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Enhancing Low-Latency and High-Reliability Data Forwarding for In-Vehicle Time-Sensitive networks

IEEE Transactions on Vehicular Technology 2025年

作者： Sun, Haitong Zhang, Haijun Guan, Wanqing Liu, Xiangnan Ma, Hui Leung, Victor C.M. Hebei Key Laboratory of Space-Air-Ground Intelligent Communication China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Department of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China

To satisfy the low delay, low jitter, and high success rate requirements for in-vehicle networks, IEEE 802.1 Task group proposed Time-Sensitive Networking (TSN), which has aroused increasing attention in managing time-critical services in networks. This paper resolves the following issue: how to minimize the worst-case response time (WCRT) of frame forwarding in TSN under no-wait scenarios to guarantee low-latency transmission, stability, and high success rate for in-vehicle TSN networks? Currently, some work resolves this problem by leveraging integer linear programming, and others apply heuristic methods to avoid the time complexity problem, but the success rate is affected since they are prone to get trapped in local optima. To reach a balance between time-complexity and success rate, a scheme named Frame Dynamic Priority Scheduling Mechanism (FDPSM) is proposed, where the frame dynamic priority levels are set up and frames are scheduled accordingly. The target is to maximize the success rate and minimize the WCRT for in-vehicle networks. We evaluate the proposed mechanism from aspects such as traffic forwarding time, size of windows, number of hops, and the network load, together with success rate and the simulation results demonstrate the advantages of the FDPSM for in-vehicle TSN networks. © 2025 IEEE. All rights reserved,

关键词： Integer programming

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A graph attention network integrated with gated spiking neural P systems for session-based recommendation

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Expert Systems with Applications 2025年 286卷

作者： Bai, Xinzhu Peng, Hong Huang, Yanping Wang, Jun Yang, Qian Ramírez-De-Arellano, Antonio School of Computer and Software Engineering Xihua University Chengdu610039 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China Department of Computer Science and Artificial Intelligence Research Group of Natural Computing University of Seville Sevilla41012 Spain

With the development of deep learning technology, the recommender system began to use deep neural networks for modeling, which enables the recommendation system to better capture the complex patterns and features in the data. Session-based recommendation has also gradually received attention, especially for scenarios which deal with user behavior sequences and real-time requirements in a session. However, existing session-based recommendation methods tend to ignore users’ long-term interest changes, and there are still challenges for context understanding. In addition, session-based recommendation also has drift problems (i.e., the short-term behavior of users does not necessarily represent their real interests), which will affect the accuracy of recommendations. Therefore, this paper proposes a novel graph attention network, termed GAN-GSNP. This network combines a gated spiking neural P system with an extra balancing effect (EB-GSNP) to alleviate the drift problem in session-based recommendation by processing both global and local embeddings simultaneously. In addition, the GAN-GSNP model also proposes a multi-branch feature extraction strategy, which uses Self-Attention and feedforward networks in parallel to extract the high-order features embedded globally by users, so as to capture the global dependencies within the session or between different items, and make full use of the user's contextual information. Experimental results on two real datasets demonstrate the effectiveness and accuracy of the proposed GAN-GSNP model. © 2025 Elsevier Ltd

关键词： Deep neural networks

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Impact of Softwarization in Microservices-based SDN Controller

Impact of Softwarization in Microservices-based SDN Controll...

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2022 European Wireless Conference, EW 2022

作者： Scotece, Domenico Arzo, Sisay Tadesse Bassoli, Riccardo Foschini, Luca Devetsikiotis, Michael Fitzek, Frank H.P. Department of Computer Science and Engineering University of Bologna Bologna Italy Department of Electrical and Computer Engineering University of New Mexico United States Deutsche Telekom of Communication Networks Technische Universität Dresden Dresden Germany Research Group Technische Universität Dresden Dresden Germany Cluster of Excellence Dresden Germany

ISBN: (纸本)9781713865698

software-defined networking decouples control and data plane in softwarized networks. This allows for centralized management of the network. However, complete centralization of the controller's functions raises the issue of the central point of failure, latency, and scalability. Distributed controller deployment is adopted to optimize scalability and latency problems. However, the existing controllers are monolithic, resulting in code inefficiency for distributed deployment. Recently, microservices-based SDN solutions have been started and deployed as virtual network functions, enabling flexible deployment. Nonetheless, the softwarization of network functionalities introduces network I/O performance degradation both considering deployments based on virtual machines and containers. This paper first introduces a microservices-based SDN solution based on Ryu SDN Framework. Then investigates whether running Ryu's network functionalities in a softwarized environment (e.g., virtual machine and container) would have a significant network I/O performance degradation. In particular, this work examines more in deep the Docker Container technology and analyzes its network setups. Multiple measurements were performed locally in a single machine. Our results show a comparison between microservices-based non-virtualized SDN Controller and the virtualized one, and moreover, show a comparison between different Docker Container network setups. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： software defined networking

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Modeling and HIL Real-Time Simulation of a Series Hybrid Vehicle with Regenerative Breaking for Energy Management Algorithm Testing

Modeling and HIL Real-Time Simulation of a Series Hybrid Veh...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： William K. Moreira Alvin Huynh Cassiano Rech Sheldon S. Williamson Power Electronics and Control Research Group - GEPOC Federal University of Santa Maria Santa Maria RS Brazil Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

The objective of this paper is to present the design of a real-time Hardware in-the-loop (HIL) platform for testing different models of internal combustion engine (ICE), electric machines, energy management algorithms (EMAs) and power converter topologies in a series hybrid vehicle configuration with regenerative breaking and optimal fuel consumption. The article presents the modeling of the ICE and the electrical components, as well as the design of the controllers for each element of the powertrain. The engine model is composed by table data combined with lowpass filters to provide dynamic behavior. The proposed system is composed by two Permanent Magnet Synchronous Machines (PMSMs), one ICE and one battery pack. The electric machines are modeled in synchronous reference frame, and classical controllers are implemented for controlling the electric currents, DC bus voltage and the traction motor speed. The battery is modeled utilizing coulomb counting for SOC estimation and a Thevenin based battery model. The HIL platform is composed by and OPAL-RT OP 4510 and a Texas Instruments TMS32028379D.

关键词： Analytical models Traction motors Real-time systems Hardware Batteries State of charge Energy management

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Drive-Charge Dilemma in Electric Mobility: Price of Anarchy and Data Analytics Standpoint 5

Drive-Charge Dilemma in Electric Mobility: Price of Anarchy ...

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5th International Conference on Advanced Communication Technologies and Networking, CommNet 2022

作者： Mahrez, Zineb Sabir, Fssaid Saad, Walid Badidi, Elarbi Hassan Ii University of Casablanca Nest Research Group Lri Lab Ensem Morocco University of Quebec at Montreal Departrment of Computer Science MontrealQCH2L 2C4 Canada Wireless VT Department of Computer Science and Software Engineering Virginia Tech VA United States College of Information Technology Uae University Department of Computer Science and Software Engineering United Arab Emirates

ISBN: (数字)9781665450546

ISBN: (纸本)9781665450546

The advent of electric mobility has created new costs that are closely linked to and influenced by the actions and behaviors of electric vehicle (EV) drivers. These include time-, energy-, and risk-related costs that each driver seeks to reduce by adjusting his or her strategy. In this paper, an electric vehicle charging dilemma using a non-cooperative routing game with selfish players is formulated. We assume that an electric vehicle plans a trip from one place to another and, in the process, must choose a specific route, stop at a roadside station, decide on the charging station, and charge its battery by a certain amount. The strategy of EVs to drive or stop depends on factors related to battery level, waiting status at charging stations, availability of charging types, and traffic load both on roads and at stations. The research problem aims to solve the EV driver's dilemma and determine the route that the EV driver must take to optimize his travel cost.' To solve the dilemma, we propose to build a simulation model based on input data obtained from historical records of U.S. government sources, as described in the paper. © 2022 IEEE.

关键词： Data Analytics

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer Systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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A Machine-Learning-based Surrogate Modeling Methodology for Submodel Integration in the Holistic Railway Digital Twin Platform 33

A Machine-Learning-based Surrogate Modeling Methodology for ...

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33rd CIRP Design Conference

作者： Zhou, Shiyang Meierhofer, Alexander Kugu, Ozan Xia, Yuxi Grafinger, Manfred TU Wien Institute of Engineering Design and Product Development Lehargasse 6/307 Vienna1060 Austria Virtual Vehicle Research GmbH Co-Simulation and Software Group Inffeldgasse 21a Graz8010 Austria University of Vienna Faculty of Computer Science Kolingasse 14-16 Vienna1090 Austria

A holistic railway infrastructure digital twin (DT) platform is sophisticated and consists of a series of submodels (e.g., turnouts, tracks, vehicles, etc.) that are built through various methodologies and software. However, integrating these submodels into the DT platform is tremendously challenging due to considerable computational complexity, software and interface restrictions. To this end, we designed a machine learning (ML) based surrogate modeling methodology for the submodel integration in the holistic railway infrastructure DT platform and illustrated the methodology through a case study. In this case study, an ML-based surrogate model for multibody simulation of railway vehicle-track dynamics is created, which can replace the railway vehicle-track simulation executed with the Multibody Dynamics (MBD) Simulation commercial software SimPACK. The well-built ML model can accurately and quickly predict the vehicle-track system's dynamic responses to different track irregularities. Besides, the integration process of the ML-based surrogate model into the DT platform through a standardized open-source Functional Mock-up Interface (FMI) is also proposed. The developed surrogate modeling methodology shows great promise owing to its high fidelity, which is verified by the measurement data collected from the Austrian national railway track system. The main contribution of our work lies in the well-built ML-based surrogate modeling methodology for reducing the computation complexity and time of different submodels, which facilitates the unification and integration of different submodels. Furthermore, this approach can also be applied to other submodels and help to build the holistic railway DT platform collaboratively. © 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)

关键词： Machine learning

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Characterisation of Thin Glass-Fibre Substrates for Deployable SAR Antennas

Characterisation of Thin Glass-Fibre Substrates for Deployab...

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European Conference on Antennas and Propagation, EuCAP

作者： Simone Mencarelli Annalisa Tresoldi Andrew C.M. Austin Michael J. Neve Guglielmo Aglietti Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Te Pūnaha Ātea-Auckland Space Institute University of Auckland Auckland New Zealand Communication Systems and Networks Group University of Bristol Bristol UK

This paper investigates the electrical characteristics of a thin glass-fibre membrane to be employed as a support and dielectric substrate for a new kind of deployable antenna mechan-ical structure designed for space-borne Synthetic Aperture Radar sensors on nano-satellite platforms. The approach outlined in this paper relies on cost-effective test fixtures built from material samples originally designed for mechanical testing. A simple Time Domain Reflectometry / Transmissometry (TDR/T) procedure enables the extraction of the material's complex permittivity.

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A Chatbot Assistant for Reducing Risk in Machinery Design

A Chatbot Assistant for Reducing Risk in Machinery Design

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Padma Iyenghar Michael Kieviet Elke Pulvermueller Juergen Wuebbelmann innotec GmbH Erlenweg 12 Melle Germany Faculty of Engineering and Computer Science Osnabrueck University of Applied Sciences Germany Software Engineering Research Group University of Osnabrueck Germany

In this paper, a novel chatbot for risk reduction as an aid during machinery design is presented. The general workflow of the chatbot involves the identification of the hazard described by the user using a neural network model followed by an interactive dialog based conversation, in which the risk reduction measures are outlined. A prototype implementation of the chatbot presents the steps to generate and pre-process the training data for Artificial Intelligence (AI) based models. Different neural network models are trained and evaluated for the proposed risk reduction chatbot. A comparative study is presented by employing an in-depth qualitative and quantitative evaluation. The work presented in this paper shows significant promise in ensuring safety awareness, thereby aiding in implementing functional safety in the early stages of machinery design and development.

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Experimentation on NN Models for Hazard Identification in Machinery Functional Safety

Experimentation on NN Models for Hazard Identification in Ma...

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IEEE International Conference on Industrial Informatics (INDIN)

The use of Artificial Intelligence (AI) in machinery functional safety can enhance efficiency and accuracy by automating tasks previously carried out by humans. This paper presents an experimental evaluation of Neural Network (NN) models for hazard identification in machinery functional safety. The systematic study includes own implementations of NN models using open source building blocks and the use of an open source conversational AI framework with various pipeline configurations. The paper provides a comparative analysis of the qualitative and quantitative parameters for the models and configurations.

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