Research at the Forefront
Ekkono’s research function consists of a team of Data Scientists – where of three PhDs – and our Machine Learning Engineers. Its a multi-talented team which create great synergies when our customers challenges are tackled and discussed in relation to the latest research findings and beyond. Often practical challenges like cpu, memory constraints or just the implementation itself forces the team to take another look at research findings to enhance and adapt them for real world use.
Being at the forefront of research, using it in our daily corporate life, is crucial to stay on top our product development. At Ekkono we have leading Data Scientists in the fields of Incremental Learning and Energy Efficient Algorithms. Our research areas stretch from Sensor Data, to Machine Learning Algorithms and Resource-Constrained Devices. To extend our field of research and further contribute to the academia as well as the industry, Ekkono is involved in a number of joint research projects.
IVVES: Industrial Grade Verification of Evolving Systems
The use of AI and complex, evolving systems (ES), i.e. systems that rapidly change, either due to fast iteration cycles in development or due to their capability to self-adapt and learn, will grow significantly in automation, computation and novel digital services. Targeting the challenges in verification and validation of AI and evolving systems, IVVES will systematically develop Artificial Intelligence approaches for robust and comprehensive, industrial-grade V&V of “embedded AI”, i.e. machine-learning for control of complex, mission-critical evolving systems and services covering the major industrial domains in Europe.
Sponsor: Vinnova (European ITEA3)
Time frame: October 2019 – September 2022
Partners: The IVVES consortium consists of 32 partners spread over five countries, for example ABB, Bombardier, RISE – Research institute of Sweden, Ekkono Solutions, Philips Healtcare, University of Helsinki and Centre de recherche informatqiue de Montréal.
DMrail: Digitization of maintenance for a sustainable transport infrastructure
Digitally connecting the transportation infrastructure facilitates the application of a vast number of emerging technologies like autonomous vehicles, predictive maintenance, traffic flow control, and green wave transports. The railway network is a critical part of a multimodal transportation system that crosses paths with road and marine transport networks.
The DMrail project aims at improving existing state-of-practice maintenance systems in order to increase the operational uptime of rail infrastructure. Innovative methods for continuous health monitoring, data driven decision making, and efficient allocation of maintenance resources will be utilized to this aim. DMrail addresses the digitalization dimension of the InfraSweden 2030 and brings us one step closer to a connected transportation vision.
Sponsor: Vinnova (Infra Sweden 2030)
Time frame: June 2019 – May 2022
Partners: Bombardier Transportation Sweden, Ekkono Solutions, Järnvägsklustret, RISE Research Institutes of Sweden, and KTH Royal Institute of Technology