Publications associées à Ageing-aware battery discharge prediction with deep learning

Inversing the actuation cycle of dielectric elastomer actuators for a facial prosthesis

Paolo Germano, Yves Perriard, Yoan René Cyrille Civet, Alexis Boegli, Thomas Guillaume Martinez, Stefania Maria Aliki Konstantinidi, Quentin Philippe Mario De Menech

Dielectric Elastomer Actuators (DEAs) are a type of smart material described as compliant capacitors. They show impressive performances as soft actuators, such as a high strain and fast response. Nonetheless, replicating natural muscle function with DEAs h ...

Society of Photo-Optical Instrumentation Engineers (SPIE)2024

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Florent Evariste Forest, Yunhong Che

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Electrochemical devices and computer science: water/thermal management of proton exchange membrane fuel cells and electrolyzers in different scales

Hossein Pourrahmani

The current restrictions on the registration of combustion engines in different countries and the harmful impacts of fossil fuels on the environment and human health have motivated decision-makers to use batteries and/or fuel cells as alternatives for comb ...

EPFL2023

Optimal Depth of Discharge for Electric Batteries with Robust Capacity-Shrinkage Estimator

Thomas Alois Weber

A user’s benefit from the energy stored in a battery over its lifetime depends on the time-varying characteristics of the battery, which are in turn affected by the chosen usage behavior. Both the capacity shrinkage and the number of lifetime cycles are st ...

2024

Dynamic Behavior of Spatially Confined Sn Clusters and Its Application in Highly Efficient Sodium Storage with High Initial Coulombic Efficiency

Xu Xu, Kumar Varoon Agrawal, Lei Zhang, Kangning Zhao

Advanced battery electrodes require a cautious design of microscale particles with built-in nanoscale features to exploit the advantages of both micro- and nano-particles relative to their performance attributes. Herein, the dynamic behavior of nanosized S ...

Wiley-V C H Verlag Gmbh2024

DEMO toroidal field coil fast discharge unit integration studies

Roberto Guarino, Alberto Ferro

The Fast Discharge Units (FDUs) of the Superconducting (SC) Toroidal Field (TF) coils in the European demonstration fusion power plant DEMO warrant the machine integrity over its full lifetime against severe failure events, such as SC coil quenches or any ...

Elsevier Science Sa2024

Integrated Wireless Power, Data Communication, and Thermal Sensing System for Autonomous Multisite Brain Implants

Implanted medical devices (IMDs) have been widely developed to support the monitoring and recording of biological data inside the body or brain. Wirelessly powered IMDs, a subset of implantable electronics, have been proposed to eliminate the limitations r ...

EPFL2024

Flexible free-standing Fe-CoP-NAs/CC nanoarrays for high-performance full lithium-ion batteries

Chen Huang, Kangning Zhao

In this study, a flexible, free-standing Fe -doped CoP nanoarrays electrode for superior lithium -ion storage has been successfully fabricated. The electrode combines the advantages of a Fe -doping and a flexible carbon cloth (CC) support, resulting in a h ...

Academic Press Inc Elsevier Science2024

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Lakmal Buddika Meegahapola

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

EPFL2024

A pre-fatigue training strategy to stabilize LiCoO2 at high voltage

Lei Zhang, Rui Xia, Kangning Zhao

Layered cathodes are among the most promising cathodes for high-energy-density Li-ion batteries, yet hindered by the structural degradation from both bulk strain and surface oxygen loss at high voltage (above 4.5 V). Herein, we report a pre-fatigue trainin ...

Royal Soc Chemistry2024

Partial discharge localization in power transformer tanks using machine learning methods

Farhad Rachidi-Haeri, Marcos Rubinstein, Hamidreza Karami

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Nature Publishing Group2024

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

David Atienza Alonso, Giovanni Ansaloni, Flavio Ponzina, Amirhossein Shahbazinia, José Angel Miranda Calero, Jonathan Dan

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

IEEE2024

Characterization of low-temperature plasmas generated by dielectric barrier discharges for bacterial inactivation

Lorenzo Ibba

Low-temperature plasmas (LTPs) at atmospheric pressure hold great promise for disinfection and sterilization applications. When compared to traditional sterilization technologies like autoclaving, LTPs may offer several benefits, including reduced energy c ...

EPFL2024

Predicting the long-term collective behaviour of fish pairs with deep learning

Francesco Mondada, Alexandre Massoud Alahi, Vaios Papaspyros

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

2024

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Alireza Modirshanechi

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

EPFL2024

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Thiên-Anh Claris Nguyen, Marc Conrad Russwurm, Gaston Jean Lenczner

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

MEMS-based Tunable Metasurface for Reflective Display Applications

Dorian Giraud Herle

In an era where portable electronic devices are indispensable for a wide range of activities, the need for displays that provide both long-lasting battery life and excellent visibility in different lighting conditions is increasingly important. Emissive di ...

EPFL2024

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Seyed Mohammad Mahdi Johari

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

EPFL2024

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)

Jean-Philippe Thiran, Roser Vinals Terres

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

Zenodo2024

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (2/6)

Jean-Philippe Thiran, Roser Vinals Terres

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

EPFL Infoscience2024