Technological change impacts all of Berger-Levrault’s business, which researches, tests, develops and deploys new solutions for its customers, based on artificial intelligence technology.
With the proliferation of data and increasing computing power, the phenomenon of digital transformation has been speeding up in recent years and has had an impact on all of Berger-Levrault’s business.
We use an artificial intelligence approach to improve product performance and support our customers in adapting their businesses. Our ambition is to make personal and professional life simpler and faster, and to improve control and predictive maintenance processes.
Artificial intelligence covers a whole range of different notions that Berger-Levrault uses according to the concept of heuristic computing, that is, according to a “problem-solving method not based on a formal model and which does not necessarily lead to a solution”. The artificial intelligence techniques we use, which are both computational and combinatorial, allow us to design reliable business products for presenting and indexing information, acquiring and structuring knowledge, assessing situations and planning actions.
Since 2009, the Berger-Levrault research and innovation team has harnessed artificial intelligence and placed it at the heart of its work. No less than eight projects, in partnership with laboratories and universities in France, Spain and Morocco, are currently underway.
To design more suitable software for our purposes and to analyze how our clients use our tools and interfaces, we rely on artificial intelligence algorithms and more particularly on multi-agent systems (MAS). We hope by these methods to be able to recreate the navigation paths of our users in our interfaces and thus understand the uses of our tools in the field. This is an essential step in improving our solutions.
Today, the ageing of the population and the increase of life expectancy are leading to an ever-growing number of people in a state of incapacity and fragility. Noting the lack of availability in specialized institutions, an alternative is home based care (HBC). The management of these structures and more precisely of the planning of the people involved is very complicated and often manual. A multitude of constraints must be taken into account when planning interventions. The production of a manual schedule is a real challenge! To meet this need, we are exploring the creation of a planning generation tool that can take into account as many constraints and criteria as possible relating to the HBC structure.
Two types of artificial intelligence technologies are studied :
Berger-Levrault and the Université Technologique de Troyes have been working for two years on the acceptability and possible interactions of a non-humanoid social mobile robot in the support of professional care practices and the relationship in institutions for dependent persons. Our sociological research program clearly has a very technological context.
Starting from usage situations, it is a question of studying the uses of communicating robots by observing and then analyzing sequences of actions that promote the possibilities of co-participation and collaboration assisted by a robot.
What are the ethical forms in the use of robotic artifacts? Indeed, technologies are not neutral. In addition, artificial intelligence can embed artificial moral agents. How can we consider a new way of life with dependency, by acting with robotic machines that are socially acceptable?
Energy efficiency is defined as a lower energy consumption for the same service provided. It has made significant progress through technology, price increases and increased awareness of waste. Energy efficiency is the first potential source of domestic energy by 2020 and will be – according to the will of stakeholders, public authorities and society as a whole – a key market of the future and a creative innovation sector. In this context, it is necessary to implement the tools that will enable us all to capture, understand and control our energy expenditures. In this dynamic Berger-Levrault is leading a series of research projects on sustainable development. For example, we are developing a prototype dashboard to analyze data from different sensors for temperature, electricity and gas consumption, heating, water flow, etc.
In this effort, we identified that a recurring problem is the trust that can be placed in the sensors and the data they produce. To solve this problem we have launched two initiatives implementing artificial intelligence techniques:
New technologies of the industrial Internet of Things, massive data processing and intelligent systems are now commonly used and increasingly efficient. In our equipment management markets, they are at the heart of the industry of the future (Plant 4.0), intelligent buildings and the smart city!
In this digital revolution, the expectations of technical services and asset managers can be high and the gains real. In this perspective, we are conducting research on automatic learning from data to build a multi-channel software platform capable of real-time data analysis from communicating sensors, digital mock-ups and management systems. Thus, the technical services will be able to improve the operation of the equipment (configuration of the equipment, predictive maintenance, energy efficiency, etc.) thanks to statistical evaluation and the generation of predictive models.
To do this, we are looking to build autonomous software agents capable of learning from observations to automatically model the behaviour of equipment in their environments. On this project, we are collaborating with our industrial client ALSTEF Automation, which is a manufacturer of handling and conveying systems (for example, airport baggage sorting systems).
Have you ever had trouble finding the right keywords to get the right result in a search engine? Have you ever had to search for information in the middle of a large volume of documents? Imagine that you could delegate tasks to a personal assistant who would be an artificial intelligence.
This is the challenge of research work recently launched in the Berger-Levrault research laboratory. The objective is to identify and make the best use of natural language processing techniques in order to be able to analyze and query large volumes of textual and unstructured documents.
In this context, Berger-Levrault has just signed a partnership agreement with the Universidad Oberta de Catalunya in Spain. The objective will be to facilitate the construction of natural language assistants, to automatically analyze text corpuses and to build generic assistants that can be tailored to the requirements, available services, data and context of our different clients.
Our cities, our houses, our factories, our offices are filled with objects, equipment, plants, vehicles, and so on. For a local authority, maintaining, locating and inventorying all the heritage at its disposal can be a heavy, difficult and extremely costly task.
Computer-Aided Management and Maintenance (CAMM) software is a solution to support and facilitate these management tasks. Solutions provided by Berger-Levrault such as ATAL II or CARL Source are answers to this problem. Nevertheless, planning interventions requires knowing the existence and location of the equipment to be maintained. Managers often rely on manual accounting work that is tedious and difficult to perform.
In this area, the Berger-Levrault research team is committed to studying and developing automatic object recognition systems based on artificial intelligence techniques. Two approaches are being tested:
Recognition on the basis of satellite image or low altitude image
Project 1 : Location and cadastral management
Project 2 : Location of street furniture using aerial data
The recognition of urban objects on aerial images is particularly difficult due to their small size on the images and their variable shape and appearance. Given these characteristics, urban objects are difficult to detect and conventional detection approaches do not offer satisfactory performance. For these reasons, specific recognition methods had to be developed.*
Recognition based on images and/or 3D ground scanning
Project 3 : Localization of street furniture using 3D data
Project 4 : Inventorying office furniture from a mobile device
Another method for mapping and analyzing urban objects is to inventory them from images captured on the ground. We are experimenting with a new method based on the use of LiDAR cameras and sensors. The difficulty lies in the ability of a system to dynamically recognize objects from flat images and 3D acquisition in the form of scatterplots.
Improving the sanitation of rainwater and wastewater is a challenge for public health, environmental protection and the protection of water resources from pollution. Unfortunately, improving sanitation cannot be achieved with today’s means. The maps and geographical data of our networks are still largely on an analog basis, which makes it difficult to use and update them. In addition, in public databases, the attributes associated with the various objects making up the network are often incomplete.
It is in this context that Berger-Levrault has chosen to focus on the mapping aspects of urban sanitation networks by tackling the many problems associated with them. First of all, we used artificial intelligence technologies to automatically detect sewer plates on aerial images. These plates constitute the nodes of the water network which will then be crossed with other textual information automatically collected to determine slopes, geometries, materials, structures, etc. Obtaining and crossing this information will make it possible to reconstruct a map of a “probable” underground network, simulate hydraulic flows under the city and therefore facilitate stormwater and wastewater management.