Modélisation graphique probabiliste pour la maîtrise des risques, la fiabilité et la synthèse de lois de commande des systèmes complexes

Mes travaux de recherche sont menés au Centre de Recherche en Automatique de Nancy (CRAN), dans le département Ingénierie des Systèmes Eco-Techniques (ISET) sous la responsabilité de B. Iung et de A. Thomas et le département Contrôle - Identification - Diagnostic (CID) sous la responsabilité de D. Maquin et de G. Millerioux.L’objectif principal de mes recherches est de formaliser des méthodes de construction de modèles probabilistes représentant les bons fonctionnements et les dysfonctionnements d’un système industriel. Ces modèles ont pour but de permettre l’évaluation des objectifs de fonctionnement du système (exigences opérationnelles, performances) et les conséquences en termes de fiabilité et de maîtrise des risques (exigences de sûreté). Ceci nécessite de modéliser les impacts de l’environnement sur le système et sur ses performances, mais aussi l’impact des stratégies de commande et des stratégies de maintenance sur l’état de santé du système.Pour plus de détails.A travers les différents travaux de thèses et collaborations, j’ai exploité différents formalismes de modélisation probabilistes. Les apports majeurs de nos contributions se déclinent en 3 points :• La modélisation des conséquences fonctionnelles des défaillances, structurée à partir des connaissances métiers. Nous avons développés les principes de modélisation par Réseau Bayésien (RB) permettant de relier la fiabilité et les effets des états de dégradation des composants à l’architecture fonctionnelle du système. Les composants et les modes de défaillances sont alors décrits naturellement par des variables multi-états ce qui est difficile à modéliser par les méthodes classiques de sûreté de fonctionnement. Nous proposons de représenter le modèle selon différents niveaux d'abstraction en relation avec l’analyse fonctionnelle. La modélisation par un modèle probabiliste relationnel (PRM) permet de capitaliser la connaissance par la création des classes génériques instanciées sur un système avec le principe des composants pris sur étagère.• Une modélisation dynamique de la fiabilité des systèmes pris dans leur environnement. Nous avons contribué lors de notre collaboration avec Bayesia à la modélisation de la fiabilité des systèmes par Réseau Bayésien Dynamique (RBD). Un RBD permet, grâce à la factorisation de la loi jointe, une complexité inférieure à une Chaîne de Markov ainsi qu’un paramétrage plus facile. La collaboration avec Bayesia a permis l’intégration dans Bayesialab (outil de modélisation) de ces extensions et notamment l’utilisation de paramètres variables dans le temps élargissant la modélisation des RBD à des processus Markoviens non homogènes.• La synthèse de la loi de commande pour l’optimisation de la fiabilité du système. Nous travaillons sur l’intégration de la fiabilité dans les objectifs de commande des systèmes sous contrainte de défaillances ou de défauts. Nous posons aujourd’hui le problème dans un contexte général de commande. Nous proposons une structuration du système de commande intégrant des fonctions d’optimisation et des fonctions d’évaluation de grandeurs probabilistes liées à la fiabilité du système. Nos travaux récents sont focalisés sur l’intégration, dans la boucle d’optimisation de la commande, des facteurs issues d’une analyse de sensibilité de la fiabilité du système par rapport aux composants

Systèmes cognitifs artificiels : du concept au développement de comportements intelligents en robotique autonome

Les travaux présentés dans le cadre de cette habilitation à diriger des recherches s’appuient sur le principe de la robotique développementale et plus particulièrement sur le paradigme de l’énaction. L’idée n’est donc pas de développer un robot intelligent, mais plutôt de concevoir un robot qui soit capable de le devenir. L’originalité du travail présenté dans ce mémoire repose sur le fait que le système cognitif artificiel est décomposé en deux parties distinctes : la première regroupe des processus cognitifs « inconscients » et la deuxième concerne les processus cognitifs « conscients ». Les processus cognitifs inconscients correspondent aux aptitudes (pré-programmées ou apprises) fonctionnant de manière quasi-automatique, alors que les processus cognitifs conscients contribuent au développement et à l’apprentissage de nouvelles aptitudes. La cognition associée au robot est donc le résultat d’un processus de développement par lequel le robot devient progressivement plus habile et acquiert les connaissances lui permettant d’interpréter le monde qui l’entoure.Ce mémoire se décompose en trois grandes parties. La première partie correspond à un curriculum vitae détaillé présentant l’ensemble de mon parcours professionnel. La deuxième partie est consacrée à la présentation plus approfondie de mes activités de recherches qui se sont focalisées sur le développement de systèmes cognitifs artificiels appliqués à la robotique avec des applications dans les domaines de la locomotion bipède, la perception et l’acquisition autonome de connaissances ainsi que les systèmes multi-robots et l’intelligence distribuée. Enfin, la troisième partie est une compilation de quatre articles de revue représentatives de l’ensemble de mes travaux de recherches

Rethinking Pedagogy: Exploring the Potential of Digital Technology in Achieving Quality Education

(First Paragraph) The Mahatma Gandhi Institute of Education for Peace and Sustainable Development (MGIEP) is UNESCO’s Category 1 education Institute in the Asia-Pacific region devoted to education for peace and sustainable development, as enshrined in SDG Target 4.7. UNESCO MGIEP promotes the use of digital learning platforms where teachers and students can co-create and share a highly interactive learning experience. With the rise of the internet, there has been a proliferation of online content and digital resources intended to support teaching and learning, albeit widely varying in quality. Digital education media and resources, if carefully designed and implemented, have a significant potential to be mobilized on a massive scale to support transformative learning for building sustainable, flourishing societies

Proceedings of the 5th International Workshop "What can FCA do for Artificial Intelligence?", FCA4AI 2016(co-located with ECAI 2016, The Hague, Netherlands, August 30th 2016)

International audienceThese are the proceedings of the fifth edition of the FCA4AI workshop (http://www.fca4ai.hse.ru/). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification that can be used for many purposes, especially for Artificial Intelligence (AI) needs. The objective of the FCA4AI workshop is to investigate two main main issues: how can FCA support various AI activities (knowledge discovery, knowledge representation and reasoning, learning, data mining, NLP, information retrieval), and how can FCA be extended in order to help AI researchers to solve new and complex problems in their domain. Accordingly, topics of interest are related to the following: (i) Extensions of FCA for AI: pattern structures, projections, abstractions. (ii) Knowledge discovery based on FCA: classification, data mining, pattern mining, functional dependencies, biclustering, stability, visualization. (iii) Knowledge processing based on concept lattices: modeling, representation, reasoning. (iv) Application domains: natural language processing, information retrieval, recommendation, mining of web of data and of social networks, etc

Too much, too late? Drug prescribing for older people near the end of life

Background. The burden of drugs prescribed to older persons at the end of life has recently drawn increasing scrutiny. Growing evidence suggests that patients with lifelimiting diseases and poor prognosis are prescribed drugs that may do more harm than good or treatments that have little chance of achieving their benefit during the patients’ short remaining lifespan. The overall aim of this thesis was to evaluate the quality of drug prescribing in older adults near the end of life. Except for Study III, all studies are based on routinely collected administrative and healthcare data with national coverage in Sweden. Study I. We found that, throughout their last year of life, older adults (n=511 843) used an increasing number of prescription drugs. The proportion of older adults exposed to ≥10 drugs increased from 30.3% one year before death to 47.2% during the last month of life. Polypharmacy was fuelled not only by the initiation of symptomatic drugs to ensure comfort but also by the frequent continuation of long-term preventive treatments and medicines prescribed for the management of chronic diseases that may otherwise lead to short-term complications. Study II. Older adults who died with solid cancer (n=151 201) often continued to receive preventive drugs until the very end of life. Over the course of the last 12 months of life, there was little change in the receipt of antihypertensive agents (absolute change -0.3%, 95% CI -0.6 to 0.0), vitamin K antagonists (+1.5%, 95% CI 1.1 to 1.9), antiplatelet agents (- 1.5%, 95% CI -1.8 to -1.2), statins (-4.7%, 95% CI -5.0 to -4.4), bisphosphonates (-0.3%, 95% CI -0.4 to -0.2), or vitamins (+1.0, 95% CI 0.8 to 1.2). During the last year before death, median drug costs amounted to $1482 (interquartile range [IQR],$700-$2896]) per person, including$213 (IQR, $77-$490) for preventive therapies. We found important differences between cancer types with regard to the use and costs of preventive drugs, which can be explained only in part by age and chronic multimorbidity. Study III. Forty European experts in geriatrics, clinical pharmacology, and palliative medicine from 10 different countries participated in a Delphi consensus panel to identify drugs deemed “often adequate”, “questionable”, or “often inadequate” for use in older patients aged ≥75 years with an estimated life expectancy of 3 months or less. Drug classes rated as “often adequate” are predominantly indicated for symptom management and comfort care. Among the drugs and drug classes considered “questionable” for use near the end of life, a vast majority are prescribed for the longterm management of non-life-threatening chronic conditions or for the secondary prevention of chronic diseases that may otherwise quickly lead to serious clinical complications. Finally, drugs defined as “often inadequate” encompasses mostly drugs and supplements prescribed for primary prevention or as part of a long-term strategy of secondary or tertiary prevention. Study IV. By applying the list mentioned above to a cohort of 58 415 older persons who died from conditions potentially amenable to palliative care in 2015, we found that 32% continued and 14% initiated at least one drug considered “often inadequate” during their last three months of life. Excluding older adults who died from acute and potentially unpredictable fatal events had little if any influence on the results. Conclusion. Older people are prescribed an increasing number of drugs as they approach the end of life. A sizeable fraction of these drugs is not directed towards the relief of distressing symptoms but instead aims at prolonging survival and managing chronic comorbidities. We have developed a consensus-based set of explicit criteria for delineating drugs that are “often adequate” from those deemed “questionable” or “often inadequate” for use in older persons at the end of life. In the absence of high-quality data from randomised clinical trials and sufficiently robust observational studies, these criteria can be used not only to provide guidance at the bedside but also to generate comparable epidemiological evidence across patient groups, care settings, regions, and countries

Actes des 29es Journées Francophones d'Ingénierie des Connaissances, IC 2018

International audienc

The Democratization of Artificial Intelligence

After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms

The Democratization of Artificial Intelligence: Net Politics in the Era of Learning Algorithms

After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms

The Democratization of Artificial Intelligence

After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms

Proceedings of the 9th Arab Society for Computer Aided Architectural Design (ASCAAD) international conference 2021 (ASCAAD 2021): architecture in the age of disruptive technologies: transformation and challenges.

The ASCAAD 2021 conference theme is Architecture in the age of disruptive technologies: transformation and challenges. The theme addresses the gradual shift in computational design from prototypical morphogenetic-centered associations in the architectural discourse. This imminent shift of focus is increasingly stirring a debate in the architectural community and is provoking a much needed critical questioning of the role of computation in architecture as a sole embodiment and enactment of technical dimensions, into one that rather deliberately pursues and embraces the humanities as an ultimate aspiration