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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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A Robustness Enhanced Capsule Network with Regularization 6

A Robustness Enhanced Capsule Network with Regularization

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6th International Conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Zeng, Ru Song, Yan Li, Min Qin, Yuzhang Ni, Tingxuan University of Shanghai for Science and Technology Department of Control Science and Engineering Shanghai China Shanghai Institute of Microsystem and Information Technology Shanghai China

ISBN: (纸本)9798350371253

This paper aims to guarantee the robustness of capsule networks (CapsNets) by regularization in the construction of primary capsules and higher-level capsules. A novel CapsN et, namely robustness enhanced CapsNet with regularization (RE-CapsN et) has been put forward by exploiting the equivariant spatial relationship and the invariant descriptors of parts. The main idea of RE-CapsNet is three-fold: 1) the regularization for poses and descriptors of capsules combined with attention modules is introduced to establish the primary capsules;2) the utilization of regularization contributes greatly to the robustness, and the adoption of attention modules and regularization gives rise to good interpretability of RE-CapsNet;3) the comparison experiments on several datasets reveal the RE-CapsNet generalizes better on unfamiliar viewpoints with affine transformations. © 2023 IEEE.

关键词： Image classification

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How to improve model performance on affNIST? 9

How to improve model performance on affNIST?

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9th IEEE Smart World Congress, SWC 2023

作者： Zeng, Ru Song, Yan Li, Min University of Shanghai for Science and Technology Department of Control Science and Engineering Shanghai China Shanghai Institute of Microsystem and Information Technology Shanghai China

ISBN: (纸本)9798350319804

The affNIST dataset is a common choice for evaluating the model robustness to affine transformations. However, there is a lack of comprehensive study on how to improve classification accuracy on affNIST. In an effort to close this gap, we design an experiment on MNIST to investigate the effects of various transformations that are also used to construct affNIST. The experiment reveals that the robustness of models to translation serves a dominant influence on their performance on affNIST. Based on the findings, a simple yet effective method is developed, which contributes greatly to the improvement on the robustness of models to translation and helps achieve record-high accuracy on affNIST. However, our experiment revealed that the enhanced models did not perform well when generalized to rotated images of MNIST. This suggests that affNIST may not be a suitable benchmark dataset for assessing model robustness to all types of affine transformations. © 2023 IEEE.

关键词： affNIST dataset classification robustness translation equiv-ariance

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Recent technology Advancements in Smart City Management:A Review

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Computers, Materials & Continua 2024年第12期81卷 3617-3663页

作者： Chiranjeevi Karri JoséJ.M.Machado Joao Manuel R.S.Tavares Deepak Kumar Jain Suresh Dannana Santosh Kumar Gottapu Amir H.Gandomi Department of Mechanical Engineering Faculty of EngineeringUniversity of PortoPorto4200-465Portugal Department of Electronics and Communication Engineering Andhra University College of EngineeringAndhra UniversityVisakhaptnam530003Andhra PradeshIndia Laboratory of lntelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Artificial IntelligenceDalian University of TechnologyDalian116024China Department of Electronics and Communication Engineering GMR Institute of TechnologyRajamVizianagaram532127Andhra PradeshIndia Department of Civil Engineering Gayatri Vidya Parishad College of Engineering(GVPCE)Visakhapatnam530048Andhra PradeshIndia Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia University Research and Innovation Center(EKIK) óbuda UniversityBudapest1034Hungary

The rapid population growth,insecure lifestyle,wastage of natural resources,indiscipline behavior of human beings,urgency in the medical field,security of patient information,agricultural-related problems,and automation requirements in industries are the reasons for invention of *** cities aim to address these challenges through the integration of technology,data,and innovative *** a smart city involves integrating advanced technologies and data-driven solutions to enhance urban living,improve resource efficiency,and create sustainable *** review presents five of the most critical technologies for smart and/or safe cities,addressing pertinent topics such as intelligent traffic management systems,information and communications technology,blockchain technology,re-identification,and the Internet of *** challenges,observations,and remarks of each technology in solving problems are discussed,and the dependency effects on the technologies’performance are also *** deep learning models for various applications are *** models performance,their dependency on dataset size,type,hyper-parameters,and the non-availability of labels or ground truth are discussed.

关键词： Smart cities intelligent traffic management systems internet of things deep learning blockchain technology

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3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-controlled GWO-AOA

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Computer Systems Science & engineering 2023年第6期45卷 2483-2503页

作者： K.Sreelakshmy Himanshu Gupta Om Prakash Verma Kapil Kumar Abdelhamied A.Ateya Naglaa F.Soliman Department of Instrumentation and Control Engineering Dr.B.R.Ambedkar National Institute of Technology JalandharPunjab144027India Department of Industrial and Production Engineering Dr.B.R.Ambedkar National Institute of Technology JalandharPunjab144027India Department of Electronics and Communication Engineering Zagazig UniversityZagazigSharqia44519Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous *** increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past ***,not a single optimization algorithms can solve all kind of optimization problem ***,there is dire need to integrate metaheuristic for general *** address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional *** performance of the developed QGA has been compared against the various *** simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.

关键词： Archimedes optimisation algorithm grey wolf optimisation path planning reinforcement learning unmanned aerial vehicles

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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LiDAR Point Cloud Map Coding Framework Based on Quadric Surface Fitting and Scene Construction 5

LiDAR Point Cloud Map Coding Framework Based on Quadric Surf...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Sun, Xuebin Wang, Jinxi Yang, Xiaofei School of Electrical Automation and Information Engineering Tianjin University Tianjin China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Shanghai Institute of Optics and Fine Mechanics Department of Aerospace Laser Technology and System Shanghai China

ISBN: (纸本)9798350355925

Due to rapid 3D acquisition advancements, LiDAR point clouds are prevalent in emerging applications like heritage preservation, mobile robotics, and remote sensing. Yet, the sheer data volume poses challenges for transmission and storage, especially in autonomous driving. This paper introduces an efficient coding framework for compressing large dense LiDAR point cloud maps. By recording timestamps and quaternion matrices during map construction, we reduce map compression to sequence compression. The scans are segregated into intra-frames and inter-frames. For intra-frames, a quadric surface fitting intra-prediction technique eliminates spatial redundancy. For inter-frames, by constructing the 3D scene using encoded intra-frames, the inter-frame can be simulated according to its posture and LiDAR intrinsic parameters. Coding focuses on the difference between actual and predicted point cloud data. Decoding reverses this process, reconstructing the point cloud map from the decoded sequence and quaternion matrices. KITTI dataset experiments show the point cloud map can be averagely compressed to 3.43% of its original size while maintaining centimeter-level precision. This outperforms both octree and Google Draco methods. © 2024 IEEE.

关键词： Mobile robots

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R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional Gradients 25th

R-CONV: An Analytical Approach for Efficient Data Reconstru...

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25th International Conference on Web information Systems engineering, WISE 2024

作者： Eltaras, Tamer Ahmed Malluhi, Qutaibah Savino, Alessandro Carlo, Stefano Di Qayyum, Adnan Politecnico Di Torino Control and Computer Engineering Department Turin10129 Italy Qatar University Doha Qatar Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9789819605750

In the effort to learn from extensive collections of distributed data, federated learning has emerged as a promising approach for preserving privacy by using a gradient-sharing mechanism instead of exchanging raw data. However, recent studies show that private training data can be leaked through many gradient attacks. While previous analytical-based attacks have successfully reconstructed input data from fully connected layers, their effectiveness diminishes when applied to convolutional layers. This paper introduces an advanced data leakage method to efficiently exploit convolutional layers’ gradients. We present a surprising finding: even with non-fully invertible activation functions, such as ReLU, we can analytically reconstruct training samples from the gradients. To the best of our knowledge, this is the first analytical approach that successfully reconstructs convolutional layer inputs directly from the gradients, bypassing the need to reconstruct layers’ outputs. Prior research has mainly concentrated on the weight constraints of convolution layers, overlooking the significance of gradient constraints. Our findings demonstrate that existing analytical methods used to estimate the risk of gradient attacks lack accuracy. In some layers, attacks can be launched with less than 5 % of the reported constraints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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Computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Multilayer core–shell structured FeNi_(3)@C with enhanced interfacial polarization for microwave absorbers

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Nano Research 2025年第2期18卷 833-842页

作者： Shaohan Guo Junying Zhang Chengyou Lin Jin Ge Song Bi Zhi-Ling Hou College of Mathematics and Physics Beijing University of Chemical TechnologyBeijing 100029China School of Physics and Optoelectronic Engineering Beijing University of TechnologyBeijing 100124China National Key Laboratory of Electromagnetic Information Control and Effects Shenyang Aircraft Design and Research InstituteShenyang 110035China 501 Department Xi’an Research Institute of High-TechXi’an 710025China

Magnetoelectric composites can achieve magneto-electric synergy to optimize impedance ***,it is challenging to enhance the broadband absorption performance for thin thickness ***,FeNi_(3)@C composite with magnetic alloy core and N-doped macroporous multilevel layered carbon shell was constructed by hierarchical interface *** uniform multilevel layered carbon shell could induce interfacial polarization and multiple scattering,and further endows enhanced electromagnetic attenuation and optimized impedance *** ultra-thin broadband absorber with an effective absorption bandwidth of 7.4 GHz is achieved at a thickness of only 1.6 ***,full-band absorption from 2 to 18 GHz at a thickness of 9.8 mm is realized through metastructure *** work provides an alternative strategy to prepare coreshell structured magnetoelectric composites for ultra-thin and broadband absorbers.

关键词： covalent organic frameworks(COFs) FeNi_(3)@C core-shell structure interfacial polarization impedance matching

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