Machine Learning

All posts tagged Machine Learning

iBridge presented at Stålbyggnadsdagen 2021

Peter Rosengren 22nd November 2021

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.

iBridge-Pres-Matningar-for-bedomning-av-Vasterbrons-tillstandDownload

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Peter RosengreniBridge presented at Stålbyggnadsdagen 2021

IoT BRIDGE welcomes Jacob Nyman

Peter Rosengren 14th August 2021

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.

Jacob Nyman

Machine Learning Specialist

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Peter RosengrenIoT BRIDGE welcomes Jacob Nyman

New method to predict life expectancy of machines

Peter Rosengren 1st July 2021

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:

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Peter RosengrenNew method to predict life expectancy of machines

CNet one of finalists in Fortum AI Hackathon

Peter Rosengren 30th November 2017

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.

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Peter RosengrenCNet one of finalists in Fortum AI Hackathon