Zobrazeno 1 - 10
of 2 000
pro vyhledávání: '"WANG Ruiqi"'
This paper proposes a high-speed transceiver-based method for implementing a digital-to-time converter (DTC). A real-time decoding technique is introduced to inject time information into high-speed pattern data. The stability of the high-speed clock
Externí odkaz:
http://arxiv.org/abs/2412.06330
Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data across various sectors. Anomalies in web service data, for example, can signal critical incidents such as system failures or server malfun
Externí odkaz:
http://arxiv.org/abs/2411.02465
Recent advances in machine learning and hardware have produced embedded devices capable of performing real-time object detection with commendable accuracy. We consider a scenario in which embedded devices rely on an onboard object detector, but have
Externí odkaz:
http://arxiv.org/abs/2410.18919
Autor:
Cai, Jinjin, Wang, Ruiqi, Zhao, Dezhong, Yuan, Ziqin, McKenna, Victoria, Friedman, Aaron, Foot, Rachel, Storey, Susan, Boente, Ryan, Vhaduri, Sudip, Min, Byung-Cheol
Audio-based disease prediction is emerging as a promising supplement to traditional medical diagnosis methods, facilitating early, convenient, and non-invasive disease detection and prevention. Multimodal fusion, which integrates features from variou
Externí odkaz:
http://arxiv.org/abs/2410.09289
Autor:
Gupte, Arjun, Wang, Ruiqi, Venkatesh, Vishnunandan L. N., Kim, Taehyeon, Zhao, Dezhong, Min, Byung-Cheol
Multi-human multi-robot teams combine the complementary strengths of humans and robots to tackle complex tasks across diverse applications. However, the inherent heterogeneity of these teams presents significant challenges in initial task allocation
Externí odkaz:
http://arxiv.org/abs/2409.16266
Trust is essential in human-robot collaboration. Even more so in multi-human multi-robot teams where trust is vital to ensure teaming cohesion in complex operational environments. Yet, at the moment, trust is rarely considered a factor during task al
Externí odkaz:
http://arxiv.org/abs/2409.16009
Task allocation in multi-human multi-robot (MH-MR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches oft
Externí odkaz:
http://arxiv.org/abs/2409.13824
Preference-based reinforcement learning (PbRL) has shown significant promise for personalization in human-robot interaction (HRI) by explicitly integrating human preferences into the robot learning process. However, existing practices often require t
Externí odkaz:
http://arxiv.org/abs/2409.13822
Autor:
Zhao, Dezhong, Wang, Ruiqi, Suh, Dayoon, Kim, Taehyeon, Yuan, Ziqin, Min, Byung-Cheol, Chen, Guohua
Preference-based reinforcement learning (PbRL) shows promise in aligning robot behaviors with human preferences, but its success depends heavily on the accurate modeling of human preferences through reward models. Most methods adopt Markovian assumpt
Externí odkaz:
http://arxiv.org/abs/2409.13683
Autor:
Wang, Ruiqi, Wang, Zichen, Gao, Peiqi, Li, Mingzhen, Jeong, Jaehwan, Xu, Yihang, Lee, Yejin, Baum, Carolyn M., Connor, Lisa Tabor, Lu, Chenyang
With advancements in computer vision and deep learning, video-based human action recognition (HAR) has become practical. However, due to the complexity of the computation pipeline, running HAR on live video streams incurs excessive delays on embedded
Externí odkaz:
http://arxiv.org/abs/2409.05662