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  • 1.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Sweden.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Abid, Nosheen
    Luleå University of Technology, Sweden.
    Pahlavan, Maryam
    Luleå University of Technology, Sweden.
    Sabah Sabry, Sana
    Luleå University of Technology, Sweden.
    Liwicki, Foteini
    Luleå University of Technology, Sweden.
    Liwicki, Marcus
    Luleå University of Technology, Sweden.
    Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning2022In: Vol. 3 (2022): Proceedings of the Northern Lights Deep Learning Workshop 2022, Septentrio Academic Publishing , 2022, Vol. 3Conference paper (Refereed)
    Abstract [en]

    Building open-domain conversational systems (or chatbots) that produce convincing responses is a recognized challenge. Recent state-of-the-art (SoTA) transformer-based models for the generation of natural language dialogue have demonstrated impressive performance in simulating human-like, single-turn conversations in English.This work investigates, by an empirical study, the potential for transfer learning of such models to Swedish language. DialoGPT, an English language pre-trained model, is adapted by training on three different Swedish language conversational datasets obtained from publicly available sources: Reddit, Familjeliv and the GDC. Perplexity score (an automated intrinsic metric) and surveys by human evaluation were used to assess the performances of the fine-tuned models. We also compare the DialoGPT experiments with an attention-mechanism-based seq2seq baseline model, trained on the GDC dataset. The results indicate that the capacity for transfer learning can be exploited with considerable success. Human evaluators asked to score the simulated dialogues judged over 57% of the chatbot responses to be human-like for the model trained on the largest (Swedish) dataset. The work agrees with the hypothesis that deep monolingual models learn some abstractions which generalize across languages. We contribute the codes, datasets and model checkpoints and host the demos on the HuggingFace platform.

  • 2.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    The Inhibitor: ReLU and Addition-Based Attention for Efficient Transformers2024In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence , 2024, Vol. 38, no 21, p. 23445-23446Conference paper (Refereed)
    Abstract [en]

    To enhance the computational efficiency of quantized Transformers, we replace the dot-product and Softmax-based attention with an alternative mechanism involving addition and ReLU activation only. This side-steps the expansion to double precision often required by matrix multiplication and avoids costly Softmax evaluations but maintains much of the core functionality of conventional dot-product attention. It can enable more efficient execution and support larger quantized Transformer models on resource-constrained hardware or alternative arithmetic systems like homomorphic encryption. Training experiments on four common benchmark tasks show test set prediction scores comparable to those of conventional Transformers with dot-product attention. Our scaling experiments also suggest significant computational savings, both in plaintext and under encryption. The ReLU and addition-based attention mechanism introduced in this paper may enable privacy-preserving AI applications operating under homomorphic encryption by avoiding the costly multiplication of encrypted variables.

  • 3.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Forsgren, Henrik
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Linge, Helena
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    HEIDA: Software Examples for Rapid Introduction of Homomorphic Encryption for Privacy Preservation of Health Data2023In: Studies in health technology and informatics, Vol. 302, p. 267-271Article in journal (Refereed)
    Abstract [en]

    Adequate privacy protection is crucial for implementing modern AI algorithms in medicine. With Fully Homomorphic Encryption (FHE), a party without access to the secret key can perform calculations and advanced analytics on encrypted data without taking part of either the input data or the results. FHE can therefore work as an enabler for situations where computations are carried out by parties that are denied plain text access to sensitive data. It is a scenario often found with digital services that process personal health-related data or medical data originating from a healthcare provider, for example, when the service is delivered by a third-party service provider located in the cloud. There are practical challenges to be aware of when working with FHE. The current work aims to improve accessibility and reduce barriers to entry by providing code examples and recommendations to aid developers working with health data in developing FHE-based applications. HEIDA is available on the GitHub repository: https://github.com/rickardbrannvall/HEIDA.

  • 4.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Forsgren, Henrik
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Linge, Helena
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Santini, Marina
    RISE Research Institutes of Sweden, Digital Systems, Prototyping Society.
    Salehi, Alireza
    RISE Research Institutes of Sweden, Digital Systems, Prototyping Society.
    Rahimian, Fatemeh
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Homomorphic encryption enables private data sharing for digital health: Winning entry to the Vinnova innovation competition Vinter 2021-222022In: 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Conference paper (Refereed)
    Abstract [en]

    People living with type 1 diabetes often use several apps and devices that help them collect and analyse data for a better monitoring and management of their disease. When such health related data is analysed in the cloud, one must always carefully consider privacy protection and adhere to laws regulating the use of personal data. In this paper we present our experience at the pilot Vinter competition 2021-22 organised by Vinnova. The competition focused on digital services that handle sensitive diabetes related data. The architecture that we proposed for the competition is discussed in the context of a hypothetical cloud-based service that calculates diabetes self-care metrics under strong privacy preservation. It is based on Fully Homomorphic Encryption (FHE)-a technology that makes computation on encrypted data possible. Our solution promotes safe key management and data life-cycle control. Our benchmarking experiment demonstrates execution times that scale well for the implementation of personalised health services. We argue that this technology has great potentials for AI-based health applications and opens up new markets for third-party providers of such services, and will ultimately promote patient health and a trustworthy digital society.

  • 5.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sandin, Fredrik
    Luleå University of Technology, Sweden.
    Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers2023In: Energies, E-ISSN 1996-1073, Vol. 16, no 5, article id 2255Article in journal (Refereed)
    Abstract [en]

    This study investigates the use of transfer learning and modular design for adapting a pretrained model to optimize energy efficiency and heat reuse in edge data centers while meeting local conditions, such as alternative heat management and hardware configurations. A Physics-Informed Data-Driven Recurrent Neural Network (PIDD RNN) is trained on a small scale-model experiment of a six-server data center to control cooling fans and maintain the exhaust chamber temperature within safe limits. The model features a hierarchical regularizing structure that reduces the degrees of freedom by connecting parameters for related modules in the system. With a RMSE value of 1.69, the PIDD RNN outperforms both a conventional RNN (RMSE: 3.18), and a State Space Model (RMSE: 2.66). We investigate how this design facilitates transfer learning when the model is fine-tuned over a few epochs to small dataset from a second set-up with a server located in a wind tunnel. The transferred model outperforms a model trained from scratch over hundreds of epochs.

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  • 6.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Lulea University of Technology, Sweden.
    Mattsson, Louise
    RISE Research Institutes of Sweden.
    Lundmark, Erik
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Vesterlund, Mattias
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Data center excess heat recovery: A case study of apple drying2020In: ECOS 2020 - Proceedings of the 33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2020 Local Organizing Committee , 2020, p. 2165-2174Conference paper (Refereed)
    Abstract [en]

    Finding synergies between heat producing and heat consuming actors in an economy provides opportunity for more efficient energy utilization and reduction of overall power consumption. We propose to use low-grade heat recovered from data centers directly in food processing industries, for example for the drying of fruit and berries. This study analyses how the heat output of industrial IT-load on servers can dry apples in a small-scale experimental set up. To keep the temperatures of the server exhaust airflow near a desired set-point we use a model predictive controller (MPC) re-purposed to the drying experiment set-up from a previous work that used machine learning models for cluster thermal management. Thus, conditions with for example 37 C for 8 hours drying can be obtained with results very similar to conventional drying of apples. The proposed solution increases the value output of the electricity used in a data center by capturing and using the excess heat that would otherwise be exhausted. The results from our experiments show that drying foods with excess heat from data center is possible with potential of strengthening the food processing industry and contribute to food self-sufficiency in northern Sweden.

  • 7.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Lulea University of Technology, Sweden.
    Mattsson, Louise
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Lundmark, Erik
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Vesterlund, Mattias
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Data center excess heat recovery: A case study of apple drying2020In: ECOS 2020 - Proceedings of the 33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2020 Local Organizing Committee , 2020, p. 2165-2174Conference paper (Refereed)
    Abstract [en]

    Finding synergies between heat producing and heat consuming actors in an economy provides opportunity for more efficient energy utilization and reduction of overall power consumption. We propose to use low-grade heat recovered from data centers directly in food processing industries, for example for the drying of fruit and berries. This study analyses how the heat output of industrial IT-load on servers can dry apples in a small-scale experimental set up. To keep the temperatures of the server exhaust airflow near a desired set-point we use a model predictive controller (MPC) re-purposed to the drying experiment set-up from a previous work that used machine learning models for cluster thermal management. Thus, conditions with for example 37 C for 8 hours drying can be obtained with results very similar to conventional drying of apples. The proposed solution increases the value output of the electricity used in a data center by capturing and using the excess heat that would otherwise be exhausted. The results from our experiments show that drying foods with excess heat from data center is possible with potential of strengthening the food processing industry and contribute to food self-sufficiency in northern Sweden. 

  • 8.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sarkinen, Jeffrey
    RISE Research Institutes of Sweden.
    Svartholm, Joar
    RISE Research Institutes of Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Digital Twin for Tuning of Server Fan Controllers2019In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, p. 1425-1428Conference paper (Refereed)
    Abstract [en]

    Cooling of IT equipment consumes a large proportion of a modern data centre’s energy budget and is therefore an important target for optimal control. This study analyses a scaled down system of six servers with cooling fans by implementing a minimal data driven time-series model in TensorFlow/Keras, a modern software package popular for deep learning. The model is inspired by the physical laws of heat exchange, but with all parameters obtained by optimisation. It is encoded as a customised Recurrent Neural Network and exposed to the time-series data via n-step Prediction Error Minimisation (PEM). The thus obtained Digital Twin of the physical system is then used directly to construct a Model Predictive Control (MPC) type regulator that executes in real time. The MPC is then compared in simulation with a self-tuning PID controller that adjust its parameters on-line by gradient descent.

  • 9.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Siltala, Mikko
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sarkinen, Jeffrey
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Vesterlund, Mattias
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Summers, Jon
    EDGE: Microgrid Data Center with Mixed Energy Storage2020In: e-Energy 2020 - Proceedings of the 11th ACM International Conference on Future Energy Systems, Association for Computing Machinery, Inc , 2020, p. 466-473Conference paper (Refereed)
    Abstract [en]

    Low latency requirements are expected to increase with 5G telecommunications driving data and compute to EDGE data centers located in cities near to end users. This article presents a testbed for such data centers that has been built at RISE ICE Datacenter in northern Sweden in order to perform full stack experiments on load balancing, cooling, micro-grid interactions and the use of renewable energy sources. This system is described with details on both hardware components and software implementations used for data collection and control. A use case for off-grid operation is presented to demonstrate how the test lab can be used for experiments on edge data center design, control and autonomous operation. © 2020 Author.

  • 10.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Stark, Tina
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eriksson, Mats
    Arctos Labs Scandinvia AB, Sweden.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Cost Optimization by Energy Aware Workload Placement for the Edge Cloud Continuum2022Report (Other academic)
    Abstract [en]

    This report investigates the problem of where to place computation workload in an edge-cloud network topology considering the trade-off between the location specific cost of computation and data communication.

    Download full text (pdf)
    fulltext
  • 11.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Stark, Tina
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eriksson, Mats
    Arctos Labs Scandinavia AB, Sweden.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Cost Optimization for the Edge-Cloud Continuum by Energy-Aware Workload Placement2023In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, Association for Computing Machinery , 2023, p. 79-84Conference paper (Refereed)
    Abstract [en]

    This article investigates the problem of where to place the computation workload in an edge-cloud network topology considering the trade-off between the location-specific cost of computation and data communication. For this purpose, a Monte Carlo simulation model is defined that accounts for different workload types, their distribution across time and location, as well as correlation structure. Results confirm and quantify the intuition that optimization can be achieved by distributing a part of cloud computation to make efficient use of resources in an edge data center network, with operational energy savings of 4–6% and up to 50% reduction in its claim for cloud capacity.

  • 12.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Lulea University of Technology, Sweden.
    Öhman, J.
    Lulea University of Technology, Sweden.
    Kovacs, G.
    Lulea University of Technology, Sweden.
    Liwicki, M.
    Lulea University of Technology, Sweden.
    Cross-encoded meta embedding towards transfer learning2020In: ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (i6doc.com) , 2020, p. 631-636Conference paper (Refereed)
    Abstract [en]

    In this paper we generate word meta-embeddings from already existing embeddings using cross-encoding. Previous approaches can only work with words that exist in each source embedding, while the architecture presented here drops this requirement. We demonstrate the method using two pre-trained embeddings, namely GloVE and FastText. Furthermore, we propose additional improvements to the training process of the metaembedding. Results on six standard tests for word similarity show that the meta-embedding trained outperforms the original embeddings. Moreover, this performance can be further increased with the proposed improvements, resulting in a competitive performance with those reported earlier.

  • 13.
    Efkarpidis, Nikolaos
    et al.
    University of Applied Sciences and Arts Northwestern Switzerland, Switzerland.
    Imoscopi, Stefano
    Universita della Svizzera italiana, Switzerland.
    Bratukhin, Aleksey
    University for Continuing Education Krem, Austria.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Franzl, Gerald
    University for Continuing Education Krem, Austria.
    Leopold, Thomas
    Institute of Computer Technology, Austria.
    Bauer, Valentin
    Institute of Computer Technology, Austria.
    Goranovic, Andrija
    Institute of Computer Technology, Austria.
    Wilker, Stefan
    Institute of Computer Technology, Austria.
    Yang, Chen-Wei
    Luleå University of Technology, Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Geidl, Martin
    University of Applied Sciences and Arts Northwestern Switzerland, Switzerland.
    Sauter, Thilo
    Institute of Computer Technology, Austria.
    Proactive Scheduling of Mixed Energy Resources at Different Grid Levels2024In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, E-ISSN 1949-3037, Vol. 15, p. 952-Article in journal (Refereed)
    Abstract [en]

    The optimal utilisation of distribution grids requires the proactive management of volatilities caused by mixed energy resources installed into different grid levels, such as buildings, energy communities (ECs) and substations. In this context, proactive control based on predictions for energy demand and generation is applied. The mitigation of conflicts between the stakeholders' objectives is the main challenge for the control of centralized and distributed energy resources. In this paper, a bi-level approach is proposed for the control of stationary battery energy storage systems (SBES) supporting the local distribution system operator (DSO) at the transformer level, as well as distributed energy resources (DERs) operated by end customers, i.e., EC-members. Model predictive control (MPC)- based and hybrid approaches merging rule- and MPC-based control schemes are evaluated. Simulation studies based on a typical European low voltage (LV) feeder topology yield the performance assessment in terms of technical and economic criteria. The results show an advantage of hybrid approaches with respect to the DSO's cost savings from peak shaving. From the EC's perspective, both hybrid and MPC-based schemes can achieve effective cost savings from proactive energy management.

  • 14.
    Forsgren, Henrik
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Vesterlund, Mattias
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Minde, Tor Björn
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Homomorphic Encryption Enables Data and Algorithm Confidentiality for Remote Monitoring and Control: An Application to Data Center Systems2023In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, Association for Computing Machinery , 2023, p. 85-90Conference paper (Refereed)
    Abstract [en]

    The design of intelligent algorithms used for device monitoring and control can be costly and is an investment that must be protected against reverse engineering by competitors. An algorithm can be safeguarded by running remotely from the cloud instead of locally on the equipment hardware. However, such a setup requires that sensitive data is sent from the device to the cloud. Fully Homomorphic Encryption (FHE) is an emerging technology that offers a solution to this problem since it enables computation on encrypted data. A cloud service using FHE can protect its proprietary algorithms while simultaneously offering customer data confidentiality. The computational overhead for the technology is, however, still very high. This work reports on a practical investigation of using FHE for data center remote control problems: What applications are feasible today? And at what cost?

  • 15.
    Fredriksson, Sebastian
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eleftheriadis, Lackis
    Ericsson Research, Sweden.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Bäckman, Nils
    Delta Electronics AB, Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    ANIARA: Experimental Investigation of Micro Edge Data Centers with Battery Support on Power-Constrained Grids2023In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, Association for Computing Machinery , 2023, p. 72-78Conference paper (Refereed)
    Abstract [en]

    As the demand for data privacy and low latency grows, edge computation carried out at edge data center nodes is believed to become increasingly important for future telecom applications. Providers must consider various factors, including power consumption, thermal dynamics, and the ability to maintain high-quality service, in addition to deployment and service orchestration. This paper presents a detailed description of two different prototype edge data centers designed to investigate the power performance and thermal dynamics of edge nodes under various applied services. The prototypes were developed and tested at the RISE ICE Datacenter research facility. We present the results of power flow experiments in which input current from the grid was limited while the computational load was maintained using the energy stored in batteries. We further discuss implications for placing edge data center nodes in locations with temporal power constraints and opportunities for participation in support services at the grid level.

  • 16.
    Heimerson, Albin
    et al.
    Lund University, Sweden.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sjölund, Johannes
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eker, Johan
    Luleå University of Technology, Sweden; Ericsson Research, Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Towards a Holistic Controller: Reinforcement Learning for Data Center Control2021In: Proceedings of the Twelfth ACM International Conference on Future Energy Systems, Association for Computing Machinery , 2021, p. 424-429Conference paper (Refereed)
    Abstract [en]

    The increased use of cloud and other large scale datacenter IT services and the associated power usage has put the spotlight on more energy-efficient datacenter management. In this paper, a simple model was developed to represent the heat rejection system and energy usage in a small DC setup. The model was then controlled by a reinforcement learning agent that handles both the load balancing of the IT workload, as well as cooling system setpoints. The main contribution is the holistic approach to datacenter control where both facility metrics, IT hardware metric and cloud service logs are used as inputs. The application of reinforcement learning in the proposed holistic setup is feasible and achieves results that outperform standard algorithms. The paper presents both the simplified DC model and the reinforcement learning agent in detail and discusses how this work can be extended towards a richer datacenter model.

  • 17.
    Heimerson, Albin
    et al.
    Lund University, Sweden.
    Sjölund, Johannes
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eker, Johan
    Lund University, Sweden; Ericsson Research, Sweden.
    Adaptive Control of Data Center Cooling using Deep Reinforcement Learning2022In: Proceedings - 2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Conference paper (Refereed)
    Abstract [en]

    In this paper, we explore the use of Reinforcement Learning (RL) to improve the control of cooling equipment in Data Centers (DCs). DCs are inherently complex systems, and thus challenging to model from first principles. Machine learning offers a way to address this by instead training a model to capture the thermal dynamics of a DC. In RL, an agent learns to control a system through trial-and-error. However, for systems such as DCs, an interactive trial-and-error approach is not possible, and instead, a high-fidelity model is needed. In this paper, we develop a DC model using Computational Fluid Dynamics (CFD) based on the Lattice Boltzmann Method (LBM) Bhatnagar-Gross-Krook (BGK) algorithm. The model features transient boundary conditions for simulating the DC room, heat-generating servers, and Computer Room Air Handlers (CRAHs) as well as rejection components outside the server room such as heat exchangers, compressors, and dry coolers. This model is used to train an RL agent to control the cooling equipment. Evaluations show that the RL agent can outperform traditional controllers and also can adapt to changes in the environment, such as equipment breaking down. 

  • 18.
    Linge, Helena
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Secure Sharing of Health-Related Data: Research Description of the VINTER, DELFIN, and HEIDA Projects2023In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 302, p. 143-144Article in journal (Refereed)
    Abstract [en]

    The need for secure and integrity-preserved data sharing has become increasingly important in the emerging era of changed demands on healthcare and increased awareness of the potential of data. In this research plan, we describe our path to explore the optimal use of integrity preservation in health-related data contexts. Data sharing in these settings is poised to increase health, improve healthcare delivery, improve the offering of services and products from commercial entities, and strengthen healthcare governance, all with a maintained societal trust. The HIE challenges relate to legal boundaries and to the importance of maintaining accuracy and utility in the secure sharing of health-related data.

  • 19.
    Paszkowsky, N. A.
    et al.
    RISE Research Institutes of Sweden.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Carlstedt, Johan
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Milz, M.
    Luleå University of Technology, Sweden.
    Kovacs, G.
    Luleå University of Technology, Sweden.
    Liwicki, M.
    Luleå University of Technology, Sweden.
    Vegetation and Drought Trends in Sweden's Mälardalen Region-Year-on-Year Comparison by Gaussian Process Regression2020In: 2020 Swedish Workshop on Data Science, SweDS 2020, Institute of Electrical and Electronics Engineers Inc. , 2020Conference paper (Refereed)
    Abstract [en]

    This article describes analytical work carried out in a pilot project for the Swedish Space Data Lab (SSDL), which focused on monitoring drought in the Mälardalen region in central Sweden. Normalized Difference Vegetation Index (NDVI) and the Moisture Stress Index (MSI)-commonly used to analyse drought- A re estimated from Sentinel 2 satellite data and averaged over a selection of seven grassland areas of interest. To derive a complete time-series over a season that interpolates over days with missing data, we use Gaussian Process Regression, a technique from multivariate Bayesian analysis. The analysis show significant differences at 95% confidence for five out of seven areas when comparing the peak drought period in the dry year 2018 compared to the corresponding period in 2019. A cross-validation analysis indicates that the model parameter estimates are robust for temporal covariance structure (while inconclusive for the spatial dimensions). There were no signs of over-fitting when comparing in-sample and out-of-sample RMSE.

  • 20.
    Rizk, Aya
    et al.
    Luleå University of Technology, Sweden.
    Seidelin, Catherine
    University of Copenhagen, Denmark.
    Kovács, György
    Luleå University of Technology, Sweden.
    Liwicki, Marcus
    Luleå University of Technology, Sweden.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Defining Beneficiaries of Emerging Data Infrastructures Towards Effective Data Appropriation: Insights from the Swedish Space Data Lab2021In: Proc of 27th International Conference on Information and Software Technologies, ICIST 2021, Springer Science and Business Media Deutschland GmbH , 2021, Vol. 1486CCIS, p. 32-47Conference paper (Refereed)
    Abstract [en]

    The increasing collection and usage of data and data analytics has prompted development of Data Labs. These labs are (ideally) a way for multiple beneficiaries to make use of the same data in ways that are value-generating for all. However, establishing data labs requires the mobilization of various infrastructural elements, such as beneficiaries, offerings and needed analytics talent, all of which are ambiguous and uncertain. The aim of this paper is to examine how such beneficiaries can be identified and understood for the nascent Swedish space data lab. The paper reports on the development of persona descriptions that aim to support and represent the needs of key beneficiaries of earth observation data. Our main results include three thorough persona descriptions that represent the lab’s respective beneficiaries and their distinct characteristics. We discuss the implications of the personas on addressing the infrastructural challenges, as well as the lab’s design. We conclude that personas provide emerging data labs with relatively stable beneficiary archetypes that supports the further development of the other infrastructure components. More research is needed to better understand how these persona descriptions may evolve, as well as how they may influence the continuous development process of the space data lab.

  • 21.
    Sarkinen, Jeffrey
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Experimental Analysis of Server Fan Control Strategies for Improved Data Center Air-based Thermal Management*2020In: 2020 19th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), 2020, p. 341-349Conference paper (Refereed)
    Abstract [en]

    This paper analyzes the prospects of a holistic air-cooling strategy that enables synchronisation of data center facility fans and server fans to minimize data center energy use. Each server is equipped with a custom circuit board which controls the fans using a proportional, integral and derivative (PID) controller running on the servers operating system to maintain constant operating temperatures, irrespective of environmental conditions or workload. Experiments are carried out in a server wind tunnel which is controlled to mimic data center environmental conditions. The wind tunnel fan, humidifier and heater are controlled via separate PID controllers to maintain a prescribed pressure drop across the server with air entering at a defined temperature and humidity. The experiments demonstrate server operating temperatures which optimally trade off power losses versus server fan power, while examining the effect on the temperature difference, ∆T. Furthermore the results are theoretically applied to a direct fresh air cooled data center to obtain holistic sweet spots for the servers, revealing that the minimum energy use is already attained by factory control. Power consumption and Power Usage Effectiveness (PUE) are also compared, confirming that decreasing the PUE can increase the overall data center power consumption. Lastly the effect of decreased server inlet temperatures is examined showing that lower inlet temperatures can reduce both energy consumption and PUE.

  • 22.
    Siltala, Mikko
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Zhou, Quan
    Aalto University, Finland.
    Physical and Data-Driven Models for Edge Data Center Cooling System2020In: 2020 Swedish Workshop on Data Science, SweDS 2020, Institute of Electrical and Electronics Engineers Inc. , 2020Conference paper (Refereed)
    Abstract [en]

    Edge data centers are expected to become prevalent providing low latency computing power for 5G mobile and IoT applications. This article develops two models for the complete cooling system of an edge data center: One model based on the laws of thermodynamics and one data-driven model based on LSTM neural networks. The models are validated against an actual edge data center experimental set-up showing root mean squared errors (RMSE) for most individual components below 1 °C over a simulation period of approximately 10 hours; which compares favourably to state-of-the-art models. 

1 - 22 of 22
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