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arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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Service Function Chaining in LEO Satellite Networks via Multi-Agent Reinforcement Learning

Service Function Chaining in LEO Satellite Networks via Mult...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Khai Doan Marios Avgeris Aris Leivadeas Ioannis Lambadaris Wonjae Shin Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Department of Software and IT Engineering École de Technologie Supérieure Montreal QC Canada Korea University School of Electrical Engineering Seoul South Korea

Low-earth-orbit satellite networks (LSNs) offer an enhanced global connectivity and a wide range of applications such as disaster response and military operations, among others. Each specific application can be represented by a service function chain (SFC) in which each function is considered as a task in the application. Our objective is to optimize the long-term system performance by minimizing the average end-to-end delay of SFC deployments in LSNs. To achieve this, we formulate a dynamic programming (DP) problem to derive an optimal placement policy. To overcome the computational intractability, the need for statistical knowledge of SFC requests, and centralized decision-making challenges, we present a multi-agent Q-learning approach where satellites act as independent agents. To facilitate performance convergence in non-stationary agents' environments, we let agents to collaborate by sharing designated learning parameters. In addition, agents update their Q-tables via two distinct rules depending on selected actions. Extensive experimentation shows that our approach achieves convergence and performance relatively close to the optimum obtained by solving the formulated DP equation.

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Performance Evaluation of Peer-to-Peer Distributed Microgrids Coordination for Voltage Regulation

Performance Evaluation of Peer-to-Peer Distributed Microgrid...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Gan, Houchao Wang, Jing Lin, Yashen Bhela, Siddharth Bilby, Chris Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO80401 United States Clarkson University Electrical and Computer Engineering PotsdamNY13699 United States Siemens Technology PrincetonNJ United States Holy Cross Energy Glenwood SpringsCO81601 United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

This paper presents the performance evaluation of a peer-to-peer microgrids coordination algorithm for sub-transmission systems. As distributed energy resources (DERs) in distribution system start to show negative impact to the bulk power system, a paradigm shift is needed for transmission planning and operation. Because distribution substations are located far from the sub-transmission system, and it is hard to use traditional centralized control for real-time control and coordination. Thus, distributed control is a natural choice because it requires less communication and central computation. In this paper, each distribution substation is treated as a microgrid, and the peer-to-peer distributed microgrids control is formulated as a real-time optimal power flow problem to reduce the negative impact in sub-transmission systems. A distributed primal-dual optimization algorithm is adopted to solve the problem. Validation of the peer-to-peer algorithm is performed through the simulation of a real-world sub-transmission system composing of many distribution systems with high renewable penetration. Simulation results show that the peer-to-peer algorithm can achieve satisfactory voltage regulation performance in sub-transmission system by coordinating and controlling DERs in distribution systems. © 2022 IEEE.

关键词： Real time control

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UAV propeller frequency and angle analysis using lidar and sliding windows

UAV propeller frequency and angle analysis using lidar and s...

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Intermountain engineering, Technology and Computing (IETC)

作者： Zachary L. Crennen Trevor C. Vannoy Wyatt W. Weller Riana Chatterjee Joseph A. Shaw Bradley M. Whitaker Computer Science Colorado School of Mines Golden Colorado USA Earth and Environmental Sciences Los Alamos National Laboratory Los Alamos New Mexico USA Electrical and Computer Engineering Montana State University Bozeman Montana USA Computer Science Arizona State University Tempe Arizona USA Optical Technology Center Montana State University Bozeman Montana USA

ISBN: (数字)9798331512828

ISBN: (纸本)9798331512835

While extensive research has been conducted on UAV detection using various modalities, limited work has addressed characterizing the frequency and angle of rotating propellers. Identifying frequencies could help solve the problem of classifying birds vs drones. This study aims to answer the question: "How accurately can the frequencies of a stationary UAV rotor be identified at various angles in the spectral analysis of lidar pulses?" We propose a lidar-based solution to detect both frequency and angle through spectral analysis. Using a sliding window to identify the propeller signal in a spectrogram, we found that frequency measurement is highly dependent on the beam’s position on the rotor and the angle between them. Our results indicate that frequency measurements are more accurate for beams positioned directly on the rotor blade and when the angle between the rotor and the beam approaches 90 degrees. However, predicting the angle of the propeller proved to be inconclusive.

关键词： Laser radar Accuracy Propellers Rotors Autonomous aerial vehicles Frequency measurement Reliability Spectral analysis Spectrogram Drones

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Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction

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computers in Biology and Medicine 2024年 176卷 108565-108565页

作者： Ma, Hongcheng Wu, Yajing Tang, Yongqiang Chen, Rui Xu, Tao Zhang, Wensheng School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal. © 2024 Elsevier Ltd

关键词： Electroencephalography

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Radar-Communications Waveform Co-Design Over-the-Air Using the WISCANet SDR Network

Radar-Communications Waveform Co-Design Over-the-Air Using t...

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IEEE National Conference on Radar

作者： Shammi A. Doly Alex R. Chiriyath Andrew Herschfelt Jacob Holtom Shankarachary Ragi Daniel W. Bliss School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ Center for Wireless Information Systems and Computational Architectures (WISCA) Arizona State University Tempe AZ USA Department of Electrical Engineering South Dakota School of Mines & Technology Rapid SD

Many waveform co-design studies demonstrate theoretical performance enhancements but rarely provide a clear path toward implementing a tractable solution. To address this limitation, we developed a waveform co-design technique to maximize the joint radar-communications network's joint per-formance and a computationally tractable method for optimizing it. This waveform co-design technique is based on the theory of partially-observable Markov decision processes (POMDPs), which we solve using an approximate dynamic programming approach called nominal belief-state optimization (NBO). The POMDP framework's natural look-ahead feature allows us to trade between the short-term and long-term performance of both radar and communications tasks, which allows it to adapt to changes in system requirements and environmental conditions. Using the WISCANet over-the-air experimental radio testbed, we implement a simple joint radar-communications system and demonstrate this waveform design and optimization technique in a hardware-in-the-loop over-the-air demonstration. We further extend the problem by proposing a real-time waveform optimization solution using a Kalman Filter in a dynamic environment.

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Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning

arXiv

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arXiv 2023年

作者： Liu, Yuhan Wang, Pengyu Lee, Chang-Hun Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance. © 2023, CC BY-NC-SA.

关键词： Maneuverability

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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Barrier Certificate based Safe Control for LiDAR-based systems under Sensor Faults and Attacks

Barrier Certificate based Safe Control for LiDAR-based Syste...

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IEEE Conference on Decision and Control

作者： Hongchao Zhang Shiyu Cheng Luyao Niu Andrew Clark Electrical and Systems Engineering Department McKelvey School of Engineering Washington University in St. Louis St. Louis Department of Electrical and Computer Engineering University of Washington Seattle

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Autonomous Cyber-Physical systems (CPS) fuse proprioceptive sensors such as GPS and exteroceptive sensors including Light Detection and Ranging (LiDAR) and cameras for state estimation and environmental observation. It has been shown that both types of sensors can be compromised by malicious attacks, leading to unacceptable safety violations. We study the problem of safety-critical control of a LiDAR-based system under sensor faults and attacks. We propose a framework consisting of fault tolerant estimation and fault tolerant control. The former reconstructs a LiDAR scan with state estimations, and excludes the possible faulty estimations that are not aligned with LiDAR measurements. We also verify the correctness of LiDAR scans by comparing them with the reconstructed ones and removing the possibly compromised sector in the scan. Fault tolerant control computes a control signal with the remaining estimations at each time step. We prove that the synthesized control input guarantees system safety using control barrier certificates. We validate our proposed framework using a UAV delivery system in an urban environment. We show that our proposed approach guarantees safety for the UAV whereas a baseline fails.

关键词： Laser radar Fault tolerant systems Urban areas Propioception Fault tolerant control Sensor fusion Sensor systems

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Architecture for Compact Photonic Downconversion of Broadband RF Signals

Architecture for Compact Photonic Downconversion of Broadban...

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CLEO: Science and Innovations, S and I 2022

作者： O'Malley, Nathan P. McKinzie, Keith A. Alshaykh, Mohammed S. Liu, Junqiu Leaird, Daniel E. Kippenberg, Tobias J. McKinney, Jason D. Weiner, Andrew M. School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Electrical Engineering Department King Saud University Riyadh11421 Saudi Arabia LausanneCH-1015 Switzerland U.S. Naval Research Laboratory WashingtonDC20375 United States

We demonstrate the use of a dual comb photonic system for downconversion and disambiguation of RF signals ranging from 4.3 GHz to 17.3 GHz. Our system has future potential for miniaturization, a key for deployment in ...

ISBN: (纸本)9781557528209

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