A flexible laboratory platform for multi-disciplinary electrical engineering courses

Aguirre, Marcos; Sood, Vijay K.; Pimentel, Julio G

doi:10.1109/ccece.2015.7129355

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…They focused on controller development for optimum power extraction of the PV pannel. A time delay based high-speed BLDC controller has been adopted in [15], [16]. Therein, the BLDC motor controller yields better stability.…”

Section: Frequently Used Symbols R Smentioning

confidence: 99%

Fault-Tolerant BLDC Motor-Driven Pump for Fluids With Unknown Specific Gravity: An Experimental Approach

Khan

Miah

2020

IEEE Access

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This paper presents an experimental approach for the design of a brushless DC (BLDC) motordriven pump that is intended to pump fluids with unknown specific gravity. We propose a control mechanism that will enable the pump to operate at a higher efficiency irrespective of fluid types. Experimental results from a prototype pump system controlled by two different open-loop controllers are used for determining the optimized closed-loop control algorithm to run the pump while tracking its higher efficiency point. A set of computer simulations is conducted based on a conventional finite-element method to design the best possible BLDC motor, which is employed in the proposed fault-tolerant and efficient pump. Experimental results are presented to validate the efficiency of the proposed pump and also compared with that of a commercial motor design simulator.

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Section: Frequently Used Symbols R Smentioning

confidence: 99%

Fault-Tolerant BLDC Motor-Driven Pump for Fluids With Unknown Specific Gravity: An Experimental Approach

Khan

Miah

2020

IEEE Access

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BLDC Motor-Driven Fluid Pumping System Design: An Extrapolated Active Learning Case Study for Electrical Machines Classes

et al. 2020

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Construction and Optimization of Deep Learning-based Innovative Experimental Platform for Mathematical Intelligence in Electricity Major

Wang,

Xiong

2024

Applied Mathematics and Nonlinear Sciences

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Due to the lack of management capability of the model development process on traditional experiment platforms, they cannot meet the continuous experimentation, reproducibility, and traceability needs of university researchers in the model development process. For this reason, the authors propose research on the design and optimization of a platform for digital intelligence innovation experimentation that is combined with deep learning. Through a theoretical analysis of the key technologies used in the deep learning development platform, the authors summarise the design structure of the deep learning innovation experiment platform. The algorithm management module, training management module, and model deployment module primarily construct this structure. Aiming at the problem of the slow computation speed of the platform in large data sets and high-dimensional space, a gradient descent algorithm is used to optimise the platform. The platform is validated and analyzed in terms of performance testing and application effects. The results show that the experimental platform optimized by the gradient descent algorithm has a higher throughput than the traditional experimental platform, and the difference is 0.1~1.5. Also, class A’s total score on experimental reflection ability (2.58±0.877) was significantly higher than class B’s (1.48±0.377) after the experiment, and the p-value was less than 0.05, which showed that the new experimental platform was more likely to improve students’ experimental reflection ability than the old way of teaching.

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A flexible laboratory platform for multi-disciplinary electrical engineering courses

Cited by 3 publications

References 6 publications

Fault-Tolerant BLDC Motor-Driven Pump for Fluids With Unknown Specific Gravity: An Experimental Approach

Fault-Tolerant BLDC Motor-Driven Pump for Fluids With Unknown Specific Gravity: An Experimental Approach

BLDC Motor-Driven Fluid Pumping System Design: An Extrapolated Active Learning Case Study for Electrical Machines Classes

Construction and Optimization of Deep Learning-based Innovative Experimental Platform for Mathematical Intelligence in Electricity Major

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