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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rinaldi, Gianmario Ferrara, Antonella Menon, Prathyush P University of Exeter Faculty of Environment Science and Economy Department of Engineering EX4 4QF United Kingdom University of Pavia Computer and Biomedical Engineering Department of Electrical Pavia Italy

ISBN: (纸本)9798350316339

The electrification of the transportation sector plays a crucial role in reducing the global carbon footprint. Among the various solutions, catenary-based electric vehicles have attracted a great deal of interest from researchers and practitioners. This article considers a vehicle powered by a battery energy storage system (BESS) and a catenary system and proposes a robust finite-time longitudinal speed control strategy. Drawing inspiration from the Second-Order Sliding Mode (2-SOSM) methodology, the approach ensures that the control input remains within the limits set by the maximum torque of the motor. The strategy demonstrates robustness against uncertainties and disturbances, eliminating the need to know the model parameters. The simulation case study considers the effects of real road data, including slope variations, on power management. A comparative analysis with the conventional Proportional Integral (PI) method highlights the superior performance of the proposed strategy. © 2024 IEEE.

关键词： Carbon footprint

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AIGC-assisted Federated Learning for Vehicular Edge Intelligence: Vehicle Selection, Resource Allocation and Model Augmentation

arXiv

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arXiv 2025年

作者： Qiang, Xianke Chang, Zheng Min, Geyong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom

To leverage the vast amounts of onboard data while ensuring privacy and security, federated learning (FL) is emerging as a promising technology for supporting a wide range of vehicular applications. Although FL has great potential to improve the architecture of intelligent vehicular networks, challenges arise due to vehicle mobility, wireless channel instability, and data heterogeneity. To mitigate the issue of heterogeneous data across vehicles, artificial intelligence-generated content (AIGC) can be employed as an innovative data synthesis technique to enhance FL model performance. In this paper, we propose AIGC-assisted Federated Learning for Vehicular Edge Intelligence (GenFV). We further propose a weighted policy using the Earth Mover’s Distance (EMD) to quantify data distribution heterogeneity and introduce a convergence analysis for GenFV. Subsequently, we analyze system delay and formulate a mixed-integer nonlinear programming (MINLP) problem to minimize system delay. To solve this MINLP NP-hard problem, we propose a two-scale algorithm. At large communication scale, we implement label sharing and vehicle selection based on velocity and data heterogeneity. At the small computation scale, we optimally allocate bandwidth, transmission power and amount of generated data. Extensive experiments show that GenFV significantly improves the performance and robustness of FL in dynamic, resource-constrained environments, outperforming other schemes and confirming the effectiveness of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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CFTD: Core Fusion Time Series Dense Encoder for Intelligent Prediction with Edge AI in Social IoT Systems

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IEEE Internet of Things Journal 2025年

作者： Lu, Yao Zhang, Renyu Suo, Ziheng Zhang, Jing Liu, Lu Min, Geyong Anhui University School of Computer Science and Technology Hefei China Durham University Department of Computer Science Durham United Kingdom University of Exeter Department of Computer Science Exeter United Kingdom

The rapid development of the Internet-of-Things (IoT) has transformed human interaction with the world. The Social Internet-of-Things (SIoT) integrates social, emotional, and behavioral aspects into traditional IoT, creating an intelligent network that connects various smart devices. By combining Edge Computing with Artificial Intelligence (AI), data can be processed and analyzed directly on edge devices, improving processing efficiency. However, limited edge resources hinder local AI training, requiring cloud-based training of high-precision models, which are then deployed on edge devices for inference. In time series forecasting, Multilayer Perceptrons (MLPs) are widely used for their computational efficiency but often overlook correlations between time series. Some models adopt channel mixing mechanisms to improve modeling capability but increase computational complexity. To address this problem, we propose the Core Fusion Time Series Dense Encoder (CFTD) model, which incorporates a Core Extract-Distribute (COED) module for efficient channel fusion. Extensive experiments on real-world datasets demonstrate that this novel CFTD model achieves excellent predictive performance compared to the state-of-the-art models. © 2014 IEEE.

关键词： Edge computing

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A BTN-Based Method for Multi-Entity Bitcoin Transaction Analysis and Influence Assessment

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Distributed Ledger Technologies: Research and Practice 2024年第4期3卷 1-23页

作者： Yan Wu Liuyang Zhao Jia Zhang Leilei Shi Lu Liu John Panneerselvam Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhengjiang Jiangsu China and School of Computer Science and Communication Engineering Jiangsu University Zhengjiang Jiangsu China School of Computer Science and Communication Engineering Jiangsu University Zhengjiang Jiangsu China Department of Computer Science University of Exeter Exeter UK School of Computing and Mathematic Sciences University of Leicester Leicester UK

Bitcoin transaction analysis is valuable for examining Bitcoin events. However, most of the existing methods are inadequate for dealing with transactions involving multiple entities. Furthermore, existing Bitcoin transaction analysis methods neglect to evaluate the influence of different entities on a Bitcoin event. This article aims to overcome such limitations by introducing a novel method for multi-entity Bitcoin transaction analysis along with proposing a method for multi-entity influence assessment based on the Bitcoin transaction network (BTN) model. To overcome the loss of tracking information, a Bitcoin gene operation named compound dyeing is devised and incorporated into the BTN simulation. After obtaining the simulation results, a method for multi-entity transaction behavior analysis is presented to identify and visualize the interactions among entities precisely and effectively. Furthermore, four influence indices with suitable visualization methods are proposed based on the features of the BTN to measure the business and trading influences of different entities. A real-world case study, the *** coin loss event, is analyzed to demonstrate the effectiveness and efficiency of the proposed methods.

关键词： Blockchain computer forensics Bitcoin

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TEN YEARS OF THE HYBRID SIMULATION TRACK: REFLECTIONS AND VISION FOR THE FUTURE

TEN YEARS OF THE HYBRID SIMULATION TRACK: REFLECTIONS AND VI...

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2024 Winter Simulation Conference, WSC 2024

作者： Anagnostou, Anastasia Brailford, Sally Eldabi, Tillal Mustafee, Navonil Tako, Antuela Modelling & Simulation Group Dept. of Computer Science Brunel University London United Kingdom Southampton Business School University of Southampton Southampton United Kingdom School of Management University of Bradford Bradford United Kingdom Centre for Simulation Analytics & Modelling University of Exeter Business School Exeter United Kingdom Nottingham Business School Nottingham Trent University Nottingham United Kingdom

ISBN: (纸本)9798331534202

The Hybrid Simulation (HS) track was included in the Winter Simulation Conference (WSC) proceedings as a full conference track for the first time in 2014. A decade has passed since that inaugural track, and HS research and practice has seen impressive advancements during this time. This paper, based on a high-level review of the published works in the last ten years of the HS track, reflects on its successes and challenges and sets the scene for the future of the field. The paper is authored by the HS track organizers, both past and present, who report on the track’s history, the nature of HS applications, the modeling tools and software available, as well as implementation challenges and the users' perspective. Finally, the paper discusses the future of HS. © 2024 IEEE.

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Sensitivity analysis of the response of a scaled power cable for application in offshore renewable energy 6th

Sensitivity analysis of the response of a scaled power cable...

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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Khalid, F. Thies, P.R. Moideen, R. Venugopal, V. Badhurshah, R. Srinil, N. Faculty of Environment Science and Economy University of Exeter Penryn Campus United Kingdom School of Engineering University of Edinburgh Edinburgh United Kingdom Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781032905570

Effective operation of offshore renewable energy systems relies on the robust design of reliability-critical components like dynamic power cables. These cables endure significant loads induced by waves, currents, and platform motion. Current design practices recommend the adoption of compliant shapes such as the lazy wave to reduce bending and tensile stress. A combination of numerical modeling and scaled physical testing mitigates reliability risks, ensuring robust power transmission. To this end, this paper investigates the sensitivity of a scaled dynamic cable assembly using a structural finite element software, OrcaFlex, for tank testing at the Flowave facility. Parameters like cable stiffness, material density, and metocean conditions are examined to understand the model’s sensitivities. The cable assembly, modelled at 1:50 scale in a lazy wave configuration, considers the current-induced response of the cable. The results identify uncertainties in numerical modeling to enhance cable design robustness. © 2025 The Author(s).

关键词： Sensitivity analysis

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IFVONet: Integrating Inter-Frame Variation and Occlusion Awareness for 3D Hand Mesh Reconstruction

IFVONet: Integrating Inter-Frame Variation and Occlusion Awa...

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Smart World Congress (SWC), IEEE

作者： Shiyao Wu John Panneerselvam Lu Liu Rajeev Raman Tianjin Huang School of Computing and Mathematical Sciences University of Leicester Leicester UK Department of Computer Science University of Exeter Exeter UK

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Reconstructing 3D hand meshes from video files is significantly challenging due to objects in the video often occluding the hand during manipulation. These occlusions can greatly reduce the quality of information extracted from the obscured regions and decrease temporal hand coherence over time. Existing approaches focus primarily on global occlusion regions but overlook temporal hand coherence, which limits their performance. Herein, we propose a novel framework called IFVONet, designed to improve 3D hand mesh reconstruction by effectively capturing inter-frame variations and improving the recovery of global occlusions. IFVONet comprises three key components: (1) Pixel-Domain Variation Module for identifying inter-frame variations across adjacent frames, enhancing temporal hand coherence. (2) Enhanced Global Occlusion Recovery Module for integrating hand information into global occlusion representation, thereby improving the accuracy of occlusion feature recovery. (3) Hand Regression Module for dynamically aggregating hand information from inter-frame variations and globally recovered occlusion features into comprehensive hand representations, ultimately leading to enhanced 3D hand reconstruction. Extensive experiments on the HO3D-v2 and HO3D-v3 datasets demonstrate that our proposed IFVONet achieves state-of-the-art performance on both 3D hand mesh reconstruction and pose estimation.

关键词： Hands Deep learning Three-dimensional displays Accuracy Pose estimation Coherence Benchmark testing Transformers Data mining

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A Survey of Multi-objective Evolutionary Algorithm Based on Decomposition: Past and Future

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IEEE Transactions on Evolutionary Computation 2024年

作者： Li, Ke University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Exeter Department of Computer Science ExeterEX4 4RN United Kingdom

Decomposition has been the mainstream approach in the classic mathematical programming for multi-objective optimization and multi-criterion decision-making. However, it was not properly studied in the context of evolutionary multi-objective optimization until the development of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In this article, we present a comprehensive survey of the development of MOEA/D from its origin to the current state-of-the-art. In order to be self-contained, we start with a step-by-step tutorial that aims to help a novice quickly get onto the working mechanism of MOEA/D. Then, selected major developments of MOEA/D are reviewed according to its core design components including subproblem formulations, selection mechanisms and reproduction operators. Besides, we also overviews some further developments for constraint handling, large-scale problems, computationally expensive objective functions, preference incorporation, and real-world applications. In the final part, we shed some lights on emerging directions for future developments. © 1997-2012 IEEE.

关键词： Optimization algorithms

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I-Card: A Generative AI-Supported Intelligent Design Method Card Deck 25

I-Card: A Generative AI-Supported Intelligent Design Method ...

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

作者： Chen, Liuqing Cheang, Wengteng Jiang, Zhaojun Xu, Yuan Cai, Zebin Sun, Lingyun Childs, Peter Han, Ji Hansen, Preben Zuo, Haoyu College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China School of Computer Science and Engineering Central South University Hunan Changsha China International Design Institute Zhejiang University Zhejiang Hangzhou China Dyson School of Design Engineering Imperial College London London United Kingdom Department of Innovation Technology and Entrepreneurship University of Exeter Exeter United Kingdom Dept of Computer and Systems Sciences Stockholm University Kista Sweden

ISBN: (纸本)9798400713941

A design method card deck helps designers understand and provoke thinking by presenting each method in a simple format and allow designers to switch between methods seamlessly by maintaining the same simple format across the deck. However, recent observations have shown designers hesitate to use a card deck due to the lack of support, while other tools have provided identified support with generative AI. Through a formative study, we identified the specific support designers need when applying the design method cards and intentions in integrating generative AI. Accordingly, we developed the intelligent design method card deck, I-Card, which integrates generative AI to provide applicable design methods, design knowledge and data support, and interactive and dynamic support. A user study demonstrates that I-Card improved the design efficiency and applicability by offering personalized guidance, enhanced decision-making with comprehensive data generation and provided more design inspiration via interactive support. © 2025 Copyright held by the owner/author(s).

关键词： Product design

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Multi-objective Reinforcement Learning Based Multi-microgrid System Optimisation Problem 11th

Multi-objective Reinforcement Learning Based Multi-microgrid...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Xu, Jiangjiao Li, Ke Abusara, Mohammad Department of Computer Science University of Exeter Exeter United Kingdom Department of Engineering University of Exeter Exeter United Kingdom

ISBN: (纸本)9783030720612

Microgrids with energy storage systems and distributed renewable energy sources play a crucial role in reducing the consumption from traditional power sources and the emission of CO2. Connecting multi microgrid to a distribution power grid can facilitate a more robust and reliable operation to increase the security and privacy of the system. The proposed model consists of three layers, smart grid layer, independent system operator (ISO) layer and power grid layer. Each layer aims to maximise its benefit. To achieve these objectives, an intelligent multi-microgrid energy management method is proposed based on the multi-objective reinforcement learning (MORL) techniques, leading to a Pareto optimal set. A non-dominated solution is selected to implement a fair design in order not to favour any particular participant. The simulation results demonstrate the performance of the MORL and verify the viability of the proposed approach. © 2021, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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