About the project
Objective
In collaboration with Karolinska University Hospital (KUH) and Karolinska Institute (KI), the PI and Co-PI of KTH propose the ISPP postdoc project EMERDENSY to develop trust-worthy machine learning algorithms with explainable outcomes and then use the algorithms for the design of Early Warning Systems (EWS).
Background
Artificial Intelligence (AI) can be used to detect infection. Often, doctors and nurses cannot be sure about the growth of infection due to the absence of clearly visible symptoms. Once infection starts, the body’s immune system starts to fight bacteria and viruses. Physiological parameters of the body, such as heart rate, blood pressure, breathing patterns, and temperature, change slowly.
AI can detect subtle changes, but humans cannot. AI can also predict infection type and patient deterioration. The medical care team will then spend precious time deciding on life-saving interventions. This heralds the use of AI-based early warning systems (EWS).
The big question: can we trust the AI systems, mainly its core called machine learning for data analysis and predictions? Can the machine learning algorithms explain their predictions to the healthcare personnel?
Partner Postdoc(s)
Yogesh Todarwal
Main supervisor
Saikat Chatterjee, Associate Professor, Division of Information Science and Engineering at KTH
Co-supervisor
Sebastiaan Meijer, Professor and Vice Dean, Division of Health Informatics and Logistics at KTH
Watch the recorded presentation at the Digitalize in Stockholm 2023 event:
About the project
Objective
Atlas Copco is a world leader in safety-critical assembly tools widely used in industries like automotive, aviation, and mobile phones. To maintain its world-leading position, Atlas Copco would like to error-proof its handheld tightening tools to ensure that depending on the assembly, the bolts are tightened with the right tightening program and in the right sequence, and no bolts are left untightened. Atlas Copco would like to use an AI/ML-based sensor fusion system to achieve this. The form factor, power budget, latency, and cost requirements rule out the use of COTS like GPUs, TPUs, or FPGAs. ASICs (Application Specific Integrated Circuits) would meet these constraints, but they take too long, are very expensive, and require specialist competence that Atlas Copco lacks. KTH has developed a Lego-inspired design framework called SiLago that provides ASIC-comparable energy efficiency and form factor but allows non-specialists like mechatronic and AI/ML experts from Atlas Copco to create a ready-to-manufacture custom chip.
The objective of this demo project is to validate the claim of SiLago by showing that it can be used by non-specialists to implement this challenging AI/ML system and meet its constraints.
Background
Industry 4.0 or the Fourth Industrial Revolution refers to a significant transformation in how manufacturing and other industries operate, characterized by the integration of digital technologies, automation, data exchange, and smart systems. To align its product offerings with the Industry 4.0 vision, Atlas Copco has launched a pilot project to error-proof its tightening tools to improve the assembly process’s reliability, flexibility, and productivity. To achieve these objectives, it is developing an AI/ML-based sensor fusion system. Energy and cost-efficient implementation of such systems is a well-recognized challenge, and many large actors like Tesla, Google, etc. have opted for an ASIC-based design. However, the design cost of ASICs runs in 100s of MUSDs and requires large volumes and/or large profits to justify them. This often restricts ASICs as a solution to large actors.
To democratize access to ASIC-like efficiency for small actors and non-specialists, KTH has proposed a Lego-inspired design framework called SiLago. The impact of this demo project will be well beyond this specific use case from Atlas Copco; it will open the doors for ecologically and economically scalable energy-efficient digitalization in many sectors, including scientific super-computing.
Crossdisciplinary collaboration
This demo project spans multiple disciplines: mechatronics, AI and ML algorithms, sensor informatics, computer architecture, and VLSI design. It is a collaboration between KTH and Atlas Copco.
About the project
Objective
The project aims to develop an integrated digital infrastructure system to enhance the level of automation for smart construction. The initial goal will involve the creation of models for the digital twin of the robotic environment on construction sites. The digital twin will be used for remote real-time monitoring, prediction, optimization and multi-robot task planning and control. The results will be tested and applied to a practical Skanska use case.
Background
Construction sites today still rely to a large amount on manual labour, and the vision for the future is to leverage automation equipment (machines, robots) to the largest extent possible in order to speed up the production cycle, enhance quality while also reducing human risks, carbon emissions and costs. Smart construction, in essence, a flexible automation process, requires a stringent digitalization of the construction site in terms of real-time digital twins of products and production systems paired with advanced algorithms for the control of machines, the coordination of robots and the assurance of safety at the workspace.
The project strives to realise such systems, leading to fundamental research challenges and practical implementations in relevant use cases. The project developments can lay the foundation for future activities by forming and evolving a consortium nucleus.
Crossdisciplinary collaboration
The project partners are Ericsson AB, Skanska and KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event:
About the project
Objective
To develop an AI-driven information retrieval system for connecting engineers with existing enterprise design knowledge in a transparent and semantic manner.
Background
Engineers with design experience predating computational tools are retiring. At the same time, widespread and informal use of generative language modeling cheapens documentation, threatening to bury records of human creativity. We work with our industry partner NEKTAB (Nordic Electric Power Technology AB) to use AI-based language models for the structuring and semantic retrieval of multimodal artifacts of engineering design. Rather than generatively guess at answers, our method emphasizes transparency in connecting questions to actual instances of prior documented information, an important feature for preserving engineering knowledge.
Crossdisciplinary collaboration
This involves collaboration between computer scientists and mechanical engineers, and involves fields of natural language processing, data engineering, solid mechanics, and engineering design.
About the project
Objective
The project’s primary goal is to devise strategies for mitigating losses in properties covered by the City of Stockholm’s proprietary insurance firm, St Erik. To lay the foundation for a loss reduction strategy concerning fire and water losses, the project involves the combination and analysis of insurance data and administrative building-related data. This information will be supplemented with details regarding loss reduction measures taken at the individual building level. The results of the analysis will be used to put in place actual loss reduction measures.
Background
The City of Stockholm insures its buildings via St Erik. Among the insured buildings are the three major housing companies, along with the city’s real estate office. The housing companies Stockholmshem AB, Svenska Bostäder AB, and Familjebostäder AB own about 70,000 apartments. Currently, the City of Stockholm’s insurance company is experiencing an upward trend in insured losses. This trend is expected to continue in the future, even due to the impact of climate change. Improving loss prevention measures is crucial to enhancing resilience.
In collaboration with the City of Stockholm and its municipal companies, the project strives to both identify and implement effective loss prevention. The work will contribute to better adapting the City of Stockholm to the consequences of climate change.
Crossdisciplinary collaboration
The project partner is the City of Stockholms insurance company, St Erik AB.
About the project
Objective
Our goal is to detect breeding places using drones. The detection of the breeding places will happen in two steps. First, the drones will identify areas that need closer investigation at around 300m heights. In the second step, drones visit the waypoints. When coming to a potential breeding place, the task of the drone is to identify if the water is indeed a potential breeding place and whether or not it contains mosquito larvae. The project is expected to investigate several potential solutions to this problem. Once a breeding place with mosquito larvae is detected, the public health authorities and building owners will be informed to ensure removal.
Background
Dengue and Zika are two arboviral viruses that affect a significant portion of the world population. In Sri Lanka alone, the number of dengue cases has been substantial in recent years, with more than 150000 cases and 440 dengue deaths reported in 2017. While there is no direct correlation between the income level of the people and the possibility of being infected by the dengue virus, the economic impact on the poor is much larger despite free healthcare being available in Sri Lanka. The principal vector species of Dengue and Zika viruses are the mosquitoes Aedes aegypti and Aedes albopictus. They breed in very slow-flowing or standing water pools. It is important to reduce and control such potential breeding grounds to contain the spread of these diseases.
Crossdisciplinary collaboration
The researchers in the team represent the Information Science and Engineering at KTH and the Connected Intelligence Unit at RISE Research Institute of Sweden. The project cooperates with Kasun De Zoysa, University of Colombo, Sri Lanka.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event:
About the project
Objective
The Digitalizing Mental Healthcare Access in Uganda (DiMHA) project is about using the ongoing global digital transformation to improve people’s mental health, thereby contributing toward healthy lives and wellbeing for all. The project will improve access to available limited mental health services. The digital portal will facilitate support to many people through accessible information automation and digital triaging to ensure that those most in need get access to the limited mental healthcare expertise and services. The project will also provide a basis for future digitalization of mental health services in Uganda, which can be replicated in the entire East African Region. The proposed call centre will have an Interactive Voice Response (IVR) component in which carefully curated mental health information in audio format shall be recorded in the major languages spoken in Uganda.
Background
Low and middle-income countries like Uganda bear a disproportionate mental health burden, with 80-90% of persons with mental health disorders in these countries having limited access to appropriate care. The COVID-19 outbreak has exacerbated the situation because social distancing rules mean that patients (and health workers) have difficulties accessing services, despite COVID-19-related increased incidence of mental ill-health such as anxiety disorders and stress. Key challenges to mental health services in Uganda include inadequate mental health facilities and human resources such as psychiatrists, nurses, psychologists and counsellors. Other barriers include poverty, stigma and disenfranchisement of people with mental illness, and lack of accessible, accurate mental health information.
Crossdisciplinary collaboration
The partnership is composed of a multidisciplinary team to respond to the corresponding cross-disciplinary responses required of the project. The researchers in the team represent the Computer and Systems Science department at Stockholm University (SU) and the Digital Health department at RISE Research Institute of Sweden. The Ugandan team includes research fellow Vincent Michael Kiberu at Makerere University’s College of Health Sciences, Dr Juliet Nakku the Executive director of Butabika National Referral Mental Hospital and Dr Johnblack Kabukye from Uganda Cancer Institute.
Watch the recorded presentation at Digitalize in Stockholm 2022 event: