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Networked dynamic systems with higher-order interactions: stability versus complexity

National Science Review 2024年第9期11卷 291-301页

作者： Ye Wang Aming Li Long Wang Center for Systems and Control College of Engineering Peking University Center for Multi-Agent Research Institute for Artificial IntelligencePeking University

The stability of complex systems is profoundly affected by underlying structures, which are often modeled as networks where nodes indicate system components and edges indicate pairwise interactions between nodes. However, such networks cannot encode the overall complexity of networked systems with higher-order interactions among more than two nodes. Set structures provide a natural description of pairwise and higher-order interactions where nodes are grouped into multiple sets based on their shared traits. Here we derive the stability criteria for networked systems with higher-order interactions by employing set structures. In particular, we provide a simple rule showing that the higher-order interactions play a double-sided role in community stability—networked systems with set structures are stabilized if the expected number of common sets for any two nodes is less than one. Moreover, although previous knowledge suggests that more interactions(i.e. complexity) destabilize networked systems, we report that,with higher-order interactions, networked systems can be stabilized by forming more local sets. Our findings are robust with respect to degree heterogeneous structures, diverse equilibrium states and interaction types.

关键词： networked system set structure higher-order interaction stability criteria

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TOGA-Based Fuzzy Grey Cognitive Map for Spacecraft Debris Avoidance

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IEEE Transactions on Fuzzy systems 2025年第6期33卷 1793-1802页

作者： Qin, Chenhui Liu, Yuanshi Wang, Tong Qiu, Jianbin Li, Min Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150080 China Beijing Institute of Control Engineering Beijing100094 China

The optimization of fuzzy grey cognitive map (FGCM) can enhance the decision quality of the system in managing uncertainties and incomplete information. Addressing this issue requires a method that effectively balances the competing demands of the speed and precision in the optimization process. Therefore, a trade-off genetic algorithm (TOGA) is proposed to refine the FGCM optimization process in this paper. Firstly, a modified fitness function with a penalty term is designed to improve the convergence rate, which can drive the FGCM population to obtain the optimal solution during the evolutionary process. Secondly, an adaptive genetic mechanism based on horizontal comparison and longitudinal assessment, is designed to strike a balance between accelerating convergence and avoiding the dilemma of falling into local optima. Finally, in the simulation section, the effectiveness of the proposed method is validated by optimizing FGCM using synthetic datasets and applying to spacecraft debris threat avoidance scenario. © 1993-2012 IEEE.

关键词： Space debris

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A generic optimization and learning framework for Parkinson disease via speech and handwritten records

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10673-10693页

作者： Yousif, Nada R. Balaha, Hossam Magdy Haikal, Amira Y. El-Gendy, Eman M. Computer and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Parkinson’s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features;and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches. © 2022, The Author(s).

关键词： Feature extraction

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Cooperative decision-making algorithm with efficient convergence for UCAV formation in beyond-visual-range air combat based on multi-agent reinforcement learning

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Chinese Journal of Aeronautics 2024年第8期37卷 311-328页

作者： Yaoming ZHOU Fan YANG Chaoyue ZHANG Shida LI Yongchao WANG School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China

Highly intelligent Unmanned Combat Aerial Vehicle(UCAV)formation is expected to bring out strengths in Beyond-Visual-Range(BVR)air *** Multi-Agent Reinforcement Learning(MARL)shows outstanding performance in cooperative decision-making,it is challenging for existing MARL algorithms to quickly converge to an optimal strategy for UCAV formation in BVR air combat where confrontation is complicated and reward is extremely sparse and *** to solve this problem,this paper proposes an Advantage Highlight Multi-Agent Proximal Policy Optimization(AHMAPPO)***,at every step,the AHMAPPO records the degree to which the best formation exceeds the average of formations in parallel environments and carries out additional advantage sampling according to ***,the sampling result is introduced into the updating process of the actor network to improve its optimization ***,the simulation results reveal that compared with some state-of-the-art MARL algorithms,the AHMAPPO can obtain a more excellent strategy utilizing fewer sample episodes in the UCAV formation BVR air combat simulation environment built in this paper,which can reflect the critical features of BVR air *** AHMAPPO can significantly increase the convergence efficiency of the strategy for UCAV formation in BVR air combat,with a maximum increase of 81.5%relative to other algorithms.

关键词： Unmanned combat aerial vehicle(UCAV)formation Decision-making Beyond-visual-range(BVR)air combat Advantage highlight Multi-agent reinforcement learning(MARL)

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A comprehensive framework towards segmenting and classifying breast cancer patients using deep learning and Aquila optimizer

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7897-7917页

作者： Balaha, Hossam Magdy Antar, Esraa Raffik Saafan, Mahmoud M. El-Gendy, Eman M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Breast cancer is among the major frequent types of cancer worldwide, causing a significant death rate every year. It is the second most prevalent malignancy in Egypt. With the increasing number of new cases, it is vital to diagnose breast cancer in its early phases to avoid serious complications and deaths. Therefore, routine screening is important. With the current evolution of deep learning, medical imaging became one of the interesting fields. The purpose of the current work is to suggest a hybrid framework for both the classification and segmentation of breast scans. The framework consists of two phases, namely the classification phase and the segmentation phase. In the classification phase, five different CNN architectures via transfer learning, namely MobileNet, MobileNetV2, NasNetMobile, VGG16, and VGG19, are applied. Aquila optimizer is used for the calculation of the optimal hyperparameters of the different TL architectures. Four different datasets representing four different modalities (i.e., MRI, Mammographic, Ultrasound images, and Histopathology slides) are used for training purposes. The framework can perform both binary- and multi-class classification. In the segmentation phase, five different structures, namely U-Net, Swin U-Net, Attention U-Net, U-Net++, and V-Net, are applied to identify the region of interest in the ultrasound breast images. The reported results prove the efficiency of the suggested framework against current state-of-the-art studies. © 2023, The Author(s).

关键词： Magnetic resonance imaging

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Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

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Neural Computing and Applications 2025年第5期37卷 3435-3454页

作者： Hussen, Noha Salem, Mofreh Eldesouky, Ali I. Sakr, Noha Elghamrawy, Sally Computers and Control Systems Engineering Department Mansoura University Mansoura Egypt Computer Engineering Department Misr Higher Institute for Engineering and Technology Mansoura Egypt

The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the development of robust and efficient drone detection systems has become paramount. Traditional detection methods such as radar and acoustic sensors have limitations in detecting small drones and can be costly to implement. This can lead to lower detection rates and increased security breaches and safety concerns. However, prevailing methodologies often falter in achieving real-time detection with requisite precision and efficacy, particularly within complex operational environments. Motivated by the imperative to surmount these challenges, our research endeavors to introduce a pioneering real-time drone detection framework. Our study encapsulates several seminal advancements. Firstly, we proffer a groundbreaking framework that synergizes advanced integration technique based on texture feature extraction and pattern recognition techniques for real-time drone detection to increase accuracy to detect drones in different conditions such as bad weather and low resolution. Secondly, we introduce an intuitive graphical user interface, enhancing the usability and accessibility of the system in real-time scenario. Lastly, through exhaustive evaluation and comparative analysis, we substantiate the superior performance of our framework in terms of accuracy, precision, and real-time detection capabilities compared to conventional DDS methodologies. Its stability and effectiveness render it a compelling solution for security-focused entities, notably those within air force and military systems. Our experimental results reveal a commendable accuracy rate of 97%, affirming the reliability and precision 98% and recall parameter 98% of our framework in accurately identifying and detecting drones, thus surpassing recent models in the field. © The Author(s), under exclusive

关键词： Graphical user interfaces

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Internal and external disturbances aware motion planning and control for quadrotors

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IET Cyber-systems and Robotics 2024年第3期6卷 23-30页

作者： Xiaobin Zhou Miao Wang Can Cui Yongchao Wang Chao Xu Fei Gao Institute of Cyber‐Systems and Control College of Control Science and EngineeringZhejiang UniversityHangzhouChina Huzhou Institute Zhejiang UniversityHuzhouChina

Resilient motion planning and control,without prior knowledge of disturbances,are crucial to ensure the safe and robust flight of *** development of a motion planning and control architecture for quadrotors,considering both internal and external disturbances(i.e.,motor damages and suspended payloads),is ***,the authors introduce the use of exponential functions to formulate trajectory *** choice is driven by its ability to predict thrust responses with minimal computational ***,a reachability analysis is incorporated for error dynamics resulting from multiple *** analysis sits at the interface between the planner and controller,contributing to the generation of more robust and safe spatial–temporal ***,the authors employ a cascade controller,with the assistance of internal and external loop observers,to further enhance resilience and compensate the *** authors’benchmark experiments demonstrate the effectiveness of the proposed strategy in enhancing flight safety,particularly when confronted with motor damages and payload disturbances.

关键词： Internal and external disturbances reachability analysis resilient motion planning and tracking

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Stability in Inverse Problem of Determining Two Parameters for the Moore-Gibson-Thompson Equation with Memory Terms

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Journal of systems Science & Complexity 2024年第6期37卷 2368-2389页

作者： FU Songren CHEN Liangbiao ZHANG Ji-Feng Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Automation and Electrical Engineering Zhongyuan University of TechnologyZhengzhou 450007China

In this paper,the authors consider the inverse problem for the Moore-Gibson-Thompson equation with a memory term and variable diffusivity,which introduce a sort of delay in the dynamics,producing nonlocal effects in *** H¨older stability of simultaneously determining the spatially varying viscosity coefficient and the source term is obtained by means of the key pointwise Carleman estimate for the Moore-Gibson-Thompson *** the sake of generality in mathematical tools,the analysis of this paper is discussed within the framework of Riemannian geometry.

关键词： Carleman estimate memory term Moore-Gibson-Thompson equation stability Riemannian geometry

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A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing

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Digital Communications and Networks 2024年第3期10卷 645-654页

作者： Xu Li Gwanggil Jeon Wenshuo Wang Jindong Zhao School of Computer and Control Engineering Yantai UniversityYantai264005China Department of Embedded Systems Engineering Incheon National UniversityAcademyro-119Incheon22012Republic of Korea

The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy *** a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy ***,introducing signature technology leads to additional signature verification *** the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature ***,it is necessary to design an efficient multi-signature verification scheme while ensuring *** this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature *** our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data *** results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.

关键词： 5G network Crowd-sensing Privacy protection Ring signature Batch verification

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Joint Fuel-optimal control of the Velocity and Power-split of Hybrid Electric Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-12页

作者： Borsboom, Olaf Salazar, Mauro Keulen, Thijs van Control Systems Technology section Department of Mechanical Engineering Eindhoven University of Technology Eindhoven MB The Netherlands

This paper explores the eco-driving problem of parallel hybrid electric vehicles, intended to drive a certain distance within a limited amount of time, where the longitudinal vehicle velocity and powertrain controls are optimized to minimize the fuel consumption. In particular, we incorporate Pontryagin's Minimum Principle (PMP) and singular control theory in an optimization framework to find the fuel-optimal velocity and power-split control policy for the prime mover and the electric machine with global optimality guarantees. In addition, we present reformulations and derivations, so that the same problem can be solved jointly using another framework based on convex optimization, with the same global optimality properties, employing methods originally derived for timeoptimal control of race cars. Thereby, we formally show the equivalence between the eco-driving and the racing problem. We showcase both our frameworks with numerical solutions, drawing three comparisons: First, we solve the velocity and power-split problem, both sequentially and jointly, using the PMP framework. We show that the latter can improve the fuel consumption by 2.6 %. Second, we benchmark the PMP and the convex framework by solving the joint problem with both methods and observe a discrepancy of 0.14% in terms of the resulting fuel energy consumption. Finally, in a numerical study addressing the performance of both methods individually, we observe that the efficiency of the PMP and the convex framework are strongly dependent on the stopping criteria and the discretization step size, respectively. Authors

关键词： Convex optimization

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