Upcoming events

September 26, 2019
Swiss Swedish Innovation Initiative - AI, Big Data & XR
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February, 2020 
Swiss Nordic Bio
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Preliminary Agenda | SWII AI, BIG DATA & XR

Venue: ABB Baden, Switzerland

Moderator: Mr. Ulf Borbos, Project Manager, Swedish Incubator & Science parks (SISP)

Mr. Stefan Ramseier, Head of Corporate Research Center ABB Switzerland (5min)

H.E. Jan Knutsson, Ambassador of Sweden to Switzerland & Liechtenstein (5min)

Dr. Thierry Calame, Member of the Board of Directors, Innosuisse (5min)

Swiss-Swedish Innovation Initiative Ms. Maja Zoric, Senior Project Manager, Business Sweden (5min)

EUREKA-Eurostars: Boost Innovation Across Borders Ms. Colette John-Grant, National Project Coordinator, Innosuisse (10min)


AI, Big Data, VR/AR in the Aerospace Industry (15min)
Lisa Åbom, CTO, Saab Aeronautics

Synopsis: The aerospace industry is characterised by high technology products, high availability and safety requirements, comparatively low production volumes, relatively high costs and variation of configurations through options, modifications, and upgrades during the long life cycle. Products, services and organisations are in this aerospace environment more and more digitized and connected with existing and/or future applications for AI and AR/VR in an environment with big data.

Making Traffic Automation Real at Zenuity - What are the Challenges? (15min)
Jonas Ekmark, Product Area Owner, New Technology, Zenuity AB

Synopsis: Traffic automation functionality has great advantages in terms of traffic safety and making productive time available for individuals. However, the paradigm shift is challenging in many aspects. The need for development, research and innovation will be discussed from Zenuity's point of view. Zenuity is a joint venture started by Volvo Cars and Autoliv in 2017, focusing on developing software for driver assist and traffic automation.

AM Digital Chain
Dr. Vladimir Navrotsky, Chief Technology Officer, Siemens Power and Gas

Synopsis: Siemens continue to focus on acceleration of AM technology industrialization. Digitalization is a backbone of AM technology and platform for AM successful industrialization. Therefore, the whole AM digital chain is under consideration and focus at Siemens. We are working with design tools, process simulation, pattern recognition, big data handling and visualization, machine learning and robotics. Collaboration with AM equipment suppliers, end users of AM, IT companies and Univer-sities is a part of Siemens approach in AM digital chain development.

Innovation collaboration opportunity with Sandvik

Hexagon – Enabling Technologies Shaping Smart Change
Dr. Bernd Reimann, Head of AI Center, Hexagon AB

Synopsis: Hexagon empowers its customers to shape smart change across geospatial, industrial, mining and agricultural applications. Here Hexagon’s Innovation Hub develops and deploys enabling technologies such as IIoT, AR/VR, and both cloud and edge analytics with big data, machine and deep learning. This talk will highlight several real-world use cases and point out research and collaboration opportunities in autonomous connected ecosystems such as smart factories, cities, construction sites, plants, mines and farms.


Industrial AI, Challenges and Use Cases
Dr. Christopher Ganz - Group VP Digital R&D

Synopsis: An industrial environment has requirements that are not obviously addressed by most machine learning algorithms. To fulfill them, AI needs to be incorporated into industrial systems. This requires a combination of different approaches and algorithms in one intelligent system.

Smart Solutions for Zero Defect Multi Stage Machining (SDM prel.)
Mr. Håkan Dahlquist, Quality and Research Manager, GF Machining Solutions System 3R Int. AB

Synopsis: The project focuses on developing nobel compensation and adaptive solutions to attain a zero defect multi-stage machining. The material condition in relation to residual stresses, workholding and machining induced errors and distortions are considered. Industrial cases from the aerospeace and automotive components manufacturing will be included. The project is a continuation project from prior.

Connected cars in the IoT Era
Alexandru Rusu, Research Director in IoT & Mahmoud Zgolli Researcher, Swisscom

Synopsis: Cities are getting smarter every day. Authorities are therefore increasingly using information and communication technologies to enrich and enhance urban transport, resource management, energy usage and public safety. All these services can become much more efficient and relevant through the use of connected sensors, big data and mobile applications. For bigger distribution and higher efficiency of these tools, they should be implemented and used by connected cars. This connectivity combined with the big amount of gathered data can result in several applications ranging from traffic safety and efficiency, infotainment, parking assistance, roadside assistance, remote diagnostics, and telematics to autonomous self-driving vehicles and global positioning systems.

Innovation with 5G for the Digital Economy
Friederike Hoffman, Head of Mobile Business Solutions, Enterprise Swisscom AG & Frank Henschke, Head of Networks and Managed Services Sales Western Europe at Ericsson AG

Synopsis: Technology evolution is relentlessly pushing forward and will play a major role in the evolution into Industry 4.0. 5G is a crucial enabler, designed to unlock the potential of flexible production to meet the customization need from the market, such as for Smart Factory. As manufacturing equipment is asked to be installed more flexible and manufacturing process are demanded to become more tight and more integrated, reliable and performing wireless connectivity is crucial. Leveraging a global standard which enables identical global setups of manufacturing infrastructure is a huge enabler for efficiency.


Each elevator pitch is 3 minutes long.

Ultra-low-power, ultra-low-latency neuromorphic solutions for the era of IoT
Dr. Ning Qiao, CEO of aiCTX 

Synopsis: The upcoming era of IoT calls for energy-efficient, lower-latency solutions and resolutions for privacy concerns. The ultra-low-power, ultra-low-latency neuromorphic processors developed by aiCTX are 1000x times more energy-efficient and 10x time faster in responsiveness than the state-of-art deep learning solutions, making it an ideal solution for edge computing and the era of IoT.

Federated Edge Machine Learning
Dr. Rikard König, CTO, Ekkono Solutions AB 

Synopsis: Edge Machine Learning drives the evolution of IoT. Instant, personalized training, based on granular, high-frequency sensor data, enables predictive maintenance, self-optimization, etc. of machines, vehicles and other devices. Combining this with crowdsourcing of the learnings from many devices, introduces new opportunities while transferring a fraction of data by uploading the machine learning models instead of the raw data.

Neuromorphic Vision for High-Performance Intelligent Industrial Vision
Dr. Kynan Eng, CEO iniVation AG 

Synopsis: Vision applications in industrial and real-world environments require fast response. The iniVation Dynamic Vision Platform provides unprecedented low-latency performance for a range of applications. It combines custom hardware and software with a user-friendly development environment to accelerate the deployment of high-performance vision solutions.

Rapid Chemical Reaction Simulations of Industrial Systems
Christoffer Pichler, Lead Software Engineer, Tailored Chemistry AB

Synopsis: Process modeling can be an important development and optimization tool in combustion- and chemical process industry. Today, Tailored Chemistry has a novel method for tailoring of rapid and accurate chemical models, powered by Swarm Intelligence algorithms. Next step is to make more extensive use of artificial intelligence, in order to make models for the cutting-edge development.

Sparking the 4th Industrial Revolution by Thinking Spatial
Nilson Kufus, co-founder and CEO, Nomoko

Synopsis: The 4th industrial revolution is about connecting the physical and digital worlds. For this to happen, software needs to understand the world we live in and hence needs a machine- readable world. The Mirror World will deliver this machine readable world and can therefore be seen as the world wide web of the 4th industrial revolution. Nomoko builds the infrastructure for the 4th industrial revolution.

EmbeDL: Deep Learning from R&D to High-Performance, Cost and Energy Efficient AI-Powered Embedded Systems
Hans Salomonsson, CEO and Co-founder EmbeDL AB 

Synopsis: Deep Learning is driving the current AI boom and can be used to solve challenging problems previously unsolvable by computers. The company has developed EmbeDL - a technology that helps companies bring Deep Learning from R&D to embedded systems by advanced optimisations, resulting in high-performance, cost and energy efficient products.

A data-driven approach towards Industry 5.0 business models
Roy Chikballapur, CEO at MachIQ

Synopsis: The current business models of machinery companies, where over 50% of their gross margins come from spare parts, sales is neither aligned with the interests of their customers, nor sustainable. Using a data-driven approach to transforming machinery business into insurance and subscription-based revenue models not only aligns interests between the two parties but is also vastly more profitable and disruption proof.

Synthetic Data for Your Intelligent Autonomous System
Anton Kloek, Data Scientist, Synthetic Data Solution AB

Synopsis: Modern AI enables incredible advances in Intelligent Autonomous Systems, but access to high quality data is still a limiting factor. Synthetic data enables faster, more affordable, and agile AI innovation.

Enabling smart maintenance with high-quality labeled sensor data
Dr. Rajet Krishnan, Cofounder, Viking Analytics

Synopsis: One of the most critical enablers for smart manufacturing (i.e., predictive maintenance, process optimization etc.) is high quality sensor data that has been processed and labelled by a domain expert. You are "data ready" for smart manufacturing when you realize this enabler. Viking Analytics’ unique platform Multiviz can be used by domain experts for converting raw real-time and historical sensor data to useful labeled data.


Machine Learning and Big Data in Quality Assurance for AM
Mr. Eduard Hryha, Professor, Department of Industrial and Materials Science, Chalmers University of Technology, Gothenburg

Synopsis: Exploration of the in-sity quality assurance tools in AM enables significant increase in robustness in AM processes and allows to minimize/avoid the need for expensive post-AM quality assurance. However, huge amount of data and high process speed require development of the efficient data mining and machine learning procedures to develop efficient closed-loop control algorithms.

Innovation collaboration opportunity with Linköping University 
Mr. Fredrik Heintz, Associate Professor Computer Science & Mr. Johan Ölvander, Professor Machine Design, Linköping University

Synopsis: The AI and Robotics research at Linköping University spans the whole spectrum from physical artifacts and how they interact with humans in intelligent collaborative and autonomous systems. The research integrates diverse disciplines such as sensor fusion, perception, knowledge representation, reasoning and learning, planning and decision making, and control with design and cognition. The majority of the research activities are conducted in collaboration with industrial partners and encompass a broad range of sectors including aerospace, transport, energy and medicine. LiU also provides interdisciplinary access to major research laboratory equipment and resources through the national Wallenberg AI, Autonomous Systems and Software Program (WASP) and National Supercomputer Center (NSC).

Innovation collaboration opportunity with University of Fribourg (TBC)

AI for Monitoring and Control of Dynamical Processes
Dr. Kilian Wasmer, Group Leader for Dynamical Processes and Deputy Head at the Laboratory for Advanced Material Processing, Empa

Synopsis: Since the beginning of the industrialisation, there is a demand for in situ and real-time monitoring and control of highly dynamical processes. On the one hand, such processes have been investigated via complex experiments and simulations. This approach has brought a significant amount of understanding. Still, in many cases it was not sufficient to reduce significantly or supress failure. On the other hand, artificial intelligence is a new trend that proved itself as a very powerful tool to overcome both the complexity and/or the large amount of data to be treated, even without an understanding of the process. In this presentation, we will show successful examples of combining fundamental understanding of a dynamical process, state-of-the art sensors, in particular acoustic emission and artificial intelligence.

Predictive Maintenance with Deep Learning for Industry
Philipp Schmid, Head Robotics & Machine Learning, CSEM

Synopsis: In predictive maintenance, the focus is on identifying signs of random failures at an early stage and predicting ageing processes. CSEM has developed predictive maintenance software that links machine data to an intelligent system via neural networks. The intelligent system works in three steps: 1. detecting a deterioration of a machine, 2. predicting how the condition of the machine will develop, and 3. identifying the components responsible for the malfunction.


Each time slot is 20 Minutes, all meetings are booked online


Networking, drinks and finger food.

Contact person:
Ms. Ulrika Hallström, Consultant,
Business Sweden Switzerland

Direct phone: +43 699 123 515 30
E-Mail: ulrika.hallstrom@business-sweden.se