Publications associées à Identification of AC Distribution Networks With Recursive Least Squares and Optimal Design of Experiment

Publications associées (14)

Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

Devis Tuia, Javiera Francisca Castillo Navarro, Valérie Zermatten

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datas ...

2025

Graph Neural Networks With Adaptive Structures

Zepeng Zhang

Graph neural networks (GNNs) have made significant progress in various machine learning tasks. Despite their success, many existing GNN models are shown to be vulnerable to adversarial attacks, creating a stringent need to build robust GNN architectures. I ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Robust and Personalized Federated Learning: Handling Data Corruption and Heterogeneity

Ljubomir Rokvic

Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling collaborative model training across decentralized devices while preserving data privacy. However, FL's success is highly contingent on the quality and integrity ...

EPFL2025

Adaptive Interface-Pinns (Adai-Pinns) for Transient Diffusion: Applications to Forward and Inverse Problems in Heterogeneous Media

Brice Tanguy Alphonse Lecampion

We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs ...

Elsevier BV2025

Short-term risk-management for hydro-wind-solar hybrid energy system considering hydropower part-load operating characteristics

Shushu Zhang

Nowadays, this type of hydropower units is frequently forced to operate in the part-load region due to their complementary operation with uncertain wind and solar power. This poses potential risk to hydro-wind-solar hybrid energy system (HWSHES). However, ...

2024

Quantum Variational Algorithms: Exploring Applications and Potentials Amid Trainability and Classical Simulability Limitations

Su Yeon Chang

Since its proposal, quantum computing has made significant strides in various domains. Among the different emerging techniques, Variational Quantum Algorithms (VQAs) have become one of the most promising approaches in the current era, where quantum computa ...

EPFL2024

Enhancing multivariate, multi-step residential load forecasting with spatiotemporal graph attention-enabled transformer

Wenlong Liao

Short-term residential load forecasting (STRLF) holds great significance for the stable and economic operation of distributed power systems. Different households in the same region may exhibit similar consumption patterns owing to the analogous environment ...

2024

Neural Distributed Controllers with Port-Hamiltonian Structures

Giancarlo Ferrari Trecate, Muhammad Zakwan

Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly ...

Institute of Electrical and Electronics Engineers Inc.2024

Distributed Machine Learning Targeting Embedded Systems for Epilepsy Detection

Saleh Baghersalimi

Epileptic seizure detection and monitoring is critical in healthcare, particularly for individuals requiring continuous oversight. Current methods, primarily based on electroencephalogram (EEG) technologies, face limitations due to their complexity in term ...

EPFL2024

Unconstrained Learning of Networked Nonlinear Systems via Free Parametrization of Stable Interconnected Operators

Giancarlo Ferrari Trecate, Luca Furieri, Danilo Saccani, Leonardo Massai

This paper characterizes a new parametrization of nonlinear networked incrementally L2 -bounded operators in discrete time. The distinctive novelty is that our parametrization is free - that is, a sparse large-scale operator with bounded incremental L2 gai ...

Institute of Electrical and Electronics Engineers Inc.2024

Bootstrapping Autonomous Driving Radars with Self-Supervised Learning

Haitham Al Hassanieh, Junfeng Guan

The perception of autonomous vehicles using radars has attracted increased research interest due its ability to operate in fog and bad weather. However, training radar models is hindered by the cost and difficulty of annotating largescale radar data. To ov ...

IEEE2024

A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation

Thomas Simon Spencer Trigo Trindade

We consider the problem of efficiently simulating stochastic models of chemical kinetics. The Gillespie stochastic simulation algorithm (SSA) is often used to simulate these models; however, in many scenarios of interest, the computational cost quickly bec ...

2024

MixedLAB: Mixed Reality to Teach Students Experimental Knowledge in Microfabrication

Jürgen Brugger, Qinglan Shan

We are developing a new approach to leverage Mixed Reality (MR) affordances for teaching microfabrication processes, such as performed in a cleanroom. Our goal is to design interactive MR content to improve microfabrication and cleanroom training practices ...

2024

Materials science experiments by robotic automation

Vincenzo Scamarcio

The increasing demand for precision, reproducibility, and scalability in scientific research has driven the development of advanced robotic systems for laboratory automation. This thesis presents the design, implementation, and validation of a mobile robot ...

EPFL2024