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  • 51.
    Wang, W.
    et al.
    Computer Science Department, University of Pittsburgh, Pittsburgh, PA, United States.
    Mosse, D.
    Computer Science Department, University of Pittsburgh, Pittsburgh, PA, United States.
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Packet priority assignment for wireless control systems of multiple physical systems2019In: Proceedings - 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, Vol. 107, p. 143-150Conference paper (Refereed)
    Abstract [en]

    Wireless control systems (WCSs) have gained much attention lately, due to their easy deployment and flexibility compared to wired control systems. However, this comes at the cost of possibly increased network delay and packet losses, that can significantly impact the control system performance, and possibly its stability. Such problems become even more relevant if the network is shared among different control systems, and thus becomes a scarce resource, like in Industrial Internet of Things applications. In this paper, we describe how to assign packet priorities dynamically when there are many physical systems sharing a given network, aiming at minimizing the performance degradation of the WCS. Towards that, we present a network model including both delay and packet losses, both of which are very important for the control system performance. Our solution is evaluated over two different use cases to show the generality of the approach: the WCS for a set of inverted pendula, and the WCS for small modular reactors in a nuclear power plant. The results show that the proposed approach allows for a more stable performance even in presence of highly nonlinear systems, sensitive to time-varying delays, as well as in presence of high network interference.

  • 52.
    Weidl, Galia
    et al.
    Mälardalen University, School of Sustainable Development of Society and Technology. University of Stuttgart.
    Madsen, Anders
    HUGIN Expert A/S, Denmark.
    Dahlquist, Erik
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Decision support on complex industrial process operation2008In: Bayesian networks: Apractical guide to applications / [ed] Olivier Pourret, Patrik Naim and Bruce Marcot, Chichester, UK: John Wiley & sons , 2008, 1st, p. 311-328Chapter in book (Other (popular science, discussion, etc.))
    Abstract [en]

    A practical guide to applications of Bayesian Networks. Theoretical aspects of BNs together with a number of applications in different areas like sensor validation, process industry applications, forensic science, terrorism risk management, robotics risk management, classification of wines, pavement and bridge management, and enhancing human cognition.

  • 53.
    Wickberg, Philip
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Fattouh, Anas
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Afshar, Sara
    Volvo Construct Equipment, Emerging Technol Dept, Eskilstuna, Sweden.
    Sjöberg, Johan
    Volvo Autonomous Solut, Eskilstuna, Sweden.
    Bohlin, Markus
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Dynamic Maps Requirements for Autonomous Navigation in Construction Sites2022In: The 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA22), 2022Conference paper (Refereed)
    Abstract [en]

    Construction sites are a special kind of off-road environment that needs dedicated dynamic maps to enable autonomous navigation in such terrains. In this paper, challenges for autonomous navigation on construction sites are first identified. Later, requirements for dynamic maps for autonomous navigation on construction sites are proposed based on the identified challenges.

  • 54.
    Xiong, Ning
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Building similarity metrics reflecting utility in case-based reasoning2006In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 17, no 4, p. 407-416Article in journal (Refereed)
    Abstract [en]

    Fundamental to case-based reasoning is the idea that similar problems have similar solutions. The meaning of the concept of "similarity" can vary in different situations and remains an issue. Since we want to identify and retrieve truly useful or relevant cases for problem solving, the metrics of similarity must be defined suitably to reflect the utility of cases for solving a particular target problem. A framework for utility-oriented similarity modeling is developed in this paper. The main idea is to exploit a case library to obtain adequate samples of utility from pairs of cases. The task of similarity modeling then becomes the customization of the parameters in a similarity metric to minimize the discrepancy between the assessed similarity values and the utility scores desired. A new structure for similarity metrics is introduced which enables the encoding of single feature impacts and more competent approximation of case utility. Preliminary experimental results have shown that the proposed approach can be used for learning with a surprisingly small case base without the risk of over-fitting and that it yields stable system performance with variations in the threshold selected for case retrieval.

  • 55.
    Zhao, Jinli
    et al.
    Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China.
    Tian, Zhen
    Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China.
    Ji, Haoran
    Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China.
    Ji, Jie
    Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wu, Jianzhon
    Institute of Energy, School of Engineering, Cardiff University, Cardiff, United Kingdom.
    Li, Peng
    Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China.
    Wang, Chengshan
    Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China.
    Peer-to-Peer electricity trading of interconnected flexible distribution networks based on Non-Cooperative games2023In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 145, article id 108648Article in journal (Refereed)
    Abstract [en]

    With the integration of power electronic devices represented by soft open points (SOPs), distribution networks have gradually evolved into interconnected flexible distribution networks (FDNs). Considering the deregulation of electricity market and user privacy, multiple stakeholders have participated in the operation of FDNs. Peer-to-peer (P2P) electricity trading is promising to alleviate operational problems of interconnected FDNs. As multiple regions pursue the maximum profits individually, non-cooperative game methods can be utilized to realize fair profit allocation in P2P trading. In this paper, a non-cooperative game-based P2P trading method is proposed to meet the electricity trading needs of multi-region interconnected FDNs. First, based on non-cooperative games, a two-layer P2P electricity trading framework is established to realize cost reduction and voltage profile improvement of multi-region interconnected FDNs. Then, a P2P trading adjustment mechanism is designed to improve the operational profits of SOP, in which spatial active power trading adjustment, temporal dispatching of energy storage (ES) link and reactive power support are incorporated. Finally, the effectiveness of the proposed method is verified based on a practical distribution network with four-terminal SOP in Tianjin. The results show that the proposed P2P electricity trading method can promote the economic operation performance of interconnected FDNs and improve the operational profit of SOP.

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