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April 5, 2018 | Plastic Omnium implements model-driven engineering

Papyrus 250Automotive equipment manufacturer Plastic Omnium is implementing a model-driven engineering (MDE) method leveraging Papyrus, an MDE environment developed by List*, a CEA Tech institute.

Plastic Omnium turned to List to make the processes used to design and develop its fuel tanks and diesel-emissions-reduction systems more efficient. The company is now taking steps to replace its traditional approach—writing specifications in natural language—with a more powerful method. List is helping Plastic Omnium to use model-driven engineering to express its product specifications in a dedicated language.

List researchers are using Papyrus, a design and modelling environment that employs a foundation of common, widely-used design languages. The researchers developed a specific usage of SysML, a general-purpose modelling language, and development processes tailored to Plastic Omnium's needs and working methods.

The result is a software platform Plastic Omnium can use to draw up organized specifications that can be leveraged by information systems. The specifications are more coherent, complete, and detailed and contain fewer redundancies than conventional specifications.

The concept was tested successfully on several pilot projects concerning the design of diesel-emissions-reduction systems. The tests revealed significant improvements in productivity and product quality and indicated possible development cost savings. Based on these results, Plastic Omnium is now implementing the tool at its R&D center with the support of Atos and C-Mind, which provide industrial customers with services related to the Papyrus platform.

*List earned the prestigious Institut Carnot seal in 2006 (Institut Carnot TN@UPSaclay).




March 29, 2018 | Remote computing just got more secure

calcul distant 250List*, a CEA Tech Institute, implemented homomorphic encryption technologies that enable "blind"—in other words, totally confidential—computation on data stored on remote servers. Several industrial applications have been developed using the technique.

Homomorphic encryption allows computation on data without knowing the values of that data. Here's how it works: The data is computed exclusively in encrypted form; the result is also encrypted and requires an encryption key to decipher. In research conducted under the EU HC@Works project, List applied its cryptocomputing technology (or compiler) to actual use cases provided by industrial partners, including Atos, which hopes to use the technology in its future e-healthcare platform, which will offer day-to-day in-home patient monitoring services.

One of the major hurdles to the widespread adoption of the technology is that encrypted data requires much more space and much more time to compute—1,000 times more—than unencrypted data. List researchers overcame this challenge by interfacing two different encryption systems. In layman's terms, a traditional encryption system ensures simplified encryption of the data. The encrypted data does not take up any more space than unencrypted data and can thus be sent to Atos' cloud infrastructure. The data is then "trans-encrypted" by a homomorphic encryption system before being processed. Test results are sent from the cloud server to the patient's doctor, who is the only person to possess the encryption key to unscramble the data. The source data can only be read by its owner.

The project has produced some encouraging results, with latency of less than two minutes. The platform is currently being tested in real-world conditions. An open-source version* of the compiler was made available in early 2018 to support the creation of a European community uniting professionals from industry and academia.


*List earned the prestigious Institut Carnot seal in 2006 (Institut Carnot TN@UPSaclay).




March 19, 2018 | World-first in non-destructive testing of flexible subsea risers

cnd 03 2018 250List, a CEA Tech institute, helped to develop a particularly innovative embedded system that combines several non-destructive testing technologies. TechnipFMC, a provider of services to the oil and gas industry, will use the system to inspect flexible subsea risers.

Non-destructive testing techniques are not well-suited to complex parts with multi-layered structures like subsea pipes. Until now, there has been no reliable means of inspecting flexible subsea risers with multiple layers of steel rebar, for example. The IRIS (In-Service Riser Inspection System) R&D project, led by List in partnership with TechnipFMC, is combining several technologies to enable the detection of any defects in the various layers of reinforcements (breaks, cracks in the thermoplastic ducts, etc.) in real time.

An ultrasonic NDT method developed by TechnipFMC is used to detect defects in the outer layer. This method is coupled with an electromagnetic wave technique developed by List to inspect the metal reinforcements. A sensor was designed and patented specifically for the project. It enables the NDT tools to see "through" the outer reinforcements to the inner ones. Another sensor uses capacitive measurement techniques to determine whether there is any water inside the thermoplastic ducts. Ultimately, a marine-grade X-ray source will be added to the system, giving it tomography capabilities.

The entire package will be integrated into an ROV (remotely-operated vehicle)-type system launched from the deck of a supply vessel and able to reach depths of 2,000 meters. The ROV will lock onto and move along the flexible riser independently.

TechnipFMC plans to start using the new NDT system for several offshore oil and gas projects this year.

*List earned the prestigious Institut Carnot seal in 2006 (Institut Carnot TN@UPSaclay).



March 16, 2018 | Structural Health Monitoring (SHM): a pioneering initiative

SHM 1France’s first National Structural Health Monitoring Day was held on Thursday, March 15 at Digiteo Saclay. The purpose of the event was to get stakeholders in SHM technologies and markets thinking about ways to effectively support development in this emerging field.

Structural Health Monitoring, or SHM, leverages integrated sensors to give structures smart capabilities for the purposes of detecting defects and predicting failures. SHM can reduce maintenance costs, limit the downtime of critical industrial components and infrastructures, and produce more reliable diagnostics and predictions than conventional methods.

SHM draws on know-how in a variety of disciplines: materials, non-destructive testing (NDT), sensors, embedded systems, and communications networks—but also data analysis and modeling. SHM can be used by virtually any industry—from aeronautics and civil engineering to energy and more—and is closely aligned with industry’s current shifts towards the digitization of processes and artificial intelligence.

The world’s first SHM systems have now made it out of the lab and onto the market.[1] In France, research labs need targeted support to transform their innovations into marketable solutions.

The March 15 event brought in more than 100 people with a broad range of backgrounds spanning academic research and industry. One of the key take-aways of the day was a shared commitment to creating a professional association to promote SHM. List[2], a CEA Tech institute, and IFSTAR, who co-organized the event with the support of Precend and COFREND, plan to continue to work together to build on the momentum created on Structural Health Monitoring Day.

[1] The first-ever SHM system was certified for the aeronautics industry in 2016; Permasense, a spinoff of Imperial College London that developed a wireless corrosion monitoring system for high-temperature pipelines, was acquired in 2017 at a high valuation.

[2] List earned the prestigious Institut Carnot seal in 2006 (Institut Carnot TN@UPSaclay).



March 12, 2018 | Ambient intelligence: MobileMii gets more advanced features

mobile mii 250List*, a CEA Tech institute, recently unveiled some advanced new features added to its MobileMii Smart Home ambient intelligence platform, which can now recognize activities in addition to actions. These capabilities will create opportunities in smart home automation and in-home monitoring for assisted living applications, for example.

The first MobileMii platform, released two years ago, could detect the behavior of people in their homes in real time, using location and posture to identify specific actions. The platform recently got some advanced new features. It now uses state-of-the-art video analysis to identify typical household activities like cleaning, cooking, eating a meal, working, and more.

The new features leverage machine learning techniques entailing the statistical analysis of different activities in a database of 45-minute videos produced specifically for the project. Around 50 volunteers performed a range of predetermined activities so that the videos could capture variations in a given task from one person to another. The initial prototype—the only one of its kind in the world—is already performing very well, with a 75% successful-recognition rate based on the analysis of data from a single camera.

The research, which is ongoing, is now focusing on improvements to the algorithm, the integration of object recognition, and higher-level reasoning. The innovation could be used in in-home services, an application currently being investigated under the ITEA3 Emospaces project.

*List earned the prestigious Institut Carnot seal in 2006 (Institut Carnot TN@UPSaclay).