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arXiv 2021年

作者： van Meer, Max Poot, Maurice Portegies, Jim Oomen, Tom The Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology Netherlands The Delft Center for Systems and Control Delft University of Technology Delft Netherlands The Applied Analysis Group Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Feedforward control is essential to achieving good tracking performance in positioning systems. The aim of this paper is to develop an identification strategy for inverse models of systems with nonlinear dynamics of unknown structure using input-output data, which directly delivers feedforward signals for a-priori unknown tasks. To this end, inverse systems are regarded as noncausal nonlinear finite impulse response (NFIR) systems, and modeled as a Gaussian Process with a stationary kernel function that imposes properties such as smoothness and periodicity. The approach is validated experimentally on a consumer printer with friction and shown to lead to improved tracking performance with respect to linear feedforward. Copyright © 2021, The Authors. All rights reserved.

关键词： Friction

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Managing Control Software Variability in Cyber-Physical Production systems: The V4rdiac Approach

SSRN

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SSRN 2024年

作者： Fadhlillah, Hafiyyan Sayyid Meixner, Kristof Greiner, Sandra Fernández, Antonio Manuel Gutiérrez Rabiser, Rick Christian Doppler Laboratory VaSiCS Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria Christian Doppler Laboratory SQI TU Wien Favoritenstraße 9-11 Vienna1040 Austria Information Systems Engineering TU Wien Favoritenstraße 9-11 Vienna1040 Austria Software Engineering Group Institute of Computer Science University of Bern Neubrückstraße 10 Bern3012 Switzerland Department of Computer Languages and Systems Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain

Cyber-Physical Production systems (CPPSs) are highly configurable and versatile production systems that utilize diverse hardware components through control software. Managing control software variants in CPPS development is challenging as variation points in the software stem from multiple engineering disciplines. Furthermore, there is a lack of mechanisms to explicitly describe control software variants as part of the software. In this paper, we present an extension of the Variability for 4diac (V4rdiac) approach and investigate its usefulness in managing control software variants based on multidisciplinary variability in CPPS development and delta modeling. We extend the approach by considering its application engineering activities to produce client-specific control software variants. We also present a novel mechanism to visualize deltas in control software to reduce the effort of implementing and maintaining deltas. We apply V4rdiac in two case studies and conduct an additional user study with four participants from industry to evaluate its usefulness. The evaluation results confirm that the engineers were able to successfully manage the control software variants. The results also show that V4rdiac can be easily learned and increases industrial practitioners’ productivity in managing control software. © 2024, The Authors. All rights reserved.

关键词： Application programs

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A sensitivity framework to assess the impact of weather on electric power networks

A sensitivity framework to assess the impact of weather on e...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Ahmed, Arif McFadden, Fiona Stevens Rayudu, Ramesh Massier, Tobias Robinson Research Institute Smart Power and Renewable Energy Systems Group School of Engineering and Computer Science !!!TUMCREATE 1 CREATE Way #10-02 CREATE Tower Singapore Robinson Research Institute Victoria University of Wellington Smart School of Engineering and Computer Science Wellington New Zealand Victoria University of Wellington Smart Power and Renewable Energy Systems Group School of Engineering and Computer Science Wellington New Zealand TUMCREATE 1 CREATE Way 10-02 CREATE Tower Singapore138602 Singapore

ISBN: (纸本)9781728155081

This manuscript provides an analytical sensitivity framework to assess the impacts of individual weather parameters on conductor temperature and to determine the weather parameters that have greater potential to impact power networks and hence power system studies, which can be applied to any region around the globe. Application to weather parameter ranges possible across all of New Zealand (NZ) shows that the conductor temperature and hence the power network is most sensitive to the wind speed followed by the wind angle, and the solar irradiance, when paired with the ambient temperature. The sensitivity analysis framework is expected to aid utilities in assessing the region-specific impact of weather parameters on their network and hence improving their planning, operation, and analysis. © 2020 IEEE.

关键词： Sensitivity analysis

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The Jade Gateway to Exergaming: How Socio-Cultural Factors Shape Exergaming Among East Asian Older Adults

arXiv

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arXiv 2024年

作者： Mogavi, Reza Hadi Son, Juhyung Yang, Simin Wang, Derrick M. Choong, Lydia Alhilal, Ahmad Zhou, Peng Yuan Hui, Pan Nacke, Lennart E. The Department of Systems Design Engineering University of Waterloo Canada The Cheriton School of Computer Science University of Waterloo Canada HCI Games Group Stratford School of Interaction Design and Business University of Waterloo ON Canada Guangzhou China Hong Kong University of Science and Technology Hong Kong Aarhus University Denmark Hong Kong University of Science and Technology University of Helsinki Hong Kong Helsinki Finland Finland

Exergaming, blending exercise and gaming, improves the physical and mental health of older adults. We currently do not fully know the factors that drive older adults to either engage in or abstain from exergaming. Large-scale studies investigating this are still scarce, particularly those studying East Asian older adults. To address this, we interviewed 64 older adults from China, Japan, and South Korea about their attitudes toward exergames. Most participants viewed exergames with a positive inquisitiveness. However, socio-cultural factors can obstruct this curiosity. Our study shows that perceptions of aging, lifestyle, the presence of support networks, and the cultural relevance of game mechanics are the crucial factors influencing their exergame engagement. Thus, we stress the value of socio-cultural sensitivity in game design and urge the HCI community to adopt more diverse design practices. We provide several design suggestions for creating more culturally approachable exergames. Copyright © 2024, The Authors. All rights reserved.

关键词： Blending

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Reinforcement Learning Infused MAC for Adaptive Connectivity

Reinforcement Learning Infused MAC for Adaptive Connectivity

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IEEE Conference on Wireless Communications and Networking

作者： Dinesh Kumar Sah Ali Nauman Muhammad Ali Jamshed Korhan Cengiz Nikola Ivković Vedran Uroš Division of Networked and Embedded Systems Research Group Data Communication Mälardalens University Västerås Sweden Department of Information and Communication Engineering Yeungnam University Gyeongbuk Republic of Korea College of Science and Engineering University of Glasgow United Kingdom Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Faculty of Organization and Informatics University of Zagreb Varaždin Croatia Polytechnic “Marko Marulić” in Knin Knin Croatia

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.

关键词： 6G mobile communication Adaptive systems Redundancy Reinforcement learning Media Access Protocol Throughput Resource management

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Enclaves in the clouds

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Queue 2020年第6期18卷

作者： Singh, Jatinder Cobbe, Jennifer Quoc, Do Le Tarkhani, Zahra Compliant and Accountable Systems Research Group Department of Computer Science and Technology University of Cambridge United Kingdom Compliant and Accountable Systems Group Department of Computer Science and Technology University of Cambridge United Kingdom Systems Engineering Group TU Dresden Germany Scontain UG Systems Research Group Cambridge University Computer Laboratory United Kingdom

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EFaR 2023: Efficient Face Recognition Competition

arXiv

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arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well. © 2023, CC BY-NC-SA.

关键词： Face recognition

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Point-KAN: Leveraging Trustworthy AI for Reliable 3D Point Cloud Completion With Kolmogorov Arnold Networks for 6G-IoT Applications

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IEEE Internet of Things Journal 2025年

作者： Sangaiah, Arun Kumar Anandakrishnan, Jayakrishnan Kumar, Sujith Bian, Gui-Bin AlQahtani, Salman A. Draheim, Dirk National Yunlin University of Science and Technology International Graduate School of AI Douliu 64002 Taiwan National Institute of Technology Puducherry Karaikal 609609 India Chinese Academy of Sciences Institute of Automation National Key Laboratory for Multi-modal Artificial Intelligence Systems Beijing 100190 China King Saud University College of Computer and Information Sciences New Emerging Technologies and5G Network and Beyond Research Chair Department of Computer Engineering Riyadh Saudi Arabia Tallinn University of Technology Information Systems Group School of Information Technologies Tallinn 12618 Estonia

3D point clouds are data points defining the morphology of environments, and completion refers to the reconstruction of missing points. 6G Internet of Things (6G-IoT) connected with 3D mapping devices needs reliable, consistent, high-fidelity real-time point cloud completion for accurate environment registration. Trustworthy AI, modeled with dependable Deep Learning (DL), enables reliable and robust point completion with spatial-geometrical consistency for deployment with 6G-IoT devices. Although several DL-based completion techniques are integrated with 6G-IoT devices, they have reliability issues, limiting key trustworthy AI characteristics. This research focuses on the reliability and robustness aspects of trustworthy AI to propose Point-KAN, a dependable real-time 3D point cloud completion model for 6G IoT-connected 3D mapping devices. Point-KAN integrates multi-head attention and Kolmogorov-Arnold Networks (KAN) within the modules of Attention Enhanced-Embedded Feature Collector (AEFC) and KAN-Enhanced Feature Mapper (KEFM) for trustworthy point cloud completion. Empirical evaluations on the ShapeNet demonstrate the superiority of Point-KAN against state-of-the-art (SOTA). Results concrete Point-KAN’s evolution as a trustworthy AI framework ensures reliability and robustness for real-time deployment in 6G-IoT-connected devices, facilitating 3D environment mapping. © 2014 IEEE.

关键词： 3D Point Cloud Completion 6G IoT Kolmogorov-Arnold Networks (KANs) Trustworthy AI

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Subsentence extraction from text using coverage-based deep learning language models

arXiv

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arXiv 2021年

作者： Lim, JongYoon Sa, Inkyu Ahn, Ho Seok Gasteiger, Norina Lee, Sanghyub John MacDonald, Bruce CARES Department of Electrical Computer and Software Engineering University of Auckland New Zealand CSIRO Data61 Robotics and Autonomous Systems Group Dynamic Platforms

Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience and computer science. This stems from its high-degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper. © 2021, CC BY.

关键词： Sentiment analysis

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AI-Powered Anomaly Detection to Strengthen Internet of Things Security and Forestall Cyber Attacks in Networked Device Environments 3

AI-Powered Anomaly Detection to Strengthen Internet of Thing...

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3rd International Conference on Advancement in Computation and computer Technologies, InCACCT 2025

作者： Varalakshmi, S. Kottur, Suresh Sasikumar, R. Saravanan, K. Kanungo, Tanaya Annamalai, Balamurugan Department of ECE Bharath Institute of Higher Education and Research Tamil Nadu Chennai India IT Infrastructure Architect/IT Systems V-Soft Consulting Group Inc 101 Bullitt Ln Suite 205 LouisvilleKY40222 United States Department of Computer Science & Engineering R.M.D Engineering College Tamil Nadu Kavaraipettai India Department of Information Technology R.M.K Engineering College Tamil Nadu Kavaraipettai India Department of ECE St. Peter's College of Engineering and Technology Tamil Nadu Avadi India Department of EEE Shree Sathyam College of Engineering and Technology Tamil Nadu Sangagiri India

ISBN: (纸本)9798331543891

As millions of devices are linked, the IoT has revolutionized numerous businesses by automating and data-driven decision-making. Due to the proliferation of linked devices, cybercriminals target IoT environments for security breaches. This research examines AI-powered anomaly detection systems for real-time threat discovery and mitigation in IoT security. Advanced machine learning and deep learning models based on artificial intelligence can detect suspicious activity in networked environments and early security breaches. Artificial intelligence systems learn from network traffic and device behaviors to respond to emerging cyber threats, unlike rule-based security solutions. The study found that AI-driven anomaly detection can prevent data breaches, malware outbreaks, and DDoS attacks. This study compares artificial intelligence algorithms for real-time anomaly detection across IoT frameworks. Supervised, unsupervised, and hybrid learning models are used. It also discusses the challenges of large-scale AI system deployment, including the need for high-quality labelled datasets for training and computational resource constraints. The results suggest that AI-enabled anomaly detection could become a crucial part of IoT security, adapting to the growing number of connected devices. © 2025 IEEE.

关键词： Unsupervised learning

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