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  • 1.
    Aramrattana, Maytheewat
    et al.
    VTI Swedish National Road and Transport Research Institute, Sweden.
    Habibovic, Azra
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system.
    Englund, Cristofer
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system. Halmstad University, Sweden.
    Safety and experience of other drivers while interacting with automated vehicle platoons2021Inngår i: Transportation Research Interdisciplinary Perspectives, ISSN 2590-1982, Vol. 10, artikkel-id 100381Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    It is currently unknown how automated vehicle platoons will be perceived by other road users in their vicinity. This study explores how drivers of manually operated passenger cars interact with automated passenger car platoons while merging onto a highway, and how different inter-vehicular gaps between the platooning vehicles affect their experience and safety. The study was conducted in a driving simulator and involved 16 drivers of manually operated cars. Our results show that the drivers found the interactions mentally demanding, unsafe, and uncomfortable. They commonly expected that the platoon would adapt its behavior to accommodate a smooth merge. They also expressed a need for additional information about the platoon to easier anticipate its behavior and avoid cutting-in. This was, however, affected by the gap size; larger gaps (30 and 42.5 m) yielded better experience, more frequent cut-ins, and less crashes than the shorter gaps (15 and 22.5 m). A conclusion is that a short gap as well as external human–machine interfaces (eHMI) might be used to communicate the platoon's intent to “stay together”, which in turn might prevent drivers from cutting-in. On the contrary, if the goal is to facilitate frequent, safe, and pleasant cut-ins, gaps larger than 22.5 m may be suitable. To thoroughly inform such design trade-offs, we urge for more research on this topic. © 2021 The Author(s)

  • 2.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Sarkinen, Jeffrey
    RISE Research Institutes of Sweden.
    Svartholm, Joar
    RISE Research Institutes of Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Summers, Jon
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Digital Twin for Tuning of Server Fan Controllers2019Inngår i: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, s. 1425-1428Konferansepaper (Fagfellevurdert)
    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.

  • 3.
    Dejke, Valter
    et al.
    RISE Research Institutes of Sweden, Material och produktion, Kemi, biomaterial och textil.
    Eng, Matthias
    RISE Research Institutes of Sweden.
    Brinkfeldt, Klas
    RISE Research Institutes of Sweden, Digitala system, Smart hårdvara.
    Charnley, Josephine
    Lusstech, UK.
    Lussey, David
    Lusstech, UK.
    Lussey, Chris
    Lusstech, UK.
    Development of prototype low-cost qtss™ wearable flexible more enviro-friendly pressure, shear, and friction sensors for dynamic prosthetic fit monitoring2021Inngår i: Sensors, E-ISSN 1424-8220, Vol. 21, nr 11, artikkel-id 3764Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    There is a current healthcare need for improved prosthetic socket fit provision for the masses using low-cost and simple to manufacture sensors that can measure pressure, shear, and friction. There is also a need to address society’s increasing concerns regarding the environmental impact of electronics and IoT devices. Prototype thin, low-cost, and low-weight pressure, shear, and loss of friction sensors have been developed and assembled for trans-femoral amputees. These flexible and conformable sensors are simple to manufacture and utilize more enviro-friendly novel magnetite-based QTSS™ (Quantum Technology Supersensor™) quantum materials. They have undergone some initial tests on flat and curved surfaces in a pilot amputee trial, which are presented in this paper. These initial findings indicate that the prototype pressure sensor strip is capable of measuring pressure both on flat and curved socket surfaces in a pilot amputee trial. They have also demonstrated that the prototype shear sensor can indicate increasing shear forces, the resultant direction of the shear forces, and loss of friction/slippage events. Further testing, amputee trials, and ongoing optimization is continuing as part of the SocketSense project to assist prosthetic comfort and fit. © 2021 by the authors

  • 4.
    Ebrahimiyan, Hamide
    et al.
    Urmia University, Iran.
    Balador, Ali
    RISE Research Institutes of Sweden.
    Nikoui, Tina
    Islamic Azad University, Iran.
    A Cost-Aware Resource Management Technique for Cloud and Edge Environment2022Inngår i: MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2022, s. 1165-1170Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Fog computing plays an important role to improve the efficiency of time-sensitive applications, cost reduction, and proper data management. In this paper, the above goals are made possible by processing some time-sensitive data and aggregating the rest of the data, by the modules in the fog data manager and associated storage servers, before sending it to the cloud servers. In this paper, we set the optimal workload allocation to the cloud and fog system by considering the compromise between costs of energy consumption of cloud and fog system, transfer bandwidth and revenue losses due to the WAN propagation delay, and time constraints in the Fog and Cloud system. The mixed linear programming obtained by IBM ILOG CPLEX software is solved to achieve the lowest possible cost by optimally allocating loads to fog devices and cloud servers. In addition, we show that using this approach, the total cost can be reduced by about 29%. 

  • 5.
    Farooqui, Ashfaq
    et al.
    RISE Research Institutes of Sweden, Säkerhet och transport, Elektrifiering och pålitlighet.
    Claase, Tijsse
    Eindhoven University of Technology, Netherlands.
    Fabian, Martin
    Chalmers University of Technology, Sweden.
    On Active Learning for Supervisor Synthesis2024Inngår i: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 21, s. 78-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Supervisory control theory provides an approach to synthesize supervisors for cyber-physical systems using a model of the uncontrolled plant and its specifications. These supervisors can help guarantee the correctness of the closed-loop controlled system. However, access to plant models is a bottleneck for many industries, as manually developing these models is an error-prone and time-consuming process. An approach to obtaining a supervisor in the absence of plant models would help industrial adoption of supervisory control techniques. This paper presents, an algorithm to learn a controllable supervisor in the absence of plant models. It does so by actively interacting with a simulation of the plant by means of queries. If the obtained supervisor is blocking, existing synthesis techniques are employed to prune the blocking supervisor and obtain the controllable and non-blocking supervisor. Additionally, this paper presents an approach to interface the with a PLC to learn supervisors in a virtual commissioning setting. This approach is demonstrated by learning a supervisor of the well-known example simulated in Xcelgo Experior and controlled using a PLC. interacts with the PLC and learns a controllable supervisor for the simulated system. Note to Practitioners—Ensuring the correctness of automated systems is crucial. Supervisory control theory proposes techniques to help build control solutions that have certain correctness guarantees. These techniques rely on a model of the system. However, such models are typically unavailable and hard to create. Active learning is a promising technique to learn models by interacting with the system to be learned. This paper aims to integrate active learning and supervisory control such that the manual step of creating models is no longer needed, thus, allowing the use of supervisory control techniques in the absence of models. The proposed approach is implemented in a tool and demonstrated using a case study. 

  • 6.
    Heimerson, Albin
    et al.
    Lund University, Sweden.
    Sjölund, Johannes
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Eker, Johan
    Lund University, Sweden; Ericsson Research, Sweden.
    Adaptive Control of Data Center Cooling using Deep Reinforcement Learning2022Inngår i: Proceedings - 2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Konferansepaper (Fagfellevurdert)
    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. 

  • 7.
    Joborn, Martin
    et al.
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system. Linköping University, Sweden.
    Ranjbar, Zohreh
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system.
    Understanding causes of unpunctual trains: Delay contribution and critical disturbances2022Inngår i: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 23, artikkel-id 100339Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper we define new concepts and metrics for improved understanding of causes to unpunctual trains. The metrics are denoted delay contribution and critical disturbance. Delay contribution can be interpreted as how much a specific disturbance contributes to the delay of a train and the critical disturbances can be interpreted as the disturbances that made the train become unpunctual. The metrics are applied in a test case with trains in southern Sweden. The results show that the metrics can provide a complementary view regarding causes to unpunctuality compared to standard methods and are able to pinpoint disturbances that made trains become unpunctual and separate them from disturbances that have less impact on the punctuality. The methods are useable in the continuous work to improve railway performance e.g., by prioritizing maintenance work that give best impact on punctuality. © 2022 The Authors

  • 8.
    Mochaourab, Rami
    et al.
    Dresden University of Technology, Germany.
    Jorswieck, E.
    Dresden University of Technology, Germany.
    Resource Allocation in Protected and Shared Bands: Uniqueness and Efficiency of Nash Equilibria2009Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Motivated by the question, is non-cooperative spectrum sharing desirable or not, we consider a scenario utilizing protected and shared bands. In a static non-cooperative setting consisting of two communication system pairs, we study the existence, uniqueness and efficiency of a fixed point of the iterative water-filling algorithm which corresponds to the Nash equilibrium. There exist several sufficient conditions for the convergence of the algorithm in the literature mostly based on the contraction mapping theorem. We derive necessary and sufficient conditions for convergence by relating the game to supermodular games. There, the best response dynamics is globally convergent when a unique Nash equilibrium exists. In order to understand the loss in efficiency due to non-cooperation, we study the Price of Anarchy of the system. We show that the performance of the noncooperative system cannot fall below two third of that of the cooperative system in the high signal to noise ratio regime. Theoretical results are illustrated by numerical simulations for a simplified system scenario.

  • 9.
    Mochaourab, Rami
    et al.
    Dresden University of Technology, Germany.
    Zorba, N.
    University of Jordan, Jordan.
    Jorswieck, E
    Dresden University of Technology, Germany.
    Nash Equilibrium in Multiple Antennas Protected and Shared Bands2012Inngår i: Proceedings of the International Symposium on Wireless Communication Systems 2012, 2012, s. 101-105Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Two non-cooperating cells are considered, each with its protected band to provide service to its high priority users. A shared band for the two cells is employed to deliver service to low priority users. We formulate the situation between the two cells as a non-cooperative game and study its Nash equilibrium. We prove that the game belongs to a class of games called supermodular games which have several interesting properties, such as the global stability of a unique Nash equilibrium. We provide a sufficient condition for the existence of a unique Nash equilibrium and study its efficiency by extensive simulations.

  • 10.
    Penttinen, Olle
    et al.
    RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.
    Ulveström, Marcus
    RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.
    Karlsson, Kristina
    RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.
    Andersson, Veronika
    RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.
    Andersson, Håkan
    RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.
    Pettersson, Johan
    RISE Research Institutes of Sweden, Samhällsbyggnad, Infrastruktur och betongbyggande.
    Büker, Oliver
    RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.
    Towards flow measurement with passive accelerometers2021Inngår i: Flow Measurement and Instrumentation, ISSN 0955-5986, E-ISSN 1873-6998, Vol. 80, artikkel-id 101992Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The aim of this project has been to find suitable methods for flow measurement and characterization with passive accelerometers. The objectives were twofold. Firstly, the process industry could make use of such a sensor for process surveillance. Secondly, the water utilities of today lack simple and cost-efficient alternatives to equip their ageing infrastructures with online flow meters. These kinds of efforts are necessary for the realization of smart maintenance and for the decrease of the currently increasing amount of maintenance needs water utilities of today are experiencing. Liquid flowing in a pipe generates vibrations, detectable with accelerometers fitted along the pipe exterior. The correlated sound from synchronized accelerometers experience a lag which is dependent on the flow rate. Also, if the acquired sound is further processed, there exist a possibility to extract enough features to estimate some additional characteristics, in this case temperature. Experiments were performed at two nominal temperatures, 20 °C and 40 °C. A deep neural network was constructed for non-linear regression purposes to predict flow velocities based on lag and mean frequencies of the vibrations. Further, a proof of concept for this methodology was shown which reached a root mean square deviation from 100.8 L/min to 171.1 L/min for a nominal flow range of 0 to 1500 L/min. In addition, we train a k-nearest neighbour classifier to predict the nominal temperature of our validation dataset with 83 percent accuracy. The work was performed at RISE Research Institutes of Sweden, serving as Sweden's national metrology institute for liquid flow and acoustics. © 2021 The Authors

  • 11.
    Voronov, Alexey
    et al.
    RISE Research Institutes of Sweden.
    Andersson, Jonas
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system.
    Englund, Cristofer
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system. Halmstad University, Sweden.
    Cut-ins in Truck Platoons: Modeling Loss of Fuel Savings2021Inngår i: Towards Connected and Autonomous Vehicle Highways, Springer Science and Business Media Deutschland GmbH , 2021, s. 11-26Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Reducing fuel consumption is one of the major benefits of platooning. While introducing platooning in mixed traffic, surrounding traffic will interfere with the platoon, risking a loss in fuel savings. In this work, a method for estimating potential fuel loss due to cut-ins in platoons is presented. Based on interviews with truck drivers with experience from platooning, and naturalistic data from previous research, we estimate the potential loss of fuel savings due to cut-ins and compare two scenarios with different amounts of traffic. The results show that platoons spend as much as 20% of time in cut-ins on typical European roads, reducing fuel savings in platooning from 13% down to 10%. Consequently, avoiding cut-ins has a positive environmental effect worth considering. 

  • 12.
    Wärff, Christoffer
    RISE Research Institutes of Sweden, Samhällsbyggnad, Infrastruktur och betongbyggande.
    Operational digital twins for water resource recovery facilities – Rationale, components, and case studies2023Rapport (Annet vitenskapelig)
    Abstract [en]

    Digital twins (DT) for water resource recovery facilities (WRRF) are different from regular process models. They require 1) a physical plant twin; 2) automatic data exchange with the real plant; 3) possibility to dynamically update models when or if required. Their use has the potential to improve understanding of plant behaviour and unmeasured variables; move towards proactive decision making at the plants when including influent forecasts; improve data quality control when comparing simulation results to measured values; and be used for predictive maintenance. The model used in a DT can be mechanistic (i.e., describing underlying mechanisms/physics), data driven (empirical, based on observed relationships between variables) or a combination of both (hybrid model). Most of the commercially available (mechanistic) wastewater process simulators include the option to use them in (near) real time as digital twins. Fault detection is important for DTs to avoid use of faulty input data. Methods range from dimensional reduction techniques to process models and statistical control charts. Automated methods for gap filling and corrections of sensor values based on laboratory measurements can be used to correct faulty data. Forecasts of influent flow rate and concentration of pollutants can be useful for optimization and “what if”-scenarios. Forecast models can be data driven (e.g., many examples with time series models and artificial neural networks available in the literature) or detailed mechanistic models. Common for most examples is that weather forecasts (temperature and precipitation) are used, and the model accuracy of course depend on the quality of the forecast. Automatic calibration can be used for both data driven/hybrid models (i.e., re-training) and mechanistic models. For mechanistic models, examples in the literature include simple changing of measured influent fractions or settler solids separation efficiency to global optimization of multiple variables over a plant-wide model. Automatic calibration can be done at fixed intervals or based on performance evaluation. Model predictive control (MPC) has been widely studied in simulated settings, with few real examples for WRRFs. For digital twins, the possibility to combine a mechanistic model with influent forecasts and numerical optimisation for, e.g., setpoints over a future time interval to achieve a certain goal is promising. The faster control applications can then be handled using regular PID-controllers. Few examples of implemented digital twins for WRRFs have so far been published in the literature. Here, one example of a digital twin is presented. It includes automatic data transfer, automatic calibration, and forecasts, but is (at the time of writing based on the available literature) only used as an advisory tool and not for direct control. Digital twins of water resource recovery facilities are complex with many different parts and models that work together. They can be used for fault detection, predictions, and optimization/control. This report summarizes some of the components that can be used to build digital twins, which ones to include of course depends on the scope and goals of the specific project. In all cases, the flow of data from collection to use must be well designed to avoid unnecessary interruptions in operation.

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  • 13.
    Zenden, Ivo
    et al.
    RISE Research Institutes of Sweden.
    Wang, Han
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Iacovazzi, Alfonso
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Vahidi, Arash
    RISE Research Institutes of Sweden.
    Blom, Rolf
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Raza, Shahid
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    On the Resilience of Machine Learning-Based IDS for Automotive Networks2023Inngår i: proc of IEEE Vehicular Networking Conference, VNC, IEEE Computer Society , 2023, s. 239-246Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Modern automotive functions are controlled by a large number of small computers called electronic control units (ECUs). These functions span from safety-critical autonomous driving to comfort and infotainment. ECUs communicate with one another over multiple internal networks using different technologies. Some, such as Controller Area Network (CAN), are very simple and provide minimal or no security services. Machine learning techniques can be used to detect anomalous activities in such networks. However, it is necessary that these machine learning techniques are not prone to adversarial attacks. In this paper, we investigate adversarial sample vulnerabilities in four different machine learning-based intrusion detection systems for automotive networks. We show that adversarial samples negatively impact three of the four studied solutions. Furthermore, we analyze transferability of adversarial samples between different systems. We also investigate detection performance and the attack success rate after using adversarial samples in the training. After analyzing these results, we discuss whether current solutions are mature enough for a use in modern vehicles.

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