All posts tagged Machine Learning
From 25-27 March 2025, CNet joined other consortium members of the MATISSE project for a plenary meeting in Rome.
It was an intensive few days with strategic workshops, technical planning, and interactive sessions to strengthen the MATISSE framework for model-based engineering of trusted Digital Twins for industrial systems.
CNet contributes to this work by managing the use case on Digital Twins for critical infrastructures.
MATISSE is a European research project bringing together 30 partners from seven countries to develop an advanced framework for efficient engineering and validation of industrial systems using Digital Twins.
By integrating Digital Twins with model-based, data-driven, cloud technologies, MATISSE aims to simulate, test, and predict system behaviours, enhancing both productivity and quality of industrial processes.
CNet is responsible for one of the use cases on Digital Twins for critical infrastructures. This will advance the existing work that CNet Group is doing on Digital Twins for bridge structures, machine learning and cloud technologies.
The project is financed by HORIZON-KDT-JU (Key Digital Technologies Joint Undertaking, now Chips Ju) and Vinnova with a total budget of €5,9 million. Apart from Sweden, participating countries inlcude France, Austria, Italy, Portugal, Finland, and Turkey.
John Leander, from CNets subsidiary IoTBridge, presented the project iBridge on this year’s highlight for the steel construction industry, Stålbyggnadsdagen 2021, which was held 28th October in Stockholm.
iBridge aims to develop and demonstrate a full-scale digital instrumentation of bridges. The purpose is to complement manual inspections and to accelerate the industrialization of digital measurement and inspection services. The overall goal is to automate and make available existing knowledge that can reduce the costs for bridge management while guaranteeing safety.
Sweden municipalities alone manage more than 29,000 bridges, in addition to privately owned bridges and bridges owned by Swedish business and industry.
CNets subsidary IoT BRIDGE welcomes a new member to the team, Jacob Nyman.
Mr. Nyman, who majored at KTH, School of Electrical Engineering and Computer Science (EECS) specializes in machine learning and will complement the teams expertise which consists of experienced IoT as well as cloud architects and developers, bridge construction scientists, sensor instrumentation experts and business and process developers.
Our goal is to advance the digitization of bridge operation and maintenance and to contribute to the renewal and better use of the Swedish and European transport infrastructure.
Machine Learning Specialist
CNets has sponsored a master thesis analyzing a new method to create a health indicator for machines. The master thesis was handed in and evaluated by KTHs School of Electrical Engineering and Computer Sciences (EECS).
The work is based on distance measurements transformed into a vector space through a feed-forward neural network. The neural network is trained using a multi-objective optimization algorithm to optimize criteria that are desired in a health indicator. The constructed health indicator is used as input to a gated recurrent unit (a neural network that handles sequential data) to predict the remaining life of a system in question.
The Master thesis Machinery Health Indicator Construction using Multi-objective Genetic Algorithm Optimization of a Feed-forward Neural Network based on Distance was carried out by Jacob Nyman in the field of machine learning:
There are four global megatrends that shape the energy sector: climate change and resource efficiency; urbanisation; new technologies and digitalisation; and active customers. These megatrends will bring profound changes not only to how energy is produced and sold to customers, but also to how it is consumed in our daily lives. Fortum is now looking for novel AI-assisted services for customers’ homes and have organised a hackathon challenge AI Comes Home. CNet has been selected as one of the finalists for this hackathon that will take place 7-8 December. We will participate with a intelligent digital assistant that combines both video and voice input to guide consumers in living a more sustainable life.