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Chattering-Free Fault-Tolerant Cluster Control and Fault Direction Identification for HIL UAV Swarm With Pre-Specified Performance

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 183-197页

作者： Pei-Ming Liu Xiang-Gui Guo Jian-Liang Wang Daniel Coutinho Lihua Xie the Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing the Shunde Graduate School of University of Science and Technology Beijing IEEE Autonomous Intelligent Systems Department Hangzhou Innovation Institute of Beihang University the Department of Automation and Systems Universidade Federal de Santa Catarina the School of Electrical and Electronic Engineering Nanyang Technological University

In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop(HIL) swarm unmanned aerial vehicles(UAVs) in the presence of possible nonidentical and unknown direction faults(NUDFs) in the yaw *** control strategy begins with the design of a pre-specified performance event-triggered observer for each individual *** observers estimate the outputs of the human controlled UAVs, and simultaneously achieve the distributed design of actual control signals as well as cluster consensus of the observer *** is worth mentioning that these observers require neither the high-order derivatives of the human controlled UAVs' output nor a priori knowledge of the initial conditions. The fault-tolerant controller realizes the pre-specified performance output regulation through error transformation and the Nussbaum function. It should be pointed out that there are no chattering caused by the jump of the Nussbaum function when a reverse fault occurs. In addition, to provide a basis for further solving the problem of physical malfunctions, a fault direction identification algorithm is proposed to accurately identify whether a reverse fault has occurred. Simulation results verify the effectiveness of the proposed control and fault direction identification strategies when the reverse faults occur.

关键词： Chattering-free cluster consensus fault direction identification human-in-the-loop (HIL) nonidentical and unknown direction faults (NUDFs) pre-specified performance swarm unmanned aerial vehicles (UAVs)

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Recent advances in photopolymerization 3D printing of alumina-ceramic

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Progress in Natural science:Materials International 2025年第1期35卷 1-30页

作者： M.Irfan Hussain Min Xia XiaoNa Ren Zhen Shen Muhammad Jamil Changchun Ge Institute of Powder Metallurgy and Advanced Ceramics School of Material Science and Engineering University of Science and Technology Beijing State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics

Digital light processing(DLP) 3D printing has a huge potential for manufacturing intricate and customized ceramic parts with high precision and cost-effectiveness. Research in this field contributes to material innovation,opening the avenues for new process designs in both scientific and industrial sectors. However, the implementation of DLP 3D printing technology in ceramic research has not yet reached the maturity level as that in polymer and tissue engineering. Necessarily, a holistic in-depth literature reporting the successful integration of alumina ceramics within DLP 3D printing technology is urgently needed. This review, systematic examines recent progress in DLP technology, focusing on photopolymer resins that incorporate UV-sensitive monomers, photoinitiators, and dispersants, as well as their synergistic effects on achieving high-quality printing, desirable material properties, and enhanced performance. Further, the review discusses key factors including post-processing characteristics such as debinding and sintering, which influence microstructure, and defect formation including microcracks, porosity and voids. Finally, the challenges associated with printing and sintering are highlighted,aiming to identify focused focused development pathways and potential solution to optimize outcomes. This analysis clarifies existing challenges and also propsoes future applications for DLP technology in the aluminaceramic field.

关键词： Al2O3 ceramics Applications Digital light processing (DLP) Post-processing Resin

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Broadband single-polarization single-mode low confinement loss hollow core anti-resonant fiber with semi-elliptical nested tube

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Optical and Quantum Electronics 2025年第4期57卷 1-12页

作者： Lin, Wen Fei, Yihong Wang, Ning Jia, Hongzhi Engineering Research Center of Optical Instruments and Systems Ministry of Education Shanghai Key Laboratory of Modern Optical Systems School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

In this paper, a hollow-core anti-resonant optical fibre containing a semi-elliptical nested tube is proposed, which has the characteristics of single-polarization, large bandwidth, single-mode and low confinement loss. By optimizing the structural design, the polarization extinction ratio (PER) of the fiber reaches 21,0183 at a wavelength of 1.55 μm, and the y-polarized fundamental mode loss is only 0.00086 dB/m. By changing the material and thickness of the semi-ellipsoidal nested tube, a large bandwidth of 280 nm was achieved in the wavelength range of 1.51-1.78 μm, with loss limited to less than 0.001 dB/m within the bandwidth range. The PER and higher-order mode extinction ratio of 4870, 1487, respectively. The loss remains below 0.01 dB/m when the bending radius exceeds 4.5 cm. Therefore, it is expected that this optical fiber can be used in optical devices such as fiber optic sensors, fiber optic lasers and fiber optic gyroscopes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Single mode fibers

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Flexible, Super-stable, and Multifunctional Capacitive Pressure Sensors with Tilted Micropillar Array by Inclined Laser Ablation and Demolding

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IEEE Sensors Journal 2025年第6期25卷 9367-9374页

作者： Xu, Tianlong Li, Kaixin Zhou, Yihao Zhang, Dawei Sheng, Bin College of Optical Electrical and Computer Engineering Unversity of Shanghai for Science and Technology Shanghai 200093 China. Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instruments and System Shanghai 200093 China

Flexible capacitive pressure sensors have garnered considerable interest across diverse applications, including medical monitoring, electronic skin, and robotic tactile systems, owing to their straightforward fabrication process, low energy consumption, and robust stability. In this paper, we introduce a new inclined laser ablation (ILA) combined with demolding technique for the fabrication of flexible capacitive sensors featuring a microstructured dielectric layer composed of a tilted micropillar array (TMA). The design parameters of TMA were systematically optimized, including the tilt angle of the micropillars, array gap, aspect ratio, and bottom film thickness. The resulting capacitive pressure sensor, equipped with 75° tilted micropillar arrays (aspect ratio of 5:1), demonstrates exceptional sensitivity (1.12 kPa-1) within the low-pressure range (0-2 kPa), rapid response and recovery times, a low pressure detection limit of 0.25 Pa, and transient response stability factor as high as 243.9. The proposed capacitive pressure sensor was validated for the detection and recognition of human motions when attached to various body parts. Additionally, the sensors also demonstrated significant potential for diverse applications, including tactile sensing, and Morse code communication. © 2025 IEEE.

关键词： Aspect ratio

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Stability and bifurcation analysis of delayed tri-trophic food chain model in poisoned environment with fear effect and additional food

Stability and bifurcation analysis of delayed tri-trophic fo...

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作者： Ramasamy, Sivasamy Banjerdpongchai, David Park, PooGyeon Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of

One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of additional food to predators in a delayed tri-trophic food chain model, where interference among the species is determined by Holling type-II functional response. The proposed model incorporates the fact that the growth rates of prey and middle predators are decreased due to the fear of middle and special predators, respectively. The additional food is supplied to both predators by either nature or external agencies. Furthermore, the poisoned prey in the predator's environment is taken into account. In the proposed model, we use two time delays: one for the growth term of the middle predator and another for the gestation delay of the special predator. We determine the conditions for the existence of ecologically feasible equilibrium points and their local and global stability. In addition, we establish the conditions for the existence of Hopf bifurcation around interior equilibrium to seek periodic behavior in nondelayed and delayed models. Numerical investigations are performed to justify the proposed theoretical findings through phase portraits, time series of solutions, and bifurcation diagrams. We observe that the chaotic dynamics of a tri-trophic food chain model can be controlled by the proper choice of the fear effect and by supplying additional food parameters. As a result, our findings provide more ecological insights into the dynamics of the delayed tri-trophic food chain model with the fear effect and additional food supply. © 2023 John Wiley & Sons, Ltd.

关键词： Hopf bifurcation

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Collaborative Routing and Charging/Discharging Scheduling of Electric Autonomous Vehicles in Coupled Power-Traffic Networks: A Multi-Objective Approach

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IEEE Internet of Things Journal 2025年第11期12卷 17753-17764页

作者： Chu, Kai-Fung Chen, Tianlun Xie, Yue Lam, Albert Y.S. Song, Yue Iida, Fumiya University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering Department of Geography Hong Kong Fano Labs Hong Kong Tongji University Department of Control Science and Engineering Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems Shanghai200120 China

Autonomous vehicles (AVs) are vehicles that traverse on the road without active human intervention. With a coordinator, AVs can be connected to provide high-efficiency transport services, such as AV-based public transport networks. The controller can manage the network by coordinating the transport request assignment, traveling, and charging/discharging schedule. On the other hand, AVs are likely to be electric and benefit the smart grid via vehicle-to-grid technology. A well-designed mobility network connecting electric AVs (EAVs) and smart grid can substantially reduce unnecessary travel and energy costs. In this paper, we aim to maximize utilities in the AV-based public transport network and the power distribution network for the vehicle network containing EAVs, charging stations, and distributed power generations. We formulate the assignment and scheduling problem as a multi-objective mixed-integer program. To solve the optimization problem, we develop a hybrid heuristic approach based on Non-Dominated Sorting Genetic Algorithm II and branch-and-bound algorithms. Experiments are conducted on a modified 15-bus distribution system and a simulated traffic network. The results show that the proposed strategy effectively minimizes the total travel and energy purchase cost by 21%. This study provides valuable insights on vehicle coordination for multiple tasks, offering visionary guidance for stakeholders engaged in multifaceted transportation endeavors. © 2014 IEEE.

关键词： Vehicle-to-grid

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Mitigating the Impact of ReRAM I-V Nonlinearity and IR Drop via Fast Offline Network Training

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 951-960页

作者： Lee, Sugil Fouda, Mohammed E. Quan, Chenghao Lee, Jongeun Eltawil, Ahmed E. Kurdahi, Fadi Ulsan National Institute of Science and Technology Department of Electrical Engineering Ulsan44919 Korea Republic of University of California at Irvine Center for Embedded and Cyber-Physical Systems IrvineCA92697 United States King Abdullah University of Science and Technology CEMSE Division Thuwal23955 Saudi Arabia

ReRAM crossbar arrays (RCAs) have the potential to provide extremely high efficiency for accelerating deep neural networks (DNNs). However, one crucial challenge for RCA-based DNN accelerators is functional inaccuracy due to nonidealities present in RCA hardware. While nonideality-aware training (NAT) could be used to mitigate the effect of nonidealities, with currently available methods it would take months to train even a medium size convolutional neural network (CNN). In this article we propose a nonideality prediction method that enables very fast training of RCA-based neural networks, and show its feasibility through NAT of DNNs. Our key ideas include 1) weight-centric nonideality modeling and 2) data-dependence elimination by tailored input randomization. Our experimental results using a multilayer perceptron and CNNs demonstrate that our method is very fast (100∼ 15 000× faster training speed) while achieving much better-crossbar-level accuracy (2 ∼ 90× lower-RMS error) and post-retraining validated accuracy than previous methods. © 1982-2012 IEEE.

关键词： Deep neural networks

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Ecology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure

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Artificial life 2025年第2期31卷 129-152页

作者： Moreno, Matthew Andres Rodriguez-Papa, Santiago Dolson, Emily University of Michigan Department of Ecology and Evolutionary Biology Center for the Study of Complex Systems Michigan Institute for Data and AI in Society Michigan State University Department of Computer Science and Engineering Michigan State University Department of Computer Science and Engineering Program in Ecology Evolution Behavior

Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution and have been hypothesized to influence phylogenetic structure. For instance, they can help explain natural history, steer behavior of contemporary evolving populations, and influence the efficacy of application-oriented evolutionary optimization. Likewise, in inquiry-oriented Artificial Life systems, these drivers constitute key building blocks for open-ended evolution. Here we set out to assess (a) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure;(b) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure;and (c) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Furthermore, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest a potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers' phylometric signatures from each other and to appropriately normalize phylogenetic metrics.

关键词： agent-based simulation Artificial Life Ecology evolution evolutionary dynamics phylogenetic analysis phylogeny metrics

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Robust Authentication: Leveraging Hardware Fingerprints and AI to Enhance Security Against Spoofing

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IEEE Access 2025年 13卷 86912-86926页

作者： Nimmy, K. Jain, Kurunandan Sachin, S.M. Abekaesh, P.A. Venkitasubramaniam, Parv Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Amrita Vishwa Vidyapeetham Center for Cybersecurity Systems and Networks Amritapuri India Lehigh University Department of Electrical & Computer Engineering United States

Fingerprint authentication blends biometric precision with user convenience, leveraging unique ridge patterns for fast, reliable, and tamper-resistant identity verification. However, it remains vulnerable to spoofing attacks, where adversaries create synthetic fingerprints using materials like gelatin. To mitigate this, multi-factor authentication (MFA) enhances security by verifying both the fingerprint and the user’s device, restricting access to authorized hardware and complicating attacks. Although Photo Response Non-Uniformity (PRNU) has been explored for device authentication, its reliability diminishes under varying lighting conditions. To address this, we propose an AI-driven approach to generate robust hardware fingerprints from fingerprint scanners, enabling high-accuracy device identification. Our method, trained on 2,000 fingerprint images from 10 scanners, achieves an impressive 98% accuracy in identifying the source scanner. By integrating fingerprint biometrics, hardware fingerprints, and passwords, our novel MFA protocol significantly enhances security against spoofing and forgery attacks. Through security analysis, we demonstrate its effectiveness in preventing unauthorized access. Finally, we validate our authentication framework using an AI-powered binary classifier model, showcasing its practicality for real-world security applications. © 2013 IEEE.

关键词： AI-based classification hardware fingerprint multifactor authentication (MFA) mutual authentication protocol photo-response non-uniformity (PRNU)

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