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Reliable detection of unknown transient change profile by the FMA test.
No full textInternational audienceThe sequential reliable transient change detection by the Finite Moving Average (FMA) test is considered. Unlike the traditional quickest change detection, which assumes that the post-change period is infinitely long, sometimes it is necessary to detect a change with a delay upper-bounded by . All detections that exceed the required time to alert are assumed missed. A transient change occurs at an unknown (but non random) change-point
An initial investigation for employing ACH depth function in degradation model selection: A case study with real data
No full textInternational audienceIn degradation modeling, stochastic processes often do not meet the classical properties necessary for traditional goodness-of-fit tests. This paper presents an initial investigation into employing the ACH depth function and its potential in degradation model selection. We commence by presenting various stochastic processes as degradation models and their selection criteria. Subsequently, we delve into the ACH depth function, highlighting its potential in this context. Through simulated data, we assess the application of this functional depth measure for model selection. The methodology's validity is further reinforced by its application to real-world data, underscoring its effectiveness.</div
Technology development for nano-pillars fabrication in silicon carbide to enhance light collection from color centers
No full textInternational audienc
Détectabilité des défauts en présence de paramètres de nuisance linéaires et tenant compte du bruit hétéroscédastique des images numériques
No full textInternational audienceThis paper addresses two general problems of imaging systems used for visual inspection and defect detection. On the one hand, the inspected object should be carefully removed in order to detect a potential defect or an anomaly. On the other hand, one of the features of imaging systems is that the noise level depends on the image intensity and so does the detectability of defects. In addition, due to the aging of the acquisition system (LEDs, Reflector), the intensity of the illumination decreases gradually over time. The present paper addresses jointly the impact of aging imaging systems and its ensuing impact on the detectability of a defect in the presence of linear nuisance parameters and signal-dependent noise
Predictive Degradation Modelling Using Artificial Intelligence: Milling Machine Case Study
No full textInternational audiencePredictive degradation modelling using artificial intelligence involves employing artificial intelligence techniques to anticipate the deterioration or aging of systems, equipment, or materials over time. This approach is particularly valuable in various industries such as manufacturing, healthcare, energy, and transportation, where the timely prediction of degradation can enable proactive maintenance, reduce downtime, and enhance overall system reliability. In degradation modelling, the first hitting/passage time refers to the moment when degradation stochastic process or degradation random variable reaches a predetermined threshold or specific value for the first time, which is crucial in predicting the remaining useful life and making informed decisions regarding maintenance schedules and asset management strategies. Units that fail before reaching a degradation threshold often indicates premature failures, which is a significant concern in reliability analysis as it suggests that the units did not degrade as expected and failed earlier than anticipated. To address this challenge in this article, the first hitting degradation value is introduced to be modelled through artificial intelligence technics. Furthermore, the milling machine degradation data is used to model the machine status using LSTM model, and the degradation trend is predicted using sequential models to forecast the machine status
Characterization of the life cycle carbon footprint of the automobile industry in the Republic of Korea by environmentally extended input-output model
No full textInternational audienceThe automobile industry is a major economic driver in Republic of Korea (Korea). However, little is known about its climate change contribution. This study estimates the direct and indirect greenhouse gases (GHG) emissions of the Korean automobile industry for the first time, by using a 2017 environmentally extended input-output (EEIO) model integrated by energy balance, GHG inventory and input-output table of 2017. The results show that the final demand of Korean automobile industry led to 8.4% of national GHG emissions in 2017, mostly because of indirect emissions embodied in the supply chain. The study also found that the Scope 1, Scope 2 and Scope 3 emissions on average accounted for 3.0%, 2.9%, and 94.7%, respectively. This highlights the contribution of the upstream supply chain such as primary metals (40.9%) and electricity (32.5%) when assessing the GHG emissions. Finally, the study underscores that carbon taxes could have a significant impact on the competitiveness of automobile export. Overall, this study provides valuable insights on countermeasures by identifying the GHG emissions characteristics of the automobile industry. The results of the study could be used to develop policies and strategies to reduce the GHG emissions and promote sustainable practices in the Korean automobile industry
Co-construction d'un outil d'assistance à l'analyse de textes par théorisation ancrée
No full textNational audienceCet article présente notre projet doctoral visant à améliorer l'assistance des chercheurs en sciences humaines utilisant la méthode par théorisation ancrée (GTM) dans l'analyse qualitative. L'objectif de ce projet est de créer un outil informatique aligné sur cette méthode, en mettant l'accent sur l'exploration des données, la lecture des entretiens et l'articulation de cas. La méthodologie implique la collaboration avec des chercheurs et étudiants. L'outil informatique prévu vise à simplifier le codage, guider la décision d'enquête, explorer les données textuelles à l'aide de l'intelligence artificielle, et soutenir l'analyse théorique par des visualisations augmentées
Retour réflexif sur l'utilisation d'une approche socio-métabolique pour l'analyse de la résilience des Systèmes Agri-Alimentaires : le cas du projet REAACC
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Maintenir la scolarisation des refus scolaires : conceptualisation d'un protocole d'accueil scolaire utilisant les technologies numériques.
No full textInternational audienceSchool Refusal (SR) can affect one pupil per class and last for several months or years. These pupils, who are more or less absent from school, represent a major challenge for schoolteachers, who find it difficult to understand these disorders and put in place solutions to keep them in school. For their part, SR pupils talk about the need to be able to build a future for themselves at school. An in-school reception protocol with face-to-face and distance learning sessions is drawn up on the basis of the needs of the pupils and the difficulties of dealing with them in the school environment. A contact person ensures good communication between all those involved. Digital technology is proposed as a mediator between the teachers, who send the educational content provided in class, and the anxious pupils who refuse to attend school. In this way, a new ecological intervention and monitoring system based on a program of actions has been devised to meet the needs of SR pupils