Posts from category "réalisation"

Programme VILAGIL : Action Aménagement Urbain

IRIT & LERASS – Toulouse University


Simulation, réalité virtuelle, interaction phygitale

Design and development of a simulation platform to anticipate the impact of development projects on territories, particularly in terms of mobility.       

Simulation, rendu 3D et Interaction avancée

Scientific goals

Approach structured around 3 complementary research axes:

- Agent-based simulation : Simulate how traffic is distributed (qualitatively and quantitatively) in terms of

  • Means of transport used,
  • Distribution during the day
  • Geographical distributionand as a function of the combination of
  •  Urban development decisions (e.g., new bus station, subway, bicycle terminal)
  • Changes in human behavior
  • Economic considerations, PLU, etc.

-    3D display,  Virtual Reality : Make the results of the simulation understandable in a context of multi-actor mediation (from the architect to the citizens)

  • Represent visually and in 3D the envisaged developments
  • Tend towards a realistic representation

-    Managing the simulation thanks to Augmented Reality : Enable any decision-maker to simply express their suggestions in terms of planning using innovative interaction techniques

  • Immersive interactive visualization
  • Phygital Interaction: mixing digital data (representation means) and physical model (manipulation means)
  • Gestural interaction


le projet Interreg SUDOE Tr@nsnet a été accepté

Le 21 octobre 2020, le comité de programmation s’est réuni par vidéoconférence afin d’approuver les projets du quatrième et dernier appel à projets du programme Sudoe 2014-2020. 17 projets ont été approuvés dont 11 dans l’axe 1 et 6 dans l’axe 5, avec un montant FEDER programmé de 16 312 826.78 €.

Tr@nsnet : Living-Labs pour une transition écologique par l’intégration et l’interconnexion de réseaux hétérogènes complexes.

Porteur : Relations internationales de l'Université Toulouse IIIPaul Sabatier

Responsables MP Gleizes et G Zissis

Partenaires : Université Polyechnique de Madrid, Université de Lisbonne, Univesrité de Beira Interior Université de La Rochelle, FUSEAM Barcelone, CIRCE Zaragosse, CTA Séville

Date de début   01/10/2020

Date de fin       31/03/2023

Résumé : Tr@nsnet s’inscrit dans le défi de la Transition Écologique (TE) en proposant de définir un modèle de Living Lab (LL) dans un contexte d’Innovation Ouverte (OI). L’objectif est de créer un modèle générique de LL pour les universités (LLU), adaptable à la sphère privée. Il sera testé et validé sur des expérimentations; des réplications de démonstrateurs technologiques existants (smart light, IoT Home, coupl.électrique/thermique,..) et la création de nouveaux démonstrateurs (seconde vie des batteries, cycle de l’eau, mobilités,..). Ces expérimentations technologiques seront aussi des résultats importants du projet. Elles permettront d’évaluer l’intégration de réseaux de services complexes. Le modèle LLU proposé combinera le modèle du Cube d’Harmonisation (HC-Enoll), du Réseau Européen des Living Labs (Enoll), avec les outils du Regulatory Sandbox (RS), en prenant en compte les exigences commerciales et réglementaires (secteurs d’activités/territoires) des innovations. Tr@nsnet apportera un avantage qualitatif et profitera aux écosystèmes d’innovation de chaque région en ouvrant la recherche publique à l’industrie et aux utilisateurs.

Seront impliqués 5 campus universitaires comme bancs d’essai, ils profiteront aussi de l’expérience de 3 LL publics-privés de la zone SUDOE. Tous les partenaires de Tr@nsnet, contribueront ainsi à la TE : 5 universités seront prêtes à soumettre un dossier de labellisation auprès d’Enoll et 3 centres technologiques élargiront les connaissances en matière d’innovation. La diversité de ces partenaires et des régions auxquelles ils appartiennent, nous confronte à différentes approches qui enrichissent le projet et permettent des activités complémentaires. Tr@nsnet se veut innovant, il promeut la recherche technologique et l’étude de la réglementation qui favorise la croissance des entreprises innovantes, la responsabilisation et la protection des droits des consommateurs.

Dynamic Learning of the Environment for Eco-Citizen Behavior

Context Presentation

The development of sustainable smart cities requires the deployment of Information and Communication Technology (ICT) to ensure better services and available information at any time and everywhere. As IoT devices become more powerful and low-cost, the implementation of an extensive sensor network for an urban context can be expensive.

This thesis addresses the problem of estimating missing information in urban contexts. The objective is to estimate accurate environmental information where physical sensors are not available. The proposed solution, HybridIoT, uses the Adaptive Multi-Agent System (AMAS) to estimate accurate environmental information under conditions of uncertainty arising from the urban application context in which the project is applied, such as openness, heterogeneity and large-scale, which have not been explored by the state-of-the-art solutions.

illustration - Davide Guastella


Smart city, Cooperative Multi-Agent Systems, Missing Information Estimation, Heterogeneous Data Integration

Scientific goals

- Limiting the number of ad hoc devices to be deployed in an urban environment

- The exploitation of heterogeneous data acquired from mobile, intermittent devices

- Real-time processing of information

- Self-calibration of the system


Toward a Data Lake

Context Presentation

neOCampus is a large operation with different kinds of projects and actors. Started in 2013, its goal is to improve the university campus user’s everyday life through data analysis for people, fluid consummation reduction, reduce building environmental footprint, etc.… Overall, it tends to make the campus smarter. All those projects have one common point: data. Including images, sensor logs, administrative data, configurations, we can find every kind of data and each must be stored somewhere.

This project is centered around this problem with a data management system architecture which is the data lake.The conception of this kind of solution must include handling every kind of data and making it possible to follow the life of a data from the input to the usage in a project. It does not only have to store every kind of data, it is needed to know what is stored, where and in the proper format to use it in the easiest way. When a new data has arrived, the system will automatically rawly store it, find the more valuable format, extract information from this data and make this knowledge available for any purpose.

datalake - Vincent-Nam Dang


Data Lake, Data Driven Project, Big Data, Data Management, Data Analysis

Scientific goal

•    To develop a datalake architecture to change the architecture of the data management system in neOCampus.

Contacts, franç,

Stream Analysis and Filtering for Reliability and Post-processing of Sensor Big Data

Context Presentation

Anomaly detection in real fluid distribution applications is a difficult task, especially when we seek to accurately detect different types of anomalies and possible sensor failures. Our case study is based on a real context: sensor data from the SGE (Rangueil campus management and operation service in Toulouse).

We propose an automatic pattern-based method for anomaly detection in time-series called Composition-based Decision Tree (CDT). We use a modified decision tree and Bayesian optimization to avoid manual tuning of hyper-parameters. Our method uses sequences of patterns to identify remarkable points corresponding to multiple anomalies. The compositions of patterns existing into time-series are learned through an internally generated decision tree and then simplified using Boolean algebra to produce intelligible rules.

Our approach automatically generates decision rules for anomaly detection. All our experiments were carried out on real and synthetic data. We show that our method is precise for classifying anomalies compared to other methods. It also generates rules that can be interpreted and understood by experts and analysts, who they can adjust and modify.

Image_IBK - ines ben kraiem


Anomaly detection, Time-series, Machine learning, Classification rules

Scientific goals

•    To detect different types of anomalies observed in real deployment

•    To generate interpretable rules for anomaly detection

•    To use learning methods for anomaly detection on static and continuous data


Collective Learning for Robotics

Context Presentation

The current digital transformation requires the creation of autonomous applications that can be adapted to complex, dynamic, heterogeneous, and unpredictable environments. These systems must be equipped with proactive learning capabilities.

To this end, Self-Adaptive Multi-Agent principle allows the decentralization and self-observation of the learning process. Each knowledge granule is an autonomous agent that cooperates with its neighbors to improve learning from exogenous and endogenous feedbacks. Detecting and solving concurrences, conflicts and incompetence leads to active endogenous learning.This work on an adaptive decentralized learning mechanism is applied to the learning of a robotic arm inverse kinematics.

Illustration Bruno Dato - Bruno Dato


Self-Adaptive Learning, Endogenous Learning, Adaptive Multi-Agent Systems, Robotics

Scientific goals

•    Design a Self-Learning System

•    Lifelong and Endogenous Learning

•    Genericity and Scalability


SANDFOX Project: Optimizing the Relationship between the User Interface and Artificial Intelligence to Improve Energy Management in Smart Buildings

Context Presentation

This research project deals with energy efficiency in buildings to mitigate the climate change. Buildings are the highest source of energy consumption worldwide. However, a large part of this energy is wasted, mainly due to poor buildings management. Therefore, being accurately informed about consumptions and detecting anomalies are essential steps to overcome this problem. Currently, some software exists to record, store, archive, and visualize big data such as the ones of a building, a campus, or a city. Yet, they do not provide Artificial Intelligence (AI) able to automatically analyze the streaming data to detect anomalies and send alerts, as well as adapted reports to the different stakeholders.

The system designed in the SANDFOX project has for objective to fill this gap. To improve the energy management, an innovative system should aim at visualizing the streaming data, editing reports, and detecting anomalies, for different stakeholders, such as policy makers, energy man-agers, researchers, technical staff or end-users of these buildings.

The paper presents the User-Centred Design approach that was used to collect the required needs from different stakeholders. The developed AI system is called SANDMAN (semi-Supervised ANomaly Detection withMulti-AgeNt systems). It processes data in a time constrained manner to detect anomalies as early as possible. SANDMAN is based on the paradigm of self-adaptive multi-agent systems. The results show the robustness of the AI regarding the detection of noisy data, of different types of anomalies, and the scaling.

 SANDFOX_image-neocampus2020 - Berangere Lartigue


Anomaly detection, dashboard, multi-agent system, smart buildings, energy management

Scientific goals

•    Anomalies detection in smart buildings streaming data by AI,

•    Restitution of the information to different stakeholders through an adapted dashboard.


Embedded Multi Gas Sensors for Indoor Air Quality Monitoring

Context Presentation

The measurement of indoor air quality is important for health protection against chemical and gaseous pollutants ... The indoor air can contain many pollutants such as CO, CO2, NO2 and VOCs. These pollutants exist in different materials and products that can be used in housing (furniture, cleaners ...), but can be also coming from human activities or outside source. In this case, the detection, measurement and monitoring of these gazeuse contaminants is necessary.

In view of its high performance and low cost, the innovative gas multi-sensor based on metal oxides semiconductors for analyzing and controlling indoor air quality is a good alternative to electrochemical and infrared sensors. This project is currently in progress in LAAS in collaboration with the LCC and Laplace and as part of a thesis funded by neOCampus and the Occitanie region.

This thesis focuses on the characterization of multiple MOX-based gas sensors and integrates these multi-sensors in electronic card to achieve a connected object to control the indoor air quality in offices and classrooms in University Paul Sabatier in Toulouse. The gas multi-sensor is a microsystem composed by four sensors on a microchip, realized to detect target gases.  



Multi-sensors, MOS, Indoor Air Quality, Smart Building, neOCampus

Scientific goals

•    To characterize new nanomaterials (SnO2, CuO, ZnO, WO3 ...) designed by the LCC by using an experimental set-up,

•    To define an operating protocol by trying different operating modes.


Network: Case of the Rangueil Campus.

Context Presentation

For several decades, the hot water for heating and domestic hot water has been a major issue because it accounts for approximately 55% of heating needs in France for both commercial and residential buildings. District Heating Systems have been considered as a most economical, efficient, and environmentally friendly solution for providing heating services to the building stock of large cities. This is essentially related to the reason that these systems can use renewable energies.

The Rangueil’s scientific campus, benefits from a superheated water district heating system built in the 1960’s. It has recently been connected to a biomass boiler that covers approximately 82% of the demand. Campus buildings undergoes significant evolutions (renovations, new buildings). The network consists of 4 main loops, 63 primary substations, 97 buildings, 11 km of underground network and cover a surface of 576 000 m². Our aim is to study the performance of this heating system, starting from one heating loop.

Picture1 - Khouloud Koteich


Rangueil’s District Heating Systems (DHS), Dynamic modeling, MATLAB / Simulink.

Scientific goals

•    The objective is to do a pre-study of the entire system by studying just one loop

•    Identify the parameters influencing the performance of a district heating system

•    Develop on MATLAB / Simulink a model of one loop of the district heating system

•    Analysis of data to identify the impacts of recent evolutions of the network and buildings.


Study of Environmental and Socio-Economical Impacts of Lighting Systems

Context Presentation

When it comes to evaluate the quantifiable effects of products or services on the environment, Life Cycle Assessment (LCA) is probably the most efficient and recognized tool. Thanks to a “cradle to grave” approach, LCA identifies and quantifies, throughout the life of products, the physical flows of matter and energy associated with human activities (extraction of raw materials, manufacturing of the product, distribution, use, collection and disposal towards end-of-life). For each of its flows correspond impact indicators which allow to establish the overall potential impact of the system on our environment. With regard to lighting, “smart” technologies have made it possible to improve energy efficiency during use phase and thus greatly limit its impact on the environment.

Before the development of these new technologies, lighting represented 14% of European consumption and 19% of global electricity consumption (2009). Today, the UNEP (United Nations Environment Program) estimates it at 15 % worldwide (2,940 TWh) for 5% of global greenhouse gas emissions (1,150 million tonnes of CO2). However, despite major advances in terms of energy efficiency, many direct or indirect impacts on our environment, our health, well-being and productivity are not considered, and we can no longer neglect these impacts.

It is then necessary to define a new methodology, which will allow the extension of the classic LCA by taking into account several health and social criteria, in particular regarding the potential ”impacts on human” ( blue light and impacts on circadian rhythms); the "impacts on ecosystems" (light pollution, potential impacts on insects and plants population); the several “uses of light” (residential, commercial, public lighting, etc.); or even "social acceptability on and by the user of the system" (security, comfort, working conditions, etc.). The aggregation of these criteria, with a classic life cycle assessment and a life cycle cost analysis (cumulative cost of a product throughout its life cycle), will give a global vision (economic, social and environmental) of the potential impacts of lighting and will helps to offer a decision support tool for establishing coherent and appropriate strategies around the transformation of our lighting systems.

 light-bulbs - Kévin BERTIN


Life Cycle Assessment, LED, Lighting Systems, Environmental Impacts

Scientific goal

•    Evaluating the global impacts of lighting technologies, and helping the decision making regarding lighting strategies.


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