Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Jiandong Qiu"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract Nonholonomic constrained wheeled mobile robot (WMR) trajectory tracking requires the enhancement of the ground adaptation capability of the WMR while ensuring its attitude tracking accuracy, a novel dual closed-loop control structure is deve
Externí odkaz:
https://doaj.org/article/d62a45bd521f4eab8a967aac169db4a3
Publikováno v:
Energies, Vol 17, Iss 17, p 4505 (2024)
The forecasting of charging demand for electric vehicles (EVs) plays a vital role in maintaining grid stability and optimizing energy distribution. Therefore, an innovative method for the prediction of EV charging load demand is proposed in this stud
Externí odkaz:
https://doaj.org/article/7578ba8ec6624bbba0562b164e4021dc
Publikováno v:
Sensors, Vol 24, Iss 14, p 4638 (2024)
In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic freque
Externí odkaz:
https://doaj.org/article/5123d9b50a25488d9dd4c8da1508d80a
Publikováno v:
Energies, Vol 17, Iss 12, p 2831 (2024)
The charging behavior of electric vehicle users is highly stochastic, which makes the short-term prediction of charging load at electric vehicle charging stations difficult. In this paper, a data-driven hybrid model optimized by the improved dung bee
Externí odkaz:
https://doaj.org/article/a7b68b824dfe401aa5d353d1835fb0b1
Publikováno v:
Journal of Advanced Transportation, Vol 2024 (2024)
With the emergence of Level 4 automated vehicles, it is necessary to investigate the impact of these vehicles on mode choice. Previous studies have looked at the potential benefits and drawbacks of automated vehicles, but there has been little resear
Externí odkaz:
https://doaj.org/article/1b9814cf9e024a33b3c2eb653b3d7c89
Publikováno v:
Journal of Advanced Transportation, Vol 2024 (2024)
This paper introduces a bilevel programming model for optimizing transit network departure frequency. In the upper-level model, user satisfaction is reflected by considering congestion effects in the cost function. The lower-level assignment model si
Externí odkaz:
https://doaj.org/article/9ab50aee28b74c918f22decf31528c41
Autor:
Jiandong Qiu, Shenghui Jiang, Jianqiang Wang, Jing Feng, Junbing Chen, Chao Dong, Yunshui Jiang, Daolai Zhang
Publikováno v:
Frontiers in Earth Science, Vol 11 (2024)
Introduction: The Holocene mud deposits that extend from the Yangtze River mouth to the Taiwan Strait along the Zhejiang–Fujian coast, East China Sea (ECS), have attracted considerable research attention. However, there is a lack of consensus regar
Externí odkaz:
https://doaj.org/article/e11745d7897049cfb21b9924a1079a90
Publikováno v:
IET Image Processing, Vol 17, Iss 5, Pp 1520-1533 (2023)
Abstract The proportion of insulators in aerial power patrol images is small and the background of overhead lines is complex, often leading to incomplete and inaccurate detection of insulators. Therefore, an algorithm for detecting insulator targets
Externí odkaz:
https://doaj.org/article/00942de4327c48a98abc79d126db4401
A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation
Publikováno v:
Sensors, Vol 24, Iss 6, p 1966 (2024)
This study addresses the ongoing challenge for learning-based methods to achieve accurate object detection in foggy conditions. In response to the scarcity of foggy traffic image datasets, we propose a foggy weather simulation algorithm based on mono
Externí odkaz:
https://doaj.org/article/fd7de2b8b278456a93be904b4a11b025
Publikováno v:
Archives of Transport, Vol 64, Iss 4, Pp 107-117 (2022)
Aiming at the problem that automated guided vehicle (AGV) is difficult to locate accurately due to the influence of environment and time drift when it works in the indoor intelligent storage system. In this paper, an extended kalman filtering (EKF) f
Externí odkaz:
https://doaj.org/article/13c66e93bc3041a88c351e2459229e6c