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PEOPLE@HES-SO - Verzeichnis der Mitarbeitenden und Kompetenzen
PEOPLE@HES-SO - Verzeichnis der Mitarbeitenden und Kompetenzen

PEOPLE@HES-SO
Verzeichnis der Mitarbeitenden und Kompetenzen

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Ouerhani Nabil

Ouerhani Nabil

Professeur-e HES ordinaire

Hauptkompetenzen

Human-Machine Interaction

Human-Robot Interaction

Multimodal interaction

IoT Internet Of Things

Multi-agent systems

Intelligent Agents

Industry 4.0

  • Kontakt

  • Lehre

  • Forschung

  • Publikationen

  • Konferenzen

Hauptvertrag

Professeur-e HES ordinaire

Haute Ecole Arc - Ingénierie
Espace de l'Europe 11, 2000 Neuchâtel, CH
DING

Nabil Ouerhani is a Professor in Computer Science at the Haute Ecole Arc // HES-SO. Besides his teaching activities, Nabil Ouerhani is leading the applied research group "Interaction technologies" which main research focus is : Human-Machine Interaction (HMI); Human-Robot Interaction (HRI) and Machine-Machine Interaction (MMI). The research group leverages ICT technologies like IoT, cyber-physical systems, mobile technologies / wearables, Multi-agent intelligent systems, human action understanding in order to conceive and develop innovative Industry 4.0 oriented applications for and in collaboration with industrial partners.

BSc HES-SO en Informatique - Haute Ecole Arc - Ingénierie
  • Java Enterprise Edition
  • Qualité du Logiciel

Laufend

BonsAPPs - AI-as-a-Service for the Deep Edge

Rolle: Mitgesuchsteller/in

Financement: European Commission (H2020)

Description du projet :

BonsAPPs will develop a fully functional (TRL-8), scalable AI-as-a-Service layer (AI-aaS) that will interoperate with the AI on demand platform as an external service. The service layer will enhance an existing AI platform (Bonseyes Marketplace) to cover experimentation, benchmarking, deployment and secure licensing of AI solutions at the Deep Edge. Project-funded Use Cases will demonstrate how Bonseyes Marketplace Platform simplifies time-consuming non-functional tasks in AI design, produces AI at a lower cost and offers specific means to scale innovations once put into the market. Two rounds of use cases will drive supply from AI professionals and demand from End Users to explore the potential gains of AI at the Deep Edge. End users, mainly SMEs/non-tech users lacking internal innovation capacities, will receive guidance in launching Industry Challenges fitting their needs. Specialized AI developers and integrators (AI Talents) will engage with them into an open innovation cycle to solve challenges; Edge AI Apps will be developed and integrated in Solutions at the Deep Edge using different deployment platforms. Coordinated by an AI SME-aware of existing barriers to bring AI innovations into the market, eight complementary partners participate: six technology partners in charge of frontend, backend , security and licensing and interoperability with development platforms, (will also provide technical support to the Use Case Owners); a specialist in Open Call Management; a specialist in digital business coordinating communication and dissemination. AI Talents involved will also have the possibility, under specific re-use licensing framework, of commercializing these results (develop new Apps/Solutions for other end users). Engagement of all relevant audiences (AI4EU community, AI researchers/developers, non-tech SMEs, clusters and value-chain leaders) is secured by the outreach capacities of partners to startups, corporate-led/RTO-led AI and Digital Innovation Hubs.

Forschungsteam innerhalb von HES-SO: Ouerhani Nabil , Pazos Escudero Nuria

Partenaires académiques: Tutschku Kurt, BTH; Benini Luca, University of Bologna

Partenaires professionnels: LLewllynn Tim, NVISO SA; Bonnefous Jean-Marc, Bonseyes Community Association; Zafalon Roberto, ST Microelectronic, Italy; Zrazinska Izabela, Funding Box; Carrasco Alexandra, ISDI

Durée du projet: 01.01.2021 - 31.12.2023

Montant global du projet: 5'750'000 CHF

Statut: Laufend

Decolleteur 4.0 - AI-enabled solution for automatic generation of Swiss-Type lathe machine-tool programs.

Rolle: Hauptgesuchsteller/in

Financement: Innosuisse

Description du projet :

Decolleteur 4.0 is the starting point for a 7 years roadmap established by our industrial partners (Tornos SA and CNC Software Europe SA)    with the goal to develop and commercialize a disruptive AI-enabled solution for automatic machine-tools programming and setup allowing manufacturing industry to boost productivity and    agility. The project has the ambition to lay the cornerstones of an artificial intelligence enabled solution for 1) automatic generation of Swiss-Type lathe machining programs from 3D part models and 2) highly assisted tool-machine setup. The solution aims at reducing by 50\%the lead time    and the effort of programming Swiss-Type lathe machine-tools.

Forschungsteam innerhalb von HES-SO: Ouerhani Nabil , Montavon Raphaël

Partenaires académiques: Weiss Lukas, Inspire AG; Gamberdella Luca, IDSIA // SUPSI

Partenaires professionnels: Neuenschwander Patrick, Tornos SA; Saner Matthieu, CNC Software Europe

Durée du projet: 01.06.2019 - 30.10.2021

Montant global du projet: 1'217'809 CHF

Statut: Laufend

Abgeschlossen

Robots collaboratifs apprenants par démonstration
AGP

Rolle: Mitarbeiter

Description du projet : Les robots industriels et collaboratifs sont de nos jours de plus en plus utilisés dans l'industrie. Cependant déployer un robot afin d'effectuer une tâche ou alors reprogrammer un robot pour effectuer une tâche différente que la tâche initiale pour lequel il a été installé reste complexe et chronophage. Ainsi, durant ce projet, une nouvelle manière permettant de simplifier la programmation d'un robot est proposée. L'opérateur effectue l'opération manuellement. Les gestes de l'opérateur et les mouvements de l'outil qu'il tient sont enregistrés à l'aide d'un capteur HTC VIVE Tracker. Ainsi, les points de la trajectoire sont obtenus. A la fin de la démonstration, l'opérateur peut visualiser les points enregistrés à l'aide des lunettes Microsoft HoloLens. Si nécessaire, la trajectoire enregistrée peut être filtrée afin de la lisser et/ou supprimer certains points aberrant. Une fois la trajectoire validée, celle-ci peut être rejouée par le robot. Les points de la trajectoire sont fournis à MoveIt, un générateur de trajectoire pour robot s'exécutant sur ROS (Robot Operating System). Celui-ci génère de manière automatique une trajectoire pour chaque articulation du robot. La trajectoire calculée est ensuite simplement transmise au contrôleur du robot qui l'exécute. Cette solution permet ainsi de programmer les trajectoires d'un robot par une simple démonstration. Elle peut être utilisée avec des robots collaboratifs mais également avec des robots industriels standards.

Forschungsteam innerhalb von HES-SO: Rizzotti Aïcha , Moor Lucien , Kunze Marc , Ouerhani Nabil , Depierraz Luc , Jeanneret Loïck , Muller Hugo , Bracamonte Javier

Partenaires académiques: HES-SO Rectorat; IAI; Technologie d'interaction

Durée du projet: 02.02.2020 - 31.08.2022

Montant global du projet: 220'000 CHF

Statut: Abgeschlossen

TherMoMac - Cyber-Physical Systems for Modelling and Compensation of Thermal Deviations in Turning Machine-Tools

Rolle: Hauptgesuchsteller/in

Financement: HES-SO

Description du projet :

TherMoMac est un projet pluridisciplinaire qui a su mobiliser différentes compétences de la HES-SO comme la mécanique, les systèmes embarqués et l’informatique afin d’améliorer l’état de l’art dans le domaine de la compensation thermique des machines-outils. Le projet a permis la mise au point d’une méthodologie hybride de prédiction de l’erreur thermique des machines en combinant des modèles déterministes (modélisation par éléments finis) et des modèles non déterministes (apprentissage automatique / Machine Learning). Les méthodes mises en place donnent des excellents résultats en termes de précision de prédiction même pour des machines dont la dérive thermique est dans l’ordre de quelques micromètres.

Forschungsteam innerhalb von HES-SO: Ouerhani Nabil , Haas Patrick , Rizzotti Aïcha , Loehr Bernard

Partenaires académiques: Ouerhani Nabil, HE-Arc

Durée du projet: 01.01.2018 - 31.12.2020

Montant global du projet: 220'000 CHF

Statut: Abgeschlossen

ECOMAC25 - Cyber-Physical System (CPS) for a 25% reduction of the energy consumption on Tornos tool-machines.

Rolle: Hauptgesuchsteller/in

Financement: Innosuisse

Description du projet :

This project aims at conceiving and developing a cyber-physical system and a software toolbox that enables the reduction of power consumption of Swiss Type Lathe machine-tools by 25%. The optimization approach combines deterministic methods that rely on domain knowledge and expertise in machines-tools and non-deterministic methods that rely on data-driven Machine Learning. Machine and process data are collected in real-time using an Industrial Internet of Things (IIoT) solution based on a wireless sensor network.

Forschungsteam innerhalb von HES-SO: Ouerhani Nabil , Jeannerat Claude

Partenaires professionnels: Neuenschwander Patrick, Tornos SA

Durée du projet: 01.09.2017 - 31.01.2020

Montant global du projet: 439'062 CHF

Statut: Abgeschlossen

Bonseyes - An open platform for the development of systems of artificial intelligence from cloud to edge devices

Rolle: Partner/in

Requérant(e)s: LLewllynn Tim, NVISO SA

Financement: European Commission (H2020)

Description du projet :

Bonseyes is an open and expandable AI platform. It will transform AI development from a cloud centric model, dominated by large internet companies, to an edge device centric model through a marketplace and an open AI platform. In contrast to existing solutions that require a high level of expertise, time, and cost to add AI to embedded products, Bonseyes provides access to advanced tools and services that can be obtained through a marketplace and eco-system of collaborative leading academic and industrial partners. This will allow for a major reduction in cost and time to enable products with cognitive and AI capabilities at an European and global level. Bonseyes will enable Europe to become a leading global player in the coming “AI-as-a-Service” economy.

Forschungsteam innerhalb von HES-SO: Ouerhani Nabil , Pazos Escudero Nuria

Partenaires académiques: Fricker Samuel, FHNW; Deniz Oscar, UCLM; Tuzschku Kurt, BTH; Ostler Daniel, Technische Universität München (TUM); Storkey Amos, University of Edinburgh; Goumas Georgios, INSTITUTE OF COMMUNICATION AND COMPUTER SYSTEMS

Partenaires professionnels: LLewllynn Tim, NVISO SA

Durée du projet: 01.01.2017 - 31.12.2019

Montant global du projet: 7'404'518 CHF

Url des Projektstandortes: https://www.bonseyes.eu/

Statut: Abgeschlossen

Etude d'une micro-usine autonome et interconnectée capable de produire des pièces microtechniques diverses
AGP

Rolle: Mitarbeiter

Requérant(e)s: Conception des moyens de production

Financement: HES-SO Rectorat

Description du projet : Le point de départ du concept de micro-usine est la problématique de réduction des temps de trajet domicile-travail et le désengorgement des zones périurbaines. Pour replacer l'usine dans la ville ou les régions désindustrialisées, elle doit être peu polluante, faible consommatrice d'énergie, à faible pollution sonore et facilement reconfigurable. Une chaîne de micromachines « simples » peut avantageusement remplacer une machine-outil traditionnelle pour fabriquer de petites pièces de précision en répondant au besoin suivant : ' Produire des pièces de petite taille avec des équipements de petite dimension, qui assurent une meilleure maîtrise des dispersions ' Disposer d'équipements de production flexibles et facilement reconfigurables ' Bénéficier d'une capacité de production de précision pour des petites et des moyennes séries ' Réduire la consommation d'espace et d'énergie ' Spécialiser les machines sur un faible nombre d'opérations plutôt que de disposer de machines encombrantes capables de tout réaliser La faisabilité technique de la micro-usine 4.0 que nous souhaitons étudier découle d'une idée originale de nos étudiants de 3ème année en conception de produit et design (filière IDE-CED). Le faible encombrement de la machine Micro5 autorise non seulement sa mise en ligne flexible, mais également une verticalisation en une sorte de micro-usine flexible en « étagère », ce qui permet d'optimiser la place utilisée dans un encombrement donné. Celle-ci sera accompagnée d'une étude C-lean permettant de valoriser les gains économiques et sociétaux futurs que cette vision autorise.

Forschungsteam innerhalb von HES-SO: Rouvé Nicolas , Jupille Dany , Jeannerat Claude , Riess Raymond , Gay des Combes Arnaud , Bourquin Vincent , Visinand Steve , Gillioz Simon , Grandi Athos Shasa , Steulet Mathieu , Ouerhani Nabil , Amez-Droz Philippe , Montavon Raphaël , Kurz Leo , Bouchardy Loïc , Lambrughi Alessandro , Santos De Pinho Dylan , Monney Nils , Murith Noé , Loehr Bernard , Pazos Escudero Nuria , Pasquier Richard

Partenaires académiques: FR - EIA - Institut SeSi; Conception des moyens de production; TTN - IDEc / Conception des moyens de production

Durée du projet: 03.04.2017 - 31.10.2019

Montant global du projet: 138'060 CHF

Statut: Abgeschlossen

Crowd-Sourcing au service de l'amélioration de l'Ergonomie des Interfaces web en se basant sur des solutions « cloud » combinant des méthodes empiriques et analytiques
AGP

Rolle: Hauptgesuchsteller/in

Financement: HES-SO Rectorat

Description du projet : L'Ergonomie des Interfaces utilisateurs représente un attribut de qualité déterminant pour le succès de solutions interactives auprès du public cible. Ceci est particulièrement vrai pour les interfaces de sites web. Malgré le nombre important de publications sur le sujet, les tests d'ergonomie des interfaces utilisateurs, en Suisse, connaissent un niveau d'industrialisation basique. L'approche empirique avec des analyses « manuelles » reste la plus utilisée, avec deux limites majeures : des coûts élevés et une mise en 'uvre laborieuse (laboratoire, mobilisation des testeurs, etc). L'objectif principal de ce projet est de poser les pierres angulaires d'une solution automatisée d'évaluation des interfaces web pour rendre leur analyse ergonomique quantitative, reproductible et moins coûteuse, balisant ainsi le chemin pour une industrialisation des tests d'ergonomie. Pour ce faire, le projet avait pour vocation d'évaluer la qualité des capteurs bon marché (notamment pour le suivi du regard, c'est le modèle eyetribe qui a été retenu) pour analyser le comportement d'un utilisateur et de s'appuyer sur les pratiques des ergonomes professionnels pour automatiser une partie de leurs tâches. L'objectif initial était d'automatiser l'analyse complète pour produire un rapport sur l'utilisabilité de l'interface utilisateur, mais la collaboration avec les ergonomes professionnels nous a montré que cet objectif n'était pas pertinent : une grande part de leur travail repose de des observations et des discussions qui nécessitent un expert humain. Il existe cependant le besoin d'un outil pour faciliter le dépouillement des informations collectées, leurs représentations et l'exploration de ces résultats. C'est donc dans cette direction que le projet a été orienté.

Forschungsteam innerhalb von HES-SO: Baudin Carole , Geslin Philippe , Bussy Gaëtan , Ferrez Pierre , Visinand Steve , Ouerhani Nabil , Maître Gilbert , Grunenwald David , Maillard Laura , Mihet Andreea , Roduit Pierre

Partenaires académiques: VS - Institut Systèmes industriels; Conception de produits centrée utilisateurs; Technologie d'interaction; Ouerhani Nabil, Technologie d'interaction

Durée du projet: 01.10.2014 - 28.02.2017

Montant global du projet: 250'000 CHF

Statut: Abgeschlossen

2024

Platform-agnostic digital twins for safer human-robot collaboration
Buchkapitel ArODES

Aïcha Rizzotti, Loïck Jeanneret, Brendan Studer, Javier Bracamonte, Nabil Ouerhani

Dans Yurish, Sergey Y., Advances in robotics and automatic control  (pp. 25-44). 2024,  s.l. : IFSA Publishing

Link zur Publikation

L'usine du futur : Digital - Dark - Adaptive - Creative
Buchkapitel

Ouerhani Nabil, Sokhn Maria, Carrino Francesco, al. et

,  L'usine du futur : Digital - Dark - Adaptive - Creative. 2024,  Suisse : Georg Éditeur

Zusammenfassung:

Cet ouvrage propose une discussion autour de l'usine du futur et de son développement, soutenu notamment par l'IA. 

De la "Dark Factory" à la "Créative Factory", l’histoire industrielle peut se résumer comme une longue quête de l’automatisation de la fabrication. Les machines de plus en plus sophistiquées et digitalisées permettront d’achever cette quête. Aujourd’hui déjà, des usines industrielles fonctionnent toutes seules pendant des heures, voire des jours. Ce qu'il sera intéressant de découvrir dans la prochaine phase de la révolution industrielle, c'est à quel point nous ferons appel à l’IA pour inventer le monde de demain.

2023

Learning from demonstration and safe cobotics using digital twins
Wissenschaftlicher Artikel ArODES

Aïcha Rizzotti, Loïck Jeanneret, Brendan Studer, Javier Bracamonte, Nabil Ouerhani, Marc Kunze

Sensors transducers journal,  2023, vol. 261, no. 2, pp. 25-32

Link zur Publikation

Zusammenfassung:

The use of collaborative robots, or cobots, is nowadays continually increasing, especially in the small- and medium-sized manufacturing sector. For each particular use case, the integration and deployment of a cobot into a collaborative workspace faces a certain number of challenges. Programming industrial robots, for example, can be a relatively complex and time-consuming task. In this paper we report an accurate method to robot programming by using an optimized “learning from demonstration” technique. The operator/programmer performs in real-time the corresponding task to be automatized, and by means of a tracker sensor the programmer’s motions are captured and transmitted to the robot; the robot registers the trajectories and is now able to reproduce the human movements with high accuracy. Another fundamental issue for cobot deployment is safety. In this paper, we also present a virtual/augmented reality (VR/AR) environment to facilitate the design and operation of cobots in order to maximize human safety. The virtual reality environment operates as an aide tool during the design phase. The human operator and the robot’s digital twin work side-by-side while executing a collaborative task in a virtual reality space. Their movements are controlled and registered, and after a given period of test time, the data is analyzed to suggest modifications to ensure a safe workspace (collision free) and to increase productivity. For the regular real-time cobot operation, an augmented reality environment was developed, again, with the purpose of assuring a safe human-robot collaboration. The augmented reality environment keeps tracking permanently the cobot and the human manipulations. This system produces audio and visual alarm signals in unsafe situations and is also able to take actions, such as slowing down or stopping the robot, to preserve the physical integrity of the human operator.

2022

Data-driven thermal deviation prediction in turning machine-tool :
Wissenschaftlicher Artikel ArODES
a comparative analysis of machine learning algorithms

Nabil Ouerhani, Bernard Loehr, Aïcha Rizzotti, Dylan Santo de Pinho, Adrien Limat, Philippe Schinderholz

Procedia Computer Science,  2022, vol. 200, pp. 185-193

Link zur Publikation

Zusammenfassung:

Thermal error significantly impacts the machining precision of machine-tools. Thermal deformations in the machine-tool structure caused by the various machine heat sources is at the origin of this phenomenon. In order to ensure the expected quality of the parts, manufacturer have to run the machine-tools for hours before start producing in order to reach the machine thermal stability. This heating phase has a high negative impact on the machine productivity on one hand and on its ecological footprint on the other. This paper presents a data-driven approach to model and predict the thermal error in order to correct the tool reference position accordingly. The automatic adjustment of tool position allows to produce parts with the expected quality and precision regardless of the thermal state of the machines, which substantially increase their productivity. For this purpose, temperature sensors as well as high precision tool position measurement instruments are deployed on a Tornos SwissNano4 machine-tool. A set of experiments are conducted to collect data related to these two measurements. Four major Machine Learning algorithms are trained using a subset of the collected data and tested with the remaining data subset. Quantitative and comparative analysis shows that three of the four algorithms have a prediction with a mean Absolute Error (MAE) below 1µm and a Correlation Coefficient higher than 90%. Even classical linear regression models are able to predict the thermal error with high accuracy. Advanced Machine Learning techniques show high potential to provide a better prediction accuracy.

2021

Learning from Demonstration for Collaborative Robots
Wissenschaftlicher Artikel

Rizzotti Aïcha, , Marc Kunze, Loïc Jeanneret, Depierraz Luc, Ouerhani Nabil

Automation, Robotics & Communications for Industry 4.0, 2021

Link zur Publikation

Zusammenfassung:

This article presents ur work from research project which objective is to allow a robot to perform pick & place and assembly tasks by intuitively teaching and programming the robot trajectories from human demonstrations. Based on motion acquisition systems, we aim at developing a system capable of acquiring and analyzing the manipulation actions performed by an operator to extract their primitives and compound characteristics. A Leap Motion sensor with a 3D camera are used to acquire gestures and movements at different scales and objects position. Classification algorithms and deep learning models are used in order to recognize gestures. An expert system is allowing the translation of recognized gestures into robot trajectories. The challenge in our case is the automation of tasks through artifical intelligence. ABB's YuMi Robot is used to validate our solution.

2020

Bonseyes AI pipeline :
Wissenschaftlicher Artikel ArODES
bringing AI to you

Miguel De Prado, Jing Su, Rabia Saeed, Lorenzo Keller, Noelia Vallez, Andrew Anderson, David Gregg, Luca Benini, Tim Llewellynn, Nabil Ouerhani, Rozenn Dahyot, Nuria Pazos Escudero

ACM Transactions on Internet of Things,  2020, vol. 1, no. 4, article no. 26

Link zur Publikation

Zusammenfassung:

Next generation of embedded Information and Communication Technology (ICT) systems are interconnected and collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one of the keys for the next technological revolution: the seamless integration of AI in our daily life. However, training and deployment of custom AI solutions on embedded devices require a fine-grained integration of data, algorithms, and tools to achieve high accuracy and overcome functional and non-functional requirements. Such integration requires a high level of expertise that becomes a real bottleneck for small and medium enterprises wanting to deploy AI solutions on the Edge, which, ultimately, slows down the adoption of AI on applications in our daily life. In this work, we present a modular AI pipeline as an integrating framework to bring data, algorithms, and deployment tools together. By removing the integration barriers and lowering the required expertise, we can interconnect the different stages of particular tools and provide a modular end-to-end development of AI products for embedded devices. Our AI pipeline consists of four modular main steps: (i) data ingestion, (ii) model training, (iii) deployment optimization, and (iv) the IoT hub integration. To show the effectiveness of our pipeline, we provide examples of different AI applications during each of the steps. Besides, we integrate our deployment framework, Low-Power Deep Neural Network (LPDNN), into the AI pipeline and present its lightweight architecture and deployment capabilities for embedded devices. Finally, we demonstrate the results of the AI pipeline by showing the deployment of several AI applications such as keyword spotting, image classification, and object detection on a set of well-known embedded platforms, where LPDNN consistently outperforms all other popular deployment frameworks.

Bonseyes AI Pipeline - Bringing AI to You: End-to-end Integration of Data, Algorithms and Deployment Tools
Wissenschaftlicher Artikel

Pazos Escudero Nuria, De Prado Miguel, Saeed Rabia, Ouerhani Nabil, Tim Llewellyn, Luca Benini

ACM Transactions on Internet of Things, 2020

2019

WirelessHART-based Sensor Network for Multichannel Measurement of machine-tool Energy Consumption in Production Environments
Wissenschaftlicher Artikel

Gay des Combes Arnaud, Mueller Patrice, Pazos Escudero Nuria, Ouerhani Nabil, Neuenschwander Patrick

International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2019), Amsterdam, 2019, 2019

Link zur Publikation

Process Parameters Optimization for Energy Efficiency in Swiss-Type Machining
Wissenschaftlicher Artikel

Ouerhani Nabil, Dylan Santos De Pinho, Neuenschwander Patrick

IARCE 2019 International Conference on Industrial Automation, Robotics and Control Engineering, 2019

Link zur Publikation

Zusammenfassung:

The energy efficiency is a critical issue economically and ecologically, this is why in this project we aim to reduce the energy consumption of the machining by optimizing the process parameters : material feed rate, spindle rotation speed and the depth of cut. We employ a genetic algorithm for which we investigated and compared four different fitness method which approximate experiments done on a Swiss DT13 of Tornos.

2018

GSGS'18 :
Buch ArODES
3rd Gamification & Serious Game Symposium : health and silver technologies, architecture and urbanism, economy and ecology, education and training, social and politics

Stéphane Gobron, Florence Quinche, Yassin Aziz Rekik, Nabil Ouerhani, Samuel Rossetti, Olivier Reutenauer, Gordan Savicic, Antoine Widmer

2018,  Delémont : HE-Arc, HES-SO,  108 p.

Link zur Publikation

Zusammenfassung:

The GSGS’18 conference is at the interface between industrial needs and original answers by highlighting the playful perspective to tackle technical, training, ecological, management and communication challenges. Bringing together the strengths of our country, this event provides a solid bridge between academia and industry through the intervention of more than 40 national and international actors. In parallel with the 53 presentations and demos, the public will be invited to participate actively through places of exchange and round tables.

2017

A Computer Vision System to Localize and Classify Wastes on the Streets
Wissenschaftlicher Artikel

Ouerhani Nabil, Saeed Rad, Jean-Philippe Thiran, Andreas Von Kaenel, André Droux, François Tièche

International Conference on Computer Vision Systems, 2017

2016

iNUIT :
Wissenschaftlicher Artikel ArODES
Internet of Things for Urban Innovation

Francesco Carrino, Elena Mugellini, Omar Abou Khaled, Nabil Ouerhani, Juergen Ehrensberger

Future Internet,  2016, vol. 8, no. 18

Link zur Publikation

Zusammenfassung:

Internet of Things (IoT) seems a viable way to enable the Smart Cities of the future. iNUIT (Internet of Things for Urban Innovation) is a multi-year research program that aims to create an ecosystem that exploits the variety of data coming from multiple sensors and connected objects installed on the scale of a city, in order to meet specific needs in terms of development of new services (physical security, resource management, etc.). Among the multiple research activities within iNUIT, we present two projects: SmartCrowd and OpEc. SmartCrowd aims at monitoring the crowd’s movement during large events. It focuses on real-time tracking using sensors available in smartphones and on the use of a crowd simulator to detect possible dangerous scenarios. A proof-of-concept of the application has been tested at the Paléo Festival (Switzerland) showing the feasibility of the approach. OpEc (Optimisation de l’Eclairage public) aims at using IoT to implement dynamic street light management and control with the goal of reducing street light energy consumption while guaranteeing the same level of security of traditional illumination. The system has been tested during two months in a street in St-Imier (Switzerland) without interruption, validating its stability and resulting in an overall energy saving of about 56%.

iNUIT: Internet of Things for Urban Innovation
Wissenschaftlicher Artikel

Ouerhani Nabil, Carrino Francesco, Abou Khaled Omar, Mugellini Elena, Ehrensberger Jürgen

Future Internet Journal, 2016

2022

Robot, Automation and Process control - state of technology and challenges
Konferenz

Ouerhani Nabil

Swiss Advanced Manufacturing Summer Events (SAMCE), 15.09.2022 - 16.09.2022, ETH Zurich

L’agilité, une révolution dans la production industrielle
Konferenz

Ouerhani Nabil

"Petit déjeuner Agilité" canton du Jura, 30.03.2022 - 30.03.2022, Delémont, Switzerland

2021

Innovation & Talents: Two competitive pillars for the Swiss industry
Konferenz

Ouerhani Nabil

Swiss Innovation Forum, 10.11.2021 - 10.11.2021, Brno, Czeck Rebublic

Tool Position Measurement Methods for Data-Driven Thermal Error Compensation in  High Precision Turning Machine-Tool
Konferenz

Ouerhani Nabil

International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2021), 04.11.2021 - 06.11.2021, Online

Zusammenfassung:

This paper presents part of the results of a research project which aims at modelling and predicting the thermal deviation for a Swiss-Type Lathe in order to estimate the thermal error in real-time and correct the Tool Center Point position accordingly. This thermal compensation is implemented into the Computerized Numerical Control (CNC) of the machine.

The paper focuses on the high precision measurement of the thermal deviation in changing thermal conditions. We have tested and evaluated two techniques of tool position measurement. The first method measures the thermal deviation indirectly through the machined part. Concretely, a test part with various, yet known diameters is machined over a period of 8 hours. The different machined parts are labelled and their diameters are precisely measured. The difference between the theoretical diameters and the effectively machined ones are calculated and considered as proportional to the thermal error of the machine-tool. The second measurement technique is based on probing using a TESA axial probe GT21. The probe is installed so that it measures exactly the relative position of the tool  tip to the spindle. The experiment consist in heating the machine-tool without machining. A part program is simply executed without any raw material in order to avoid measurement perturbation due to material removal. The probes measure, continuously, the relative position of the tool tip.

A set of experiments have been conducted to evaluate the precision of both measurement methods. The indirect measurement method (through the measurement of parts diameters) provides acceptable results for Tornos DT13 machine-tool. The experimental data show a clear correlation between the temperature variation and the part diameter variations. The maximum thermal deviation observed for the DT13 machine-tool amounts to 37μm. However, this method fails to precisely measure the thermal deviation of a Tornos Swiss-Nano machine-tool which is thermally more stable than the DT13. The maximum thermal deviation measured at the SwissNano amounts to 6 μm. The direct probing based measurement method was, however, able to precisely capture the thermal deviation of the SwissNano machine. Using this measurement method, we were able to clearly correlate the thermal deviation and the temperature variations.

We used the latter thermal deviation measurement method to train a Multi Layer Perceptron model (MLPRegressor()) in order to predict the thermal error based on a temperature vector measured at different positions of the SwissNano machine. Validation with different test data (as compared to the training data), shows a correlation of 97.3% for the X-axis and 89.4% for the Y-axis. The Mean Absolute Error (MAE) for the X-axis is 0.167 μm and for the Y-axis is 0.183 μm. The results clearly show the potential to precisely predict the thermal error that can be used to continuously adjust the tool reference position. 

Daten & Intelligenz
Konferenz

Ouerhani Nabil, Samuel Fricker

F&E-KONFERENZ ZU INDUSTRIE 4.0, 12.04.2021 - 12.04.2021, Brugg, Switzerland

IoT-enabled solution for dynamic street light control and management
Konferenz

Ouerhani Nabil

IoT Week, 12.04.2021 - 09.06.2017, Geneva, Switzerland

Learning from demonstration for collaborative robots
Konferenz ArODES

Aïcha Rizzotti, Marc Kunze, Loïc Jeanneret, Luc Depierraz, Nabil Ouerhani

Proceedings of the 1st IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0 (ARCI’ 2021)

Link zur Konferenz

Zusammenfassung:

This article presents ur work from research project which objective is to allow a robot to perform pick & place and assembly tasks by intuitively teaching and programming the robot trajectories from human demonstrations. Based on motion acquisition systems, we aim at developing a system capable of acquiring and analyzing the manipulation actions performed by an operator to extract their primitives and compound characteristics. A Leap Motion sensor with a 3D camera are used to acquire gestures and movements at different scales and objects position. Classification algorithms and deep learning models are used in order to recognize gestures. An expert system is allowing the translation of recognized gestures into robot trajectories. The challenge in our case is the automation of tasks through artifical intelligence. ABB's YuMi Robot is used to validate our solution.

2020

Cyber-Physical System for Data-Driven Modelling and Prediction of Thermal Deviation in Turning Machine-Tools
Konferenz

Ouerhani Nabil, Rizzotti Aïcha, Loehr Bernard, Santos De Pinho Dylan, Schindelholz Philippe

4th International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2020), 13.09.2020 - 14.09.2020, Prague, Czech Republic

Multi-Agent System based solution for operating agile and customizable micro-manufacturing systems
Konferenz

Ouerhani Nabil, Santos De Pinho Dylan, Gay des Combes Arnaud, Mathieu Steuhlet, Jeannerat Claude

4th International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2020), 13.09.2020 - 14.09.2020, Prague, Czech Republic

TherMoMac – Data-Driven Thermal Behavior Modelling of Machine-Tools
Konferenz

Ouerhani Nabil, Rizzotti Aïcha, Haas Patrick

F&E-KONFERENZ ZU INDUSTRIE 4.0, 05.02.2020 - 05.02.2020, ETH Zurich, Switzerland

Zusammenfassung:

The main objective of the project is to explore a hybrid method involving deterministic techniques based on finite element simulation and non-deterministic techniques based on data-driven deep learning to model the thermal behaviour of machine tools. A certainty-based information fusion technique provides a good potential to enhance the precision of thermal error estimation compared to the individual techniques. During the presentation, we will present the first results of the project and show the future work to be conducted.

MiLL – Micro Lean Lab
Konferenz

Ouerhani Nabil, Jeannerat Claude, Grize Philippe

F&E-KONFERENZ ZU INDUSTRIE 4.0, 05.02.2020 - 05.02.2020, ETH Zurich, Switzerland

2019

IoT meets distributed AI :
Konferenz ArODES
deployment scenarios of Bonseyes AI applications on FIWARE

Lucien Moor, Lukas Bitter, Miguel De Prado, Nuria Pazos Escudero, Nabil Ouerhani

Proceedings of 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC), 29-31 October 2019, London, United Kingdom

Link zur Konferenz

Zusammenfassung:

Bonseyes is an Artificial Intelligence (AI) platform composed of a Data Marketplace, a Deep Learning Toolbox, and Developer Reference Platforms with the aim of facilitating tech and non-tech companies a rapid adoption of AI as an enabler for their business. Bonseyes provides methods and tools to speed up the development and deployment of AI solutions on low power Internet of Things (IoT) devices, embedded computing systems, and data centre servers. In this work, we address the deployment and the integration of Bonseyes AI applications in a wider enterprise application landscape involving different applications and services. We leverage the well-established IoT platform FIWARE to integrate the Bonseyes AI applications into an enterprise ecosystem. This paper presents two AI application deployment and integration scenarios using FIWARE. The first scenario addresses use cases where edge devices have enough compute power to run the AI applications and there is only need to transmit the results to the enterprise ecosystem. The second scenario copes with use cases where an edge device may delegate most of the computation to an external/cloud server. Further, we employ FIWARE IoT Agent generic enabler to manage all edge devices related to Bonseyes AI applications. Both scenarios have been validated on concrete use cases and demonstrators.

Process Parameters Optimization for Energy Efficiency in Swiss-Type Machining
Konferenz

Ouerhani Nabil, Santos De Pinho Dylan, Patrick Neuenschwander

International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2019), 25.09.2019 - 27.09.2019, Amsterdam, Netherlands

WirelessHART-based Sensor Network for Multichannel Measurement of machine-tool Energy Consumption in Production Environments
Konferenz

Ouerhani Nabil, Gay des Combes Arnaud, Pazos Escudero Nuria, Patrick Neuenschwander, Patrice Muller

International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2019), 25.09.2019 - 27.09.2019, Amsterdam, Netherlands

nternet of Things enables street light optimization
Konferenz

Ouerhani Nabil

2nd HES-SO / INARTIS Conference, 20.05.2019 - 20.05.2019, Geneva, Switzerland

Digital Manufacturing
Konferenz

Ouerhani Nabil

BusinessIn conference, 09.04.2019 - 09.04.2019, Neuchâtel, Switzerland

2018

Virtual reality puzzle game for musculoskeletal disorders prevention
Konferenz ArODES

Maria Sisto, Mohsen Zare, Nabil Ouerhani, Jean-Claude Sagot, Stéphane Gobron

Proceedings of GSGS'18: 3rd Gamification Serious Game Symposium, 5-6 July 2018, Neuchâtel, Switzerland

Link zur Konferenz

Seamless integration of coarse and fine movements for fluid interaction in Serious Games
Konferenz

Ouerhani Nabil, Lucien Moor

3rd Gamification and Serious Game Symposium, 05.07.2018 - 06.07.2018, Neuchâtel, Switzerland

Real-Time sensing for RULA implementation in a Musculoskeletal Disorder Prevention Serious Game
Konferenz

Ouerhani Nabil, Divernois Margaux

3rd Gamification and Serious Game Symposium, 05.07.2018 - 06.07.2018, Neuchâtel, Switzerland

Virtual reality serious game for musculoskeletal disorder prevention
Konferenz ArODES

Maria Sisto, Mohsen Zare, Nabil Ouerhani, Christophe Bolinhas, Margaux Divernois, Bernard Mignot, Jean-Claude Sagot, Stéphane Gobron

Proceedings of 5th International Conference on Augmented Reality, Virtual Reality and Computer Graphics (AVR), 24-27 June, Otranto, Italia

Link zur Konferenz

Zusammenfassung:

Musculo Skeletal Disorders (MSDs) is the most common disease in the workplaces causing disabilities and excessive costs to industries, particularly in EU countries. Most of MSDs prevention programs have focused on a combination of interventions including training to change individual behaviors (such as awkward postures). However, little evidence proves that current training approach on awkward postures is efficient and can significantly reduce MSDs symptoms. Therefore, dealing with awkward postures and repetitive tasks is the real challenge for practitioners and manufacturers, knowing that the amount of risk exposure varies increasingly among workers depending on their attitude and expertise as well as on their strategy to perform the task. The progress in MSDs prevention might come through developing new tools that inform workers more efficiently on their gestures and postures. This paper proposes a potential Serious Game that immerses industrial workers using Virtual Reality and helps them recognize their strategy while performing tasks and trains them to find the most efficient and least risky tactics.

2017

IoT Games for hybrid gamer interaction
Konferenz

Ouerhani Nabil, Divernois Margaux, Beurret Stéphane, Jean-Bernard Rossel

Gamification & Serious Games Symposium, 30.06.2017 - 01.07.2017, Neuchâtel, Switzerland

A study of transitional virtual environments
Konferenz ArODES

Maria Sisto, Nicolas Wenk, Nabil Ouerhani, Stéphane Gobron

Proceedings of the 4th International Conference, Augmented Reality, Virtual Reality, and Computer Graphics AVR 17, Ugento, Italy, June 12-15, 2017, part 1

Link zur Konferenz

Zusammenfassung:

Due to real world physical constraints (e.g. walls), experimenting a virtual reality phenomenon implies transitional issues from one virtual environment (VE) to another. This paper proposes an experiment which studies the relevance of smooth and imperceptible transitions from a familiar and pleasurable virtual environment to a similar workplace as a mean to avoid traumatic experiences in VR for trainees. Specifically, the hereby work assumes that the user conciousness regarding virtual environment transitions is a relevant indicator of positive user experience during those. Furthermore, serious games taking place in purely virtual environments have the advantage of coping with various workplace configurations and tasks that the trainee can practice. However, the virtual world of serious games should be carefully designed in order to avoid traumatic experiences for trainees. The results presented stem from an empirical evaluation of user experience conducted with 80 volunteers. This evaluation shows that more than one-third of the participants did not even notice the VE global change.

Hybrid and Flexible Computing Architectures for Deep Learning Systems
Konferenz

Ouerhani Nabil, Pazos Escudero Nuria, François Tièche, De Prado Miguel, Sara Carola, Lucien Moor, Lukas Bitter

Zoom Innovation on Consumer Electronics (ZINC), 01.06.2017 - 01.06.2017, Novi Sad, Serbia

2016

IoT-based dynamic street light control for smart cities use cases
Konferenz ArODES

Nabil Ouerhani, Nuria Pazos Escudero, Marco Aeberli, Michael Muller

Proceedings of 2016 International Symposium on Networks, Computers and Communications (ISNCC)

Link zur Konferenz

Zusammenfassung:

This paper presents a real-world proven solution for dynamic street light control and management which relies on an open and flexible Internet of Things architecture. Substantial contribution is brought at the interoperability level using novel device connection concept based on model-driven communication agents to speed up the integration of sensors and actuators to Internet of Things platforms. The paper shows also results from real-world tests with deployed dynamic street lights in urban spaces. The proposed dynamic light control solution permits an energy saving of about 56% compared to classical static, time-based street light control.

Dynamic street-parking optimisation
Konferenz ArODES

Nuria Pazos, M. Müller, Matthieu Favre-Bulle, K. Brandt-dit-Grieurin, Olivier Hüsser, M. Aeberli, Nabil Ouerhani

Proceedings of the 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 23-25 March 2016, Crans-Montana, Switzerland

Link zur Konferenz

Zusammenfassung:

An instrument to improve the quality of life in large cities, helping to reduce the car traffic, is presented in this paper. It will result in a mobile guidance software that will help the drivers looking for a parking place to find it efficiently. SmartPark relies on available parking information systems, as well as on new sensors or even on social data inputs. A fixed magnetic on-street sensor and a video processing smart camera have been developed and prototypes of both devices were tested. Their data is available through a cloud-based Internet of Things infrastructure and continuously updated every few seconds. Databases will be built over time enabling data mining methods to infer parking availability models over time which will be used, eventually, by the algorithms feeding the mobile application.

2015

ConnectOpen - automatic integration of IoT devices
Konferenz ArODES

Nuria Pazos, Michael Müller, Marco Aeberli, Nabil Ouerhani

Proceedings of the 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), 14-16 December 2015, Milan, Italy

Link zur Konferenz

Zusammenfassung:

There exists, today, a wide consensus that Internet of Things (IoT) is creating a wide range of business opportunities for various industries and sectors like Manufacturing, Healthcare, Public infrastructure management, Telecommunications and many others. On the other hand, the technological evolution of IoT facing serious challenges. The fragmentation in terms of communication protocols and data formats at device level is one of these challenges. Vendor specific application architectures, proprietary communication protocols and lack of IoT standards are some reasons behind the IoT fragmentation. In this paper we propose a software enabled framework to address the fragmentation challenge. The framework is based on flexible communication agents that are deployed on a gateway and can be adapted to various devices communicating different data formats using different communication protocol. The communication agent is automatically generated based on specifications and automatically deployed on the Gateway in order to connect the devices to a central platform where data are consolidated and exposed via REST APIs to third party services. Security and scalability aspects are also addressed in this work.

Dynamic street light management :
Konferenz ArODES
towards a citizen centred approach

Nabil Ouerhani, Nuria Pazos Escudero, Marco Aeberli, Julien Senn, Stéphane Gobron

Proceedings of 2nd Conference "Smart Cities", 30 May 2015, Agadir, Morocco

Link zur Konferenz

Zusammenfassung:

This paper presents a novel approach towards dynamic street light control, which combines advanced Information and Communication Technologies (ICT) and citizens' involvement and engagement. Our proposal is based on the citizens' involvement which would strongly increases the efficiency and perfromance of technological solutions in smart city context. We believe that serious Games have the potential to strengthen people motivation in this context.

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