S.V. Shojaedini; M. Rahimi Nejad; R. Kasbgar Haghighi
Abstract
In this article a new method is introduced for geolocating of signal emitters which is based on evolutionary computation (EC) concept. In the proposed method two well-known members of EC techniques including Bees Algorithm (BA) and Genetic Algorithm (GA), are utilized to estimate the positions of emitters ...
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In this article a new method is introduced for geolocating of signal emitters which is based on evolutionary computation (EC) concept. In the proposed method two well-known members of EC techniques including Bees Algorithm (BA) and Genetic Algorithm (GA), are utilized to estimate the positions of emitters by optimizing the hyperbola equations which have been resulted from Time Difference of Arrival (TDOA) of their radiated signals. To show the effectiveness of the EC concept in positioning the simulation is carried for linear and nonlinear moving emitters in presence of several amounts of noise. Then obtained results are compared with Maximum Likelihood (ML) estimator as one of the most common approaches among traditional methods. The results showed better performance of the EC family compared to ML in such way that they estimate the position of emitters even up to 33% and 30% more accurate than ML in presence of 5 and 10 percent of noise respectively. Furthermore the comparison among the examined methods belong to EC family shows that BA leads to the accuracy of 3 to 12 percent better than GA in estimating positions of radiation sources.
M. Ahmadi Darmani; H. Hooshyar
Abstract
Axial Flux Permanent Magnet (AFPM) machines are attractive candidates for Electric Vehicles (EVs) applications due to their axial compact structure, high efficiency, high power and torque density. This paper presents general design characteristics of AFPM machines. Moreover, torque density of the machine ...
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Axial Flux Permanent Magnet (AFPM) machines are attractive candidates for Electric Vehicles (EVs) applications due to their axial compact structure, high efficiency, high power and torque density. This paper presents general design characteristics of AFPM machines. Moreover, torque density of the machine which is selected as main objective function, is enhanced by using Genetic Algorithm (GA) and variation of PM characteristics, based on sizing equation and Finite Element Analysis (FEA). Then, torque ripple of the motor is reduced according to the effect of PM characteristics on Torque Ripple Factor (TRF). The designed machine produces sinusoidal back-EMF waveform. The torque density is improved and the torque ripple is reduced. The results are validated by using 3D-FEA (FEA) . Furthermore, to assess the obtained results by FEA method, an advanced vehicle simulator (ADVISOR) software is used to demonstrate the performance improvement over the Europe test drive cycles.
