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Geometric attitude tracking control of quadrotor UAVs with adaptive extended state observers

ISA Transactions 2025年 160卷 289-297页

作者： Wang, Liping Chen, Jiapeng Huang, Yuyuan Zhu, Yongbin Pei, Hailong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou510640 China

This paper studies a geometric attitude tracking control problem of quadrotor unmanned aerial vehicles (UAVs) with adaptive extended state observers (AESOs). Through coordinate transformation, the error dynamic of AESO can be transformed into a canonical form which is easier to analyze by linear time-varying theory. In the presence of unknown disturbances and system uncertainties, the estimated error and stability of AESO can be analyzed. Simultaneously, a geometric tracking controller is developed on SO(3) with AESOs which guarantees that the error exponentially converges to a bounded set related to the estimated error. Some examples and a flight test are given to verify the availability of the control scheme. © 2025

关键词： Unmanned aerial vehicles (UAV)

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Fabrication of bamboo-inspired continuous carbon fiber-reinforced SiC composites via dual-material thermally assisted extrusion-based 3D printing

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材料科学技术（英文版） 2025年第5期208卷 92-103页

作者： Sai Li Haitian Zhang Zhongliang Lu Fusheng Cao Ziyao Wang Yan Liu Xiaohui Zhu Shuai Ning Kai Miao Shaoyu Qiu Dichen Li State Key Laboratory for Manufacturing Systems Engineering School of Mechanical EngineeringXi'an Jiaotong UniversityXi'an 710049China State Key Laboratory for Manufacturing Systems Engineering School of Mechanical EngineeringXi'an Jiaotong UniversityXi'an 710049China School of Intelligent Manufacturing Modern Industry Xinjiang UniversityUrumqi 830046China Science and Technology on Reactor Fuel and Materials Laboratory Nuclear Power Institute of ChinaChengdu 610213China

Ceramic matrix composites(CMCs)structural components encounter the dual challenges of severe mechanical conditions and complex electromagnetic environments due to the increasing demand for stealth technology in aerospace *** address various functional requirements,this study integrates a biomimetic strategy inspired by gradient bamboo vascular bundles with a novel dual-material 3D print-ing *** distinct bamboo-inspired structural configurations Cf/SiC composites are designed and manufactured,and the effects of these different structural configurations on the CVI process are *** method is utilized to characterize the relationship between interface bonding strength and mechanical *** results reveal that the maximum flexural strength and fracture toughness reach 108.6±5.2 MPa and 16.45±1.52 MPa m1/2,respectively,attributed to the enhanced crack propagation resistance and path caused by the weak fiber-matrix ***,the bio-inspired configuration enhances the dielectric loss and conductivity loss,exhibiting a minimum reflection loss of-24.3 dB with the effective absorption band of 3.89 *** work introduces an innovative biomimetic strategy and 3D printing method for continuous fiber-reinforced ceramic composites,expanding the ap-plication of 3D printing technology in the field of CMCs.

关键词： 3D printing Cf/SiC composites Mechanical properties Electromagnetic wave absorption

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Enhancing fairness of trading environment:discovering overlapping spammer groups with dynamic co‑review graph optimization

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Cybersecurity 2025年第2期8卷 242-269页

作者： Chaoqun Wang Ning Li Shujuan Ji Xianwen Fang Zhen Wang Shandong Provincial Key Laboratory of Wisdom Mine Information Technology Shandong University of Science&TechnologyQingdao 266590ShandongChina Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui University of Science and TechnologyHuainanChina

Within the thriving e-commerce landscape,some unscrupulous merchants hire spammer groups to post misleading reviews or ratings,aiming to manipulate public perception and disrupt fair market *** phenomenon has prompted a heightened research focus on spammer groups *** the e-commerce domain,current spammer group detection algorithms can be classified into three categories,i.e.,Frequent Item Mining-based,graph-based,and burst-based ***,existing graph-based algorithms have limitations in that they did not adequately consider the redundant relationships within co-review graphs and neglected to detect overlapping members within spammer *** address these issues,we introduce an overlapping spammer group detection algorithm based on deep reinforcement learning named ***,the algorithm filters out highly suspicious products and gets the set of reviewers who have reviewed these ***,taking these reviewers as nodes and their co-reviewing relationships as edges,we construct a homogeneous co-reviewing ***,to efficiently identify and handle the redundant relationships that are accidentally formed between ordinary users and spammer group members,we propose the Auto-Sim algorithm,which is a specifically tailored algorithm for dynamic optimization of the co-reviewing graph,allowing for adjustments to the reviewers’relationship network within the ***,candidate spammer groups are discovered by using the Ego-Splitting overlapping clustering algorithm,allowing overlapping members to exist in these ***,these groups are refined and ranked to derive the final list of spammer *** results based on real-life datasets show that our proposed DRL-OSG algorithm performs better than the baseline algorithms in Precision.

关键词： Spammer groups Homogeneous network Redundant relationships Overlapping members Deep reinforcement learning Ego-splitting algorithm

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Unsupervised Outlier Detection with Reinforced Noise Discriminator

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ACM Transactions on Intelligent systems and technology 2025年第2期16卷 1-26页

作者： Zhang, Zhongping Liu, Daoheng Zhu, Jinwei Wu, Youxi Hebei Key Laboratory of Computer Virtual Technology and System Integration School of Information Science and Engineering Yanshan University Qinhuangdao China School of Information Science and Engineering Yanshan University Qinhuangdao China School of Artificial Intelligence Hebei University of Technology Tianjin China

Outlier detection is one of the hot topics in the field of machine learning and data mining. At present, there are many kinds of outlier detection algorithms. The accuracies of traditional outlier detection algorithms are often affected by unique parameters, and an increase in the amount of data and the dimensions of the data can seriously affect their efficiency and effectiveness. Methods based on generative adversarial networks (GANs) can solve the above problems, but they are unacceptable since the model often collapses during the training period. In this article, to solve the problems of curse of dimensionality and model collapse, we propose a novel reinforced noise discriminator (RND) method for unsupervised outlier detection in tabular data. We consider outlier detection as a binary classification problem. Thus, we apply a learnable reinforced discriminator and generate a large number of potential outliers with a uniform distribution and potential outliers that are close to the original data that are used as a negative sample to train the discriminator, which learns the distribution of the original data to detect outliers. We empirically compare the proposed approach with ten state-of-the-art outlier detection methods on both synthetic and real-world tabular datasets. The experimental results show that RND outperforms its competitors in the majority of cases. The codes used to perform the experiments described in this article are available at https://***/urlhearts/r-n-d. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Generative adversarial networks

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Coherence-protected operations in hybrid superconducting circuit-magnon system

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Chinese Physics B 2025年第3期34卷 251-257页

作者： Le-Tian Zhu Xing-Yu Zhu Zhu-Cheng Yue Tao Tu Chuan-Feng Li Key Laboratory of Quantum Information Chinese Academy of SciencesUniversity of Science and Technology of ChinaHefei 230026China School of Mechanical and Electronic Engineering Suzhou UniversitySuzhou 234000China Hefei National Laboratory University of Science and Technology of ChinaChinese Academy of SciencesHefei 230088China

Hybrid systems consisting of superconducting circuits and magnon systems are a promising platform for quantum ***,realizing high-fidelity magnon state preparation and manipulation remains an outstanding challenge due to the complexity of interactions and noise sources in hybrid ***,we propose a coherence-preserving magnon state manipulation *** engineering a superconducting-magnon coupling pulse and combining it with dynamical decoupling pulses,we design a composite pulse *** demonstrate the manipulation and preparation of non-classical states of magnons with a fidelity of up to 98%under realistic *** designs significantly improve the fidelity of manipulation and robustness to noise in hybrid systems compared to existing *** results pave the way for practical applications of quantum magnonics platforms.

关键词： circuit QED quantum information with hybrid systems entanglement production

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Dynamic Regret for Distributed Online Composite Optimization

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IEEE Transactions on Automatic Control 2025年第5期70卷 3056-3071页

作者： Hou, Ruijie Yu, Yang Li, Xiuxian Tongji University Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai201800 China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai Institute of Intelligent Science and Technology Shanghai201210 China

This article focuses on online composite optimization over multiagent networks. In the distributed setting, each agent has its own local loss function, which consists of a convex, strongly convex or strongly convex and smooth function, and a time-varying nonsmooth regularizer. Two distributed online algorithms are proposed and corresponding dynamic regrets are analyzed. Two proposed algorithms are based on signs of relative states. The first algorithm obtains (Formula presented) dynamic regret bound when each local loss is a general convex composite function, where CT is the path variation. If CT can be estimated in advance for convex or strongly convex local loss with a time-varying nonsmooth regularizer, then dynamic regret bounds are, respectively, in the order of (Formula presented) and o (log T(1 + CT)). The second algorithm is based on the first one, especially for handling the local loss composed of a strongly convex and smooth function with a nonsmooth regularizer, and then obtains o (1 + CT) dynamic regret bound. In the end, numerical results are given to support the theoretical findings. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Performance of a four deep neural network structure in moiré fringe intelligent analysis

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Applied Optics 2025年第9期64卷 2208-2216页

作者： Zeng, Xinyu Chen, Yunyun Cheng, Weihao Xie, Wenzhuo Yang, Cuihong State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion Nanjing University of Information Science & Technology Nanjing210044 China Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere Nanjing University of Information Science & Technology Nanjing210044 China Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science & Technology Nanjing210044 China Nanjing University of Information Science & Technology Nanjing210044 China

Moiré fringe analysis holds significant importance in the assessment of flow fields, while deep learning is advancing rapidly and is frequently employed to handle complex computational challenges. In this paper, the performance of U-net, Unet++, Unet 3+, and ResUnet in moiré fringe analysis is compared and analyzed. The results indicate significant differences among the four neural networks in terms of the training time, prediction speed, prediction accuracy, and generalization capability regarding positional variations. Among the four neural networks, ResUnet exhibits the shortest training times, highest accuracy, and fastest prediction speeds while also exhibiting good generalization capability. Consequently, in practical applications, using ResUnet for moiré fringe analysis is an excellent choice. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Deep neural networks

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Polypropylene-based blend with enhanced breakdown strength under gamma-ray irradiation for cable insulation

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Nuclear science and Techniques 2025年第1期36卷 35-48页

作者： Bai-Xin Liu Yu Gao Jing Li Chen-Yi Guo Bo-Sen Si Jun-Guo Gao Yu Chen Bo-Xue Du School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China Key Laboratory of Engineering Dielectrics and Its Application Ministry of EducationHarbin University of Science and TechnologyHarbin 150080China State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong UniversityXi’an 710049China

This study focuses on the electrical properties and microstructure of polypropylene(PP)-based blends used for cable insulation in nuclear power plants(NPPs).The PP-based blend,comprising isotactic PP and propylene-based elastomer(PBE)at concentrations ranging from 0 to 50 wt%,underwent a melt blending process and subsequent cobalt-60 gamma-ray irradiation with doses ranging from 0 to 250 *** conductivity,trap distribution,and alternating(AC)breakdown strength were chosen to assess the insulation *** results indicate that the addition of PBE significantly improves the electrical properties of PP under *** PP,the electrical conductivity increased with irradiation,whereas the trap depth and breakdown strength decreased ***,for the blend,these changes initially exhibit opposite *** the irradiation was increased to 250 kGy,the AC breakdown strength of the blend improved by more than 21%compared to that of *** physical and chemical structures of the samples were investigated to explore the improvement *** results offer insights into the design of new cable-insulation materials suitable for NPPs.

关键词： Nuclear power plant Cable insulation Polypropylene Electrical properties Gamma-ray irradiation

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A DECOUPLED,LINEARLY IMPLICIT AND UNCONDITIONALLY ENERGY STABLE SCHEME FOR THE COUPLED CAHN-HILLIARD systems

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Journal of Computational Mathematics 2025年第3期43卷 708-730页

作者： Dan Zhao Dongfang Li Yanbin Tang Jinming Wen School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China College of Information Science and Technology Jinan UniversityGuangzhou 510632China

We present a decoupled,linearly implicit numerical scheme with energy stability and mass conservation for solving the coupled Cahn-Hilliard *** time-discretization is done by leap-frog method with the scalar auxiliary variable(SAV)*** only needs to solve three linear equations at each time step,where each unknown variable can be solved *** is shown that the semi-discrete scheme has second-order accuracy in the temporal *** convergence results are proved by a rigorous analysis of the boundedness of the numerical solution and the error estimates at different *** examples are presented to further confirm the validity of the methods.

关键词： Coupled Cahn-Hilliard system Leap-frog method Scalar auxiliary variable Error estimate

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Investigation into enhancing the sensitivity method for monitoring trace erosion product concentration in the ceramic channel of a Hall thruster

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Plasma science and technology 2025年第4期27卷 95-104页

作者： Wei XI Ximing ZHU Bowen ZHENG Zhongxi NING Daren YU School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001People’s Republic of China Key Laboratory of Aerospace Plasma Propulsion Ministry of Industry and Information TechnologyHarbin 150001People’s Republic of China

An optical emission spectroscopy(OES)method with a non-invasive measurement capability,without inducing disturbance to the discharge,represents an effective method for material ***,when the OES method is employed to monitor the trace erosion product within the ceramic channel of a Hall thruster,it becomes challenging to distinguish between signal and *** this study,we propose a model filtering method based on the signal characteristics of the Hall thruster plume *** method integrates the slit imaging and spectral resolution features of the *** this method,we extract the spectral signals of the erosion product and working gas from the Hall thruster under different operating *** results indicate that our new method performs comparably to the traditional method without model filtering when extracting atom signals from strong xenon working ***,for trace amounts of the erosion product,our approach significantly enhances the signal-to-noise ratio(SNR),enabling the identification of extremely weak spectral signals even under low mass flow rate and low-voltage *** obtain boron atom concentration of 3.91×10^(-3) kg/m^(3) at a mass flow rate of 4×10^(-7) kg/s and voltage of 200 V while monitoring a wider range of thruster operating *** new method proposed in this study is suitable for monitoring other low-concentration elements,making it valuable for materials processing,environmental monitoring and space propulsion applications.

关键词： model filtering method optical emission spectroscopy Hall thruster advanced actinometry method trace erosion product concentration(Some figures may appear in colour only in the online journal)

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