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Gotowe bibliografie tematyczne / Sideslipping

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Artykuły w czasopismach
Rozprawy doktorskie
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Gotowa bibliografia na temat „Sideslipping”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Sideslipping"

1

Li, Zhen, Muneshi Mitsuoka, Eiji Inoue, Takashi Okayasu, Yasumaru Hirai, and Zhongxiang Zhu. "Prediction of Tractor Sideslipping Behavior Using a Quasi–static Model." Journal of the Faculty of Agriculture, Kyushu University 60, no. 1 (2015): 215–18. http://dx.doi.org/10.5109/1526314.

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2

Zang, Xiao Jie, Zhi Dong Zhang, and Chang Liu. "Mechanism Modeling and Analysis of a Small Unmanned Vehicles Using Vortex Lattice Method." Applied Mechanics and Materials 496-500 (January 2014): 1068–72. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1068.

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Streszczenie:

For a certain type of small unmanned aerial vehicles (SUAV), using the vortex lattice method (VLM) estimated the aerodynamic derivatives, establishing linear small-perturbation equations based on level and non-sideslipping flight. Analyzed the static stability and mode characteristics based on the dimensionless derivative and matlab simulation. Early in the design, by mechanism modeling and performance analysis for the UAV through pneumatic calculations could get a lot of valuable reference data of the UAV. This has some practical significance for system performance analysis and flight control

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3

Ou, Jian, Xiaolong Cheng, and Pengju Zhang. "Research on Trajectory Prediction Based on Front Vehicle Sideslip Recognition." World Electric Vehicle Journal 16, no. 4 (2025): 241. https://doi.org/10.3390/wevj16040241.

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In order to solve the problem of emergency collision avoidance of autonomous vehicles when the front vehicle is unstable and sliding under high-speed conditions, a research method for the state recognition of the front side-skid vehicle and the trajectory prediction of the front side-skid vehicle was proposed. By extracting the key features of the vehicle in front of the vehicle in danger of sliding to build a skidding recognition model of the vehicle in front, a skidding recognition strategy of the vehicle in front was designed based on the extracted skidding feature indexes to judge the skid

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4

Verhaagen, Nick G., and Steven H. J. Naarding. "Experimental and numerical investigation of vortex flow over a sideslipping delta wing." Journal of Aircraft 26, no. 11 (1989): 971–78. http://dx.doi.org/10.2514/3.45869.

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5

Hebbar, Sheshagiri K., Max F. Platzer, and Chang Ho Kim. "Experimental investigation of vortex breakdown over a sideslipping canard-configured aircraft model." Journal of Aircraft 31, no. 4 (1994): 998–1001. http://dx.doi.org/10.2514/3.46598.

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6

Žuraulis, Vidas, and Edgar Sokolovskij. "Vehicle Velocity Relation to Slipping Trajectory Change: An Option for Traffic Accident Reconstruction." PROMET - Traffic&Transportation 30, no. 4 (2018): 395–406. http://dx.doi.org/10.7307/ptt.v30i4.2720.

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In this paper, the relation of the velocity of a vehicle in the slip mode to the parameters of the tire marks on the road surface is examined. During traffic accident reconstructions, the initial velocity of a sideslipping vehicle is established according to the tire mark trajectory radius, and calculations highly depend on the directly measured parameters of the tire marks, in particular cases known as yaw marks. In this work, a developed and experimentally validated 14-degree-of-freedom mathematical model of a vehicle is used for an investigation of the relation between velocity and trajecto

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7

Kumar Singh, Siddhanta, and Anand Sharma. "Revving up insights: machine learning-based classification of OBD II data and driving behavior analysis using g-force metrics." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2188–97. https://doi.org/10.11591/eei.v14i3.9398.

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This research work uses machine learning (ML) approaches to classify on-board diagnostics II (OBD II) data and g-force measures to provide a thorough analysis of driving behavior. The research paper effectively demonstrates the classification of driving behaviours using OBD II and g-force data. Driving behaviours are analyzed by using ML algorithms such as random forest (RF), AdaBoost, and K-nearest neighbors (KNN). The analysis goes beyond a summary by discussing how OBD II data, g-force metrics, and the algorithms interrelate to classify ten distinct driving behaviors (e.g., weaving, swervin

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8

Li, Ming, Haicheng Zhang, Zhongyu Lin, Yanguo Sun, and Mingshui Li. "Experimental and numerical study on the local crosswind environment of a wide streamlined bridge deck equipped with wind barriers at different angles of attack." Physics of Fluids 36, no. 5 (2024). http://dx.doi.org/10.1063/5.0208748.

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In this study, the effects of wind barriers on the crosswind environment of vehicle safety on a long-span bridge with a streamlined bridge deck were investigated at different angles of attack (AOA). The profiles of the mean and the root mean square (RMS) of the wind pressure coefficients above different traffic lanes were obtained through synchronous pressure measurements. The mean pressure fields around the bridge decks were investigated by computational fluid dynamics (CFD) simulations. The crosswind environment of vehicle safety between the deck with railings and the deck with wind barriers

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9

Siddhanta, Kumar Singh, and Sharma Anand. "Revving up insights: machine learning-based classification of OBD II data and driving behavior analysis using g-force metrics." May 19, 2025. https://doi.org/10.11591/eei.v14i3.9398.

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Streszczenie:

This research work uses machine learning (ML) approaches to classify on board diagnostics II (OBD II) data and g-force measures to provide a thorough analysis of driving behavior. The research paper effectively demonstrates the classification of driving behaviours using OBD II and g force data. Driving behaviours are analyzed by using ML algorithms such as random forest (RF), AdaBoost, and K-nearest neighbors (KNN). The analysis goes beyond a summary by discussing how OBD II data, g-force metrics, and the algorithms interrelate to classify ten distinct driving behaviors (e.g., weaving, swervin

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Rozprawy doktorskie na temat "Sideslipping"

1

Kim, Chang Ho. "Flow visualization studies of a sideslipping, canard-configured X-31A-like fighter aircraft model." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26553.

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Książki na temat "Sideslipping"

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Kim, Chang Ho. Flow visualization studies of a sideslipping, canard-configured X-31A-like fighter aircraft model. Naval Postgraduate School, 1991.

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Streszczenia konferencji na temat "Sideslipping"

1

HSU, C. H., and C. LIU. "Numerical study of vortical flow over a sideslipping delta wing." In 8th Applied Aerodynamics Conference. American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-3001.

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