About the project

Objective
The goal of the collaborative impact project is to leverage the novel technical and theoretical research results from the data-limited learning project to create added value and technical transfer to the Swedish industry (Saab and AstraZeneca) and to create interactive physical demonstrators for the Digital Futures Hub and the general public.

Background
This impact project is an extension to the 4-year project Data-Limited Learning of Complex Dynamical Systems (DLL), where the new project focuses on impact activities, software prototypes, and demonstrators. See the following for the previous DLL project.

The previous DLL project consisted of three connected sub-projects (i) bioprocessing, (ii) reinforcement learning for cyber-physical systems, and (iii) theoretical foundation. Within these sub-projects, our project has resulted in significant research results. In this new collaborative impact project, we focus on a subset of these results within the three tasks. The key aspect of the usefulness of this new project is to take the results from a theoretical or pure academic setting, to create demonstrators for the public, and to enable technical transfer to the Swedish industry.

Cross-disciplinary collaboration
This project involves co-PIs and researchers from different disciplines, including computer science, automatic control, machine learning, and biotechnology. The project is divided into three main project tasks, each aiming for separate impact activities:

About the project

Objective
Adaptive Intelligent Homes (AIH) aims to develop a suite of demonstrators, combining the cutting-edge advances made within the parent collaborative research project (AAIS) on user state recognition, human-robot handovers, multimodal referring expression generation, controllable speech synthesis, and proactively supporting users in complex tasks. AIH will extend the context-awareness and adaptiveness of those conversational robots from a laboratory environment to a real-life environment and from individual to collaborative settings, where multiple users interact with the system simultaneously. Through engaging with societal and municipal actors, the project contributes to more inclusive and accessible robotic systems with potential applications to promote healthy lifestyles and provide home care services for older adults.

Background
AI-powered agents provide an opportunity to manage, coordinate and initiate activities within and across households, contributing to healthy lifestyles and improved quality of life for users of all ages. To achieve this goal, these robotic systems must be aligned with the user’s needs, preferences, and interests, and they must adapt themselves to complex environments involving multiple users performing overlapping and interwoven household tasks, such as cooking.

Cooking in the home is not simply instruction-giving; it is a multi-party, parallelized activity requiring timing, action relevance, and adaptability from both the human and any AI assistance. The project has envisioned an adaptive intelligent kitchen assistant that could help humans prepare food and other kitchen-centric tasks to enhance healthy ageing and improve quality of life.

Cross-disciplinary collaboration
The interdisciplinary approach will allow us to synthesize the human-centric understanding of the home and kitchen environment to the technical capabilities of dialogue systems and robotic agents. This could indicate, for instance, new modalities for personalising the intelligent kitchen assistant for different user groups or linguistic styles that could be adapted to different family members’ skill sets and preferences. An important societal aspect is to explore and design for improved inclusivity of dialogue systems by recognizing diverse ways of interacting with the system based on user’s age, gender, ethnicity, or disability and investigate the long-term impact of deployment of such systems in the home environment to the quality of life and self-efficacy among end-users. Designing for improved accessibility of dialogue systems has the potential to result in more equal access to intelligent and assistive home environments.

About the project

Objective
The Explainable and Ethical Machine Learning for Knowledge Discovery from Medical Data Sources (EXTREMUM) project aims to develop a novel platform for learning from complex medical data sources with a focus on two healthcare application areas: adverse drug event detection and early detection and treatment of cardiovascular diseases.

The team will present a new framework for data management and analysis of data integration, methods for machine learning, and ethical issues related to predictive models. This project’s fundamental breakthrough is establishing a novel knowledge management and discovery framework for medical data sources. The outcome will be a set of methods and tools for integrating complex medical data sources, a set of predictive models for learning from these sources emphasising interpretability and explanatory features, and simultaneously focusing on maintaining ethical integrity in the underlying decision mechanisms that rule machine learning.

Background
One of the biggest challenges that research and business entities have been facing recently is that the data provided by today’s technologies originate from multiple data sources in massive quantities and at rapid rates. In addition to their volume, such data sources are complex and heterogeneous. In the presence of these complex and continuously growing information sources, domain scientists, data analysts, and novice users have been struggling to manage this complexity and arrive from abundant data to usable and interpretable models and exploitable domain knowledge. Towards this end, these data sources need to be monitored in real-time. Hence, data integration indexing and predictive modelling have become a major challenge. Consider, for example, the healthcare domain, where numerous data sources in the form of Electronic Health Records (EHRs), such as billing codes, registry data, and pharmaceutical data, are used for developing predictive models of, e.g., heart failure prevention and treatment progression, or adverse drug effect (ADE) detection.

An example workflow of the EXTREMUM framework starts from radiography exams, ranking them based on their severity and urgency, then moving on to their classification and tagging while eventually producing clinically relevant explanatory text.

Cross-disciplinary collaboration
The project is a collaborative effort between four research institutions: the Department of Computer and Systems Sciences at Stockholm University, the Department of Law at Stockholm University, RISE Research Institutes Sweden, Division ICT, and the Department of Automatic Control, School of Electrical Engineering and Computer Science (EECS, KTH).

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

Results

The main goal of this project is to develop and establish a novel data representation, integration, and knowledge discovery framework for medical data sources, focusing on explainable machine learning governed by legal and ethical principles. Particular emphasis will be given to healthcare data sources available in EHRs, emphasising two particular healthcare problems: (a) early prediction and treatment of cardiovascular diseases and (b) adverse drug event identification and prevention.

Project objectives, along with the current achievements and results:

Objective 1: Unified representation and integration of complex data spaces. We have explored and defined novel space representations, similarity measures, and methods for searching and indexing large and complex data spaces for complex data sources. The basic challenge is the temporal nature of the data spaces and the inherent temporal dependencies that may exist within the same and across different data sources in these spaces. Our main solutions include the adoption and employment of temporal abstractions for temporal event sequences (both time series and discrete event sequences) of univariate and multivariate events.

Objective 2: Explainable predictive models for complex data sources. Regarding the second objective, our intention is to develop novel predictive modelling algorithms that can support and exploit data sources of complex nature and heterogeneity while at the same time providing explainability of their predictions in the form of interpretable features or rule sets. Towards this end, we have developed new methods for explainable machine learning with an emphasis on example-based explanations, such as counterfactuals for time series counterfactuals, as well as for event sequences). Moreover, we have explored model-based explanations and more concretely local explanations (such as LIME and SHAP), their applicability to the medical domain, and to what extent they are trusted and can be adopted by medical practitioners. In the case of time series data, interpretability can be achieved by focusing on white-box models, such as those described by differential equations, for which we have developed and analyzed several estimation algorithms.

We have also developed novel methods to reverse engineer predictors/filters for Hidden Markov Models and Linear Dynamical Systems, rendering them explainable by “opening the box”; furthermore, by modifying the samples arriving at a learning algorithm, we are developing methods that can improve learning while preserving privacy, enforcing fairness and arriving at more interpretable models. Our future plan is to develop similar methods for counterfactual generation for forecasting (e.g., for predicting critical events in the ICU and preventing them by suggesting critical actions. Furthermore, we intend to conduct a more extensive qualitative study on the generated explanations and counterfactuals involving a larger group of medical practitioners. This will also involve a qualitative assessment and deployment of our current demonstrator tool (published recently in the hospital setting on actual use cases.

Objective 3: Adherence to legal and ethical frameworks. We have focused on the legal and ethical implications associated with the development and use of predictive modelling in relation to healthcare data analysis. To this end, the General Data Protection Regulation (GDPR) is of utmost relevance. We have identified the predominant social values promoted in the GDPR and transposed these legal rules into mathematical equivalents. The finding from this process has been interesting to the extent that the identified social values are mutually exclusive and that promoting one of them occurs at the expense of the others, necessitating a balancing act. The social values studied thus far were privacy, accuracy and explainability. The results of this examination are relevant from a legislative techniques perspective, culminating in these interdisciplinary findings being published in a legal publication venue.

Publications

We like to inspire and share interesting knowledge!

Journal articles (J)

  1. R.A.González,C.R.Rojas,S.Pan,andJ.Welsh.“Theoreticalandpracticalaspects of the convergence of the SRIVC estimator for over-parameterized models”. Automatica, 142:110355, 2022
  2. S. Pan, J. Welsh, R. A. González, and C. R. Rojas. “Consistency analysis and bias elimination of the instrumental variable based state variable filter method”. Automatica, 144:110511, 2022
  3. P. Wachel and C. R. Rojas. “An adversarial approach to adaptive model predictive control”. Journal of Advances in Applied & Computational Mathematics, 9, 135–146, 2022
  4. R. A. González, C. R. Rojas, S. Pan, and J. Welsh. “Refined instrumental variable methods for unstable continuous-time systems in closed-loop”. International Journal of Control (accepted for publication), 2022
  5. C.R.RojasandP.Wachel.“Onstate-spacerepresentationsofgeneraldiscrete-time dynamical systems”. IEEE Transactions on Automatic Control (accepted for publication), 2022
  6. R.Mochaourab,A.Venkitaraman,I.Samsten,P.Papapetrou,andC.R.Rojas,“Post- hoc Explainability for Time Series Classification: Toward a Signal Processing Perspective,” IEEE Signal Processing Magazine, Special Issue on Explainability in Data Science: Interpretability, Reproducibility, and Replicability, vol. 39, no. 4, Jul. 2022
  7. S. Greenstein, P. Papapetrou, and R. Mochaourab, “Embedding Human Values into Artificial Intelligence,” in De Vries, Katja (ed.), De Lege, Iustus förlag, Uppsala University, 2022
  8. J. Rebane, I. Samsten, L. Bornemann, and P. Papapetrou, “SMILE: A feature-based temporal abstraction framework for event-interval sequence classification”. In Data Mining and Knowledge Discovery (DAMI) 35(1): 372-399, 2021
  9. B. Djehiche, O. Mazhar, and C. R. Rojas. “Finite impulse response models: A nonasymptotic analysis of the least squares estimator”. Bernoulli, 27(2):976–1000, 2021
  10. R. A. González, C. R. Rojas, S. Pan, and J. Welsh. “Consistent identification of continuous-time systems under multisine input signal excitation”. Automatica, 133:109859, 2021
  11. I. Lourenço, R. Mattila, C. R. Rojas, X. Hu, and B.Wahlberg. “Hidden Markov models: Inverse filtering, belief estimation and privacy protection”. Journal of Systems Science and Complexity, 34:1801–1820, 2021
  12. J. Rebane, I. Samsten, and P. Papapetrou, “Exploiting Complex Medical Data with Interpretable Deep Learning for Adverse Drug Event Prediction”. In Artificial Intelligence in Medicine (AIM), 28(8): 1651-1659, 2021
  13. Locally and globally explainable time series tweaking KAIS), 62 (5): 1671- 1700, 2020
  14. R. Mattila, C. R. Rojas, V. Krishnamurthy, and B. Wahlberg. “Inverse filtering for hidden Markov models with applications to counter-adversarial autonomous systems”. IEEE Transactions on Signal Processing, 68:4987–5002, 2020

Conference papers (C)

  1. Z. Lee, M. Trincavelli, and P. Papapetrou, “Finding Local Groupings of Time Series“, In the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML/PKDD), to appear
  2. L. Mondrejevski, I. Miliou, A. Montanino, D. Pitts, J. Hollmén, and P. Papapetrou, “FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction”. In Computer-Based Medical Systems (CBMS), to appear
  3. M. Bampa, T. Fasth, S. Magnusson, and P. Papapetrou, “EpidRLearn: Learning intervention strategies for epidemics with Reinforcement Learning“. In the International Conference on Artificial Intelligence in Medicine (AIME), 2022
  4. S. Guarnizo, I. Miliou, and P. Papapetrou, “Impact of Dimensionality on Nowcasting Seasonal Influenza with Environmental Factors“. In the International Symposium on Intelligent Data Analysis (IDA), 128-142, 2022
  5. J. Parsa, C. R. Rojas, and H. Hjalmarsson. “Optimal input design for sparse system identification”. In Proceedings of the 2022 European Control Conference (ECC), 2022
  6. B. Lakshminarayanan and C. R. Rojas. “A statistical decision-theoretical perspective on the two-stage approach to parameter estimation”. In Proceedings of the 61th IEEE Conference on Decision and Control (CDC 2022) (accepted for publication), 2022
  7. I. Lourenço, R. Winqvist, C. R. Rojas, and B. Wahlberg. “A teacher-student Markov decision process-based framework for online correctional learning”. In Proceedings of the 61th IEEE Conference on Decision and Control (CDC 2022) (accepted for publication), 2022
  8. Z. Wang, I. Samsten, and P. Papapetrou, Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients. In Artificial Intelligence in Medicine (AIME), 338-348, 2021 [best student paper award]
  9. J.PavlopoulosandP.Papapetrou,CustomizedNeuralPredictiveMedicalText:AUse- Case on Caregivers. In Artificial Intelligence in Medicine (AIME), 438-443, 2021
  10. J. Rebane, I. Samsten, P. Pantelidis, and P. Papapetrou, Assessing the Clinical Validity of Attention-based and SHAP Temporal Explanations for Adverse Drug Event Predictions. In Computer-based Medical Systems (CBMS), 235-240, 2021
  11. Z. Wang, I. Samsten, R. Mochaourab, and P. Papapetrou, Learning Time Series Counterfactuals via Latent Space Representations. In Discovery Science (DS), 369- 384, 2021
  12. I. Lourenço, R. Mattila, C. R. Rojas, and B. Wahlberg. “Cooperative system identification via correctional learning”. In Proceedings of the 19th IFAC Symposium on System Identification (SYSID 2021), 2021
  13. R. A. González, C. R. Rojas, and H. Hjalmarsson. “Non-causal regularized least- squares for continuous-time system identification with band-limited input excitations”. In Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021), 2021
  14. R. A. González, C. R. Rojas, S. Pan, and J. S. Welsh. “The SRIVC algorithm for continuous-time system identification with arbitrary input excitation in open and closed loop”. In Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021), 2021
  15. Z. Lee, T. Lindgren, and P. Papapetrou, “Z-Miner: an efficient method for mining frequent arrangements of event intervals“. In ACM Knowledge Discovery and Data Mining (KDD), 524-534, 2020
  16. Z. Lee, S. Girdzijauskas, and P. Papapetrou, “Z-Embedding: A spectral representation of event intervals for efficient clustering and classification“. In the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML/PKDD), 710-726, 2020
  17. M. Bampa, P. Papapetrou, and J. Hollmen, “A clustering framework for patient phenotyping with application to adverse drug events“. In IEEE Computer-Based Medical Systems (CBMS), 177-182, 2020
  18. J. Pavlopoulos and P. Papapetrou, “Clinical predictive keyboard using statistical and neural language modeling“. In IEEE Computer-Based Medical Systems (CBMS), 293- 296, 2020
  19. Z. Lee, J. Rebane, and P. Papapetrou, “Mining disproportional frequent arrangements of event intervals for investigating adverse drug events“. In IEEE Computer-Based Medical Systems (CBMS), 293-296, 2020
  20. N. B. Kumarakulasinghe, T. Blomberg, J. Liu, A. S. Leao and P. Papapetrou, “Evaluating local interpretable model-agnostic explanations on clinical machine learning classification models“. In IEEE Computer-Based Medical Systems (CBMS), 7-12, 2020
  21. R. Mattila, I. Lourenço, V. Krishnamurthy, C. R. Rojas, and B. Wahlberg. “What did your adversary believe? Optimal smoothing in counter-autonomous systems”. In Proceedings of the 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona, Spain, 2020
  22. R. A. González and C. R. Rojas. “Finite sample deviation and variance bounds for first order autoregressive processes”. In Proceedings of the 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona, Spain, 2020
  23. R. A. González and C. R. Rojas. “A finite-sample deviation bound for stable autoregressive processes”. In Proceedings of the 2nd Conference on Learning for Decision and Control (L4DC), Berkeley, USA, 2020
  24. R. A. González, J. S. Welsh, and C. R. Rojas. “Enforcing stability through ellipsoidal inner approximations in the indirect approach for continuous-time system identification”. In Proceedings of the 21st IFAC World Congress (IFAC 2020), Berlin, Germany, 2020
  25. R. Mattila, C. R. Rojas, E. Moulines, V. Krishnamurthy, and B.Wahlberg. “Fast and consistent learning of hidden Markov models by incorporating non-consecutive correlations”. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), 2020
  26. I. Lourenço, R. Mattila, C. R. Rojas, and B. Wahlberg. “How to protect your privacy? A framework for counter-adversarial decision making”. In Proceedings of the 59th IEEE Conference on Decision and Control (CDC 2020), 2020
  27. R. Mochaourab, S. Sinha, S. Greenstein, and P. Papapetrou, “Robust Counterfactual Explanations for Privacy-Preserving SVMs,” International Conference on Machine Learning (ICML 2021), Workshop on Socially Responsible Machine Learning, Jul. 2021

Demo paper (D)

  1. R. Mochaourab, S. Sinha, S. Greenstein, and P. Papapetrou, “Demonstrator on Counterfactual Explanations for Differentially Private Support Vector Machines,” European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022), Demo Track, Sept. 2022

About the project

Objective
Our planet faces unprecedented environmental challenges, including rapid urbanization, deforestation, pollution, loss of biodiversity, melting glacier, rising sea-level, and climate change. In recent years, the world also witnessed numerous natural disasters, from droughts, heat waves and wildfires to flooding and hurricanes, killing thousands and causing billions in property and infrastructural damages. With its synoptic view and large area coverage at regular revisits, satellite remote sensing has played a crucial role in monitoring our changing planet.

The overall objective of the EO-AI4GlobalChange (EO-AI4GlobalChange: Earth Observation Big Data and Deep Learning for Change Detection and Environmental Impact Assessment: Urbanization and Forest Fire Monitoring as Examples) project is to develop innovative and robust methods for monitoring global environmental changes using Earth Observation big data and deep learning. This research will focus on three major global environmental challenges: urbanization, wildfires and flooding.

Open and free Earth observation big data such as Sentionel-1 SAR and Sentinel-2 MSI data have been used to demonstrate the novel deep learning-based methods in selected cities worldwide and various wildfire and flooding sites across the globe. For urban mapping, a novel Domain Adaptation (DA) approach using semi-supervised learning has been developed for built-up area extraction. Several novel methods have been developed for urban change detection, including a dual-stream U-Net and a Siamese Difference Dual-Task network. For early detection of active fires, Gated Recurrent Units and transformer networks have been used to improve GOES-R and VIIRS dense time series detections.

Figure 1. The Dixie Fire burned 963,309 acres (389,837 ha), was the largest single (i.e. non-complex) wildfire in recorded California history and the second-largest wildfire overall. The burned areas were clearly detected by the Sentinel-2 MSI (Left) and Sentinel-1 SAR (Right) images.

Figure 2. Started on July 13, 2021, and contained on October 25, 2021, the progression of the Dixie Fire was captured by Sentinel-1 and Sentinel-2 images.

For wildfire progression monitoring, transfer learning-based models have been evaluated to exploit Sentinel-1 SAR and Sentinel-2 MSI data. Civil contingencies agencies can use the timely and reliable information the project generates to support effective emergency management and decision-making during and after wildfires and flooding. Automatic and continuous mapping of urban areas and their changes can support sustainable and resilient city planning and contribute to monitoring the UN 2030 Urban Sustainable Development Goal (SDG 11).

Background
In recent years, the world has experienced many devastating wildfires due to human-induced climate change, most recently in Australia around the turn of 2019/2020. Wildfires kill and displace people, damage property and infrastructure, burn vegetation and harm wildlife, and cost billions of euros to fight. Up-to-date and reliable information on fire risk, active fires, fire extent, progression and damage assessment is critical for effective emergency management and decision support.

The pace of urbanization has been unprecedented. Rapid urbanization poses significant social and environmental challenges, including sprawling informal settlements, increased pollution, urban heat island, loss of biodiversity and ecosystem services, and making cities more vulnerable to disasters. Therefore, accurate and consistent information on urban changing patterns is essential to support sustainable urban development and the UN’s New Urban Agenda.

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

Notable Presentations

Conference Presentations

Results

The overall objective of the EO-AI4GlobalChange project is to develop innovative and robust methods for monitoring environmental changes using Earth Observation big data and deep learning. This research will focus on three major global environmental challenges: urbanization, wildfires and flooding.

The specific objectives are:

  1. to develop novel, automatic and globally applicable methods for effective change detection using deep learning and big data analytics to exploit all available Earth Observation data;
  2. to adapt the developed methods for continuous urban change detection, for flood mapping, and for wildfire monitoring including early detection of active fires, near real-time monitoring of wildfire progression and rapid damage estimation;
  3. to assess the environmental impacts of urbanization and wildfires on biodiversity and ecosystem services.

The research achievements are:

List of open-data repositories and developed software

Publications

We like to inspire and share interesting knowledge!

Peer-reviewed journal publications and book chapter

  1. Hafner, S. Y. Ban and A. Nascetti, 2022. Unsupervised Domain Adaptation for Global Urban Extraction Using Sentinel-1 and Sentinel-2 Data. Remote Sensing of Environment. Vol. 280,113192, https://doi.org/10.1016/j.rse.2022.113192.
  2. Hafner, S., A. Nascetti, H. Azizpour and Y. Ban, 2022. Sentinel-1 and Sentinel- 2 Data Fusion for Urban Change Detection Using a Dual Stream U-Net. IEEE Geoscience and Remote Sensing Letters, Vol. 19, pp. 1-5. doi: 10.1109/LGRS.2021.3119856.
  3. Mugiraneza, T., S. Hafner, J. Haas, Y. Ban. 2022. Monitoring urbanization and environmental impact in Kigali, Rwanda using Sentinel-2 MSI data and ecosystem service bundles. International Journal of Applied Earth Observation and Geoinformation, Vol. 109, 102775. DOI: https://doi.org/10.1016/j.jag.2022.102775.
  4. Yadav, R., A. Nascetti, Y. Ban. 2022. Deep Attentive Fusion network for Flood Detection on Uni-temporal Sentinel-1 Data. Frontiers in Remote Sensing, section Microwave Remote Sensing (Accepted).
  5. Hu, X., Y. Ban, and A, Nascetti. 2021. Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning. Remote Sensing, 13, no. 8: 1509.
  6. Hu, X., Y. Ban, and A, Nascetti. 2021. Sentinel-2 MSI data for active fire detection in major fire-prone biomes: A multi-criteria approach. International Journal of Applied Earth Observation and Geoinformation, 101, 102347.
  7. Zhang, P., Y. Ban, and A. Nascetti. 2021. Learning U-Net without Forgetting for Near Real-Time Wildfire Monitoring by the Fusion of SAR and Optical Time Series. Remote Sensing of Environment, 1-12. https://doi.org/10.1016/j.rse.2021.112467.
  8. Zhao Y, and Y. Ban. 2022. GOES-R Time Series for Early Detection of Wildfires with Deep GRU-Network. Remote Sensing. 2022; 14(17):4347. https://doi.org/10.3390/rs14174347.
  9. Furberg, D., and Y. Ban. 2021. Satellite monitoring of urbanization and environmental impacts in Stockholm, Sweden, through a multiscale approach. Urban Remote Sensing, 2nd Edition, Ed: X. Yang, Wiley.
  10. Furberg, D., Ban, Y. & Mörtberg, U., 2020. Monitoring urban green infrastructure changes and impact on habitat connectivity using high-resolution satellite data. Remote Sensing, 12(18), 3072. https://doi.org/10.3390/rs12183072.
  11. Ban, Y., Zhang, P., Nascetti, A., Bevington, A. R., Wulder, M. A., 2020. Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning. Scientific Reports, 10(1), 1–15. https://www.nature.com/articles/s41598-019-56967-x.

Peer-Reviewed Conference Papers

  1. Hafner, S., Y. Ban and A. Nascetti, 2022. Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2022, Kuala Lumpur, Malaysia.
  2. Hu, X., P. Zhang and Y. Ban, 2022. Gan-Based SAR to Optical Image Translation in Fire- Disturbed Regions. Proceedings of IGARSS’2022, Kuala Lumpur, Malaysia.
  3. Zhang, P., X. Hu, and Y. Ban, 2022. Wildfire-S1S2-Canada: A Large-Scale Sentinel-1/2 Wildfire Burned Area Mapping Dataset Based on the 2017-2019 Wildfires in Canada. Proceedings of IGARSS’2022, Kuala Lumpur, Malaysia.
  4. Zhao, Y., Y. Ban, 2022. Global Scale Burned Area Mapping Using Bi-Temporal ALOS-2 PALSAR-2 L-Band Data. Proceedings of IGARSS’2022, Kuala Lumpur, Malaysia.
  5. Yadav, R., A. Nascetti and Y. Ban, 2022. “Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data,” Proceedings, IGARSS’2022.
  6. Yadav, R., Y. Ban and A. Nascetti, 2022. “Building Change Detection using Multi-Temporal Airborne LiDAR Data,” Proceedings, XXIV ISPRS Congress (2022 edition), 6–11 June 2022, Nice, France.
  7. Hafner, S., Y. Ban and A. Nascetti, 2021. Exploring the Fusion of Sentinel-1 SAR and Sentinel-2 MSI Data for Built-Up Area Mapping Using Deep Learning. Proceedings of IGARSS’2021, Brussels, Belgium.
  8. Zhao, Y., Y. Ban and A. Nascetti, 2021. “Early Detection of Wildfires with GOES-R Time- Series and Deep GRU Network,” Proceedings of IGARSS’2021, Brussels, Belgium.

About the project

Objective
In this project, Princeton University and KTH Royal Institute of Technology plan to develop a family of C3.ai-enabled methods for learning, optimization, and system stability analysis of grid-tied inverters and power electronics-based power systems. The goal is to develop a unified machine-learning platform for power electronics, power systems, and data science research. A bottom-up approach, from modelling a single inverter to modelling a cluster of inverters connected as a microgrid, will be used as a motivating case study to show a holistic hierarchical modelling approach supported by C3.ai.

Background
Distributed and renewable energy resources are massively deployed in electric power grids, driven by the sharp cost reduction and the demand for carbon neutrality. The grid of the future will be supported by clouds of distributed and renewable energy resources. Power electronics Inverters are pervasively needed to connect renewable energy resources to the grid, thanks to their full controllability over electric power. On the other hand, to meet a wide range of grid requirements, these grid-tied inverters are commonly equipped with sophisticated control systems, which pose challenges to the stability and control of power grids, threatening the energy security of modern society.

Crossdisciplinary collaboration
This project is a collaboration between KTH and Princeton University.

Watch the recorded presentation at Digitalize in Stockholm 2022 event:

About the project

Objective
The Learning in Routing Games for Sustainable Electromobility (RoSE) project will employ large-scale simulation, learning, and game theory to develop sustainability-aware traffic routing tools. The tools will leverage and fuse heterogeneous, noisy, and often incomplete data from various sources, such as infrastructure condition data, traffic flow data, and power distribution grid data. The key contribution is to account for operational costs, infrastructure condition deterioration, and environmental externalities in the design of socially desirable, sustainable traffic routing mechanisms. We will address questions such as: How should heavy-duty vehicles be routed at scale to find a good trade-off between operational costs, sustainability, and electric power grid constraints?

Background
The transportation sector is the largest contributor to greenhouse gas emissions worldwide (about 24% of CO2 emissions in the EU and about 28% in the USA). The electrification of road transportation presents an opportunity to defer emissions from roads to electric power generation. However, the ambition to achieve zero-emission mobility requires a new, sustainability-oriented approach to transportation planning at a societal scale, respecting infrastructural constraints and individual incentives while being resilient to infrastructure component failures and data uncertainty. A promising approach is using digital traffic routing tools that maximise green energy utilisation while respecting other vital environmental constraints.

Crossdisciplinary collaboration
RoSE is a collaboration between KTH (EECS and ABE school) and MIT (Department of Civil and Environmental Engineering) in Cambridge, USA.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

About the project

Objective
This project plans to develop AI-based CFD simulation to realise accurate and fast predictions of urban climate and the built environment. This study will focus on two aspects of developing the AI-based model: the AI-based turbulence model by learning the “behaviour” of turbulence and the AI-based surrogate model. To train and test the artificial neural network (ANN) model, this project will collect experimental data on indoor and outdoor airflow from on-site and lab measurements. In terms of accuracy, the predictions by AI-based models are expected to be within a 10% difference from that of conventional CFD simulations. Regarding efficiency, AI-based models are expected to be at least ten times faster than conventional CFD simulations.

Background
The urban climate determines the environmental quality in urban areas by removing or dispersing the airborne pollutants generated by human activities or providing cleaner external (rural) air. Studying urban climate and its impact on built environments would help provide guidelines and tools for urban planners and building engineers to evaluate the environmental quality in our living space. Given the practical difficulties of performing city-scale or multi-scale experiments, accurate simulation and fast decision-supporting tools are urgently needed to provide pollutant mitigation strategies for researchers, urban planners, environmental engineers and decision-makers. The existing development of such tools has been plagued mainly by three scientific challenges: computational speed, accuracy, and robustness.

Crossdisciplinary collaboration
The researchers in the team represent the KTH ABE School, the Department of Computer Science and Engineering, and the Blekinge Institute of Technology.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Activities & Results

Find out what’s going on!

Coupled framework
We develop a coupled CFD – deep learning framework where we substitute only the turbulence model of CFD with an MLP

Flow fields from literature
A. Room simulation mixed convection indoor airflow [1]. Data used to train the MLP.

B. Office simulation with displacement ventilation [2].

C. Building array simulation outdoor airflow [3].

Compared to standard CFD simulation using RNG k-ε model the new coupled framework is:
A. 5 % faster*

B. 7 % faster*

C. 2 % faster*

Download presentation from Open Research Day 21 April as pdf.

Publications

We like to inspire and share interesting knowledge!

Calzolari, G. and Liu, W. Deep learning to replace, improve, or aid CFD analysis in built environment applications: a review. Building and Environment, 2021, 206, 108315. https://doi.org/10.1016/j.buildenv.2021.108315.

Calzolari, G. and Liu, W. A deep learning-based zero-equation turbulence model for indoor airflow simulation. Proceedings of the 5th International Conference on Building Energy and Environment (COBEE 2022), Paper No. 1196, Montreal, Canada.