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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Sabo, Aliyu Jibia, Kabir Garba Shahinzadeh, Hossein Hayati, Mohammad Mohsen Abapour, Mehdi Gharehpetian, Gevork B. Department of Electrical and Electronic Engineering Malaysia Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tabriz Iran Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798331533946

The growing demand for reliable and sustainable energy has driven researchers and engineers to explore renewable energy sources to complement traditional power grids. Wind energy conversion systems (WECS), particularly those using permanent magnet synchronous generators (PMSG), are among the most widely adopted renewable energy solutions. Integrating WECS into traditional grids introduces challenges, including power system oscillations, especially during disturbances or faults. Inertia control and power electronics converters are pivotal in mitigating these oscillations and ensuring grid stability. This review investigates the design and optimization of damping controllers, emphasizing their integration with excitation systems to enhance the dynamic performance of PMSG-based wind integrated grids. Additionally, the role of power electronics in control strategies and inertia emulation for stable grid operation is discussed. The findings highlight the critical importance of advanced control measures in addressing the inherent uncertainties of renewable energy systems integrated with conventional grids. © 2025 IEEE.

关键词： Synchronous generators

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PS-IPS: Deploying Intrusion Prevention System with machine learning on programmable switch

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Future Generation computer Systems 2024年 152卷 333-342页

作者： Lee, Alan Y.-P. Wang, Michael I.-C. Hung, Chi-Hsiang Wen, Charles H.-P. Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Computer Engineering New York University Tandon School of Engineering United States Advanced SDN Technology Research Center National Yang Ming Chiao Tung University Taiwan

Intrusion prevention is significant to avoid device damage and financial losses. Researchers have proposed various Intrusion Prevention Systems (IPS) to prevent malware, including traditional and SDN-based IPS. However, existing IPSs suffer from low throughput problems caused by detection and rule-installation delays. Here, we propose a programmable switch-base IPS (named PS-IPS), which utilizes the switch CPU and pipeline to detect malware. PS-IPS consists of four main components: (1) parser, (2) flow filter, (3) recirculation director, and (4) malware detector. According to the experiment, PS-IPS achieves a 183X throughput than the SDN-based IPS. The response time of PS-IPS is also reduced by 99.99%, showing that PS-IPS effectively prevents malware with a single programmable switch. © 2023

关键词： Machine learning

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Nonlinear coupling of closely spaced modes in atomically thin MoS2 nanoelectromechanical resonators

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Microsystems & Nanoengineering 2024年第6期10卷 581-591页

作者： S M Enamul Hoque Yousuf Steven W.Shaw Philip X.-L.Feng Department of Electrical and Computer Engineering University of FloridaGainesvilleFL 32611USA Department of Mechanical and Civil Engineering Florida Institute of TechnologyMelbourneFL 32901USA Department of Mechanical Engineering Department of Physics and AstronomyMichigan State UniversityEast LansingMI 48423USA

Nanoelectromechanical systems(NEMS)incorporating atomic or molecular layer van der Waals materials can support multimode resonances and exotic nonlinear *** we investigate nonlinear coupling of closely spaced modes in a bilayer(2L)molybdenum disulfide(MoS_(2))nanoelectromechanical *** model the response from a drumhead resonator using equations of two resonant modes with a dispersive coupling term to describe the vibration induced frequency shifts that result from the induced change in *** employ method of averaging to solve the equations of coupled modes and extract an expression for the nonlinear coupling coefficient(λ)in closed *** thermomechanical noise spectral measurements are used to calibrate the vibration amplitude of mode 2(a_(2))in the displacement *** drive mode 2 near its natural frequency and measure the shifted resonance frequency of mode 1(f_(1s))resulting from the dispersive *** model yieldsλ=0.027±0.005 pm^(-2)·μs^(-2) from thermomechanical noise measurement of mode *** model also captures an anomalous frequency shift of the undriven mode 1 due to nonlinear coupling to the driven mode 2 mediated by large dynamic *** study provides a direct means to quantifyingλby measuring the thermomechanical noise in NEMS and will be valuable for understanding nonlinear mode coupling in emerging resonant systems.

关键词： dynamics coupling nonlinear

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Classification of ASD Based on fMRI Data Using Two Hybrid Learning Approaches 19

Classification of ASD Based on fMRI Data Using Two Hybrid Le...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Gerdefaramarzi, Sara Soltani Hakimdavoodi, Hamidreza Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798331507565

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition marked by challenges in communication, heightened sensitivity to sensory input, difficulties in adapting to change, and atypical patterns of play. Since traditional methods of diagnosing this disorder rely heavily on behavioral assessments, they are highly prone to errors. Consequently, it is necessary to provide a reliable method for diagnosing this mental disorder by leveraging advanced machine-learning techniques. In this paper, two hybrid learning approaches that only use fMRI data have been designed and implemented. The first method is the simultaneous training of a deep autoencoder and a single-layer perceptron (SLP) to extract beneficial features and properly tune the model's hyperparameters for classification. The second method combines a support vector machine model with a data augmentation strategy using simultaneous training of a generative adversarial network and a deep autoencoder to prevent model overfitting. It is worth mentioning that in the two proposed methods, the Extra-Trees algorithm is initially used for dimensionality reduction and feature selection. These two methods have been implemented on the entire publicly available dataset released by the Autism Brain Imaging Data Exchange, and both results have been compared. Our study in this paper achieved a maximum accuracy of 70.8%, sensitivity of 67.4%, and specificity of 75.3%. These results indicate that our proposed approaches have performed significantly better than many recent studies in this field. © 2024 IEEE.

关键词： Generative adversarial networks

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Capacitor voltage unbalance minimization for three-phase five-level diode-clamped inverter using hexagonal hysteresis space vector modulation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 661-671页

作者： Ramkumar, R. Singh, Manmohan Ramkumar, R. Usha, S. Thentral, T. M. Thamizh Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computer Engineering and Application GLA University Mathura India Department of Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Renewable Energy University of Maroua National Advanced School of Engineering of Maroua Maroua Cameroon

This article suggests a method for diminishing the voltage unbalance in a three-phase five-level diode-clamped inverter (DCI) through the use of hexagonal hysteresis space vector modulation (HHSVM). Capacitor voltage balancing leads to enhanced system efficiency, reduced stress on components, enhanced performance, abridged electromagnetic interference, and reduced total harmonic distortion. The proposed modulation technique and its implementation are thoroughly examined in this study, along with modeling and experiment data that show how efficient the method is at lowering the capacitor voltage unbalance in the proposed five-level DCI. Capacitor voltage unbalance is reduced with the use of this HHSVM approach to 0.95%, which is a superior reduction compared to traditional PWM methods. The paper also discusses the advantages of the proposed method over other existing methods, making it a promising solution for practical applications in power electronics systems.

关键词： Diode-clamped inverter (DCI) Capacitor voltage balancing Hexagonal hysteresis space vector modulation (HHSVM) Total harmonic distortion

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Integrating Adaptive Control Allocation with Simple Adaptive Control for an Aircraft Model under Actuator Loss of Effectiveness 10

Integrating Adaptive Control Allocation with Simple Adaptive...

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Kefayat, Mohammad Hossein Kamali, Marzieh Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798331509972

The paper proposes an innovative strategy for adaptive control allocation for redundant actuators in the presence of uncertainties. We introduce a novel reconfiguration control scheme designed for aircraft experiencing actuator loss of effectiveness. The proposed method integrates adaptive control allocation with simple adaptive control (SAC) to effectively manage and compensate for the uncertainties and variations in the actuator's effect. In aircraft, adaptive control allocation is a technique that dynamically distributes control commands to control surfaces to achieve optimal flight performance, even in the event of an actuator fault. As a redundant system, the research civil aircraft model (RCAM) presents simulation results illustrating the effectiveness of the proposed method. © 2024 IEEE.

关键词： Aircraft models

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Statistical curriculum learning: An elimination algorithm achieving an oracle risk 37

Statistical curriculum learning: An elimination algorithm ac...

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37th Annual Conference on Learning Theory, COLT 2024

作者： Cohen, Omer Meir, Ron Weinberger, Nir Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Israel

We consider a statistical version of curriculum learning (CL) in a parametric prediction setting. The learner is required to estimate a target parameter vector, and can adaptively collect samples from either the target model, or other source models that are similar to the target model, but less noisy. We consider three types of learners, depending on the level of side-information they receive. The first two, referred to as strong/weak-oracle learners, receive high/low degrees of information about the models, and use these to learn. The third, a fully adaptive learner, estimates the target parameter vector without any prior information. In the single source case, we propose an elimination learning method, whose risk matches that of a strong-oracle learner. In the multiple source case, we advocate that the risk of the weak-oracle learner is a realistic benchmark for the risk of adaptive learners. We develop an adaptive multiple elimination-rounds CL algorithm, and characterize instance-dependent conditions for its risk to match that of the weak-oracle learner. We consider instance-dependent minimax lower bounds, and discuss the challenges associated with defining the class of instances for the bound. We derive two minimax lower bounds, and determine the conditions under which the performance weak-oracle learner is minimax optimal. © 2024 O. Cohen, R. Meir & N. Weinberger.

关键词： Contrastive Learning

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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computer Modeling in engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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ENTITY-CENTRIC REINFORCEMENT LEARNING FOR OBJECT MANIPULATION FROM PIXELS 12

ENTITY-CENTRIC REINFORCEMENT LEARNING FOR OBJECT MANIPULATIO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Haramati, Dan Daniel, Tal Tamar, Aviv Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Israel

Manipulating objects is a hallmark of human intelligence, and an important task in domains such as robotics. In principle, Reinforcement Learning (RL) offers a general approach to learn object manipulation. In practice, however, domains with more than a few objects are difficult for RL agents due to the curse of dimensionality, especially when learning from raw image observations. In this work we propose a structured approach for visual RL that is suitable for representing multiple objects and their interaction, and use it to learn goal-conditioned manipulation of several objects. Key to our method is the ability to handle goals with dependencies between the objects (e.g., moving objects in a certain order). We further relate our architecture to the generalization capability of the trained agent, based on a theoretical result for compositional generalization, and demonstrate agents that learn with 3 objects but generalize to similar tasks with over 10 objects. Videos and code are available on the project website: https://***/view/entity-centric-rl. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Removing Speckle Noise from Synthetic-Aperture Radar Images Using Artificial Intelligence Techniques 11

Removing Speckle Noise from Synthetic-Aperture Radar Images ...

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11th International Symposium on Telecommunication, IST 2024

作者： Moein, Fateme Taban, Mohammad Reza Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798350356250

In recent times, Synthetic-Aperture Radar (SAR) images have gained significant importance due to their extensive use in both military and civilian applications. However, these images are often compromised by speckle noise, which severely hampers their interpretability and utility across various domains. This paper explores a range of image denoising techniques, with a particular focus on deep learning-based approaches, while also considering traditional methods. Given the critical nature of the subject, real SAR images have been rigorously analyzed. The study emphasizes that speckle noise in real SAR images is far more detrimental than in simulated scenarios, revealing the limitations of artificial intelligence algorithms when used in isolation for denoising tasks. To address this challenge, the proposed algorithm in this paper first preprocesses the real data using classical methods before applying a neural network for denoising. Both quantitative metrics and visual assessments demonstrate that this hybrid approach not only outperforms standard denoising methods but also preserves critical information in SAR images more effectively. © 2024 IEEE.

关键词： Radar imaging

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