Estimation of Rotor Position in Brushless Direct Current Motor by Memory Attenuated Extended Kalman Filter

Yin, Shuai

doi:10.18280/ejee.210106

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…In general, vehicle tracking algorithms predict the vehicle position in the next frames according to the position in the previous frames. The bases of the existing vehicle tracking algorithms include Kalman filter [15], mean shift [16], particle filter [17], and tracking learning detection (TLD) [18].…”

Section: Vehicle Tracking Methodsmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Video Sequences

Wang¹

2020

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Vehicle detection and tracking are key aspects of intelligent transportation. However, the accuracy of traditional methods for vehicle detection and tracking is severely suppressed by the complexity of road conditions, occlusion, and illumination changes. To solve the problem, this paper initializes the background model based on multiple frames with a fixed interval, and then establishes a moving vehicle detection algorithm based on trust interval. The established algorithm can easily evaluate the background complexity based on regional information. After that, the authors pointed out that the correlation filtering, a classical vehicle tracking algorithm, cannot adapt to the scale changes of vehicles, due to the weak dependence of background information. Hence, a tracking algorithm that adapts to vehicle scale was designed based on background information. Finally, the proposed algorithms were proved feasible for detection and tracking moving vehicles in complex environments. The research provides a good reference for the application of computer vision in moving target detection.

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Section: Vehicle Tracking Methodsmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Video Sequences

Wang¹

2020

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“…The two most common methods for producing thermal plasma are direct current (DC) arc discharge and inductively coupled discharge [1][2][3][4][5][6]. The inductively coupled plasma (ICP) torch ionizes the working gas flowing through the quartz tube using high-frequency induction heating.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Power on Inductively Coupled Plasma Parameters

Marza

Khalaf

2022

IJP

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In this work, we studied the effect of power variation on inductively coupled plasma parameters using numerical simulation. Different values were used for input power (750 W-1500 W), gas temperature 300K, gas pressure (0.02torr), 5 tourns of the copper coil and the plasma was produced at radio frequency (RF) 13.56 MHZ on the coil above the quartz chamber. For the previous purpose, a computer simulation in two dimensions axisymmetric, based on finite element method, was implemented for argon plasma. Based on the results we were able to obtain plasma with a higher density, which was represented by obtaining the plasma parameters (electron density, electric potential, total power, number density of argon ions, electron temperature, number density of excited argon atoms) where the high density in the generated plasma provides a greater degree in material processing, which increases the efficiency of the system. These results may aid in future research towards the development of more efficient optimization of plasma parameters which are (electron density, electric potential, total power, number density of argon ions, electron temperature, and number density of excited argon atoms).

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“…Traditional vehicle tracking algorithms predict the vehicle location in consecutive frames based on its position in the previous frames. Some of the well-known tracking algorithms in this category are Kalman filter [1], mean shift [2], particle filter [3] and tracking learning detection [4]. Many methods have been proposed extending these approaches.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Signature based Correlation Filter for Vehicle Tracking in Presence of Clutters and Occlusion

S¹,

Deepu.²

2022

IJACSA

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This vehicle tracking is an important task of smart traffic management. Tracking is very challenging in presence of occlusions, clutters, variation in real world lighting, scene conditions and camera vantage. Joint distribution of vehicle movement, clutter and occlusions introduces larger errors in particle tracking based approaches. This work proposes a hybrid tracker by adapting kernel and particle-based filter with aggregation signature and fusing the results of both to get the accurate estimation of target vehicle in video frames. Aggregation signature of object to be tracked is constructed using a probabilistic distribution function of lighting variation, clutters and occlusions with deep learning model in frequency domain. The work also proposed a fuzzy adaptive background modeling and subtraction algorithm to remove the backgrounds and clutters affecting the tracking performance. This hybrid tracker improves the tracking accuracy even in presence of larger disturbances in the environment. The proposed solution is able to track the objects with 3% higher precision compared to existing works even in presence of clutters.

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Estimation of Rotor Position in Brushless Direct Current Motor by Memory Attenuated Extended Kalman Filter

Cited by 6 publications

References 10 publications

Moving Vehicle Detection and Tracking Based on Video Sequences

Moving Vehicle Detection and Tracking Based on Video Sequences

The Effect of Power on Inductively Coupled Plasma Parameters

Hybrid Deep Learning Signature based Correlation Filter for Vehicle Tracking in Presence of Clutters and Occlusion

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