Single-rotor UAV flow field simulation using generative adversarial networks

Wen, Sheng; Shen, Ningwen; Zhang, Jiantao; Lan, Yubin; Han, Jie; Yin, Xuanchun; Zhang, Quanyong; Ge, Yufeng

doi:10.1016/j.compag.2019.105004

Cited by 15 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the use of single rotor drones for agricultural plant protection has been greatly appreciated, various shortcomings still exist in this field. For instance, one of the disadvantages of using single rotor drones in agricultural plant protection is studied by Wen et al [126]. The authors showed that the rotor flow field of a single rotor UAV can cause drift of the droplets, resulting in waste and secondary disaster.…”

Section: ) Single Rotor Uavsmentioning

confidence: 99%

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

et al. 2022

View full text Add to dashboard Cite

The use of drones, formally known as unmanned aerial vehicles (UAVs), has significantly increased across a variety of applications over the past few years. This is due to the rapid advancement towards the design and production of inexpensive and dependable UAVs and the growing request for the utilization of such platforms particularly in civil applications. With their intrinsic attributes such as high mobility, rapid deployment and flexible altitude, UAVs have the potential to be utilized in many wireless system applications. On the one hand, UAVs are able to operate as flying mobile terminals within wireless/cellular networks to support a variety of missions such as goods delivery, search and rescue, precision agriculture monitoring, and remote sensing. On the other hand, UAVs can be utilized as aerial base stations to increase wireless communication coverage, reliability, and the capacity of wireless systems without additional investment in wireless systems infrastructure. The aim of this article is to review the current applications of UAVs for civil and commercial purposes. The focus of this paper is on the challenges and communication requirements associated with UAV-based communication systems. This article initially classifies UAVs in terms of various parameters, some of which can impact UAVs' communication performance. It then provides an overview of aerial networking and investigates UAVs routing protocols specifically, which are considered as one of the challenges in UAV communication. This article later investigates the use of UAV networks in a variety of civil applications and considers many challenges and communication demands of these applications. Subsequently, different types of simulation platforms are investigated from a communication and networking viewpoint. Finally, it identifies areas of future research.

show abstract

Section: ) Single Rotor Uavsmentioning

confidence: 99%

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…We kindly refer interested readers to [49] covering the literature and the current trends related to unsupervised anomaly detection for UAVs. In another context, authors in [50] investigate learning the law of the rotor flow field of a UAV. This complex task is achieved through the use of GAN where the flow field features are learned by the proposed model.…”

Section: Supervised and Unsupervised Solutions For Uavs-based Problemsmentioning

confidence: 99%

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Lahmeri

Kishk

Alouini

2021

IEEE Open J. Commun. Soc.

103

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAVs networks.

show abstract

“…In recent years, Generative Adversarial Network (GAN) [7][8][9] has been widely used in image generation, image style transfer, image repair and other fields. It is a generative unsupervised deep learning model that can use a small amount of training data to generate target data sets, making up for the problem of insufficient training data [10].…”

Section: Introductionmentioning

confidence: 99%

Information Acquisition Method of Tomato Plug Seedlings Based on Cycle-Consistent Adversarial Network

Zhang¹

2023

ATAIML

View full text Add to dashboard Cite

In order to solve the interference caused by the overlapping and extrusion of adjacent plug seedlings, accurately obtain the information of tomato plug seedlings, and improve the transplanting effect of automatic tomato transplanters, this study proposes a seedling information acquisition method based on Cycle-Consistent Adversarial Network (CycleGAN). CycleGAN is a generative unsupervised deep learning method, which can realize the free conversion of the source-domain plug seedling image and the target-domain plug label image. It collects more than 500 images of tomato plug seedlings in different growth stages as a collection image set; follows certain principles to label the plug seedling images to obtain a label image set, and uses two image sets to train the CycleGAN network model. Finally, the trained model is used to process the images of tomato plug seedlings to obtain their label images. According to the labeling principle, the correct rate of model recognition is between 91% and 97%. The recognition results show that the CycleGAN model can recognize and judge whether the seedlings affected by the adjacent seedling holes are suitable for transplanting, so the application of this method can greatly improve the intelligence level of the automatic tomato transplanters.

show abstract

Single-rotor UAV flow field simulation using generative adversarial networks

Cited by 15 publications

References 18 publications

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Information Acquisition Method of Tomato Plug Seedlings Based on Cycle-Consistent Adversarial Network

Contact Info

Product

Resources

About