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2022 International Workshop on OpenCL, IWOCL 2022

作者： Meyer, Joachim Alpay, Aksel Fröning, Holger Heuveline, Vincent Compiler Design Lab Saarland Informatics Campus Saarland University Germany Computing Systems Group Institute of Computer Engineering Heidelberg University Germany Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg University Germany

ISBN: (纸本)9781450396585

With heterogeneous programming continuously on the rise, performance portability is still to be improved. SYCL provides the nd-range parallel-for paradigm for writing data-parallel kernels. This model allows barriers for group-local synchronization, similar to CUDA and OpenCL kernels. GPUs provide efficient means to model this, but on CPUs the necessary forward-progress guarantees require the use of many (lightweight) threads in library-only SYCL implementations, rendering the nd-range parallel-for unacceptably inefficient. By adopting two compiler-based approaches solving this, the present work improves the performance of the nd-range parallel-for in hipSYCL for CPUs by up to multiple orders of magnitude on various CPU architectures. The two alternatives are compared with regard to their functional correctness and performance. By upstreaming one of the variants, hipSYCL is the first SYCL implementation to provide a well performing nd-range parallel-for on CPU, without requiring an available OpenCL runtime. © 2022 Owner/Author.

关键词： Program compilers

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Software-defined event-based camera and vision algorithms for digitizing highly dynamic manufacturing tasks

Software-defined event-based camera and vision algorithms fo...

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2024 Low-Cost Digital Solutions for Industrial Automation, LoDiSA 2024

作者： Walker, Emily Turner, Christopher J. Oyekan, John Department of Automatic Control & Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Surrey Business School The University of Surrey Surrey GuildfordGU2 7XH United Kingdom Real Time & Distributed Systems Group Department of Computer Science University of York Heslington YorkYO10 5GH United Kingdom

ISBN: (纸本)9781837242672

Event-based cameras are inspired by the way the retina in the eye process information. Rather than capturing image frames at a constant rate, event cameras generate data asynchronously, based on changes in pixel value between frames. As a result, these cameras have a high spatiotemporal accuracy, low power constraints and reduced artefacts from motion blur. Hence, they represent favourable properties for digitizing highly dynamic manufacturing tasks such as machine vibrations and rapid human assembly actions. However, the costs of event-based cameras are prohibitively high for small to medium sized enterprises (SMEs). In this research, we present the development of a rig that combines low-cost visual sensors with low-cost algorithms to perform action recognition for highly dynamic manual manufacturing tasks. We applied our algorithms to achieve an action classification system via a novel threshold-based approach and in parallel use a neural network to perform classification of objects in the video feed. The results achieved demonstrated that our approach was able to successfully identify various rapid manual tasks being performed on identified objects. In addition to SMEs, our approach would be suited for providing low-cost digitization on legacy machines. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/).

关键词： Machine vibrations

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Quantifying, Activating and Rewarding Flexibility in Renewable Energy Communities

Quantifying, Activating and Rewarding Flexibility in Renewab...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Julien Allard François Vallée Zacharie De Grève Thomas Stegen Mevludin Glavic Bertrand Cornélusse Power Systems and Markets Research Group Electrical Power Engineering Unit University of Mons Mons Belgium Department of Electrical Engineering and Computer Science University of Liège Liège Belgium

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

Demand-side flexibility from renewable energy community members increases the benefits of local production and exchanges. To effectively harness this flexibility, end-users must be rewarded for their efforts regarding the shifted energy volumes and the associated discomfort. This work quantifies these last two quantities for three types of loads (electric vehicles, thermal loads and wet appliances). Then, a two-step model is proposed which first optimally activates the offered flexibility and then shares the benefits of this activation among the renewable energy community members through a rule-based or an optimization-based approach. Results show that the community framework can further increase the benefits brought by flexible demand.

关键词： Renewable energy sources Europe Tariffs Production Pricing Thermal management Electric vehicles Smart grids Thermal loading Load modeling

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Quality of Experience Aware Task Offloading in Digital Twinning Vehicular Edge Computing

Quality of Experience Aware Task Offloading in Digital Twinn...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Mostakim Jihad Mashraba Tasnim Rodshi Abdullah Al Fahad Palash Roy Md. Abdur Razzaque Mohammad Mehedi Hassan Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green Networking Research Group University of Dhaka Bangladesh Information Systems Dept. College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Adopting Digital Twin (DT) technology in vehicular edge computing (VEC) enables efficient capture of real-time state information of applications, thereby addressing complex task scheduling problems. Existing literature studies considered only minimizing service latency for task offloading; however, there is room for exploring strategies to enhance user Quality of Experience (QoE) in timeliness and reliability domains. In this paper, we have developed an optimization framework using Mixed Integer Linear Programming (MILP), namely QuETOD, which minimizes service latency by allocating task execution responsibility to highly reliable and reputed vehicles in a DT-enabled VEC environment. The developed QuETOD framework clusters the vehicles based on the demand-supply theory of economics by considering computing resources and utilizing the multi-weighted subjective logic for getting the proper reputation update of the vehicles. The experimental results of the developed QuETOD system depict significant performance improvement in terms of QoE and reliability compared to the state-of-the-art works as high as 15% and 25%, respectively.

关键词： Processor scheduling Computational modeling Reliability theory Real-time systems Polynomials Quality of experience Servers

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Digital Shadows for Robotic Assembly in the World Wide Lab 56

Digital Shadows for Robotic Assembly in the World Wide Lab

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56th CIRP International Conference on Manufacturing systems, CIRP CMS 2023

作者： Behery, Mohamed Brauner, Philipp Kluge-Wilkes, Aline Baier, Ralph Mertens, Alexander Schmitt, Robert H. Ziefle, Martina Lakemeyer, Gerhard Knowledge-Based Systems Group Ahornstraße 55 Aachen52074 Germany Human-Computer Interaction Centre Campus-Boulevard 57 Aachen52074 Germany Laboratory of Machine Tools and Production Engineering Campus-Boulevard 30 Aachen52074 Germany Institute of Industrial Engineering and Ergonomics Eilfschornsteinstraße 18 Aachen52062 Germany

Due to the lacking standardisation of Digital Shadow (DS) of changeable assembly processes, these processes have not yet been fully integrated into the World Wide Lab (WWL). We propose generalising, standardising, and using Behaviour Trees (BTs) as standard DS for assembly in the WWL. A BT models processes in a modular, human-readable tree structure that has shown great success in many applications. BT monitor task execution giving real-time feedback to their operators. We show that BTs cross several developmental milestones of DSs and give future recommendations for further development and integration into the WWL. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing systems 2023.

关键词： Behaviour Trees Digital Shadows Industrial Internet of Things Industry 4.0 Robotic Assembly

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MLFusion: Multilevel Data Fusion using CNNS for atrial fibrillation detection

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computers in Biology and Medicine 2025年 189卷 109901-109901页

作者： John, Arlene Parhi, Keshab K. Cardiff, Barry John, Deepu Biomedical Signals and Systems Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7522 NB Netherlands Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States School of Electrical and Electronic Engineering University College Dublin DublinD04 FX62 Ireland

Data fusion, involving the simultaneous integration of signals from multiple sensors, is an emerging field that facilitates more accurate inferences in instrumentation applications. This paper presents a novel fusion methodology for multi-sensor multimodal data using convolutional neural networks (CNNs), which introduces a data-driven approach to automatically select the optimal level of information abstraction for fusion. Unlike traditional methods, the proposed model allows for a self-learning fusion process, eliminating the need for manual selection of the fusion level by the designer. The model uniquely integrates feature extraction and fusion into a unified framework, enhancing efficiency and performance. Additionally, the fusion network incorporates signal quality indicators (SQIs) as input streams, enabling the model to account for the quality of each input signal during the fusion process. The methodology is evaluated on the task of atrial fibrillation detection through the fusion of two multimodal signal inputs taken from a portion of the MIMIC III database. The fusion network, using electrocardiogram (ECG) and photoplethsymogram (PPG) signals as inputs, and trained with an average loss function, achieved an accuracy of 99.33% and a sensitivity of 99.74%. The model's robustness in the presence of noisy inputs is also analyzed, demonstrating the effectiveness of the SQI-based multi-level fusion approach. This novel methodology represents a significant advancement in data fusion by offering a fully automated, quality-aware approach to multi-sensor multimodal signal fusion. © 2025

关键词： Convolutional neural networks

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Association rule learning for threat analysis using traffic analysis and packet filtering approach

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3245-3255页

作者： Rawat, Romil Chakrawarti, Rajesh Kumar Raj, A. Samson Arun Mani, Gayathri Chidambarathanu, K. Bhardwaj, Ramakant Computer and Communication Technology Department University of Extremadura Badajoz Spain Department of Computer Science and Engineering Sushila Devi Bansal College (SDBC) Bansal Group of Institutions (BGI) Indore India Karunya Institute of Technology and Sciences Karunya Nagar Tamil Nadu Coimbatore India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Business Systems R.M.K. Engineering College Chennai India Department of Mathematics Amity University West Bengal Kolkata India

The Mirai botnet source code, which was distributed on the dark web, is still changing as malware designers modify it to produce more sophisticated varieties of the botnet—the Satori—based on the malware source code. An botnet that is tied to the Internet of Things, infested with malware, and administered by offenders creates a backdoor for cybercriminals to carry out destructive operations throughout the system. The work provides association rule learning, which reveals design characteristics and warning indications of hazards using data (a data stream) collected for darknet vulnerability research. The negative effects of threat-originating computers associated with malware scripts are investigated by looking at trends in IoT-linked applications (like destination port, TCP window lengths, and kind of service).This paper provides an study of an attack (both before and after) with the first source scripts development of a conventional IoT malware, the Satori, as well as an uncommon insight into the viral processes of an attack campaign. Trials demonstrate that the suggested system is efficient in anticipating and tracking new virus tendencies in cyberspace, demonstrating that it is a viable way for streamlining and accelerating the discovery of new illicit patterns in online activities. Satori-based mathematical modelling of destination ports, ToS, and TCP window size is provided with NICT/16 DN sensor dataset that is employed in the research. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Association rule learning Cyber-attack Dark web IoT Malware

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Support Autonomy: Exploring Player Perspectives on AI-Supported Onboarding in Video Games 25

Support Autonomy: Exploring Player Perspectives on AI-Suppor...

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Choong, Lydia Cmentowski, Sebastian Kukshinov, Eugene Tu, Joseph Nacke, Lennart E. Hci Games Group Stratford School of Interaction Design and Business University of Waterloo WaterlooON Canada Industrial Design Eindhoven University of Technology Eindhoven Netherlands Cheriton School of Computer Science University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada

ISBN: (纸本)9798400713941

Video game onboarding faces the challenge of teaching game mechanics in a fun and engaging way. Artificial intelligence (AI) solutions have become a quick fix to help users understand technology. However, little is known about how AI supports player onboarding in video games. To address this knowledge gap, this research explores player perspectives on AI-supported onboarding. We conducted a qualitative user study (n = 20) to investigate player expectations, attitudes, and concerns about AI-supported learning experiences. Players learn primarily through the lived experience of a game and value personalized guidance during onboarding. Participants emphasized the importance of maintaining control over how AI is used during onboarding and the freedom to choose their support level. Our results suggest that players want future AI-supported onboarding systems to prioritize their agency, encourage active learning, and maintain transparency throughout the learning process. We contribute to game design research by proposing balanced, player-centric AI-supported onboarding experiences in video games. © 2025 Copyright held by the owner/author(s).

关键词： Active learning

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Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification

arXiv

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arXiv 2024年

作者： Kon, Johan van de Wijdeven, Jeroen Bruijnen, Dennis Tóth, Roland Heertjes, Marcel Oomen, Tom Control Systems Technology Group Departement of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands Philips Engineering Solutions Eindhoven Netherlands Control Systems Group Electrical Engineering Eindhoven University of Technology Netherlands The HUN-REN Institute for Computer Science and Control Budapest Hungary Delft University of Technology Delft Netherlands

Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stability constraints can only be numerically verified, e.g., through solving Linear Matrix Inequalities. In this paper, an unconstrained DT-LPV-IO parameterization is developed which gives a stable model for any choice of model parameters. To achieve this, it is shown that all quadratically stable DT-LPV-IO models can be generated by a mapping of transformed coefficient functions that are constrained to the unit ball, i.e., a small-gain condition. The unit ball is then reparameterized through a Cayley transformation, resulting in an unconstrained parameterization of all quadratically stable DT-LPV-IO models. As a special case, an unconstrained parameterization of all stable DT linear time-invariant transfer functions is obtained. Identification using the stable DT-LPV-IO model with neural network coefficient functions is demonstrated on a simulation example of a position-varying mass-damper-spring system. © 2024, CC BY-NC-ND.

关键词： Parameterization

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Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification

Unconstrained Parameterization of Stable LPV Input-Output Mo...

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European Control Conference (ECC)

作者： Johan Kon Jeroen Van De Wijdeven Dennis Bruijnen Roland Tóth Marcel Heertjes Tom Oomen Departement of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Philips Engineering Solutions Eindhoven The Netherlands Control Systems Group Electrical Engineering Eindhoven University of Technology The Netherlands HUN-REN Institute for Computer Science and Control Budapest Hungary Delft University of Technology Delft The Netherlands

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

关键词： Computational modeling Neural networks Riccati equations Transfer functions Mathematical models Stability analysis Numerical models

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