Categories
Bridge project

Low-Code Programming of Spatial Contexts for Logistic Tasks in Mobile Robotics

DIREC project

Low-code programming of spatial contexts for logistic tasks in mobile robotics

Summary

Low-volume production represents a large share of the Danish manufacturing industry. An unmet need in this industry is flexibility and adaptability of manufacturing processes. Existing solutions for automating industrial logistics tasks include combinations of automated storage, conveyor belts, and mobile robots with special loading and unloading docks. 

However, these solutions require major investments and are not cost efficient for low-volume production, and today, low-volume production is often labor intensive.

Together with industrial partners, this project will investigate production scenarios where a machine can be operated by untrained personnel by using low-code development for adaptive and re-configurable robot programming of logistic tasks.

Project period: 2022-2025
Budget: DKK 7,15 million

An unmet need in industry is flexibility and adaptability of manufacturing processes in low-volume production. Low-volume production represents a large share of the Danish manufacturing industry. Existing solutions for automating industrial logistics tasks include combinations of automated storage, conveyor belts, and mobile robots with special loading and unloading docks. However, these solutions require major investments and are not cost efficient for low-volume production.

Therefore, low-volume production is today labor intensive, as automation technology and software are not yet cost effective for such production scenarios where a machine can be operated by untrained personnel. The need for flexibility, ease of programming, and fast adaptability of manufacturing processes is recognized in both Europe and USA. EuRobotics highlights the need for systems that can be easily re-programmed without the use of skilled system configuration personnel. Furthermore, the American roadmap for robotics  highlights adaptable and reconfigurable assembly and manipulation as an important capability for manufacturing.

The company Enabled Robotics (ER) aims to provide easy programming as an integral part of their products. Their mobile manipulator ER-FLEX consists of a robot arm and a mobile platform. The ER-FLEX mobile collaborative robot provides an opportunity to automate logistic tasks in low-volume production. This includes manipulation of objects in production in a less invasive and more cost-efficient way, reusing existing machinery and traditional storage racks. However, this setting also challenges the robots due to the variability in rack locations, shelf locations, box types, object types, and drop off points.

Today the ER-FLEX can be programmed by means of block-based features, which can be configured to high-level robot behaviors. While this approach offers an easier programming experience, the operator must still have a good knowledge of robotics and programming to define the desired behavior. In order to enable the product to be accessible to a wider audience of users in low-volume production companies, robot behavior programming has to be defined in a simpler and intuitive manner. In addition, a solution is needed that address the variability in a time-efficient and adaptive way to program the 3D spatial context.

Low-code software development is an emerging research topic in software engineering. Research in this area has investigated the development of software platforms that allow non-technical people to develop fully functional application software without having to make use of a general-purpose programming language. The scope of most low-code development platforms, however, has been limited to create software-only solutions for business processes automation of low-to-moderate complexity.

Programming of robot tasks still relies on dedicated personnel with special training. In recent years, the emergence of digital twins, block-based programming languages, and collaborative robots that can be programmed by demonstration, has made a breakthrough in this field. However, existing solutions still lack the ability to address variability for programming logistics and manipulation tasks in an everchanging environment.

Current low-code development platforms do not support robotic systems. The extensive use of hardware components and sensorial data in robotics makes it challenging to translate low-level manipulations into a high-level language that is understandable for non-programmers. In this project we will tackle this by constraining the problem focusing on the spatial dimension and by using machine learning for adaptability. Therefore, the first research question we want to investigate in this project is whether and how the low-code development paradigm can support robot programming of spatial logistic task in indoor environments. The second research question will address how to apply ML-based methods for remapping between high-level instructions and the physical world to derive and execute new task-specific robot manipulation and logistic actions.

Therefore, the overall aim of this project is to investigate the use of low-code development for adaptive and re-configurable robot programming of logistic tasks. Through a case study proposed by ER, the project builds on SDU’s previous work on domain-specific languages (DSLs) to propose a solution for high-level programming of the 3D spatial context in natural language and work on using machine learning for adaptable programming of robotic skills. RUC will participate in the project with interaction competences to optimize the usability of the approach.

Our research methodology to solve this problem is oriented towards design science, which provides a concrete framework for dynamic validation in an industrial setting. For the problem investigation, we are planning a systematic literature review around existing solutions to address the issues of 3D space mapping and variability of logistic tasks. For the design and implementation, we will first address the requirement of building a spatial representation of the task conditions and the environment using external sensors, which will give us a map for deploying the ER platform. Furthermore, to minimizing the input that the users need to provide to link the programming parameters to the physical world we will investigate and apply sensor-based user interface technologies and machine learning. The designed solutions will be combined into the low-code development platform that will allow for the high-level robot programming.

Finally, for validation the resultant low-code development platform will be tested for logistics-manipulation tasks with the industry partner Enabled Robotics, both at a mockup test setup which will be established in the SDU I4.0 lab and at a customer site with increasing difficulty in terms of variability.

Value creation

Making it easier to program robotic solutions enables both new users of the technology and new use cases. This contributes to the DIREC’s long-term goal of building up research capacity as this project focuses on building the competences necessary to address challenges within software engineering, cyber-physical systems (robotics), interaction design, and machine learning.

Scientific value
The project’s scientific value is to develop new methods and techniques for low-code programming of robotic systems with novel user interface technologies and machine learning approaches to address variability. This addresses the lack of approaches for low-code development of robotic skills for logistic tasks. We expect to publish at least four high-quality research articles and to demonstrate the potential of the developed technologies in concrete real-world applications.

Capacity building
The project will build and strengthen the research capacity in Denmark directly through the education of one PhD candidate, and through the collaboration between researchers, domain experts, and end-users that will lead to R&D growth in the industrial sector. In particular, research competences in the intersection of software engineering and robotics to support the digital foundation for this sector.

Societal and business value
The project will create societal and business value by providing new solutions for programming robotic systems. A 2020 market report predicts that the market for autonomous mobile robots will grow from 310M DKK in 2021 to 3,327M DKK in 2024 with inquiries from segments such as the semiconductor manufacturers, automotive, automotive suppliers, pharma, and manufacturing in general. ER wants to tap into these market opportunities by providing an efficient and flexible solution for internal logistics. ER would like to position its solution with benefits such as making logistics smoother and programmable by a wide customer base while alleviating problems with shortage of labor. This project enables ER to improve their product in regard to key parameters. The project will provide significant societal value and directly contribute to SDGs 9 (Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation).

Impact

The project will provide a strong contribution to the digital foundation for robotics based on software competences and support Denmark being a digital frontrunner in this area.

Participants

Project Manager

Thiago Rocha Silva

Associate Professor

University of Southern Denmark
Maersk Mc-Kinney Moller Institute

E: trsi@mmmi.sdu.dk

Aljaz Kramberger

Associate Professor

University of Southern Denmark
Maersk Mc-Kinney Moller Institute

Mikkel Baun Kjærgaard

Professor

University of Southern Denmark
Maersk Mc-Kinney Moller Institute

Mads Hobye

Associate Professor

Roskilde University
Department of People and Technology

Lars Peter Ellekilde

Chief Executive Officer

Enabled Robotics ApS

Partners

Categories
Bridge project

Multimodal Data Processing of Earth Observation Data

DIREC project

Multimodal data processing of Earth Observation Data

Summary

Based on Earth observations, a number of Danish public organizations build and maintain important data foundations that are used for decision-making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark.  

Together with some of these public organizations, this project aims to support the digital acceleration of the green transition by strengthening the data foundation for environmental data. There is a need for public organizations to utilize new data sources and create a scalable data warehouse for Earth observation data. This will involve building processing pipelines for multimodal data processing and designing user-oriented data hubs and analytics. 

 

Project period: 2022-2025
Budget: DKK 12,27 million

The Danish partnership for digitalization has concluded that there is a need to support the digital acceleration of the green transition. This includes strengthening efforts to establish a stronger data foundation for environmental data. Based on observations of the Earth a range of Danish public organizations build and maintain important data foundations. Such foundations are used for decision making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark.

The increasing possibilities of automated data collection and processing can decrease the cost of creating and maintaining such data foundations and provide service improvements to provide more accurate and rich information. To realize such benefits, public organizations need to be able to utilize the new data sources that become available, e.g., to automize manual data curation tasks and increase the accuracy and richness of data. However, the organizations are challenged by the available methods ability to efficiently combine the different sources of data for their use cases. This is particularly the case when user-facing tools must be constructed on top of the data foundation. The availability of better data for end-users will among others help the user decrease the cost of executing environmental law and making planning decisions. In addition, the ability of public data sources to provide more value to end-users, improves the societal return-on-investment for publishing these data, which is in the interest of the public data providers as well as their end-users and the society at large.

The Danish Environmental Protection Agency (EPA) has the option to receive data from many data sources but today does not utilize this because today’s lack of infrastructure makes it cost prohibitive to take advantage of the data. Therefore, they are expressing a need for methods to enable a data hub that provide data products combining satellite, orthophoto and IoT data. The Danish GeoData Agency (GDA) collects very large quantities of Automatic Identification System (AIS) data from ships sailing in Denmark. However, they are only to a very limited degree using this data today. The GDA has a need for methods to enable a data hub that combines multiple sources of ship-based data including AIS data, ocean observation data (sea level and sea temperature) and metrological data. There is a need for analytics on top that can provide services for estimating travel-time at sea or finding the most fuel-efficient routes. This includes estimating the potential of lowering CO2 emissions at sea by following efficient routes.

Geo supports professional users in performing analysis of subsurface conditions based on their own extensive data, gathered from tens of thousands of geotechnical and environmental drilling operations, and on public sources. They deliver a professional software tool that presents this multi modal data in novel ways and are actively working on creating an educational platform giving high school students access to the same data. Geo has an interest in and need for methods for adding live, multi modal data to their platform, to support both professional decision makers and students. Furthermore, they have a need for novel new ways of querying and representing such data, to make it accessible to professionals and students alike. Creating a testbed for combining Geo’s data with satellite feeds, combined with automated processing to interpret this data, will create new synergies and has the potential to greatly improve the visualizations of the subsurface by building detailed, regional and national 3D voxel models.

Therefore, the key challenges that this project will address are how to construct scalable data warehouses for Earth observation data, how to design systems for combining and enriching multimodal data at scale and how to design user-oriented data interfaces and analytics to support domain experts. Thereby, helping the organizations to produce better data for the benefit of the green transition of the Danish society.

The aim of the project is to do use-inspired basic research on methods for multimodal processing of Earth observation data. The research will cover the areas of advanced and efficient big data management, software engineering, Internet of Things and machine learning. The project will research in these areas in the context of three domain cases with GDA on sea data and EPA/GEO on environmental data.

Scalable data warehousing is the key challenge that work within advanced and efficient big data management will address. The primary research question is how to build a data warehouse with billions of rows of all relevant domain data. AIS data from GDA will be studied and in addition to storage also data cleaning will be addressed. On top of the data warehouse, machine learning algorithms must be enabled to compute the fastest and most fuel-efficient route between two arbitrary destinations.

Processing pipelines for multimodal data processing is the key topic for work within software engineering, Internet of Things and machine learning. The primary research question is how to engineer data processing pipelines that allows for enriching data through processes of transformation and combination. In the EPA case there is a need for enriching data by combining data sources, both from multiple sources (e.g., satellite and drone) and modality (e.g., the NDVI index for quantifying vegetation greenness is a function over a green and a near infrared band). Furthermore, we will research methods for easing the process of bringing disparate data into a form that can be inspected both by a human and an AI user. For example, data sources are automatically cropped to a polygon representing a given area of interest (such as a city, municipality or country), normalized for comparability and subjected to data augmentation, in order to improve machine learning performance. We will leverage existing knowledge on graph databases. We aim to facilitate the combination of satellite data with other sources like sensor recordings at specific geo locations. This allows for advanced data analysis of a wide variety of phenomena, like detection and quantification of objects and changes over time, which again allows for prediction of future occurrences.

User-oriented data hubs and analytics is a cross cutting topic with the aim to design interfaces and user-oriented analytics on top of data warehouses and processing pipelines. In the EPA case the focus is on developing a Danish data hub with Earth observation data. The solution must provide a uniform interface to working with the data providing a user-centric view to data representation. This will then enable decision support systems, which will be worked on in the GEO case, that may be augmented by artificial intelligence and understandable to the human users through explorative graph-based user interfaces and data visualizations. For the GPA case the focus is on a web-frontend for querying AIS data as a trajectory and heat maps and estimating the travel time between two points in Danish waters. As part of the validation the data warehouse and related services will be deployed at GDA and serve as the foundation for future GDA services.

Advancing means to process, store and use Earth observation data has many potential domain applications. To build the world class computer science research and innovation centres, as per the long-term goal of DIREC, this project focuses on building the competencies necessary to address challenges with Earth observation data building on advances in advanced and efficient big data management, software engineering, Internet of Things and machine learning.

Scientific value
The project’s scientific value is the development of new methods and techniques for scalable data warehousing, processing pipelines for multimodal data and user-oriented data hubs and analytics. We expect to publish at least seven rank A research articles and to demonstrate the potential of the developed technologies in concrete real-world applications.

Capacity building
The project will build and strengthen the research capacity in Denmark directly through the education of two PhDs, and through the collaboration between researchers, domain experts, and end-users that will lead to R&D growth in the public and industrial sectors. Research competences to address a stronger digital foundation for the green transformation is important for the Danish society and associated industrial sectors.

Societal and business value
The project will create societal and business value by providing the foundation for the Blue Denmark to reduce environmental and climate impact in Danish and Greenlandic waters to help support the green transformation. With ever-increasing human activity at sea, growing transportation of goods where 90% is being transported by shipping and a goal of a European economy based on carbon neutrality there is a need for activating marine data to support this transformation. For the environmental protection sector the project will provide the foundation for efforts to increase the biodiversity in Denmark by better protection of fauna types and data-supported execution of environmental law. The project will provide significant societal value and directly contribute to SDGs 13 (climate action), 14 (life under water) and 15 (life on land).

In conclusion, the project will provide a strong contribution to the digital foundation for the green transition and support Denmark being a digital frontrunner in this area.

Impact

The project will provide the foundation for the Blue Denmark to reduce environmental and climate impact in Danish and Greenlandic waters to help support the green transformation.  

Participants

Project Manager

Aslak Johansen

Associate Professor

University of Southern Denmark
Maersk Mc-Kinney Moller Institutee

E: asjo@mmmi.sdu.dk

Christian S. Jensen

Professor

Aalborg University
Department of Computer Science

Thiago Rocha Silva

Associate Professor

University of Southern Denmark
Maersk Mc-Kinney Moller Institute

Kristian Torp

Professor

Aalborg University
Department of Computer Science

Kristian Tølbøl Rasmusssen

Head of Visual Computing Lab

The Alexandra Institute

Mads Darø Kristensen

Principal Application Architect

The Alexandra Institute

Søren Krogh Sørensen

Software Developer

The Alexandra Institute

Oliver Hjermitslev

Visual Computing Specialist

The Alexandra Institute

Mads Robenhagen Mølgaard

Department Director

GEO
Geodata & Subsurface Models

Ove Andersen

Special Consultant

Danish Geodata Agency

Niels Tvilling Larsen

Head of Department

Danish Geodata Agency
Danish Hydrographic Office

Sarah Lønholt

Special consultant

Danish Environmental Protection Agency

Partners

Categories
SciTech project

Privacy and Machine Learning

Project type: SCITECH Project

Privacy and Machine Learning

There is an unmet need for decentralised privacy-preserving machine learning. Cloud computing has great potential, however, there is a lack of trust in the service  providers and there is a risk of data breaches. A lot of data are private and stored locally for good reasons, but combining the information in a global machine learning (ML) system could lead to services that benefit all. For instance, consider a consortium of banks that want to improve fraud detection by pooling their customers’ payment data and merge these with data from, e.g., Statistics Denmark. However, for competitive reasons the banks want to keep their customers’ data secret and Statistics Denmark is not allowed to share the required sensitive data. As another example, consider patient information (e.g., medical images) stored at hospitals. It would be great to build diagnostic and prognostic tools using ML based on these data, however, the data can typically not be shared.
The research aim of the project is the development of AI methods and tools that enable industry to develop new solutions for automated image-based quality assessment. End-to-end learning of features and representations for object classification by deep neural networks can lead to significant performance improvements. Several recent mechanisms have been developed for further improving performance and reducing the need for manual annotation work (labelling) including semi-supervised learning strategies and data augmentation. Semi-supervised learning  combines generative models that are trained without labels (unsupervised learning), application of pre-trained networks (transfer learning) with supervised learning on small sets of labelled data. Data augmentation employs both knowledge based transformations, such as translations and rotations and more general learned transformations like parameterised “warps” to increase variability in the training data and increase robustness to natural variation.
Researching secure use of sensitive data will benefit society at large. CoED-based ML solves the fundamental problem of keeping private input data private while still enabling the use of the most applied analytical tools. The CoED privacy-preserving technology reduces the risk of data breaches. It allows for secure use of cloud computing, with no single point of failure, and removes the fundamental cloud security problem of missing trust in service providers. The project will bring together leading experts in CoED and ML. It may serve as a starting point for attracting additional national and international funding, and it will build up competences highly relevant for Danish industry. The concepts developed in the project may change how organisations collaborate and allow for innovative ways of using data, which can increase the competitiveness of Danish companies relative to large international players.

October 1, 2020 – September 31, 2024 – 3,5 years.

Total budget DKK 4,7 / DIREC investment DKK 3,22

Participants

Project Manager

Peter Scholl

Assistant Professor

Aarhus University
Department of Computer Science

E: peter.scholl@cs.au.dk

Ivan Bjerre Damgaard

Professor

Aarhus University
Department of Computer Science

Christian Igel

Professor

University of Copenhagen
Department of Computer Science

Kurt Nielsen

Associate Professor

University of Copenhagen
Department of Food and Resource Economics

Rahul Rachuri

PhD Student

Aarhus University
Department of Computer Science

Hiraku Morita

Post Doc

University of Copenhagen
Department of Computer Science

Partners

Categories
SciTech project

Machine Learning Algorithms Generalisation

Project type: SCITECH Project

Machine Learning Algorithms Generalisation

AI is radically changing society and the main driver behind new AI methods and systems is machine learning. Machine learning focuses on finding solutions for, or patterns in, new data by learning from relevant existing data. Thus, machine learning algorithms are often applied to large datasets and then they more or less autonomously find good solutions by finding relevant information or patterns hidden in the data. However, it is often not well understood why machine learning algorithms work so well in practice on completely new data – often their performance surpass what current theory would suggest by a wide margin.

Being able to understand and predict when, why and how well machine learning algorithms work on a given problem is critical for knowing when they may be applied and trusted, in particular in more critical systems. Understanding why the algorithms work is also important in order to be able drive the machine learning field forward in the right direction, improving upon existing algorithms and designing new ones.

The goal of this project is to research and develop a better understanding of the generalisation capability of the most used machine learning algorithms, including boosting algorithms, support vector machines and deep learning algorithms. The result will be new generalisation bounds, both showing positive what can be achieved and negative what cannot.

This will allow us to more fully understand the current possibilities and limits, and thus drive the development of new and better methods. Ultimately, this will provide better guarantees for the quality of the output of machine learning algorithms in a variety of domains.

Researching the theoretical foundation for machine learning (and thus essentially all AI based systems) will benefit society at large, since a solid theory will allow us to formally argue and understand when and under which conditions machine learning algorithms can deliver the required quality.

As an added value, the project will bring together leading experts in Denmark in the theory of algorithms to (further) develop the fundamental theoretical basis of machine learning. Thus, it may serve as a starting point for additional national and international collaboration and projects, and it will build up competences highly relevant for Danish industry.

October 1, 2020 – September 31, 2024 – 3,5 years.

Total budget DKK 2,41 / DIREC investment DKK 1,55

Participants

Project Manager

Kasper Green Larsen

Associate Professor

Aarhus University
Department of Computer Science

E: larsen@cs.au.dk

Allan Grønlund

Postdoc

Aarhus University
Department of Computer Science

Mikkel Thorup

Professor

University of Copenhagen
Department of Computer Science

Martin Ritzert

Postdoc

Aarhus University
Department of Computer Science

Partners

Categories
Bridge project

Embedded AI

Project type: Bridge Project

Embedded AI

AI is currently limited by the need for massive data centres and centralized architectures, as well as the need to move this data to algorithms. To overcome this key limitation, AI will evolve from today’s highly structured, controlled, and centralized architecture to a more flexible, adaptive, and distributed network of devices. This transformation will bring algorithms to the data, made possible by algorithmic agility and autonomous data discovery, and it will drastically reduce the need for high-bandwidth connectivity, which is required to transport massive data sets, and eliminate any potential sacrifice of the data’s security and privacy. Furthermore, it will eventually allow true real-time learning at the edge.

This transformation is enabled by the merging of AI and IoT into “Artificial Intelligence of Things” (AIoT), and has created an emerging sector of Embedded AI (eAI), where all or parts of the AI processing is done on the sensor devices at the edge, rather than sent to the cloud. The major drivers for Embedded AI are, increased responsiveness and functionality, reduced data transfer, and increased resilience, security, and privacy. To deliver these benefits, development engineers need to acquire new skills in embedded development and systems design.

To enter and compete in the AI era, companies are hiring data scientists to build expertise in AI and create value from data. This is true for many companies developing embedded systems, for instance to control water, heat and air flow in large facilities, large ship engines or industrial robots, all with the aim to optimize their products and services.
However, there is a challenging gap between programming AI in the cloud using tools like Tensorflow, and programming at the edge, where resources are extremely constrained. This project will develop methods and tools to migrate AI algorithms from the cloud to a distributed network of AI enabled edge-devices. The methods will be demonstrated on several use-cases from the industrial partners.

In a traditional, centralized AI architecture, all the technology blocks would be combined in the cloud or at a single cluster (Edge computing) to enable AI. Data collected by IoT, i.e., individual edge-devices, will be sent towards the cloud. To limit the amount of data needed to be sent, data aggregation may be performed along the way to the cloud. The AI stack, the training, and the later inference, will be performed in the cloud, and results for actions will be transferred back to the relevant edge-devices. While the cloud provides complex AI algorithms which can analyse huge datasets fast and efficiently, it cannot deliver true real-time response and data security and privacy may be challenged.

When it comes to Embedded AI, where AI algorithms are moved to the edge, there is a need to transform the foundation of the AI Stack by enabling transformational advances, algorithmic agility and distributed processing will enable AI to perceive and learn in real-time by mirroring critical AI functions across multiple disparate systems, platforms, sensors, and devices operating at the edge. We propose to address these challenges in the following steps, starting with single edge-devices.

  1. Tiny inference engines – Algorithmic agility of the inference engines will require new AI algorithms and new processing architectures and connectivity. We will explore suitable microcontroller architectures and reconfigurable platform technologies, such as Microchip’s low power FPGA’s, for implementing optimized inference engines. Focus will be on achieving real-time performance and robustness. This will be tested on cases from the industry partners.
  2. µBrains – Extending the edge-devices from pure inference engines to also provide local learning. This will allow local devices to provide continuous improvements. We will explore suitable reconfigurable platform technologies with ultra-low power consumption, such as Renesas’ DRP’s using 1/10 of the power budget of current solutions, and Microchip’s low power FPGA’s for optimizing neural networks. Focus will be on ensuring the performance, scheduling, and resource allocation of the new AI algorithms running on very resource constrained edge-devices.
  3. Collective intelligence – The full potential of Embedded AI will require distributed algorithmic processing of the AI algorithms. This will be based on federated learning and computing (microelectronics) optimized for neural networks, but new models of distributed systems and stochastic analysis, is necessary to ensure the performance,
    prioritization, scheduling, resource allocation, and security of the new AI algorithms—especially with the very dynamic and opportunistic communications associated with IoT.

The expected outcome is an AI framework which supports autonomous discovery and processing of disparate data from a distributed collection of AI-enabled edge-devices. All three presented steps will be tested on cases from the industry partners.

 

Deep neural networks have changed the capabilities of machine learning reaching higher accuracy than hitherto. They are in all cases on learning from unstructured data now the de facto standard. These networks often include millions of parameters and may take months to train on dedicated hardware in terms of GPUs in the cloud. This has resulted in high demand of data scientists with AI skills and hence, an increased demand for educating such profiles. However, an increased use of IoT to collect data at the edge, have created a wish for training and executing deep neural networks at the edge rather than transferring all data to the cloud for processing. As IoT end- or edge-devices are characterized by low memory, low processing power, and low energy (powered by battery or energy harvesting), training or executing deep neural networks is considered infeasible. However, developing dedicated accelerators, novel hardware circuits and architectures, or executing smaller discretized networks may provide feasible solutions for the edge.

The academic partners DTU, KU, AU and CBS, will not only create the scientific value from the results disseminated through the four PhDs, but will also create important knowledge, experience, and real-life cases to be included in the education, and hence, create capacity building in this important merging field of embedded AI or AIoT.

The industry partners Indesmatech, Grundfos, MAN ES, and VELUX are all strong examples of companies who will benefit from mastering embedded AI, i.e., being able to select the right tools and execution platforms for implementing and deploying embedded AI in their products.

  • Indesmatech expect to gain leading edge knowledge about how AI can be implemented on various chip processing platforms, with a focus on finding the best and most efficient path to build cost and performance effective industrial solutions across industries as their customers are represented from most industries.
  • Grundfos will create value in applications like condition monitoring of pump and pump systems, predictive maintenance, heat energy optimization in buildings and waste-water treatment where very complex tasks can be optimized significant by AI. The possibility to deploy embedded AI directly on low cost and low power End and Edge devices instead of large cloud platforms, will give Grundfos a significant competitive advantage by reducing total energy consumption, data traffic, product cost, while at the same time increase real time application performance and secure customers data privacy.
  • MAN ES will create value from using embedded AI to predict problems faster than today. Features such as condition monitoring and dynamic engine optimization will give MAN ES competitive advantages, and
    the exploitation of embedded AI together with their large amount of data collected in the cloud will in the long run create marked advantages for MAN ES.
  • VELUX will increase their competitive edge by attaining a better understanding of the ability to implement the right level of embedded AI into their products. The design of new digital smart products with embedded intelligence, will create value from driving the digital product transformation of VELUX.

January 1, 2022 – December 31, 2024 – 3 years.

Total budget DKK 22,5 million / DIREC investment DKK 6,54 million.

Participants

Project Manager

Xenofon Fafoutis

Associate Professor

Technical University of Denmark
DTU Compute

E: xefa@dtu.dk

Peter Gorm Larsen

Professor

Aarhus University
Dept. of Electrical and Computer Engineering

Jalil Boudjadar

Associate Professor

Aarhus University
Dept. of Electrical and Computer Engineering

Jan Damsgaard

Professor

Copenhagen Business School
Department of Digitalization

Ben Eaton

Associate Professor

Copenhagen Business School
Department of Digitalization

Thorkild Kvisgaard

Head of Electronics

Grundfos

Thomas S. Toftegaard

Director, Smart Product Technology

Velux

Rune Domsten

Co-founder & CEO

Indesmatech

Jan Madsen

Professor

Technical University of Denmark
DTU Compute

Henrik R. Olesen

Senior Manager

MAN Energy Solutions

Reza Toorajipour

PhD Student

Copenhagen Business School
Department of Digitalization

Iman Sharifirad

PhD Student

Aarhus University
Dept. of Electrical and Computer Engineering

Amin Hasanpour

PhD Student

Technical University of Denmark
DTU Compute

Partners

Categories
Bridge project

HERD: Human-AI collaboration: Engaging and controlling swarms of robots and drones

DIREC project

HERD: Human-AI Collaboration

- Engaging and Controlling Swarms of Robots and Drones

Summary

Today, robots and drones take on an increasingly broad set of tasks. However, such robots are limited in their capacity to cooperate with one another and with humans. How can we leverage the potential benefits of having multiple robots working in parallel to reduce time to completion? If robots are given the task collectively as a swarm, they could potentially coordinate their operation on the fly and adapt based on local conditions to achieve optimal or near-optimal task performance.  

Together with industrial partners, this project aims to address multi-robot collaboration and design and evaluate technological solutions that enable users to engage and control autonomous multi-robot systems.

Project period: 2021-2025
Budget: DKK 17,08 million

Robots and drones take on an increasingly broad set of tasks, such as AgroIntelli’s autonomous farming robot and the drone-based emergency response systems from Robotto. Currently, however, such robots are limited in their capacity to cooperate with one another and with humans. In the case of AgroIntelli, for instance, only one robot can currently be deployed on a field at any time and is unable to respond effectively to the presence of a human-driven tractor or even another farming robot working in the same field. In the future, AgroIntelli wants to leverage the potential benefits of having multiple robots working in parallel on the same field to reduce time to completion. A straightforward way to achieve this is to partition the field into several distinct areas corresponding to the number of robots available and then assign each robot its own area. However, such an approach is inflexible and requires detailed a priori planning. If, instead, the robots were given the task collectively as a swarm, they could potentially coordinate their operation on the fly and adapt based on local conditions to achieve optimal or near-optimal task performance.

Similarly, Robotto’s system architecture currently requires one control unit to manage each deployed drone. In large area search scenarios and operations with complex terrain, the coverage provided by a single drone is insufficient. Multiple drones can provide real-time data on a larger surface area and from multiple perspectives – thereby aiding emergency response teams in their time-critical operations. In the current system, however, additional drones each requires a dedicated operator and control unit. Coordination between operators introduces an overhead and it can become a struggle to maintain a shared understanding of the rapidly evolving situation. There is thus a need to develop control algorithms for drone-to-drone coordination and interfaces that enable high-level management of the swarm from a single control console. The complexity requires advanced interactions to keep the data actionable, simple, and yet support the critical demands of the operation. This challenge is relevant to search & rescue (SAR) as well as other service offerings in the roadmap, including firefighting, inspections, and first responder missions.

For both of our industrial partners, AgroIntelli and Robotto, and for similar companies that are pushing robotics technology toward real-world application, there is a clear unmet need for approaches that enable human operators to effectively engage and control systems composed of multiple autonomous robots. This raises a whole new set of challenges compared to the current paradigm where there is a one-to-one mapping between operator and robot. The operator must be able to interact with the system at the swarm level as a single entity to set mission priorities and constraints, and at the same time, be able to intervene and take control of a single robot or a subset of robots. An emergency responder may, for instance, want to take control over a drone to follow a civilian or a group of personnel close to a search area, while a farmer may wish to reassign one or more of her farming robots to another field.

HERD will build an understanding of the challenges in multi-robot collaboration, and design and evaluate technological solutions that enable end-users to engage and control autonomous multi-robot systems. The project will build on use cases in agriculture and search & rescue supported by the industrial partners’ domain knowledge and robotic hardware. Through the research problems and aims outlined below, we seek to enable the next generation of human-swarm collaboration.

Pre-operation and on-the-fly mission planning for robot swarms: An increase in the number of robots under the user’s control has the potential to lead to faster task completion and/or a higher quality. However, the increase in unit count significantly increases the complexity of both end-user-to-robot communication and coordination between robots. As such, it is critical to support the user in efficient and effective task allocation between robots. We will answer the following research questions: (i) What are the functionalities required for humans to effectively define mission priorities and constraints at the swarm level? (ii) How can robotic systems autonomously divide tasks based on location, context, and capability, and under the constraints defined by the end-user? (iii) How does the use of autonomous multi-robot technologies change existing organizational routines, and which new ones are required?

Situational awareness under uncertainty in multi-robot tasks: Users of AI-driven (multi-)robot systems often wish to simulate robot behaviour across multiple options to determine the best possible approach to the task at hand. Given the context-dependent and algorithm-driven nature of these robots, simulation accuracy can only be achieved up to a limited degree. This inherent uncertainty negatively impacts the user’s ability to make an informed decision on the best approach to task completion. We will support situational awareness in the control of multi-robot systems by studying: (i) How to determine and visualise levels of uncertainty in robot navigation scenarios to optimise user understanding and control? (ii) What are the implications of the digital representation of the operational environment for organizational sensemaking? (iii) How can live, predictive visualisations of multi-robot trajectories and task performance support the steering and directing of robot swarms from afar?

User intervention and control of swarm subsets: Given the potentially (rapidly) changing contexts in which the robots operate, human operators will have to regularly adapt from a predetermined plan for a subset of robots. This raises novel research questions both in terms of robot control, in which the swarm might depend on a sufficient number of nearby robots to maintain communication, and in terms of user interaction, in which accurate robot selection and information overload can quickly raise issues. We will therefore answer the following research questions:

(i) When a user takes low-level control of a single robot or subset of a robot swarm, how should that be done, and how should the rest of the system respond?

(ii) How can the user interfaces help the user to understand the potential impact when they wish to intervene or deviate from the mission plans?

Validation of solutions in real-world applications: Based on the real-world applications of adaptive herbicide spraying by farming robots and search & rescue as provided by our industrial partners, we will validate the solutions developed in the project. While both industrial partners deal with robotic systems, their difference in both application area and technical solution (in-the-air vs. on land) allows us to assess the generalisability and efficiency of our solutions in real-world applications. We will answer the following research questions:

(i) What common solutions should be validated in both scenarios and which domain-specific solutions are relevant in the respective types of scenarios?

(ii) What business and organisational adaptation and innovation are necessary for swarm robotics technology to be successfully adopted in the public sector and in the private sector.

Advances in AI, computer science, and mechatronics mean that robots can be applied to an increasingly broader set of domains. To build the world class computer science research and innovation centres, as per the long-term goal of DIREC, this project focuses on building the competencies necessary to address the complex relationship between humans, artificial intelligence, and autonomous robots.

Scientific value
The project’s scientific value is the development of new methods and techniques to facilitate effective interaction between humans and complex AI systems and the empirical validation in two distinct use cases. The use cases provide opportunities to engage with swarm interactions across varying demands, including domains where careful a priori planning is possible (agricultural context) and chaotic and fast-paced domains (search & rescue with drones). HERD will thus lead to significant contributions in the areas of autonomous multi-robot coordination and human-robot interaction. We expect to publish at least ten rank A research articles and to demonstrate the potential of the developed technologies in concrete real-world applications. This project also gears up the partners to participate in project proposals to the EU Framework Programme on specific topics in agricultural robotics, nature conservation, emergency response, security, and so on, and in general topics related to developing key enabling technologies.

Capacity building
HERD will build and strengthen the research capacity in Denmark directly through the education of three PhDs, and through the collaboration between researchers, domain experts, and end-users that will lead to industrial R&D growth. Denmark has been a thought leader in robotics, innovating how humans collaborate with robots in manufacturing and architecture, e.g. Universal Robots, MiR, Odico, among others. Through HERD, we support not only the named partners in developing and improving their products and services, but the novel collaboration between the academic partners, who have not previously worked together, helps to ensure that the Danish institutes of higher education build the competencies and the workforce that are needed to ensure continued growth in the sectors of robotics and artificial intelligence. HERD will thus contribute to building the capacity required to facilitate effective interaction between end-users and complex AI systems.

Business value
HERD will create business value through the development of technologies that enable end-users to effectively engage and control systems composed of multiple robots. These technologies will significantly increase the value of the industrial partners’ products, since current tasks can be done faster and at a lower cost, and entirely new tasks that require multiple coordinated robots can be addressed. The value increase will, in turn, increase sales and exports. Furthermore, multi-robot systems have numerous potential application domains in addition to those addressed in this project, such as infrastructure inspection, construction, environmental monitoring, and logistics. The inclusion of DTI as partner will directly help explore these opportunities through a broader range of anticipated tech transfer, future market and project possibilities.

Societal value
HERD will create significant societal value and directly contribute to SDGs 1 (no poverty), 2 (zero hunger), 13 (climate action), and 15 (life on land). Increased use of agricultural robots can, for instance, lead to less soil compaction and enable the adoption of precision agriculture techniques, such as mechanical weeding that eliminates the need for pesticides. Similarly, increased use of drones in search & rescue can reduce the time needed to save people in critical situations.

Impact

The project will develop technologies that enable end-users to effectively engage and control systems composed of multiple robots.

Systems composed of multiple robots will significantly increase the value of industrial products, since current tasks can be done faster and at a lower cost, and entirely new tasks that require multiple coordinated robots can be addressed. 

News / coverage

Participants

Project Manager

Anders Lyhne Christensen

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

E: andc@mmmi.sdu.dk

Ulrik Pagh Schultz

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Mikael B. Skov

Professor

Aalborg University
Department of Computer Science

Timothy Robert Merritt

Associate Professor

Aalborg University
Department of Computer Science

Niels van Berkel

Associate Professor

Aalborg University
Department of Computer Science

Ionna Constantiou

Professor

Copenhagen Business School
Department of Digitalization

Kenneth Richard Geipel

Chief Executive Officer

Robotto

Christine Thagaard

Marketing Manager

Robotto

Lars Dalgaard

Head of Section

Danish Technological Institute
Robot Technology

Alea Scovill

Strategic Project Manager

Agro Intelligence ApS

Kasper Grøntved

PhD Student

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Maria-Theresa Oanh Hoang

PhD Student

Aalborg University
Department of Computer Science

Alexandra Hettich

PhD Student

Copenhagen Business School
Department of Digitalization

Partners

Categories
Bridge project

EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education

DIREC project

Explain me

- Learning to Collaborate via Explainable AI in Medical Education

Summary

In the Western world, approximately one in ten medical diagnoses is estimated to be incorrect, which results in the patients not getting the right treatment. The explanation may be lack of experience and training on the part of the medical staff.

Together with clinicians, this project aims to develop explanatory AI that can help medical staff make qualified decisions by taking the role as a mentor who provides feedback and advice for the clinicians. It is important that the explainable AI provides good explanations that are easy to understand and utilize during the medical staff’s workflow.

Project period: 2021-2025
Budget: DKK 28,44 million

AI is widely deployed in assistive medical technologies, such as image-based diagnosis, to solve highly specific tasks with feasible model optimization. However, AI is rarely designed as a collaborator for the healthcare professionals, but rather as a mechanical substitute for part of a diagnostic workflow. From the AI researcher’s point of view, the goal of development is to beat state-of-the-art on narrow performance parameters, which the AI may solve with superhuman accuracy.

However, for more general problems such as full diagnosis, treatment execution, or explaining the background for a diagnosis, the AI is still not to be trusted. Hence, clinicians do not always perceive AI solutions as helpful in solving their clinical tasks, as they only solve part of the problem sufficiently well. The EXPLAIN-ME initiative seeks to create AIs that help solve the overall general tasks in collaboration with the human health care professional.

To do so, we need not only to provide interpretability in the form of explainable AI models — we need to provide models whose explanations are easy to understand and utilize during the clinician’s workflow. Put simply, we need to provide good explanations.

Unmet technical needs
It is not hard to agree that good explanations are better than bad explanations. In this project, however, we aim to establish methods and collect data that allow us to train and validate the quality of clinical AI explanations in terms of how understandable and useful they are.

AI support should neither distract nor hinder ongoing tasks, giving fluctuating need for AI support, e.g. throughout a surgical procedure. As such, the relevance and utility of AI explanations are highly context- and task-dependent. Through collaboration with Zealand University Hospital we will develop explainable AI (XAI) feedback for human-AI collaboration in static clinical procedures, where data is collected and analyzed independently — e.g. when diagnosing cancer from scans collected beforehand in a different unit.

In collaboration with CAMES and NordSim, we will implement human-AI collaboration in simulation centers used to train clinicians in dynamic clinical procedures, where data is collected on the fly — e.g. for ultrasound scanning of pregnant women, or robotic surgery. We will monitor the clinicians’ behavior and performance as a function of feedback provided by the AI. As there are no actual patients involved in medical simulation, we are also free to provide clinicians with potentially bad explanations, and we may use the clinicians’ responses to freely train and evaluate the AI’s ability to explain.

Unmet clinical needs
In the Western World, medical errors are only exceeded by cancer and heart diseases in the number of fatalities caused. About one in ten diagnoses is estimated to be wrong, resulting in inadequate and even harmful care. Errors occur during clinical practice for several reasons, but most importantly, because clinicians often work alone with minimal expert supervision and support. The EXPLAIN-ME initiative aims to create AI decision support systems that take the role of an experienced mentor providing advice and feedback.

This initiative seeks to optimize the utility of feedback provided by healthcare explainable AI (XAI). We will approach this problem both in static healthcare applications, where clinical decisions are based on data already collected, and in dynamic applications, where data is collected on the fly to continually improve confidence in the clinical decision. Via an interdisciplinary effort between XAI, medical simulation, participatory design and HCI, we aim to optimize the explanations provided by the XAI to be of maximal utility for clinicians, supporting technology utility and acceptance in the clinic.

Case 1: Renal tumor classification
Classification of a renal tumor as malign or benign is an example of a decision that needs to be taken under time pressure. If malign, the patient should be operated immediately to prevent cancer from spreading to the rest of the body, and thus a false positive diagnosis may lead to the unnecessary destruction of a kidney and other complications. While AI methods can be shown statistically to be more precise than an expert physician, there is a need for extending it with explanation for a decision– and only the physicians know what “a good explanation” is. This motivates a collaborative design and development process to find the best balance between what is technically possible and what is clinically needed.

Case 2: Ultrasound Screening
Even before birth, patients suffer from erroneous decisions made by healthcare workers. In Denmark, 95% of all pregnant women participate in the national ultrasound screening program aimed at detecting severe maternal-fetal disease. Correct diagnosis is directly linked to the skills of the clinicians, and only about half of all serious conditions are detected before birth. AI feedback, therefore, comes with the potential to standardize care across clinicians and hospitals. At DTU, KU and CAMES, ultrasound imaging will be the main case for development, as data access and management, as well as manual annotations, are already in place. We seek to give the clinician feedback during scanning, such as whether the current image is a standard ultrasound plane (see figure); whether it has sufficient quality; whether the image can be used to predict clinical outcomes, or how to move the probe to improve image quality.

Case 3: Robotic Surgery
AAU and NordSim will collaborate on the assessment and development of robotic surgeons’ skills, associated with an existing clinical PhD project. Robotic surgery allows surgeons to do their work with more precision and control than traditional surgical tools, thereby reducing errors and increasing efficiency. AI-based decision support is expected to have a further positive effect on outcomes. The usability of AI decision support is critical, and this project will study temporal aspects of the human-AI collaboration, such as how to present AI suggestions in a timely manner without interrupting the clinician; how to hand over tasks between a member of the medical team and an AI system; and how to handle disagreement between the medical expert and the AI system.

In current healthcare AI research and development, there is often a gap between the needs of clinicians and the developed solutions. This comes with a lost opportunity for added value: We miss out on potential clinical value for creating standardized, high quality care across demographic groups. Just as importantly, we miss out on added business value: If the first, research-based step in the development food chain is unsuccessful, then there will also be fewer spin-offs and start-ups, less knowledge dissemination to industry, and overall less innovation in healthcare AI.

The EXPLAIN-ME initiative will address this problem:

  • We will improve clinical interpretability of healthcare AI by developing XAI methods and workflows that allow us to optimize XAI feedback for clinical utility, measured both on clinical performance and clinical outcomes.
  • We will improve clinical technology acceptance by introducing these XAI models in clinical training via simulation-laboratories.
  • We will improve business value by creating a prototype for collaborative, simulation-based deployment of healthcare AI. This comes with great potential for speeding up industrial development of healthcare AI: Simulation-based testing of algorithms can begin while algorithms still make mistakes, because there is no risk of harming patients. This, in particular, can speed up the timeline from idea to clinical implementation, as the simulation-based testing is realistic while not requiring the usual ethical approvals.

This comes with great potential value: While AI has transformed many aspects of society, its impact on the healthcare sector is so far limited. Diagnostic AI is a key topic in healthcare research, but only marginally deployed in clinical care. This is partly explained by the low interpretability of state-of-the-art AI, which negatively affects both patient safety and clinicians’ technology acceptance. This is also explained by the typical workflow in healthcare AI research and development, which is often structured as parallel tracks where AI researchers independently develop technical solutions to a predefined clinical problem, while only occasionally interacting with the clinical end-users.

This often results in a gap between the clinicians’ needs and the developed solution. The EXPLAIN-ME initiative aims to close this gap by developing AI solutions that are designed to interact with clinicians in every step of the design-, training-, and implementation process.

Impact

The project will develop explainable AI that can help medical staff make qualified decisions by taking the role as a mentor.

News / coverage

Participants

Project Manager

Aasa Feragen

Professor

Technical University of Denmark
DTU Compute

E: afhar@dtu.dk

Anders Nymark Christensen

Associate Professor

Technical University of Denmark
DTU Compute

Mads Nielsen

Professor

University of Copenhagen
Department of Computer Science

Mikael B. Skov

Professor

Aalborg University
Department of Computer Science

Niels van Berkel

Associate Professor

Aalborg University
Department of Computer Science

Henning Christiansen

Professor

Roskilde University
Department of People and Technology

Jesper Simonsen

Professor

Roskilde University
Department of People and Technology

Henrik Bulskov Styltsvig

Associate Professor

Roskilde University
Department of People and Technology

Martin Tolsgaard

Associate Professor

CAMES Rigshopitalet

Morten Bo Svendsen

Chief Engineer

CAMES Rigshospitalet

Sten Rasmussen

Professor, Head

Department of Clinical Medicine
Aalborg University

Mikkel Lønborg Friis

Director

NordSim
Aalborg University

Nessn Htum Azawi

Associate Professor,
Head of Research Unit & Renal Cancer team

Department of Urology
Zealand University Hospital

Manxi Lin

PhD Student

University of Southern Denmark
DTU Compute

Naja Kathrine Kollerup

PhD Student

Aalborg University
Department of Computer Science

Jacob Armdorf

PhD Student

University of Copenhagen
Department of Computer Science

Daniel van Dijk Jacobsen

PhD Student

Roskilde University
Department of People and Technology

Partners

Categories
Bridge project

Business Transformation and Organisational AI-based Decision Making

DIREC project

Business Transformation and Organisational AI-based Decision Making

Summary

Today, business processes in private companies and public organisations are widely supported by Enterprise Resource Planning, Business Process Management and Electronic Case Management systems, put into use with the aim to improve efficiency of the business processes. 
 


The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors and fraud, and inability to adapt the business processes. This lack of understanding, agility and control over business processes places a major burden on the companies and organisations. 
 


Together with industry, the project aims to develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.

 

Project period: 2021-2025
Budget: DKK 16,8 million

Enterprise systems generate a plethora of highly granular data recording their operation. Machine learning has a great potential to aid in the analysis of this data in order to predict errors, detect fraud and improve their efficiency. Knowledge of business processes can also be used to support the needed transformation of old and heterogeneous it landscapes to new platforms. Application areas include Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) supervision of business processes in the financial sector, supply chain management in agriculture and foodstuff supply, and compliance and optimisation of workflow processes in the public sector.

The research aim of the project to develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation. Through field studies in organizations that are using AI, BPM and process mining techniques it will be investigated how organizations implement, use and create value (both operational and strategic) through AI, BPM and process mining techniques. In particular, the project will focus on how organizational decision-making changes with the implementation of AI-based algorithms in terms of decision making skills (intuitive + analytical) of the decision makers, their roles and responsibilities, their decision rights and authority and the decision context.

Scientific value

The scientific value of the project is new methods and user interfaces for decision support and business transformation and associated knowledge of their performance and properties in case studies. These are important contributions to provide excellent knowledge to Danish companies and education programs within AI for business innovation and processes.

Capacity building

For capacity building the value of the project is to educate 1 industrial PhD in close collaboration between CBS, DIKU and the industrial partner DCR Solutions. The project will also provide on-line course material that can be used in existing and new courses for industry, MSc and PhD.

Business and societal value

For the business and societal value, the project has very broad applicability, targeting improvements in terms of effectiveness and control of process aware information systems across the private and public sector. Concretely, the project considers cases of customers of the participating industry partners within the financial sector, the public sector and within energy and building management. All sectors that have vital societal role. The industry partner will create business value of estimated 10-20MDkr increased turnaround and 2-3 new employees in 5-7 years through the generation of IP by the industrial researcher and the development of state- of-the-art proprietary process analysis and decision support tools.

Impact

The project will develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems.

Participants

Project Manager

Arisa Shollo

Associate Professor

Copenhagen Business School
Department of Digitalization

E: ash.digi@cbs.dk

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

Raghava Mukkamala

Associate Professor

Copenhagen Business School
Department of Digitalization

Morten Marquard

Founder & CEO

DCR Solutions

Søren Debois

CTO

DCR Solutions

Panagiotis Keramidis

PhD Student

Copenhagen Business School
Department of Digitalization

Partners

Categories
Bridge project

AI and Blockchains for Complex Business Processes

DIREC project

AI and Blockchains for Complex Business Processes

Summary

Today, business processes in private companies and public organizations are widely supported by Enterprise Resource Planning, Business Process Management, and Electronic Case Management systems to improve the efficiency of the processes. 

The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors, and fraud, and inability to adapt the business processes. This lack of understanding, agility and control over business processes places a major burden on companies and organizations. 

Together with industry, this project aims to develop methods and tools that enable the industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.  

 

Project period: 2021-2025

Enterprise and block chain systems generate a plethora of highly granular data recording their operation. Machine learning has a great potential to aid in the analysis of this data in order to predict errors, detect fraud and improve their efficiency. Knowledge of business processes can also be used to support the needed transformation of old and heterogeneous it landscapes to new platforms. Application areas include Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) supervision of business processes in the financial sector, supply chain management in agriculture and foodstuff supply, and compliance and optimisation of workflow processes in the public sector.

The research aim of the AI and Blockchain for Complex Business Processes project is methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, from techniques for automatic identification of business events, via the development of new rule based process mining technologies to tools for the use of process insights for business intelligence and transformation.

The project will do this through a unique bridge between industry and academia, involving two innovative, complementary industrial partners and researchers across disciplines of AI, software engineering and business intelligence from three DIREC partner universities. Open source release (under the LGPL 3.0 license) of the rule-based mining algorithms developed by the PhD assigned task 2 will ensure future enhancement and development by the research community, while simultaneously providing businesses the opportunity to include them in proprietary software.

Impact

The project will develop methods and tools that enable the industry to develop new efficient soluations for exploiting the huge amout of business data generated by entreprise systems. 

News / coverage

Participants

Project Manager

Tijs Slaats

Associate Professor

University of Copenhagen
Department of Computer Science

E: slaats@di.ku.dk

Jakob Grue Simonsen

Professor

University of Copenhagen
Department of Computer Science

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

Michel Avital

Professor

Copenhagen Business School
Department of Digitalization

Henrik Axelsen

PHD Fellow

University of Copenhagen
Department of Computer Science

Christoffer Olling Back

Staff Machine Learning Engineer

ServiceNow

Hugo López

Associate Professor

Technical University of Denmark
DTU Compute

Søren Debois

Associate Professor

IT University of Copenhagen
Department of Computer Science

Julian Bohm

Director Legal

ServiceNow

Omri Ross

Chief Blockchain Scientist

eToro

Axel Fjelrad Christfort

PhD Fellow

University of Copenhagen
Dept. of Computer Science

Partners

Categories
Bridge project

Deep Learning and Automation of Image-Based Quality of Seeds and Grains

DIREC project

Deep Learning and Automation of Image-based Quality of Seeds and Grains

Summary

Today, manual visual inspection of grain is still one of the most important quality assurance procedures throughout the value chain of bringing cereals from the field to the table.

Together with FOSS, this project aims to develop and validate a method of automated image-based solutions that can replace subjective manual inspection and improve performance, robustness and consistency of the inspection. The method has the potential of providing the grain industry with a disruptive new tool for ensuring quality and optimising the value of agricultural commodities.

Project period: 2020-2024
Budget: DKK 3,91 million

To derive maximum value from the data there is a need to develop methods of training data algorithms to automatically be able to provide industry with the best possible feedback on the quality of incoming materials. The purpose is to develop a framework which replaces the current feature-based models with deep learning methods. By using these methods, the potential is significantly to reduce the labor needed to expand the application of EyeFoss™ into new applications; e.g. maize, coffee, while at the same time increase the performance of the algorithms in accurately and reliably describing the quality of cereals.

This project aims at developing and validating, with industrial partners, a method of using deep learning neural networks to monitor quality of seeds and grains using multispectral image data. The method has the potential of providing the grain industry with a disruptive new tool for ensuring quality and optimising the value of agricultural commodities. The ambition of the project is to end up with an operationally implemented deep learning framework for deploying EyeFoss™ to new applications in the industry. In order to the achieve this, the project will team up with DTU Compute as a strong competence centre on deep learning as well as a major player within the European grain industry (to be selected).

The research aim of the project is the development of AI methods and tools that enable industry to develop new solutions for automated image-based quality assessment. End-to-end learning of features and representations for object classification by deep neural networks can lead to significant performance improvements. Several recent mechanisms have been developed for further improving performance and reducing the need for manual annotation work (labelling) including semi-supervised learning strategies and data augmentation.

Semi-supervised learning  combines generative models that are trained without labels (unsupervised learning), application of pre-trained networks (transfer learning) with supervised learning on small sets of labelled data. Data augmentation employs both knowledge based transformations, such as translations and rotations and more general learned transformations like parameterised “warps” to increase variability in the training data and increase robustness to natural variation.

Scientific value
The scientific value of the project will be new methods and open source tools and associated knowledge of their performance and properties in an industrial setup.

Capacity building
The aim of the project is to educate one PhD student in close collaboration with FOSS – the aim is that the student will be present at FOSS at least 40% of the time to secure a close integration and knowledge exchange with the development team at FOSS working on introducing EyeFossTM to the market. Specific focus will be on exchange at the faculty level as well; the aim is to have faculty from DTU Compute present at FOSS and vice-versa for senior FOSS specialists that supervise the PhD student. This will secure better networking, anchoring and capacity building also at the senior level. The PhD project will additionally be supported by a master-level program already established between the universities and FOSS.

Societal impact
Specifically, the project aims to provide FOSS with new tools to assist in scaling the market potential of the EyeFossTM from its current potential of 20m EUR/year. Adding, in a cost-efficient way, applications for visual inspection of products like maize, rice or coffee has the potential to at least double the market potential. In addition, the contributions will be of generic relevance to companies developing image-based solutions for food quality/integrity assessment and will provide excellent application and AI integration knowledge of commercial solutions already on-the-market to other Danish companies.

Impact

The project has the potential of providing the grain industry with a disruptive new tool for ensuring quality and optimising the value of agricultural commodities.

News / coverage

Participants

Project Manager

Lars Kai Hansen

Professor

Technical University of Denmark
DTU Compute

E: lkai@dtu.dk

Kim Steenstrup Pedersen

Professor

University of Copenhagen
Department of Computer Science

Lenka Hýlová

PHD Fellow

Technical University of Denmark
DTU Compute

Thomas Nikolajsen

Head of Front-end Innovation

FOSS

Toke Lund-Hansen

Head of Spectroscopy team

FOSS

Erik Schou Dreier

Senior Scientist

FOSS

Partners