Search

Your search keyword '"Rostami A"' showing total 1,036 results
Start Over Author "Rostami A" Publisher ieee
1,036 results on '"Rostami A"'

1. Adaptive Dynamic Surface Control for Uncertain Nonlinear Time-Delay Cyber-Physical Systems Under Injection and Deception Attacks

Author

Danial Rostami and Marzieh Kamali

Subjects
Cyber-physical systems, injection and deception attacks, adaptive dynamic surface control, time-delay systems, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Nowadays, cyber-physical systems (CPSs) have gained remarkable attention in electrical and control engineering. However, their vulnerability to attacks poses challenges for control. This report explores the adaptive dynamic surface control approach for CPSs against both injection and deception attacks on states and the control signal with unknown direction, suggesting using Nussbaum functions. Given the comprehensive nature of nonlinear time-delay CPSs, this research focuses on designing an adaptive resilient controller for such systems. To address the challenges of time-delay CPSs without restrictive assumptions, specific even functions are applied to resolve signal zeroing in the controller’s denominator due to time-varying delays. Utilizing these functions in dynamic surface control, presents challenges that are addressed in this study. The proposed method ensures the boundedness of all closed-loop signals and two simulation examples are given to show how well it works.

Published
2024
Full Text
View/download PDF

2. Intelligent Energy Management for Full-Active Hybrid Energy Storage Systems in Electric Vehicles Using Teaching–Learning-Based Optimization in Fuzzy Logic Algorithms

Author

Seyed Mehdi Rakhtala Rostami and Zeyad Al-Shibaany

Subjects
Energy management, full-active hybrid energy storage system, electric vehicle, ultracapacitor, TLBO fuzzy control, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Electric vehicles (EVs) are a compelling alternative for mitigating CO2-equivalent emissions. In the context of EVs, the architecture and operational efficiency of a hybrid energy storage system (HESS) are pivotal. The present study focuses on a HESS model based on a parallel full-active configuration that integrates a lithium-ion (Li-ion) battery with an ultracapacitor facilitated by two direct current-to-direct current converters. The energy management strategy governing the HESS emerges as a critical element in the overall performance of EVs. Conventionally, fuzzy control strategies have been extensively utilised at the supervisory and high-control levels; these are largely dependent on expert experience. This paper introduces an innovative fuzzy control system that employs teaching–learning-based optimisation (TLBO) in the energy management strategy domain of a HESS in EVs. This intelligent control mechanism aims to optimise various performance metrics – such as energy consumption, lithium battery output current, and peak power. The optimisation focuses on fine-tuning the parameters constituting the fuzzy rule base and membership functions. Simulation results substantiate the adaptability of the TLBO-based fuzzy energy management system in efficiently allocating power across various driving conditions. Comparative analyses are conducted with respect to the power-sharing capabilities of the proposed TLBO-fuzzy (TLBO-F), particle swarm optimisation-fuzzy (PSO-F), and non-optimisation-fuzzy (NO-F) strategies under two distinct driving conditions: the urban dynamometer driving schedule and the European extra-urban driving cycle. The main objective of this research is to extend battery lifetime by minimising both the lithium battery output power and overall energy consumption. In this way, the study aims not only to prolong the lifespan of Li-ion batteries but also to mitigate the associated energy costs for battery charging. Ultimately, the proposed method enhances the power sharing of the ultracapacitor during a span of 1400 seconds. It also confirms the battery ratio power, ${R.P}_{b}^{\Box } $ , is decreased for the proposed method (TLBO-F) to 6.4% compared to the PSO-F strategy and is decreased 35% relative to the NO-F algorithm. The battery’s state of charge is increased to 14% by implementing the TLBO-F method relative to the PSO-F and increased to 30% compared to the NO-F algorithm. This diminishes the charging and discharging rate of the battery during the driving cycle while extending the lifespan of the Li-ion battery and adheres to the ultracapacitor’s state-of-charge constraints.

Published
2024
Full Text
View/download PDF

3. All-Optical Broadband QDs Semiconductor Optical Amplifier (QDs-SOA): Inhomogeneous Broadening

Author

Mahdiyeh Eyvazi, Reza Yadipour, and Ali Rostami

Subjects
Semiconductor optical amplifier, QD-SOA, optical pumping, QDs, inhomogeneous broadening, broadband, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The escalating demand for increased traffic capacity and bandwidth in communication networks has spurred the exploration of innovative solutions. This article delves into the promising features of Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs), specifically focusing on InAs 0.4 Sb0.6 quantum dots within an InP quantum well structure. The study utilizes optical pumping to achieve population inversion in the active region, thereby enhancing efficiency and overcoming challenges associated with electrical pumping. Through numerical simulations employing the Finite Difference Time Domain (FDTD) technique, the article successfully demonstrates the amplification of a $6\mu \text{m}$ wavelength optical signal, achieving a gain of 7.8 times (8.92 dB). Furthermore, the impact of inhomogeneous and homogeneous broadenings on the amplifier’s structure and gain is thoroughly explored, exposing their crucial roles in molding the optical gain profile. In conclusion, the study posits that QD-SOAs, coupled with optical pumping, present a promising candidate for advancing communication networks by providing higher capacities and improved performance.

Published
2024
Full Text
View/download PDF

26. A Prediction of Time Series Driving Motion Scenarios Using LSTM and ESN

Author

Mohammad Reza Chalak Qazani, Farzin Tabarsinezhad, Houshyar Asadi, Chee Peng Lim, Adetokunbo Arogbonlo, Shehab Alsanwy, Shadi Mohamed, Mehrdad Rostami, and Saeid Nahavandi

Subjects
modelling and prediction, computing methodologies, signal processing, manipulators
Abstract

The motion signals are generated for a simulator user based on the visual understanding of the environment using virtual reality. In this respect, a motion cueing algorithm (MCA) is employed to reproduce the motion signals based on the real driving motion scenarios. Advanced MCAs are required to predict precise driving motion scenarios. Nonetheless, investigations on effective methods for predicting the driving motion scenarios accurately are limited. Current state-of-the-art studies mainly focus on the averaged motion signals from several simulator users pertaining to a specific map or from feedforward neural network and non-linear autoregressive. The existing methods are unable to yield precise predictions of the driving scenarios. In this research, the echo state network and long short-term memory models are employed for the first time in MCA to forecast the driving motion signals. Our evaluation proves the efficiency of our proposed methods in comparison with existing methods.

Published
2022

40. A Physical Layer Multicast Precoding and Grouping Scheme for Bandwidth Minimization

Author

Francisco J. Martin-Vega, F. Javier Lopez-Martinez, Gerardo Gómez, and Farshad Rostami Ghadi

Subjects
Mathematical optimization, General Computer Science, Multicast, Computer science, Quality of service, General Engineering, Physical layer, Transmitter power output, Precoding, 5G networks, TK1-9971, Signal-to-noise ratio, Bandwidth allocation, user grouping, Bandwidth (computing), General Materials Science, multicast precoding, Electrical engineering. Electronics. Nuclear engineering, clustering
Abstract

Physical layer multicasting exploits multiple antennas at the transmitter side to deliver a common message to a group of $K$ users. To this end, two formulations have been well addressed in the literature: i) the max-min-fair criterion, which maximizes the signal-to-noise ratio (SNR) of the worst user for a fixed transmit power; and ii) the quality of service (QoS) formulation, which minimizes the transmit power subject to a target SNR. Nevertheless, it is known that the performance and complexity of these approaches is severely degraded as the group size grows. In this paper, we propose a different formulation that aims at minimizing the required bandwidth needed to provide the multicast service. This is achieved by dividing the users into smaller groups and assigning the bandwidth required to provide a target rate to each group. Contrary to the common belief, it is shown that dividing the users into different groups that use orthogonal bandwidth allocations can lead to a smaller aggregated bandwidth than the single-group with single bandwidth allocation counterpart, if an intelligent grouping scheme is used. An iterative algorithm to derive the optimal number of groups is presented with an stopping criterion to reduce the numerical complexity. It is shown through simulation that our proposed approach greatly reduces the required bandwidth compared to existing schemes that rely on single bandwidth allocation. Interestingly, results reveal that our proposed scheme also leads to a greater SNR for a randomly chosen user, and it reduces the variance of the required bandwidth, which eases the implementation in real networks.

Published
2021

46. Partial Two-Stage Four-level Inverter for Grid-tied PV Application

Author

Jahan, Hossein Khoun, Shotorbani, Amin Mohammadpour, Noshahr, Mohammad Reza Rostami, Peimani, Mansour, Sabahi, Mehran, and Blaabjerg, Frede

Subjects
Voltage, Inverters, Total harmonic distortion, Photovoltaic systems, Leakage currents
Abstract

In this paper, a novel inverter, termed as Triple-dc-Link Inverter (TDCLI), is proposed. This inverter uses three dc links to develop a four-level ac voltage. The utilized dc links are the input dc source, which is provided by a PV unit, and two dc voltages that are developed by using a unique dc-dc stage. Hence, only one dc source is required to synthesize the TDCLI. In grid-tied Photovoltaic (PV) application of the proposed inverter, the leakage current, one of the main problems in grid-tied PV systems, is tackled. The other feature of the proposed inverter is developing a four-level boosted voltage that reduces the Total Harmonic Distortion (THD) value. The simulations are provided to lend credence to the viability and feasibility of the proposed inverter.

Published
2022

50. Optical Modeling and Characterization of Demyelinated Nerve Using Graphene-Based Photonic Structure

Author

Amir Maghoul, Ali Rostami, Mladen Veletic, Bige Deniz Unluturk, Nilojan Gnanakulasekaran, and Ilangko Balasingham

Subjects
General Computer Science, General Engineering, General Materials Science
Abstract

The myelin sheath, as an insulation layer in nerve cells, plays an essential role in neural communication and signal conduction. Loss of myelin, referred to as demyelination, is associated with many mental disorders. Detecting the tiny demyelinated parts in nerve fibers can provide early diagnosis of some mental disorders and create effective treatment plans. This paper establishes a new engineering approach for differentiation between demyelinated and myelinated axons by analyzing spectral responses resulting from the optical simulation framework. We propose computational modeling on the photonic communication of nerve fibers and develop a graphene-based neurophotonic device that can be used to detect the regions demyelinated on the nerve fiber. We first model a nanoscale thin-film configuration of the multilayered myelinated axon to evaluate photons transmission in the nerve fibers under geometric defects as demyelination. Then, the nerve’s optical characteristics are achieved by focusing on the reflectance of light incidence on the nerve model with the change of the demyelination size to distinguish demyelinated—from myelinated nerves by the spectral contrast. Undertaking the different levels of demyelination progression, we theoretically explore the variations of effective refractive index using an analytical solution technique. Ultimately, we design a nanostructure configured with silicon dioxide, graphene, and gold nanoparticles to function as a biochip recognizing myelinated axon damage under the surface plasmon effect. This device can promote a practical procedure to distinguish nanoscale demyelinated and myelinated axons, which can be utilized for neural sensing of tiny brain tissues as a neurophotonic needle.

Published
2022

Catalog

Books, media, physical & digital resources
See catalog results