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arXiv 2024年

作者： Lee, Donggil Kim, Taekyoo Lee, Seungjoon Shim, Hyungbo Center for Intelligent and Interactive Robotics Korea Institute of Science and Technology Seoul02792 Korea Republic of The Train Control and Communication Team Korea Railroad Research Institute Uiwang16105 Korea Republic of NAVER Labs Seongnam13161 Korea Republic of ASRI Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

In this paper, we propose a distributed scheme for estimating the network size, which refers to the total number of agents in a network. By leveraging a synchronization technique for multi-agent systems, we devise an agent dynamics that ensures convergence to an equilibrium point located near the network size regardless of its initial condition. Our approach is based on an assumption that each agent has a unique identifier, and an estimation algorithm for obtaining the largest identifier value. By adopting this approach, we successfully implement the agent dynamics in a fully decentralized manner, ensuring accurate network size estimation even when some agents join or leave the network. © 2024, CC BY-NC-ND.

关键词： Multi agent systems

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification

arXiv

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arXiv 2024年

作者： Peng, Ruimin Du, Zhenbang Zhao, Changming Luo, Jingwei Liu, Wenzhong Chen, Xinxing Wu, Dongrui Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China AI Platform Software Engineering Research Center Dongfeng Corporation Research & Development Institute Wuhan430072 China China Electronic System Technology Co. Ltd. Beijing100089 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD) Transformer for cross-subject EEG-based seizure subtype classification, which can be effectively trained from small labeled data. MBMD Transformer replaces all even-numbered encoder blocks of the vanilla Vision Transformer by our designed multi-branch encoder blocks. A mutual-distillation strategy is proposed to transfer knowledge between the raw EEG data and its wavelets of different frequency bands. Experiments on two public EEG datasets demonstrated that our proposed MBMD Transformer outperformed several traditional machine learning and state-of-the-art deep learning approaches. To our knowledge, this is the first work on knowledge distillation for EEG-based seizure subtype classification. © 2024, CC BY.

关键词： Electroencephalography

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Post-Quantum Privacy-Preserving Provable Data Possession Scheme Based on Smart Contracts

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Wireless communications and Mobile Computing 2023年第1期2023卷

作者： Li, Zhengwen Zhang, Tengjiao Zhao, De Zha, Yaxing Sun, Jun School of Cyberspace Security Beijing University of Posts and Telecommunications 100876 China School of Information Engineering Beijing Institute of Graphic Communication 102600 China Research and Development Center of Transport Industry of Network Security Technologies China Communication Information Technology Group Co. Ltd 100088 China China Industrial Control Systems Cyber Emergency Response Team 100040 China

Provable data possession (PDP) is a crucial means of protecting the integrity of data in the domain of cloud storage. In the post-quantum era, the PDP scheme that uses lattices relies too heavily on the third-party auditor (TPA), which is not entirely trustworthy and is easily affected by a single point of failure. Moreover, the scheme often leads to the leakage of private user data while attempting to satisfy the demand for public verification. In response to the above problems, this paper designs a protocol for post-quantum privacy-preserving PDP and uses it to develop a scheme based on smart contracts. The proposed scheme has the characteristics of being post quantum and can satisfy the demand for public verification while preserving user privacy. The property of noninteraction of the protocol can reduce transaction fees incurred owing to the frequent operation of the blockchain, and the smart contract with a deposit mechanism can ensure fair payments to all parties. The results of a theoretical analysis and experiments show that the proposed scheme is highly secure and efficient. © 2023 Zhengwen Li et al.

关键词： Smart contract

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Quick Routing Response to Link Failure in Low-Earth Orbit Satellite Networks

Quick Routing Response to Link Failure in Low-Earth Orbit Sa...

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International Conference on Computer and communications (ICCC)

作者： Zhiping Lu Ruxin Zhi Weiguo Ma School of Information and Communication Engineering Beijing University of Posts and Telecommunications State Key Laboratory of Wireless Mobile Communications China Academy of Telecommunications Technology (CATT) Beijing China Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

Low earth orbit satellite communication systems are attractive for provisioning global, high-speed and low latency Internet access services. In order to achieve global continuous multiple coverage, the number of satellites is tremendous. But Low earth orbit satellite networks are prone to instability due to unpredictable link failures and frequent topology changes. Therefore, in this paper, a novel routing schemes with quick response to link failures is first proposed. Based on Dijkstra algorithm, a scalable routing algorithm is proposed to minimize the end-to-end transmission delay. It takes advantage of the predictable constellation trajectory, and changes dynamically according to the changes of network topology. It provides the shortest routing path and an alternative path simultaneously. When a link failure is detected by one satellite, notification packets will be sent to its neighbors for adjustment to the alternative routing path. Notification packets also will be sent to the head node for the recalculation of routing table. Finally, extensive simulations have been conducted, and the results show that the proposed scheme is able to produce desired performance.

关键词： Satellites Network topology Heuristic algorithms Dynamics Low earth orbit satellites Packet loss Routing

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A First-Order Generalized Pseudo-Bayesian Method Based on Moving Horizon Estimation for Surrounding Vehicle States Estimation in Complex Environments

SSRN

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SSRN 2022年

作者： Lu, Haikuan Wang, Ping Fu, Xiuwei Chen, Hong Hu, Yunfeng State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China The Department of Control Science and Engineering School of Communication Engineering Jilin University Changchun130025 China College of Electronics and Information Engineering Tongji University Shanghai201804 China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China

Predicting the motion of surrounding vehicles is important for autonomous vehicle trajectory *** ensure the accuracy of motion prediction, it is particularly important to obtain information about the state of surrounding vehicles. For this purpose, a first-order generalized pseudo-Bayesian method based on moving horizon estimation (GPB1-MHE) is proposed in this paper. First, for vehicles, pedestrians and obstacles in complex environments, we design several different intention-based motion models to describe multiple maneuvers of vehicles during driving. Second, we replace the Kalman filter (KF) in the first-order generalized pseudo-Bayesian (GPB1) method with moving horizon estimation (MHE) to obtain GPB1-MHE. Compared with GPB1, it makes full use of the historical state information of a vehicle to improve the estimation accuracy. Finally, the effectiveness of the estimation method is verified using four operating conditions, overtaking, overtaken, pedestrian approaching, and pedestrian staying away, in a SCANeR studio and Simulink joint simulation environment. © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

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Characterizing Human Collective Behaviors During COVID-19 — Hong Kong SAR, China, 2020

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China CDC weekly 2023年第4期5卷 71-75,I0001-I0003页

作者： Zhanwei Du Xiao Zhang Lin Wang Sidan Yao Yuan Bai Qi Tan Xiaoke Xu Sen Pei Jingyi Xiao Tim K.Tsang Qiuyan Liao Eric H.YLau Peng Wu Chao Gao Benjamin J.Cowling WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkNew TerritoriesHong Kong Special Administrative RegionChina Department of Genetics University of CambridgeCambridgeCB23EHUK Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore College of Information and Communication Engineering Dalian Minzu UniversityDalian CityLiaoning ProvinceChina Department of Environmental Health Sciences Mailman School of Public HealthColumbia UniversityNew York CityNYUSA School of Artificial Intelligence Opticsand Electronics(iOpen)Northwestern Polytechnical UniversityXi’an CityShaanxi ProvinceChina

Summary What is already known about this topic?People are likely to engage in collective behaviors online during extreme events,such as the coronavirus disease 2019(COVID-19)crisis,to express awareness,take action,and... 详细信息

关键词： collective likely extreme

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A Reinforcement Learning-Based Energy Management Strategy for Fuel Cell Electric Vehicle Considering Coupled-Energy Sources Degradations

SSRN

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SSRN 2023年

作者： Huo, Weiwei Liu, Teng Li, Fajia Institute of Electromechanical Engineering Beijing Information Science & Technology University Beijing100192 China Collaborative Innovation Center of Electric Vehicles in Beijing Beijing Information Science & Technology University Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100192 China University of Jinan Jinan250024 China

The energy management strategy (EMS) is crucial for fuel cell electric vehicles (FCEV) by decreasing the equivalent fuel consumption and postponing fuel cell (FC) deterioration. An improved EMS based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) deep reinforcement learning considering energy sources degradation and economic factor is proposed in this paper. The working condition identification (WCI) implemented by ensemble bagged trees (EBT) is used to expand the diversity of EMS application scenarios. The drastic loading change of FC is avoided for minimizing the instantaneous cost (MIC), which reduces FC degradation greatly. TD3 is applied to optimize the total cost combined with WCI and MIC. The training results based on 13 classic working conditions show that the degradation of the FC system is effectively suppressed, decreasing the overall cost and accelerating the algorithm's convergence time significantly. © 2023, The Authors. All rights reserved.

关键词： Energy management

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A Spectrum Sensing Method Based on CNN-LSTM Deep Neural Network

A Spectrum Sensing Method Based on CNN-LSTM Deep Neural Netw...

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Shujian Zhang Zhan Xu Lu Tian Xiaolong Yang Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Beijing China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665405836

Spectrum sensing can effectively improve the low utilization of spectrum resources and is one of the crucial components of cognitive radio networks. This paper proposes a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network cascaded spectrum sensing model. The model uses CNN to analyze the Short-Time Fourier transform spectrogram of the blind signal. Then the generated feature vector or feature map is passed to the LSTM according to the timestamp. Finally, it detects a signal in a specific spectrum and classifies the signal type to identify multiple signals accurately. The neural network model improves the detection probability by simultaneously acquiring the spatial and temporal characteristics of the blind signal. The experimental results show that the method in this paper can detect a variety of signals with higher detection probability within a wide range of SNR, especially under the condition of low SNR.

关键词： Deep learning Fourier transforms Simulation Conferences Neural networks Sensors Convolutional neural networks

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Modulation Signal Recognition Based on Feature Fusion and Residual CNN in Fractal Noise Background 6

Modulation Signal Recognition Based on Feature Fusion and Re...

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6th International Conference on Signal and Image Processing, ICSIP 2021

作者： Chen, Di Xiong, Gang Huang, Baigang Wang, Lijun No.36 Research Institute of Cetc Science and Technology On Communication Information Security Control Laboratory Jiaxing China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Navigation and Sensing Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9780738133737

In this paper, a modulated signal recognition method based on feature fusion and ResCNN (FF-ResCNN) under generalized fractal noise background is proposed. Firstly, in the feature extraction and fusion part, considering the robustness, anti-noise and interpretability of the features, the fusion feature vectors of generalized fractal spectrum, instantaneous feature and high-order cumulant feature are constructed. Secondly, in order to suppress the possible gradient and degradation problems, the ResCNN model is used to train and classify the fusion features when the CNN model is used to further collect the deep-level abstract features. Finally, feature extraction, feature fusion and classification experiments are carried out for nine modulated signals under two noise types (GWN and fractal noise). Experimental results show that in the case of generalized fractal noise, when SNR=0dB, the recognition accuracy of signal modulation type reaches 95.42%, which verifies the superiority of the proposed method. At the same time, the simulation analysis is carried out for different feature fusion schemes, which provides a valuable reference for feature combination optimization under different noise environment. © 2021 IEEE.

关键词： Feature extraction

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