Electrical Engineering

Convolutional neural networks have been proposed as an approach for classifying data corresponding to labeled and unlabeled datasets. The fast-growing data empowers deep learning algorithms to achieve higher accuracy. Numerous trained models have been proposed, which involve complex algorithms and increasing network depth. The main challenges of implementing deep convolutional neural networks are high energy consumption, high on-chip and off-chip bandwidth requirements, and large memory footprint. Different types of on-chip communication networks and traffic distribution methods have been proposed to reduce memory access latency and energy consumption of data movement. This paper proposes a new traffic distribution mechanism on a mesh topology using distributer nodes by considering memory access mechanism in the AlexNet, VggNet, and GoogleNet trained models. We also propose a flow mapping method (FMM) based on dataflow stationary which reduces energy consumption by 8%.

Background and Objective: Nowadays, the endemic and survival of Coronavirus disease 2019 (COVID-19) are challenging for humanity healthy, which led to death and hard or soft physical diseases and economic damages in the entire world. Durability and life span of COVID-19 are the negative feature and the problematic for transferring the virus via different ways such as environmental factors. Increasing the condensation of particulate matters (PM 2.5 , the atmospheric aerosol) is also challenging for the metropolitans in autumn and winter as the critical seasons of the year. Methods: In this paper, we propose a method for analyzing the relationship between the pollutants (PM 2.5 as an aerosol and Carbon monoxide as the pollutant gas) and the number of COVID-19 infected people (CIP) to determine the COVID-19 outbreak in the pollutant seasons. We analyzed the relationship between COVID-19 outbreak and aerosol, and COVID-19 outbreak and pollutant gas using monitoring air quality monitoring stations (AQMSs) and investigating daily statistics about the number of COVID-19 infected people. We also propose an algorithm based on neural networks that forecasts the normal or critical situation of the COVID-19 outbreak based on the previously estimated the correlation coefficient (CC) and the online reported aerosol condensation from AQMSs. Results: Our work consists of estimating the CC based on the Kendall rank method in Tehran and Isfahan, two major cities of Iran, from February 20 to March 19, 2020. Experimental results show the correlation coefficient approximately 0.5 for aerosol and the number of CIP, and 0.21 for pollutant gas and the number of CIP in Tehran. Our analysis shows a direct relationship between increasing the condensation of PM 2.5 and the number of CIP. In addition, there is no relationship between the number of CIP and Carbon monoxide. We also forecast the situation of Isfahan according to the number of CIP using our proposed algorithm that the results demonstrate the critical situation. Conclusion: Aerosols increase the probability of getting infected COVID-19 due to respiratory disease and the probability of transferring the virus via particulate matters. Our work can be useful to alert people about the critical situation of the city that the results can provide a prevention approach to face COVID-19.

Purpose -The main purpose of this paper is to apply a fast analytical model of the acoustic behaviour of pulse-width modulation (PWM) controlled induction machines to a fractionalslot winding machine. A second goal is to analytically clarify the interaction between space harmonics and time harmonics in audible electromagnetic noise spectrum.

Induction motors optimal design can involve many variables and objectives, and generally requires to make several trade-offs, especially when including the audible electromagnetic noise criterion beyond the usual performance criteria. Multiobjective optimization techniques based on Pareto optimality are useful to help us finding the most interesting solutions and decide which one(s) to adopt. However, it is not always easy to analyse the Pareto-optimal solutions obtained with such methods, especially when treating more than three objectives, and Pareto fronts may contain more data than we might think. This paper briefly describes an analytical model of the variable-speed squirrel-cage induction machine which computes both its performances and sound power level of electromagnetic origin. The model is then coupled to the Non-dominated Sorting Genetic Algorithm (NSGA-II) in order to perform global optimization with respect to several objectives (e.g. noise level, efficiency and material cost). Finally, an optimization problem is solved and analysed, and some useful visualization tools of the Pareto optimal solutions and their characteristics are presented.

This paper presents a method to characterize the main magnetic force waves occurring in a sinusoidally-fed induction machine. Three main force types are identified: slotting force waves, winding force waves and saturation force waves. Slotting force waves are characterized in terms of number of nodes, velocity, propagation direction and magnitude. On the ground of the expression of these forces magnitude, a method to cancel a given magnetic force wave by properly choosing the rotor slot or stator slot opening width is presented. This new method is validated with both simulations and experiments.

The design of variable-speed electrical machines involves several fields of physics, such as electromagnetism, thermics, mechanics but also acoustics. This paper describes the analytical multi-physics models of a computer-aided design (CAD) software which is applied to inverter-fed traction induction machines. The electromagnetic model computes rotor and stator currents, the induction machine traction characteristics, and the radial air-gap flux density. The mechanical and acoustic model computes the motor audible magnetic noise level due to Maxwell forces. The thermal model based on 3D nodal network, computes the transient temperature of different parts of the motor. These fast models make it possible to couple the software with some optimization tools. Some simulation results are presented on a self-ventilated closed motor and compared to experiments.

This paper derives the analytical characterization of Maxwell radial vibrations due to Pulse-Width Modulation (PWM) supply in induction machines, and especially in traction motors supplied with an asynchronous switching frequency. The number of nodes and the velocity of these particular force waves are experimentally validated by visualizing some operational deflection shapes of the stator. It is shown that according to the switching frequency, these forces can be responsible for high magnetic noise levels during starting and braking. A simple rule to avoid PWM noise is then proposed, and applied to an industrial traction motor.

This paper presents a fully analytical model of the electromagnetic and vibro-acoustic behavior of variable-speed squirrel-cage induction machines. This model is integrated in a fast simulation tool that can be used to design motors with low magnetic noise level on their whole speed range.

Numerous empirical rules have been established in order to rightly choose the number of stator and rotor slots and limit the audible magnetic noise level radiated by induction machines. However, these rules never take into account the stator natural frequencies neither the fact that the motor is run at variable speed.

This paper derives the analytical characterization of Maxwell radial vibrations due to saturation effects in induction machines, and especially in traction motors. The number of nodes and the velocity of these particular force waves are experimentally validated by visualizing some operational deflection shapes of the stator. It is shown that according to the stator and rotor slot numbers, and stator natural frequencies, these forces can be responsible for high magnetic noise levels during electrical starting and braking. A simple rule to avoid saturation magnetic noise is then proposed, and applied to an industrial motor. A fully analytical model of the motor vibro-acoustic behavior, including saturation effects, is finally presented. Simulation results show that the new proposed motor improves magnetic noise level up to 20 dB, whereas experiments give a 15 dB improvement.

This paper presents the full analysis of the vibration behaviour responsible for audible magnetic noise in a PWM-fed fractional-slot induction machine, including theoretical predictions, numerical simulations, and experimental validations (stator modal analysis and deflection shapes visualisation). Magnetic force waves due to slotting harmonics, pulse-width modulation (PWM) harmonics and their interactions are characterised in terms of nodes number, rotation speed, and propagation direction.

An analytical model of PWM controlled induction machine was developed in order to predict its sound power level of electromagnetic origin at variable speed. The electromagnetic model calculates the stator and rotor currents with a multilayer single-phase equivalent circuit. The mechanical and acoustic parts are based on a cylindrical shell stator model. After a description of these models, this paper

Nowadays, the development of the electric motorization for the land vehicles is essential since the energetic challenge is become very critical. Electrical machines dedicated to this operating segment have some specifics of torque characteristics (high saturation level, important flux weakening) over the speed range. This paper presents the development of a tool used for the optimal acoustic and electromechanical modeling which stands out from standard analytical and Finite Element modeling respectively because of a high accuracy of calculations and a quickness of the resolution. Some results of comparison between acoustic measurement and simulation for different speed will be presented.