E. Ghasemi Mizuji, A. Abdolali,
Volume 12, Issue 2 (June 2016)
Abstract
The main purpose of this paper is to design, implement and measure a new sample of mantle cloak. A new method called mantle cloak is introduced by cloaking an object by a single, conformal meta-surface which can drastically suppress the scattering of the desired object. In this paper, a grid lattice is placed around a dielectric object as the cloaking structure. Previously, this FSS has been utilized for the cloaking of PEC object; but, since this structure has inductive equivalent circuit on the dielectric, it can be used for cloaking of dielectric cylinder. Numerical simulations of near field and far field of the designed structure are derived to prove the effectiveness of our meta-surface cloak. Also, measurement results of S11 and S21 related to cloaked structure and their comparison with single dielectric proves suitable performance of the designed cloak. The results show more than 20% bandwidth for this structure. This means that this structure is suitable for broadband operations.
H. Shayeghi, A. Ghasemi,
Volume 12, Issue 4 (December 2016)
Abstract
Microgrids is an new opportunity to reduce the total costs of power generation and supply the energy demands through small-scale power plants such as wind sources, photo voltaic panels, battery banks, fuel cells, etc. Like any power system in micro grid (MG), an unexpected faults or load shifting leads to frequency oscillations. Hence, this paper employs an adaptive fuzzy P-PID controller for frequency control of microgrid and a modified multi objective Chaotic Gravitational Search Algorithm (CGSA) in order to find out the optimal setting parameters of the proposed controller. To provide a robust controller design, two non-commensurable objective functions are formulated based on eigenvalues-domain and time-domain and multi objective CGSA algorithm is used to solve them. Moreover, a fuzzy decision method is applied to extract the best and optimal Pareto fronts. The proposed controller is carried out on a MG system under different loading conditions with wind turbine generators, photovoltaic system, flywheel energy, battery storages, diesel generator and electrolyzer. The simulation results revealed that the proposed controller is more stable in comparison with the classical and other types of fuzzy controller.
M. Ghaseminezhad, A. Doroudi, S. H. Hosseinian, A. Jalilian,
Volume 17, Issue 1 (March 2021)
Abstract
Nowadays study of input voltage quality on induction motors behavior has become a controversial subject due to the wide application of these motors in the industry. The impact of grid voltage fluctuations on the performance of induction motors can be included in this area. The majority of papers devoted to the influence of voltage fluctuations on the induction motors are focusing only on the solving of d-q state equations or steady-state equivalent circuit analysis. In this paper, a new approach to this issue is investigated by field analysis which studies the effects of voltage fluctuations on the magnetic fluxes of induction motors. New analytical expressions to approximate the airgap flux density and the torque under-voltage fluctuation conditions are presented. These characteristics are also calculated directly by the finite-element method considering the magnetic saturation and the harmonic fields. Finally, experimental results on a typical induction motor are employed to validate the accuracy of analytical and simulation results.
Ehsan Ghasemi, Seyyed Mohammad Razavi, Sajad Mohamadzadeh,
Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)
Abstract
This study proposes a descriptor-based approach combined with deep learning, which recognizes facial emotions for safe driving. Paying attention to the driver's facial expressions is crucial to address the increasing road accidents. This project aims to develop a Facial Emotion Recognition (FER) system that monitors the driver's facial expressions to identify emotions and provide instant assistance for safety control. In the initial stage, Viola-Jones face detection was employed to detect the facial region, followed by Butterworth high-pass filtering to enhance the identified region for locating the eye, nose, and mouth regions, using Viola-Jones face detection. Secondly, the Local Binary Patterns (LBP) feature descriptor is utilized to extract features from the identified eye, nose, and mouth regions. Using 3 RGB channels, the extracted features from these three regions are fed into RessNet-50 and EfficientNet deep networks. The outputs of the two deep learning models' classifiers are combined and integrated using two ensemble methods: ensemble maximum voting and ensemble mean. Based on these combining classifier rules, the performance was evaluated on the JAFFE and KMU-FED databases. The experimental results demonstrate that the proposed method can effectively and with higher accuracy than other competitors recognize emotions in the JAFFE and KMU-FED datasets. The novelty and originality of this paper lie in its significant application in the automotive industry. Implementing our proposed method in a system capable of high accuracy and precision can help mitigate numerous driving hazards. Our approach has achieved 99% and 98% accuracy on the JAFFE and KMU-FED databases, respectively. This high level of accuracy, coupled with its practical relevance, underscores the innovative nature of our work.
