RETRACTED ARTICLE: Fuzzy curvilinear path
            optimization using fuzzy regression analysis for mid vehicle collision detection and
            avoidance system analyzed on NGSIM I-80 dataset (real-road scenarios)

Prabhakaran, N.; Sudhakar, M.

doi:10.1007/s00521-018-3553-7

Cited by 11 publications

(6 citation statements)

References 26 publications

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“…The power system of electric vehicle is mainly composed of three core components: motor, battery, and power control system. The driving system plays a decisive role in the vehicle quality and performance [8][9][10]. Different drive systems mainly use different drive motors.…”

Section: Parameterization Of Vehicle Permanentmentioning

confidence: 99%

Parameterization and Performance of Permanent Magnet Synchronous Motor for Vehicle Based on Motor-CAD and OptiSLang

Yong-ling

2022

Wireless Communications and Mobile Computing

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With the popularity of electric vehicles, electric vehicles have become a hot research topic, and the performance of permanent magnet synchronous motor has become increasingly prominent in recent years. Therefore, the application of permanent magnet synchronous motor in electric vehicles has become a possibility. Based on the analysis of the existing permanent magnet synchronous motor technology for electric vehicles, this paper will start from the motor body design. In this paper, the performance parameters of permanent magnet synchronous motor meeting the requirements of vehicle operation are estimated by analyzing the parameters of electric vehicle, and the motor is modeled based on the motor design software Motor-CAD. In this paper, the parametric analysis module in the software is used to optimize the parameters of motor permanent magnet, stator slot width, air gap magnetic density, and air gap length. Finally, this paper uses OptiSLang software to simulate and analyze the permanent magnet synchronous motor. It improves the driving performance of automobile motor and reduces energy consumption, so that the permanent magnet synchronous motor can better meet the requirements of electric vehicles. The experimental results show that the optimal range of motor split ratio is large, in the range of 0.85~0.9.

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Section: Parameterization Of Vehicle Permanentmentioning

confidence: 99%

Parameterization and Performance of Permanent Magnet Synchronous Motor for Vehicle Based on Motor-CAD and OptiSLang

Yong-ling

2022

Wireless Communications and Mobile Computing

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“…In order to predict the time length T of the physical packet to be transmitted in the next time, machine learning will be applied. Typical machine learning algorithms include linear regression, logistic regression, ridge regression, and Wireless Communications and Mobile Computing support vector regression [24][25][26][27][28][29]. Linear regression [24] uses least square methods as cost function and optimizes the target model by Newton iteration.…”

Section: Related Workmentioning

confidence: 99%

“…However, linear regression may obtain local optimum solution for some applications. Logistic regression [25] is based on the probabilistic mechanism, which determines parameters by maximum likelihood estimation. However, logistic regression is a linear model in essence and may not be suitable for the vibrating samples.…”

Section: Related Workmentioning

confidence: 99%

Proactive Flexible Interval Intermittent Jamming for WAVE‐Based Vehicular Networks

Xing

et al. 2021

Wireless Communications and Mobile Computing

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In this paper, we deal with the eavesdropping issue in Wireless Access in Vehicular Environments- (WAVE-) based vehicular networks. A proactive flexible interval intermittent jamming (FIJ) approach is proposed which predicts the time length T of the physical layer packet to be transmitted by the legitimate user and designs flexible jamming interval (JI) and jamming-free interval (JF) based on the predicted T . Our design prevents eavesdroppers from overhearing the information with low energy cost since the jamming signal is transmitted only within JI. Numerical analysis and simulation study validate the performance of our proactive FIJ, in terms of jamming energy cost and overhearing defense, by comparing with the existing intermittent jamming (IJ) and FIJ.

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“…Before the emergence of deep neural networks, many methods had been used to identify objects in images based on nonautomatic features. Among these methods, we can mention the gradient histogram, optimal flow, Kalman filter, machine learning-based techniques such as principal component analysis, fuzzy methods, and classification methods such as support vector machines for classifying complex data sets [16][17][18][19][20][21][22][23]. Geometric transformations and feature extraction have also been employed [24][25][26] to detect moving objects and determine their descriptors.…”

Section: Introductionmentioning

confidence: 99%

Real‐time vehicle detection using segmentation‐based detection network and trajectory prediction

Zarei,

Moallem,

Shams

2023

IET Computer Vision

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The position of vehicles is determined using an algorithm that includes two stages of detection and prediction. The more the number of frames in which the detection network is used, the more accurate the detector is, and the more the prediction network is used, the algorithm is faster. Therefore, the algorithm is very flexible to achieve the required accuracy and speed. YOLO's base detection network is designed to be robust against vehicle scale changes. Also, feature maps are produced in the detector network, which contribute greatly to increasing the accuracy of the detector. In these maps, using differential images and a u‐net‐based module, image segmentation has been done into two classes: vehicle and background. To increase the accuracy of the recursive predictive network, vehicle manoeuvres are classified. For this purpose, the spatial and temporal information of the vehicles are considered simultaneously. This classifier is much more effective than classifiers that consider spatial and temporal information separately. The Highway and UA‐DETRAC datasets demonstrate the performance of the proposed algorithm in urban traffic monitoring systems.

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RETRACTED ARTICLE: Fuzzy curvilinear path optimization using fuzzy regression analysis for mid vehicle collision detection and avoidance system analyzed on NGSIM I-80 dataset (real-road scenarios)

Cited by 11 publications

References 26 publications

Parameterization and Performance of Permanent Magnet Synchronous Motor for Vehicle Based on Motor-CAD and OptiSLang

Parameterization and Performance of Permanent Magnet Synchronous Motor for Vehicle Based on Motor-CAD and OptiSLang

Proactive Flexible Interval Intermittent Jamming for WAVE‐Based Vehicular Networks

Real‐time vehicle detection using segmentation‐based detection network and trajectory prediction

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