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  • 71.
    Dytckov, Sergei
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Persson, Jan A.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Lorig, Fabian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Potential Benefits of Demand Responsive Transport in Rural Areas: A Simulation Study in Lolland, Denmark2022Ingår i: Sustainability, E-ISSN 2071-1050, Vol. 14, nr 6, artikel-id 3252Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In rural areas with low demand, demand responsive transport (DRT) can provide an alternative to the regular public transport bus lines, which are expensive to operate in such conditions. With simulation, we explore the potential effects of introducing a DRT service that replaces existing bus lines in Lolland municipality in Denmark, assuming that the existing demand remains unchanged. We set up the DRT service in such a way that its service quality (in terms of waiting time and in-vehicle time) is comparable to the replaced buses. The results show that a DRT service can be more cost efficient than regular buses and can produce significantly less CO2 emissions when the demand level is low. Additionally, we analyse the demand density at which regular buses become more cost efficient and explore how the target service quality of a DRT service can affect operational characteristics. Overall, we argue that DRT could be a more sustainable mode of public transport in low demand areas.

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  • 72.
    Jiang, Wei
    et al.
    Jishou Univ, Coll Informat Sci & Engn, Jishou 416000, Hunan, Peoples R China..
    Zhou, Kai-Qing
    Jishou Univ, Coll Informat Sci & Engn, Jishou 416000, Hunan, Peoples R China..
    Sarkheyli-Hägele, Arezoo
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Zain, Azlan Mohd
    Univ Teknol Malaysia, UTM Big Data Ctr, Skudai 80310, Johor, Malaysia..
    Modeling, reasoning, and application of fuzzy Petri net model: a survey2022Ingår i: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 55, s. 6567-6605Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A fuzzy Petri net (FPN) is a powerful tool to model and analyze knowledge-based systems containing vague information. This paper systematically reviews recent developments of the FPN model from the following three perspectives: knowledge representation using FPN, reasoning mechanisms using an FPN framework, and the latest industrial applications using FPN. In addition, some specific modeling and reasoning approaches to FPN to solve the 'state-explosion problem' are illustrated. Furthermore, detailed analysis of the discussed aspects are shown to reveal some interesting findings, as well as their developmental history. Finally, we present conclusions and suggestions for future research directions.

  • 73.
    Salvi, Dario
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Carl Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Ymeri, Gent
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Carrasco-Lopez, Carmen
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Tsang, Kevin C.H.
    University of Edinburgh, United Kingdom.
    Shah, Seyed Ahmar
    University of Edinburgh, United Kingdom.
    Mobistudy: Mobile-based, platform-independent, multi-dimensional data collection for clinical studies2022Ingår i: IoT 2021: Conference Proceedings, ACM Digital Library, 2022, s. 219-222Konferensbidrag (Refereegranskat)
    Abstract [en]

    Internet of Things (IoT) can work as a useful tool for clinical research. We developed a software platform that allows researchers to publish clinical studies and volunteers to participate into them using an app and connected IoT devices. The platform includes a REST API, a web interface for researchers and an app that collects data during tasks volunteers are invited to contribute. Nine tasks have been developed: Forms, Positioning, Finger tapping, Pulse-oximetry, Peak Flow measurement, Activity tracking, Data query, Queen’s College step test and Six-minute walk test. These leverage sensors embedded in the phone, connected Bluetooth devices and additional APIs like HealthKit and Google Fit. Currently, the platform is used in two clinical studies by 25 patients: an asthma management study in the United Kingdom, and a neuropathic pain management study in Spain.

  • 74.
    Ghajargar, Maliheh
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för kultur och samhälle (KS), Institutionen för konst, kultur och kommunikation (K3).
    Bardzell, Jeffrey
    Indiana University Bloomingtonm,USA.
    Smith-Renner, Alison
    Dataminr, USA.
    Höök, Kristina
    Royal Institute of Technology (KTH).
    Gall Krogh, Peter
    Aarhus University, Denmark.
    Wiberg, Mikael
    Umeå University.
    Tangible XAI2022Övrigt (Övrig (populärvetenskap, debatt, mm))
    Abstract [en]

    Computational systems are becoming increasingly smart and automated. Artificial intelligence (AI) systems perceive things in the world, produce content, make decisions for and about us, and serve as emotional companions. From music recommendations to higher-stakes scenarios such as policy decisions, drone-based warfare, and automated driving directions, automated systems affect us all.

    But researchers and other experts are asking, How well do we understand this alien intelligence? If even AI developers don’t fully understand how their own neural networks make decisions, what chance does the public have to understand AI outcomes? For example, AI systems decide whether a person should get a loan; so what should—what can—that person understand about how the decision was made? And if we can’t understand it, how can any of us trust AI?

    The emerging area of explainable AI (XAI) addresses these issues by helping to disclose how an AI system arrives at its outcomes. But the nature of the disclosure depends in part on the audience, or who needs to understand the AI. A car, for example, can send warnings to consumers (“Tire Pressure Low”) and also send highly technical diagnostic codes that only trained mechanics can understand. Explanation modality is also important to consider. Some people might prefer spoken explanations compared to visual ones. Physical forms afford natural interaction with some smart systems, like vehicles and vacuums, but whether tangible interaction can support AI explanation has not yet been explored.

    In the summer of 2020, a group of multidisciplinary researchers collaborated on a studio proposal for the 2021 ACM Tangible Embodied and Embedded (TEI) conference. The basic idea was to link conversations about tangible and embodied interaction and product semantics to XAI. Here, we first describe the background and motivation for the workshop and then report on its outcomes and offer some discussion points.

  • 75.
    Ghajargar, Maliheh
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för kultur och samhälle (KS), Institutionen för konst, kultur och kommunikation (K3).
    Bardzell, Jeffrey
    Pennsylvania State University, USA.
    Alison, Smith-Renner
    Dataminr, USA.
    Höök, Kristina
    KTH Royal Institute of Technology.
    Gall Krogh, Peter
    Aarhus University, Denmark.
    Graspable AI: Physical Forms as Explanation Modality for Explainable AI2022Ingår i: TEI '22: Proceedings of the Sixteenth International Conference on Tangible, Embedded, and Embodied Interaction, New York, USA: Association for Computing Machinery (ACM), 2022, Vol. 53, s. 1-4Konferensbidrag (Refereegranskat)
    Abstract [en]

    Explainable AI (XAI) seeks to disclose how an AI system arrives at its outcomes. But the nature of the disclosure depends in part on who needs to understand the AI and the available explanation modalities (e.g., verbal and visual). Users’ preferences regarding explanation modalities might differ, as some might prefer spoken explanations compared to visual ones. However, we argue for broadening the explanation modalities, to consider also tangible and physical forms. In traditional product design, physical forms have mediated people’s interactions with objects; more recently interacting with physical forms has become prominent with IoT and smart devices, such as smart lighting and robotic vacuum cleaners. But how tangible interaction can support AI explanations is not yet well understood.

    In this second studio proposal on Graspable AI (GAI) we seek to explore design qualities of physical forms as an explanation modality for XAI. We anticipate that the design qualities of physical forms and their tangible interactivity can not only contribute to the explainability of AI through facilitating dialogue, relationships and human empowerment, but they can also contribute to critical and reflective discourses on AI. Therefore, this proposal contributes to design agendas that expand explainable AI into tangible modalities, supporting a more diverse range of users in their understanding of how a given AI works and the meanings of its outcomes.

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  • 76.
    Hu, X.
    et al.
    School of Mathematics and Statistics Science, Ludong University, Yantai, Shandong 264025, China..
    Zhu, G.
    Marine College, Zhejiang Ocean University, Zhoushan 316022, China..
    Ma, Y.
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China..
    Li, Z.
    Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland..
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Sotelo, M.
    School of Mathematics and Statistics Science, Ludong University, Yantai, Shandong 264025, China..
    Event-Triggered Adaptive Fuzzy Setpoint Regulation of Surface Vessels With Unmeasured Velocities Under Thruster Saturation Constraints2022Ingår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, nr 8, s. 13463-13472Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This article investigates the event-triggered adaptive fuzzy output feedback setpoint regulation control for the surface vessels. The vessel velocities are noisy and small in the setpoint regulation operation and the thrusters have saturation constraints. A high-gain filter is constructed to obtain the vessel velocity estimations from noisy position and heading. An auxiliary dynamic filter with control deviation as the input is adopted to reduce thruster saturation effects. The adaptive fuzzy logic systems approximate vessel's uncertain dynamics. The adaptive dynamic surface control is employed to derive the event-triggered adaptive fuzzy setpoint regulation control depending only on noisy position and heading measurements. By the virtue of the event-triggering, the vessel's thruster acting frequencies are reduced such that the thruster excessive wear is avoided. The computational burden is reduced due to the differentiation avoidance for virtual stabilizing functions required in the traditional backstepping. It is analyzed that the event-triggered adaptive fuzzy setpoint regulation control maintains position and heading at desired points and ensures the closed-loop semi-global stability. Both theoretical analyses and simulations with comparisons validate the effectiveness and the superiority of the control scheme. 

  • 77.
    Huang, H.
    et al.
    Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing 210013, China.
    Hu, C.
    Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing 210013, China..
    Zhu, J.
    School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210013, China..
    Wu, M.
    Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing 210013, China..
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Stochastic Task Scheduling in UAV-Based Intelligent On-Demand Meal Delivery System2022Ingår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, nr 8, s. 13040-13054Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we investigate the dynamic task scheduling problem with stochastic task arrival times and due dates in the UAV-based intelligent on-demand meal delivery system (UIOMDS) to improve the efficiency. The objective is to minimize the total tardiness. The new constraints and characteristics introduced by UAVs in the problem model are fully studied. An iterated heuristic framework SES (Stochastic Event Scheduling) is proposed to periodically schedule tasks, which consists of a task collection and a dynamic task scheduling phases. Two task collection strategies are introduced and three Roulette-based flight dispatching approaches are employed. A simulated annealing based local search method is integrated to optimize the solutions. The experimental results show that the proposed algorithm is robust and more effective compared with other two existing algorithms.

  • 78.
    Zhu, G.
    et al.
    Maritime College, Zhejiang Ocean University, Zhoushan 316022, China..
    Ma, Y.
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China.
    Li, Z.
    School of Engineering, Ocean University of China, Qingdao 266110, China, and also with the Yonsei Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea.
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Sotelo, M.
    Department of Computer Engineering. University of Alcalá, 28806 Alcalá de Henares, Spain.
    Event-Triggered Adaptive Neural Fault-Tolerant Control of Underactuated MSVs With Input Saturation2022Ingår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, nr 7, s. 7045-7057Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper investigates the tracking control problem of marine surface vessels (MSVs) in the presence of uncertain dynamics and external disturbances. The facts that actuators are subject to undesirable faults and input saturation are taken into account. Benefiting from the smoothness of the Gaussian error function, a novel saturation function is introduced to replace each nonsmooth actuator saturation nonlinearity. Applying the hand position approach, the original motion dynamics of underactuated MSVs are transformed into a standard integral cascade form so that the vector design method can be used to solve the control problem for underactuated MSVs. By combining the neural network technique and virtual parameter learning algorithm with the vector design method, and introducing an event triggering mechanism, a novel event-triggered indirect neuroadaptive fault-tolerant control scheme is proposed, which has several notable characteristics compared with most existing strategies: 1) it is not only robust and adaptive to uncertain dynamics and external disturbances but is also tolerant to undesirable actuator faults and saturation; 2) it reduces the acting frequency of actuators, thereby decreasing the mechanical wear of the MSV actuators, via the event-triggered control (ETC) technique; 3) it guarantees stable tracking without the a priori knowledge of the dynamics of the MSVs, external disturbances or actuator faults; and 4) it only involves two parameter adaptations--a virtual parameter and a lower bound on the uncertain gains of the actuators--and is thus more affordable to implement. On the basis of the Lyapunov theorem, it is verified that all signals in the tracking control system of the underactuated MSVs are bounded. Finally, the effectiveness of the proposed control scheme is demonstrated by simulations and comparative results. 

  • 79.
    Bugeja, Joseph
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Jacobsson, Andreas
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    The Ethical Smart Home: Perspectives and Guidelines2022Ingår i: IEEE Security and Privacy, ISSN 1540-7993, E-ISSN 1558-4046, Vol. 20, nr 1, s. 72-80Artikel i tidskrift (Refereegranskat)
  • 80.
    Mihailescu, Radu-Casian
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmo Univ, Dept Comp Sci, Internet Things & People Res Ctr, S-20506 Malmo, Sweden..
    A weakly-supervised deep domain adaptation method for multi-modal sensor data2021Ingår i: 2021 IEEE GLOBAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS (GCAIOT), IEEE , 2021, s. 45-50Konferensbidrag (Refereegranskat)
    Abstract [en]

    Nearly every real-world deployment of machine learning models suffers from some form of shift in data distributions in relation to the data encountered in production. This aspect is particularly pronounced when dealing with streaming data or in dynamic settings (e.g. changes in data sources, behaviour and the environment). As a result, the performance of the models degrades during deployment. In order to account for these contextual changes, domain adaptation techniques have been designed for scenarios where the aim is to learn a model from a source data distribution, which can perform well on a different, but related target data distribution. In this paper we introduce a variational autoencoder-based multi-modal approach for the task of domain adaptation, that can be trained on a large amount of labelled data from the source domain, coupled with a comparably small amount of labelled data from the target domain. We demonstrate our approach in the context of human activity recognition using various IoT sensing modalities and report superior results when benchmarking against the effective mSDA method for domain adaptation.

567891011 71 - 80 av 308
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