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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6172-6185页

作者： Liu, Jiaxu Yi, Xinping Huang, Xiaowei The University of Liverpool School of Electrical Engineering Electronics and Computer Science LiverpoolL69 3GJ United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing210096 China

Hyperbolic graph convolutional networks (HGCNs) have demonstrated significant potential in extracting information from hierarchical graphs. However, existing HGCNs are limited to shallow architectures due to the computational expense of hyperbolic operations and the issue of oversmoothing as depth increases. Although treatments have been applied to alleviate oversmoothing in graph convolutional networks (GCNs), developing a hyperbolic solution presents distinct challenges since operations must be carefully designed to fit the hyperbolic nature. Addressing these challenges, we propose DeepHGCN, the first deep multilayer HGCN architecture with dramatically improved computational efficiency and substantially reduced oversmoothing. DeepHGCN features two key innovations: 1) a novel hyperbolic feature transformation layer that enables fast and accurate linear mappings;and 2) techniques such as hyperbolic residual connections and regularization for both weights and features, facilitated by an efficient hyperbolic midpoint method. Extensive experiments demonstrate that DeepHGCN achieves significant improvements in link prediction (LP) and node classification (NC) tasks compared to both Euclidean and shallow hyperbolic GCN variants. © 2024 IEEE.

关键词： Network architecture

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Prediction of segregation characterization based on granular velocity and concentration in rotating drum

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Particuology 2023年第2期73卷 17-25页

作者： Wenzheng Xiu Ra Li Quan Chen Qicheng Sun Vladimir Zivkovic Hui Yang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai200093China State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing100084China School of Engineering Newcastle UniversityNE17RUUnited Kingdom

In a binary granular system composed of two types of particles with different granule sizes and the same density,particle sorting occurs easily during the flow *** segregation pattern structure is mainly affected by the granular velocity and granular concentration in the flow *** paper reports on the experimental velocity and concentration measurement results for spherical particles in a quasi-two-dimensional rotating *** relationship between the granular velocity along the depth direction of the flow layer and granular concentration was established to characterize structures with different degrees of *** corresponding relationships between the granular velocity and concentration and the segregation pattern were further analyzed to improve the theoretical models of segregation(convection-diffusion model and continuous flow model)and provide a reference for granular segregation control in the production process.

关键词： Granular segregation Rotating drum Granular velocity Granular concentration

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Intent-Guided Trajectory Prediction for E-Scooter Riders and Bicyclists 27

Intent-Guided Trajectory Prediction for E-Scooter Riders and...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Zhengming Ding, Zhengming Chen, Yaobin Chien, Stanley Li, Lingxi Sheroy, Rini Domeyer, Joshua Tian, Renran School of Industrial Engineering Purdue University United States Tulane University Department of Computer Science United States School of Electrical and Computer Engineering Purdue University United States United States North Carolina State University Department of Industrial and Systems Engineering United States

ISBN: (纸本)9798331505929

The rapidly increasing popularity of micromobility devices, such as e-scooters and bicycles, in urban environments underscores the critical need for advanced trajectory prediction models to enhance road safety and facilitate seamless integration into intelligent transportation systems. This study introduces a novel approach to predicting the movements of bicyclists and e-scooter riders by leveraging a unique dataset that combines bird's-eye and egocentric views, collected from diverse urban settings across the United States. Employing state-of-the-art deep learning techniques, our model significantly outperforms traditional linear and polynomial regression models in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE), demonstrating superior accuracy in forecasting the trajectories of vulnerable road users. Furthermore, our approach incorporates cognitive annotations to predict crossing intentions, enriching the model's predictive capabilities. The findings highlight the potential of our method to contribute to the development of proactive safety measures and collision avoidance systems, ultimately fostering safer urban mobility landscapes for all road users. © 2024 IEEE.

关键词： Prediction models

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Surge Protection of Residential EV Charging Stations Against Direct Lightning Strikes 37

Surge Protection of Residential EV Charging Stations Against...

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37th International Conference on Lightning Protection, ICLP 2024

作者： Gektidis, Konstantinos Hadjicostas, Alexandros Peppas, George D. Politis, Zafiris Tsovilis, Thomas High Voltage Laboratory School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece School of Electrical and Computer Engineering Technical University of Crete Chania731 00 Greece Research & Development Department Raycap SA 14 Telou and Petroutsou Athens15124 Greece

ISBN: (纸本)9783800764174

A typical three-story building equipped with a residential electrical vehicle charging station is modelled within the ATP-EMTP environment. Fast-front overvoltages affecting each component of the modelled electrical installation, particularly the charger and the car, are calculated for direct negative lightning strikes to the air termination system of the installation. The assessment of the risk of failure involves examining the rated impulse withstand voltage levels of distribution boards, the residential EV charger, and the plugged car. This evaluation considers the impacts of lightning peak current and soil resistivity. A protection scheme involving the installation of surge protective devices is investigated so as to diminish the risk of failure to equipment and car. The energy absorption capability of surge protective devices is assessed and the necessity of dedicated surge protection for electric cars is discussed. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Charging stations

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Gauze:enabling communication-friendly block synchronization with cuckoo filter

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Frontiers of computer Science 2023年第3期17卷 113-123页

作者： Xiaoqiang DING Liushun ZHAO Lailong LUO Junjie XIE Deke GUO Jinxi LI College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Computer Science and Technology Xidian UniversityXi’an 710071China Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China Institute of Systems Engineering AMSPLABeijing 100141China

Block synchronization is an essential component of blockchain ***,blockchain systems tend to send all the transactions from one node to another for ***,such a method may lead to an extremely high network bandwidth overhead and significant transmission *** is crucial to speed up such a block synchronization process and save bandwidth consumption.A feasible solution is to reduce the amount of data transmission in the block synchronization process between any pair of ***,existing methods based on the Bloom filter or its variants still suffer from multiple roundtrips of communications and significant synchronization *** this paper,we propose a novel protocol named Gauze for fast block *** utilizes the Cuckoo filter(CF)to discern the transactions in the receiver’s mempool and the block to verify,providing an efficient solution to the problem of set reconciliation in the P2P(Peer-to-Peer Network)*** up to two rounds of exchanging and querying the CFs,the sending node can acknowledge whether the transactions in a block are contained by the receiver’s mempool or *** on this message,the sender only needs to transfer the missed transactions to the receiver,which speeds up the block synchronization and saves precious bandwidth *** evaluation results show that Gauze outperforms existing methods in terms of the average processing latency(about lower than Graphene)and the total synchronization space cost(about lower than Compact Blocks)in different scenarios.

关键词： block synchronization cuckoo filter probabilistic data structure

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Guest Editorial Special Issue on Self-Powered Sensors and Wearable Electronic systems

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第4期3卷 118-119页

作者： Nag, Anindya Ozioko, Oliver Kim, Woo Soo Andrews, Joseph Technische Universität Dresden Faculty of Electrical and Computer Engineering Dresden01062 Germany Dresden01069 Germany University of Derby Department of Electrical and Electronic Engineering DerbyDE22 1GB United Kingdom Simon Fraser University School of Mechatronic Systems Engineering BurnabyBCV5A 1S6 Canada University of Wisconsin-Madison Department of Mechanical Engineering and Department of Electrical and Computer Engineering MadisonWI53706 United States

Wearable sensing has recently been highly preferred due to its quick and accurate measurement of physiological parameters. These sensors have been devised using various polymers [1], [2] and nanomaterials [3], [4] suited for the chosen application. With the exponential growth of wearable electronics [5], [6], [7], there is a need to broaden their capabilities in terms of functionality and availability. Commercializing these wearable electronics needs further encouragement to use these sensors as point-of-care devices. Self-powered sensors [8], [9] are one of the growing aspects in the sector of wearable sensing. With the growing requirement for energy usage, self-powered sensing systems need to be developed to generate and harvest energy ubiquitously [10], [11]. This Special Issue highlights some of the published papers that work on using smart textiles and self-powered devices for efficient and sustainable sensing applications. © 2022 IEEE.

关键词： Wearable sensors

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Adaptive delay-energy balanced partial offloading strategy in Mobile Edge Computing networks

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Digital Communications and Networks 2023年第6期9卷 1310-1318页

作者： Shumei Liu Yao Yu Lei Guo Phee Lep Yeoh Branka Vucetic Yonghui Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyang110819China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Electrical and Information Engineering University of SydneySydneyNSW2006Australia

Mobile Edge Computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy *** this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy *** address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing *** from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power *** the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading *** experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.

关键词： Mobile edge computing(MEC) Delay Energy consumption Dynamic balance Partial computation offloading

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Research on Enterprise Risk Decision Support System Optimization based on Ensemble Machine Learning 24

Research on Enterprise Risk Decision Support System Optimiza...

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5th International Conference on computer Science and Management Technology, ICCSMT 2024

作者： Gong, Chenwei Lin, Yuzhen Cao, Jinming Wang, Jun Henry Samueli School of Engineering Department of Computer University of California Los Angeles United States School of Information Systems and Management Carnegie Mellon University Jersey City United States Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland United States School of Economics and Management Wuhan University Hubei Wuhan China

ISBN: (纸本)9798400709999

Since the advent of artificial intelligence, it has not only transformed the way we live, but is also accelerating the transformation of production methods. Machine learning is a pivotal technology that endows computers with intelligence. The primary driving force behind its advancement is the pursuit of rapid and precise knowledge acquisition. Nevertheless, the existing enterprise risk decision support system is inadequate in terms of both timeliness and effectiveness when confronted with the task of analysing vast quantities of data. Furthermore, it lacks the capacity to assimilate the expertise of managers and to facilitate interaction in an intuitive manner. By combining a variety of machine learning models, the ensemble learning method effectively leverages the advantages of different algorithms, thereby improving the accuracy and stability of risk prediction. Each model provides a unique perspective and decision-making boundaries when dealing with complex enterprise risk data, but a single model may not be adequate when faced with a particular data set or a particular type of risk. The ensemble method overcomes the problem of bias or overfitting that may be caused by a single model by combining the prediction results of multiple models, so as to obtain a more robust prediction effect. These methods optimize the final prediction results based on how well they perform during training by giving different weights to each model. This convergence strategy significantly improves the accuracy of enterprise risk assessment and helps to provide decision-makers with more reliable data support to make more informed risk management decisions in complex business environments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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On Graph Reduction in Consensus Control of Multi-agent systems: A Preliminary Study

On Graph Reduction in Consensus Control of Multi-agent Sys...

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27th RoboCup International Symposium, 2024

作者： Setayeshi, Amirali Babi, Zeynab Ezzati Saadati, Ali Sadeghnejad, Soroush Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Tehran Iran

ISBN: (纸本)9783031858581

Since the use of multi-agent systems has significantly increased over the past few decades, it is crucial to study the methods used in this area and take implementation-related practical issues into consideration. For instance, in the formulation of the consensus problem, the communication between the agents is depicted using a graph, where each edge represents a communication link between the agents. In practical terms, there will be significant operational challenges and costs associated with implementing each of these communication channels in various forms. Therefore, in this study, the graph reduction problem has been presented as an optimization problem, accompanied by six distinct cost functions. The objective is to reduce the size of a graph without compromising its fundamental characteristics. Hence, the cost functions provided are formulated to minimize the number of edges in the resulting graph while ensuring that the consensus error remains bounded. Ultimately, simulations were performed to demonstrate the effectiveness of the proposed approach. The outcomes derived from the specified set of cost functions were then compared to several conventional topologies in multi-agent systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cost functions

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