Sepúlveda-Oviedo, Edgar Hernando Travé-Massuyès, Louise Subias, Audine Pavlov, Marko Alonso, Corinne
This research proposes a method for detecting subtle faults named snail trails for their visual similarity with the trail of a snail in photovoltaic modules. Snail trails do not significantly reduce panel performance but they are the main cause of serious panel deterioration such as microcracks and delamination and can go so far as to set the panel...
EL Abed, Maha Dauvignac, Jean-Yves Lantéri, Jérôme Migliaccio, Claire
International audience
Borzooei, Sahar Tournier, Pierre-Henri Dolean, Victorita Migliaccio, Claire
International audience
Sbihi, Mohammed Couellan, Nicolas
There are many real life applications where data can not be effectively represented in Hilbert spaces and/or where the data points are uncertain. In this context, we address the issue of binary classification in Banach spaces in presence of uncertainty. We show that a number of results from classical support vector machines theory can be appropriat...
Choudhury, Nanda Mukherjee, Rohan Yadav, Rambalak Liu, Yang Wang, Wei
This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyz...
van Dreven, Jonne Boeva, Veselka Abghari, Shahrooz Grahn, Håkan Al Koussa, Jad
This study introduces a novel systematic approach to address the challenge of labeled data scarcity for fault detection and diagnosis (FDD) in District Heating (DH) systems. To replicate real-world DH fault scenarios, we have created a controlled laboratory emulation of a generic DH substation integrated with a climate chamber. Furthermore, we pres...
Prasshanth, C.V. Venkatesh, Naveen Sugumaran, V. Aghaei, Mohammadreza
Photovoltaic (PV) modules play a pivotal role in renewable energy systems, underscoring the critical need for their fault diagnosis to ensure sustained energy production. This study introduces a novel approach that combines the power of deep neural networks and machine learning for comprehensive PV module fault diagnosis. Specifically, a fusion met...
Oufadel, Ayoub Azouzoute, Alae Ghennioui, Hicham Soubai, Chaimae Taabane, Ibrahim
This article proposes a novel approach to photovoltaic panel inspection through the integration of image classification and meteorological data analysis. Utilizing two convolutional neural network models with distinct architectures for classifying thermal and red, green, blue (RGB) images of photovoltaic installations, in addition to an support vec...
Attar, Ali El Wehby, Ayoub Chbib, Fadlallah Mehrez, Hassane Aissaoui Fadlallah, Ahmad Hachem, Joel Khatoun, Rida
Cyberattacks against the Internet of Vehicles (IoV) will continue to evolve as the industry continues to adopt new and advanced connected technologies. These advances should result in a complex ecosystem that integrates different technologies (5G, 6G, Cloud, IoT, etc.) and presents a large attack surface. Denial of service (DoS) attacks are among t...
Koffi, Tresor Mourchid, Youssef Hindawi, Mohammed Dupuis, Yohan
Falls among individuals, especially the elderly population, can lead to serious injuries and complications. Detecting impact moments within a fall event is crucial for providing timely assistance and minimizing the negative consequences. In this work, we aim to address this challenge by applying thorough preprocessing techniques to the multisensor ...
