Posts from 2019-07-09

Context Presentation

When it comes to identifying and measuring the quantifiable effects of products or services on the environment, Life Cycle Assessment (LCA) is probably the most powerful and recognized tool. Thanks to a multicriterion and 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 required for the manufacture of the product, distribution, use, collection and disposal to end-of-life systems and all phases of transport). For each of its flows, there are impact indicators that establish the overall potential impact of the system on our environment.

During past years, smart lighting technologies allowed significant improvements regarding lamp efficiency during use phase (from 19% to 15% of global electricity consumption), nevertheless, there are direct or indirect impacts on our environment, health, well-being or productivity not taken into account into Life Cycle Assessment (LCA) studies, and we can’t no longer neglected them.

Figure 1: Impacts assessment of lighting systems

Scientific Goals

- How to extend LCA methodology in order to determine which lighting system is most performant regarding environmental, economic and social aspect?

- How using phase could impact on lamp overall performance (Light Loss Factor, Mean Time Before Failure and Maintenance Factor)?

- Which criteria should be used to reflect lighting impact on human health or ecosystems during use phase?

Keywords

Lighting systems, Life Cycle Assessment, Circadian effect, Life cycle Cost, Multicriterion analysis.

Contacts

kevbertin@gmail.com – bertin@laplace.univ-tlse.frEncadrants : georges.zissis@laplace.univ-tlse.fr , marc2.mequignon@free.fr

Context Presentation

- Study the behavior and cognition of titmouse in their natural environment using an electronic feeder, called an Openfeeder.

- Developed by SETE (Station of Theoretical and Experimental Ecology) and SelectDesign.

- System successfully deployed as an island (4 to 8 feeders) on 2 high altitude sites and 3 low altitude sites around SETE (fall 2018).

- A feeder = PIR sensor (detect the presence of a bird), RFID reader (identification), a door controlled by a servomotor. The bird is banded (a transponder), a software with several programmed cognitive task scenarios.

- Principle of operative conditioning (learning a stimulus/reward combination).

- Data collection by USB stick, OF by OF!

Figure 1 : an 8 OpenFeeder station

Scientific Goals

- Synchronize the clocks of the OpenFeeder on each station

- Collect data (logs): centralization on an OF

- Transmit all collected data to the Laboratory (SETE), with RF module and GSM module

- Transmit errors and anomalies in real time by SMS via the GSM module

- Transmit config. (cognitive task scenarios,.ini files)

Keywords

Birdwatch, OpenFeeder, GSM, ALPHA_TRX 433s,

Contacts

kacimi@irit.fr | thiebolt@irit.fr | mcauchoixxx@gmail.comanzilane.mmadi@irit.fr | anzilane.mmadi@univ-tlse3.fr

Context Presentation

The development of self-driving cars is increasing with 5G techniques. One of the biggest challenges posed by this domain is to protect pedestrians and to decrease accidents by detecting them before they occur. That’s why we need to decrease latency, improve reliability, optimize resource allocation and maintain connectivity… In this regard, we have proposed to preview vehicular and pedestrian traffic and send an alert message to warn them of collision risks. To achieve our goals, we started by proposing a network architecture based on an SDN approach, cell-less configuration, and decentralized computing nodes... Then we noticed that if all vehicles and pedestrians are going to communicate with the controller to send their position, the OpenFlow signaling is going to increase in the controller. So, we have simulated the up-link traffic and we have shown the interest of relieving the overload on the controller by sending position messages just in need. We developed an algorithm that estimate the time interval without future collision risks and decide the frequency of sending position messages in the up-link. Concerning the future work, we have to validate the obtained results with simulation.

Figure 1: Proposed SDN architecture

Scientific Goals

- Generate alert messages under low latency- Improve fiability and throughput- Optimize ressources allocation

Keywords

neOCampus, file, presentation, innovation, VANET, 5G, SDN …

Contacts

Chaima.Zoghlami@irit.fr, Rahim.Kacimi@irit.fr, Riadh.dhaou@irit.fr

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 incompetencies leads to active endogenous learning.This work on an adaptive decentralized learning mechanism will be applied on application domains such as robotics, autonomous vehicles and smart cities.

Figure 1: « Schema of the Learning

Figure 2: « Implementation Example Multi-Agent System » on an Industrial Robot »

Scientific Goals

- Design a Self-Learning System

- Lifelong and Endogenous Learning

- Genericity and Scalability

Keywords

Self-Adaptive Learning, Endogenous Learning, Adaptive Multi-Agent Systems, Artificial Intelligence

Contacts

bruno.dato@irit.frfrederic.migeon@irit.fr marie-pierre.gleizes@irit.fr

Context Presentation

Mobility in cities is a crucial question to improve services offered to users and to decrease pollution. Traffic simulation is an interesting tool for decision-makers for city planning. Decision relevance depends on the realism of the simulation. To ensure that, we suppose that we have a map of the terrain (road topology, buildings…) and observation points that provide traffic information (throughput, direction…). They are in limited number and do not cover all the map.This internship focuses on generating car flows on the whole road network using adaptive multi-agent systems. For that, the system determines the location, speed and direction for each vehicle based on its neighborhood. The behavior of each vehicle self-adjusts to match, at the macro-level, traffic information provided by all observation points.

Figure 1: Example of traffic simulationDynameq © INRO

Scientific Goals

- Self-calibration of simulation parameters for realistic traffic flow generation

- Self-adaptation of the simulation to handle traffic dynamics

Keywords

realistic traffic generation, multi-agent simulation, self-adaptation, cooperation

Contacts

damien.vergnet@irit.frelsy.kaddoum@irit.frmarie-pierre.gleizes@irit.fr

Context Presentation

Only a few fixed stations monitor air pollution at the urban scale, where it shows huge variation in space and time. However, the advent of low cost and miniaturized sensors paves the way to mobile sensor networks and crowdsensing systems. Bikes as a carrying platform seems promising: 1) distance tracks are longer than walking, 2) their embedded generator (dynamo) allows to create an autonomous energy system, 3) they do not pollute (and therefore do not distort data collection) and can both cover road network and pedestrian areas 4) human-carried measurement reinforces spatial coverage of the most frequented, hence the most important, areas. We instantiate it as CLUE: Cycle-based Laboratory of Urban Evolutions.

Auto-calibration of the sensor fleet and algorithms tolerant to erroneous measurements thanks to data density are two ways to face the low quality of the sensors (low accuracy, time drifting). Another challenge is keeping user privacy while sharing data without compromising their interpretability (for air pollution, human mobility).

Figure: CLUE embedded system

Scientific Goals

- equip a fleet of bicycles with a set of sensors

- collect information on mobility and air pollution

- merge the data of several sensors in a real environment and validate predictive models of pollutants used in aerology

Keywords

Distributed sensing system, human-centered measurement tool, big data, air pollution

Contacts

Christophe Bertero <christophe.bertero@laas.fr>Jean-Francois Léon <jean-francois.leon@aero.obs-mip.fr>Matthieu Roy <matthieu.roy@laas.fr>, Gilles Tredan <gilles.tredan@laas.fr>

Context Presentation

A Smart Clean Garden (SCG), is a planted filter recognised as nature based solution for water treatment of domestic water. Inspired from water quality regulation of natural rivers, a SCG shelters an enhanced biodiversity for encreased capacity of sewage in limited area of green cities. The addition of IoT as environmental sensors (moisture, NO3, Ph, conductivity,…) allows to survey and to better understand the complex system functioning inside the filter. Collected data will feed regular deterministic modeling and IA to describe the pollutant reduction process.

Figure 1 : Planted filters with 2 granulometric levels that already exist on USTH campus at Hanoi, made by Epurteck as the first SCG pilote for demonstration and logo of the project

Scientific Goals

- To demonstrate that it is possible to treat a part of domestic water of UT3 campus and producing recycled water for gardening and watering the green area of the campus

- To test the capacity building of the water purification in the planted filter by using IoT survey and environmental data modelling ?

- To identify what are the main drivers that lead to the pollutant removial in this complex system made with sediment, water , biodiversity as a micro-organisms, macro-invertebrates and plants communities, nutrients, natural organic mater and antropic molecules (fertilisers, persistant organic pollutant, medical residus, etc…

Keywords

neOCampus, smart clean, garden, water, intelligent reuse, innovation,

Contacts

Magali Gerino (magali.gerino@univ-tlse3.fr) ; Léo Garcia (leo.garcia@iut-tlse3.fr) ; Dan Tan Costa ( EPURTECK, dan@epurtek.fr)

Au cours de cette journée, nous avons partagé les projets de smart campus qui sont soutenus par les universités de La Rochelle, de Bordeaux, de Paris Est et de Toulouse. Nous avons pu découvrir les différents axes de développement choisis par les établissements ainsi que les problématiques rencontrées mais aussi les partenaires (industriels ou non) investis.

Les participants étaient :

Marwane REZZOUKI - Université de Bordeaux

Francis ALLARD - Université de La Rochelle

Patrice JOUBERT - Université de La Rochelle

Pauline BLONDE - Université de Paris-Est

Mathieu DELORME - Université de Paris-Est

Nafissa BOUTKHIL - Université de Paris-Est

Marie-Pierre GLEIZES - Université de Toulouse III