Mr. NeC B.V.
Network enabling Capability
Mr. NeC integrates its high-performance big data solutions with your enterprise systems to sustain and boost your internal business organisation.
Mr. NeC acquires for you novel business insights using latest deep (reinforcement) learning solutions.
Mr. NeC interweaves its deep learning suite with your enterprise production environment allowing you to focus on offering your clients optimised and diversified services.
(x,g)/(X*,G*): Observed / Optimized predicted context-sensitive application-domain specific data, information and knowledge flow graphs with performance attributes
Mr. NeC is all about the discovery, creation, and enabling of emerging capabilities of hybrid networks of organisations and socio-technical systems. Mr. NeC strives for leveraging those capabilities within diverse industrial and societal sectors by combining research, technology development and project development in predictive big data analytics using and extending state-of-theart deep learning technologies.
Mr. NeC is convinced that deep learning of critical systemic and environmental multi-scale and nonlocal synergetics of adaptive complex hybrid networks will ensure understanding and ultimately mastering dynamic and evolving complexity challenges and opportunities in socio-technical systems and organisations.
RESEARCH &TECHNOLOGY DEVELOPMENT
Mr. NeC investigates and develops deep learning approaches, engineering methods, simulation models and systems coupled to real-world distributed applications that will help you understand and master weak and strong emergence of cooperation amongst adaptive hybrid physical, biological and socio-economic-technical networks.
PROGRAM & PROJECT REALISATION
COGNIPLANT - COGNITIVE PLATFORM TO ENHANCE 360º PERFORMANCE AND SUSTAINABILITY OF THE EUROPEAN PROCESS INDUSTRY (October 2019 - March 2024)
COGNIPLANT project will develop and demonstrate an innovative approach for the advanced digitization and intelligent management of the process industries. This approach will be based on a novel vision to data monitoring and analysis, that will make the most of the latest developments on advanced analytics and cognitive reasoning, coupled with a disruptive use of the Digital Twin concept to improve Production plants’ operation performance by up to 68% in real time control of the productive environment, 65% in quality control of the final products and 70 % in response time to uncontrolled incidents.
The concept will be implemented by four end-users from four different SPIRE industries, one chemical industry in Austria, one aluminum refinery in Ireland, one concrete manufacturing industry in Italy and one metal industry in Spain. The COGNIPLANT solution will provide a hierarchical monitoring and supervisory control that will give a comprehensive vision of the plants’ production performance as well as the energy and resource consumption. Advanced data analytics will be applied to extract valuable information from the data collected about the processes and their effect on the production plant’s overall performance enabling to design and simulate operation plans in digital twin models based on the conclusions. As a result, optimal operation plans will be obtained that will improve the performance of those cognitive production plants. In addition, the project will demonstrate the positive impact derived from the implementation of COGNIPLANT solution that will allow industries reducing their CO2 emissions up to 20%.
A training strategy will be designed to provide a comprehensive framework for the dissemination of the project outcomes and a clear understanding of the new solution for the employees of the SPIRE sectors.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 869931.
Consortium: Ibermatica SA, Idekos Coop, Technische Universitaet Muenchen, Ingeteam, Power Technology SA, Hermes Schleifmittel G.M.B.H., Savvy Data Systems SL, Software Competence Center Hagenberg G.M.B.H., Logpickr, Mr. Nec B.V., Stam SRL, Fornaci Calce Grigolin SPA, Core Innovation and Technology OE, Aughinish Alumina Ltd, and Acería de Álava, S.A.
MRNEC will contribute to the development of deep (reinforcement) learning models for industrial process control optimization.
ACROBA - AI-Driven Cognitive Robotic Platform for Agile Production environments (January 2021 - June 2024)
ACROBA project aims to develop and demonstrate a novel concept of cognitive robotic platforms based on a modular approach able to be smoothly adapted to virtually any industrial scenario applying agile manufacturing principles. The novel industrial platform will be based on the concept of plug-and-produce, featuring a modular and scalable architecture which will allow the connection of robotic systems with enhanced cognitive capabilities to deal with cyber physical systems (CPS) in fast-changing production environments. ACROBA Platform will take advantage of artificial intelligence and cognitive modules to meet personalisation requirements and enhance mass product customisation through advanced robotic systems capable of self-adapting to the different production needs. A novel ecosystem will be built as a result of this project, enabling the fast and economic deployment of advanced robotic solutions in agile manufacturing industrial lines, especially industrial SMEs. The characteristics of the ACROBA platform will allow its cost-effective integration and smooth adoption by diverse industrial scenarios to realise their true industrialisation within agile production environments.
The platform will depart from the COPRA-AP reference architecture for the design of a novel generic module-based platform easily configurable and adaptable to virtually any manufacturing line. This platform will be provided with a decentralized ROS node-based structure to enhance its modularity. ACROBA Platform will definitely serve as a cost-effective solution for a wide range of Industrial sectors, both inside the consortium as well as additional industrial sectors that will be addressed in the future. The Project approach will be demonstrated by means of five industrial large-scale real pilots, Additionally, the Platform will be tested through twelve dedicated Hackatons and two Open calls for technology transfer experiments.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017284.
Consortium: Berner Fachhochschule, Bremer Institut fuer Produktion und Logistik GMBH, Mr. NeC B.V., Fundacion AITIIP, Universidad de la Iglesia de Deusto Entidad Religiosa, Pole EMC2, Cabka Group GMBH, Ikor Sistemas Electronicos SL, Sigma Clermont, Irish Manufacturing Research Company Limited by Guarantee, Nuevas Tecnicas de Automatizacion Industrial SL, Steripack Ireland LTD, Stam SRL, ICPE SA, Fundacion Centro de Tecnologias de Interaccion Visual y Comunicaciones, Moses Productos SL, and Prizztech LTD.
MRNEC will contribute to the development of deep (reinforcement) learning neural network architectures, models and algorithms for efficient plantwide human-robot collaboration.
Modern geometric reward functions for robot control
Our big data science services
Deep Reinforcement Learning
Development, integration and licensing of DRL suite or tools with enterprise development and production environment
Organisational and ICT architecture business requirements analysis and road mapping to capitalise on deep learning solutions
Initiation, networking, partnering and consortium formation, proposal or proposition development, project coordination, management and execution
3025 NW Rotterdam
Dr. Alfons Salden
Mr. NeC B.V. © 2021
Deep Reinforcement Learning
Nonlocal Multi-Scale Complex Interaction Network Analytics and Predictive Distributed Control
Health and Home Care
Autonomous Transport and Logistics
Smart Regions, Industry and Nations