Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Wenhu Qin"'
Publikováno v:
Batteries, Vol 10, Iss 3, p 87 (2024)
A reliable and accurate estimation of the state-of-health (SOH) of lithium batteries is critical to safely operating electric vehicles and other equipment. This paper proposes a state-of-health estimation method based on fennec fox optimization algor
Externí odkaz:
https://doaj.org/article/793b3b3446df465797ac7a4afa36dee1
Publikováno v:
Electronic Research Archive, Vol 30, Iss 11, Pp 4191-4208 (2022)
Considering the wide applications of big data in transportation, machine learning and mobile internet technology, artificial intelligence (AI) has largely empowered transportation systems. Many traditional transportation planning and management metho
Externí odkaz:
https://doaj.org/article/a48dc603cd2541a59f5aec739146f1cf
Publikováno v:
Batteries, Vol 9, Iss 4, p 224 (2023)
The accuracy of predicting the remaining useful life of lithium batteries directly affects the safe and reliable use of the supplied equipment. Since the degradation of lithium batteries can easily be influenced by different operating conditions and
Externí odkaz:
https://doaj.org/article/3201004e78924494bfae222e8781832b
Autor:
Zhonghua Yun, Wenhu Qin
Publikováno v:
IEEE Access, Vol 8, Pp 55447-55461 (2020)
The evaluation of lithium battery performance is a complex and very important issue. Generally, manufacturers perform battery burn-in tests and evaluate the performance of lithium batteries based on capacity, internal resistance, voltage, and other p
Externí odkaz:
https://doaj.org/article/ccfc782e7a734c4ba2111a473c0115fc
Publikováno v:
Remote Sensing, Vol 14, Iss 18, p 4551 (2022)
When conducting land cover classification, it is inevitable to encounter foggy conditions, which degrades the performance by a large margin. Robustness may be reduced by a number of factors, such as aerial images of low quality and ineffective fusion
Externí odkaz:
https://doaj.org/article/8283699091fc43cdae87e150d6501cba
Publikováno v:
IEEE Access, Vol 7, Pp 78515-78532 (2019)
Driving behavior has a large impact on vehicle fuel consumption. Dedicated study on the relationship between the driving behavior and fuel consumption can contribute to decreasing the energy cost of transportation and the development of the behavior
Externí odkaz:
https://doaj.org/article/7a8c47994a0c426fae6d64660714fa98
Publikováno v:
IEEE Access, Vol 7, Pp 92465-92475 (2019)
There have been some biomechanics-based control systems that have achieved better realistic virtual human motion. Yet their abilities to adapt the changing environments are weaker than the traditional control systems with characters driven by proport
Externí odkaz:
https://doaj.org/article/54704c9f65474e13885b370f0171f624
Publikováno v:
IEEE Access, Vol 7, Pp 109544-109554 (2019)
This paper presents an approach for solving the crowd navigation problem in an unknown and dynamic environment based on deep reinforcement learning. In our approach, we first make four leader agents learn how to reach their goals and avoid collisions
Externí odkaz:
https://doaj.org/article/0dcb806d76a14c07bb197d938fe8f173
Publikováno v:
IEEE Access, Vol 6, Pp 68850-68866 (2018)
Research on how risk is perceived by drivers is vital to driving behavior research and driving safety. As risk can be divided into subjective and objective risk, in this paper, we focus on modeling subjective risk perception by drivers using a deep l
Externí odkaz:
https://doaj.org/article/bd66bafa427f49cd82c28b2550f0c733
Publikováno v:
Sensors, Vol 21, Iss 7, p 2464 (2021)
Multiple-camera systems can expand coverage and mitigate occlusion problems. However, temporal synchronization remains a problem for budget cameras and capture devices. We propose an out-of-the-box framework to temporally synchronize multiple cameras
Externí odkaz:
https://doaj.org/article/8a607a4e9d304dd2a88a5334074cfba5