648 citations
- Sharif University of TechnologyIR34 papers
- Institute for Research in Fundamental SciencesIR29 papers
- University of TehranIR15 papers
- Amirkabir University of TechnologyIR8 papers
- Shahid Beheshti UniversityIR8 papers
- Payame Noor UniversityIR7 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Observatoire de la Côte d’AzurFR6 papers
- University of Applied Science and TechnologyIR6 papers
- Alzahra UniversityIR5 papers
- École Polytechnique Fédérale de LausanneCH5 papers
- Institute for Advanced Studies in Basic SciencesIR5 papers
12 papers · 1 filter
Model-Agnostic Meta-Learning for Fault Diagnosis of Induction Motors in Data-Scarce Environments with Varying Operating Conditions and Electric Drive Noise
Ali Pourghoraba, MohammadSadegh KhajueeZadeh, Ali Amini +3
Reliable mechanical fault detection with limited data is crucial for the effective operation of induction machines, particularly given the real-world challenges present in industri…
A Flexible Job Shop Scheduling Problem Involving Reconfigurable Machine Tools Under Industry 5.0
Hessam Bakhshi-Khaniki, Reza Tavakkoli-Moghaddam, Zdenek Hanzalek +1
The rise of Industry 5.0 has introduced new demands for manufacturing companies, requiring a shift in how production schedules are managed to address human centered, environmental,…
A Reconfigurable Approximate Computing RISC-V Platform for Fault-Tolerant Applications
Arvin Delavari, Faraz Ghoreishy, Hadi Shahriar Shahhoseini +1
The demand for energy-efficient and high performance embedded systems drives the evolution of new hardware architectures, including concepts like approximate computing. This paper…
MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping
Amirreza Fateh, Mohammad Reza Mohammadi, Mohammad Reza Jahed Motlagh
Few-shot Semantic Segmentation addresses the challenge of segmenting objects in query images with only a handful of annotated examples. However, many previous state-of-the-art meth…
Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms
Fatemeh Askari, Amirreza Fateh, Mohammad Reza Mohammadi
In the context of few-shot classification, the goal is to train a classifier using a limited number of samples while maintaining satisfactory performance. However, traditional metr…
Estimation Enhancing in Optoelectronic Property: A Novel Approach Using Orbital Interaction Parameters and Tight-Binding
Ali Haji Ebrahim Zargar, Ali Amini, Ahmad Ayatollahi
This paper advocates for an innovative approach designed for estimating optoelectronic properties of quantum structures utilizing Tight-Binding (TB) theory. Predicated on the compa…