Accessorial Locating for Internet of Vehicles Based on DOA Estimation in Industrial Transportation

Lv, Yingnan; Zhen, Jiaqi; Guo, Baoyu

doi:10.1155/2021/8241773

Cited by 2 publications

(1 citation statement)

References 53 publications

(52 reference statements)

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“…It assists in the detection and control of beam phasing of antennas based on signal patterns, strength, and target location [79,80]. The direction of arrival (DoA) estimation has become a popular application in military-civil research for remote object detection [81][82][83]. The fixed-antenna method for DoA estimation has limitations, hence antenna arrays have been used as an alternative in practice.…”

Section: Antenna Position Direction and Radiation Estimationmentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

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Section: Antenna Position Direction and Radiation Estimationmentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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A classification method for urban functional regions based on the transfer rate of empty cars

et al. 2021

IET Intelligent Trans Sys

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Predicting the nature of each urban functional region based on the transfer rate of empty cars plays a crucial role in constructing smart cities and urban planning. The transfer rate of empty cars describes the probability of a taxi driving from one region to another without any passengers. It can reflect the main driving directions of taxies and the main flow directions of people among different urban functional regions. Although current researches have focused on the functional regions divided by remote sensing satellite images, there is almost no discussion on determining the nature of the region through taxi behaviour. The authors consider using taxi behaviour to classify urban functional regions. Besides, the attentional spatio-temporal model (Attentional Gated Recurrent Unit, AGRU) is introduced in the work. The AGRU consists of three modules, which are the spatial feature extraction module, the temporal feature extraction module, and the attentional pooling mechanism. The model has been evaluated on the data set provided by Didi Chuxing and it has been compared with some typical models. The experimental results show that the AGRU can reflect spatio-temporal information, and its attentional pooling mechanism can distinguish whether a region is the place of departure or the destination.

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Accessorial Locating for Internet of Vehicles Based on DOA Estimation in Industrial Transportation

Cited by 2 publications

References 53 publications

Application of Machine Learning in Electromagnetics: Mini-Review

Application of Machine Learning in Electromagnetics: Mini-Review

A classification method for urban functional regions based on the transfer rate of empty cars

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