the NeurIPS-2022 Neural MMO Challenge, which attracted 500 participants and received over 1,600 submissions. Like the previous IJCAI-2022 Neural MMO Challenge, it involved agents from 16 populations surviving
Externí odkaz:
http://arxiv.org/abs/2311.03707
Autor:
Li, Xinhang, Yang, Yiying, Yuan, Zheng, Wang, Zhe, Wang, Qinwen, Xu, Chen, Li, Lei, He, Jianhua, Zhang, Lin
Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety critical nature. While multi-agent reinforcement learning (MARL) algorithms have been proposed for MVP pr
Externí odkaz:
http://arxiv.org/abs/2306.05016
The multi-vehicle pursuit (MVP), as a problem abstracted from various real-world scenarios, is becoming a hot research topic in Intelligent Transportation System (ITS). The combination of Artificial Intelligence (AI) and connected vehicles has greatl
Externí odkaz:
http://arxiv.org/abs/2210.13470
The pursuit-evasion game in Smart City brings a profound impact on the Multi-vehicle Pursuit (MVP) problem, when police cars cooperatively pursue suspected vehicles. Existing studies on the MVP problems tend to set evading vehicles to move randomly o
Externí odkaz:
http://arxiv.org/abs/2210.13015
Autor:
Li, Xinhang, Li, Zihao, Yang, Nan, Yuan, Zheng, Wang, Qinwen, Yang, Yiying, Huang, Yupeng, Song, Xuri, Li, Lei, Zhang, Lin
The expansion of renewable energy could help realizing the goals of peaking carbon dioxide emissions and carbon neutralization. Some existing grid dispatching methods integrating short-term renewable energy prediction and reinforcement learning (RL)
Externí odkaz:
http://arxiv.org/abs/2204.04612
Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture
Externí odkaz:
http://arxiv.org/abs/2203.00183