Unleash the full potential of your data
Artelys and the ANAP have been collaborating during 3 years to supply the healthcare sector with the most recent advancements in Data Science.
The MMT initiative (Man Machine Teaming) explores the possibilities of development for cognitive air combat systems.
Operating transport networks with the support of AI. This is the field explored by the online competition “Learn to Run a Power Network in a sustainable world“ launched by RTE with Artelys support.
A redesigned relationship between Man and Machine
MMT was launched and financed by the French General Direction of Armies (DGA). It is animated by the companies Dassault Aviation and Thales. Artelys is part of the ecosystem of partners, composed of French start-ups, SMEs and research centers.
As part of the Maestro project, Artelys is exploring the application of Monte-Carlo Tree Search algorithm and Reinforcement Learning to aerial trajectory optimization through Constraint Programming.
Official information: https://man-machine-teaming.com/
Data Science and AI to support crucial usecases in the healthcare sector
For 3 years, Artelys has been supporting the ANAP to implement techniques which solve concrete use cases. Those technics involve statistical data analysis, AI prototypes and have contributed to a strategy of internal acculturation to Data Science tools and Agile methodology.
Several use cases have been identified with the experts and have given rise to different services:
- Development and integration of a predictive module of the occupation rate of beds embedded in an existing tool for hospitals.
- Segmentation of the users of the web content of ANAP, according to their user profile. Statistics and clustering methods (K-Means) have been exploited to build efficient user personae.
- Analysis of the adequacy between healthcare professional demands and content offered by ANAP on its website. A list of clusters of interest have emerged thanks to a segmentation method (Hierarchical Ascendant Classification for biclustering) and led to recommendation concerning the structure of the offer.
- State of play of the real estate owned by the medico-social establishment in France. Creation and analysis of tailored indicators.
- Development of a configurable web tool using text mining to explore the thematic of Twitter posts relative to healthcare issues. Emergence of relationship between popular themes thanks to Natural Language Processing (NLP).
Gather the diverse available data streams to understand and act for road safety
The Loiret department is a member of the European project INTERREG NWE BE-GOOD and is in charge of the challenge “SAFER ROADS” whose goal is to bring characteristics by data use for road safety improvement. It means better understanding of the accident contexts and more efficient prevention actions.
One important stake is to get the data available and actionable by concerting the diverse actors of mobility sector who own it.
Artelys organized workshops in which several actors collaborated to provide data streams and specify their needs: public administration of the department (GIS, Road Observatory, Infrastructure administration), police forces (Gendarmerie Nationale), firemen (SDIS), insurance companies (Mondial Assistance, Thelem) and navigation services (Waze, Coyote).
A web platform has been developed by Artelys and made available to the public administration, stakeholders but also to citizens. It contains:
- A data warehouse with harmonized multi-source data streams and additional information on the context. This data are directly usable through requests or simple dashboard visualization.
- More than 24 000 accidents from the previous 10 years located on the road network and complemented by infrastructure and context information.
- 8 thematic dashboards to analyze influence of multiple variables on the accident risk (infrastructure, speed, traffic, seasonality, weather, accident situation, aggravating factors).
- A prediction algorithm which evaluates, on the road network, the risk level of accidents for a given future context. This algorithm is being developed and relies on supervised machine learning using decision trees.
- A clustering algorithm which has made some reference profiles of accidents emerged.
Operating transport networks with the support of artificial intelligence
In the competition ”Learn to Run a Power Network in a Sustainable World”, AI agents developed by the community have to face real-life TSO challenges.
They have to learn how to design the best set of actions on the grid in terms of robustness against realistic scenarios: consumption peaks, line overloads, intermittent renewable production, plants maintenance, but also cyber–attacks. Machine learning methods, reinforcement learning in particular, are designed to navigate in a vast multidimensional state space and converge to an optimal policy.
Artelys had several contributions at different levels of the competition, by developing four open-source Python packages.
- Grid2viz – An intuitive web application that offers interactive views into the results of Reinforcement Learning agents that ran on the competition environment (grid2op library). It has been developed with Dash.
- Chronix2Grid – A modular package that allows to generate synthetic but realistic consumption, production (solar, wind, hydraulic, nuclear and thermal), electricity loss (dissipation) and economic dispatched productions time-series for a given power grid. Parameters are tuned thanks to a set of indicators based on real-life time-series.
- ExpertOp4Grid – An “expert” agent dedicated to solve overloads in line grids using cheap but non-linear action with an expert approach inspired by recent research. For any new overloaded situations, it computes an influence graph around the overload of interest, and ranks the substations and topologies to explore, to find a solution.
- Oracle4Grid – An “oracle” agent that learns about the future by performing simulations and then finds the best course in order to maximize the cumulated reward. It provides indicators that provide benchmarks in agent performance.
See the competition on Codalab platform.
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