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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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EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin Cancer Classification 27th

EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ray, Amartya Gayen, Soumyajit Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031781032

Skin cancer poses a serious global health challenge, where timely and precise diagnosis is essential to improve patient outcomes. Recently, neural networks have proven to be highly effective tools for automated skin cancer classification, significantly advancing the field of dermatology. This paper introduces a novel approach to generate edge maps from dermoscopic images using a holistically nested edge detector model. These edge maps enhance the detection of shape and symmetry irregularities, which are key indicators of malignancy, and improve the focus on relevant regions of interest. We then propose an edge-guided dual-branch neural network, called EDB-Net, for the classification task. Branch 1 handles edge maps, while Branch 2 processes original dermoscopic images. To highlight significant regions and focus on specific lesion areas, we incorporate a novel channel-spatial synergistic attention block within Branch 2. Additionally, we introduce a unique strategy to modulate the generated attention maps using edge features extracted from the edge maps in Branch 1, creating edge-guided features that refine the overall feature representation. In the final stage, both edge-guided and attention-aided features are combined, producing more distinct and contextually relevant outputs, thereby significantly enhancing classification performance. Our model achieves accuracies of 0.927 and 0.848 on the challenging HAM10000 and ISIC 2016 datasets, respectively, without employing any data augmentation. The source code of the proposed model is available at: https://***/Cmatermedicalimageanalysis/EDB_Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Establishing the Neuron SPICE Model of Basal Ganglia in Parkinson's Disease Based on the Hodgkin-Huxley Conductance-Based Theory

Establishing the Neuron SPICE Model of Basal Ganglia in Park...

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Neural Imaging and Sensing 2025

作者： Wu, Kun-Ta Chen, Peng-Jye Chiang, Chih-Chuan Yan, Yung-Jhe Mang, Ou-Yang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Biomedical Technology and Device Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781510683549

Deep brain stimulation (DBS) is a surgical treatment used to reduce movement impairments in individuals suffering from Parkinson's disease. To ensure the stability of a DBS system, it is vital to develop an equivalent Hodgkin-Huxley neuron SPICE model that can be simulated with circuit components. Accordingly, this study proposes a neuron SPICE model for the basal ganglia (BG). First, conductance-based models for several brain regions are established in SPICE, including the internal segment of globus pallidus (GPi), subthalamic nucleus (STN), thalamus (TH), and external segment of globus pallidus (GPe). Notably, the amount of the constant bias current from other brain regions is adjusted for the healthy and Parkinsonian states respectively. Next, the synapse model connecting the neurons is constructed and classified into excitatory and inhibitory types. Subsequently, the TH is stimulated by the pulse wave from sensorimotor cortex (SMC). Eventually, the TH neuron in the healthy state responds faithfully to the stimulus with spikes, while the neuron in the Parkinson's state sometimes fails to respond. After DBS at the STN in Parkinsonian state, the TH neuron responds to the SMC similarly to the healthy state. In summary, this model provides a way to simulate Parkinson's disease founded on a conductance-based model that includes various brain regions while incorporating realistic electrical properties of the experimental environment into the simulation, such as electrodes applied in DBS and amplifier circuits for sensing signals. © 2025 SPIE.

关键词： Neurodegenerative diseases

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Distributed model predictive control without terminal cost under inexact distributed optimization

arXiv

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arXiv 2025年

作者： Liu, Xiaoyu Dimarogonas, Dimos V. Liu, Changxin Dabiri, Azita De Schutter, Bart Delft Center for Systems and Control Delft University of Technology Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper presents a novel distributed model predictive control (MPC) formulation without terminal cost and a corresponding distributed synthesis approach for distributed linear discrete-time systems with coupled constraints. The proposed control scheme introduces an explicit stability condition as an additional constraint based on relaxed dynamic programming. As a result, contrary to other related approaches, system stability with the developed controller does not rely on designing a terminal cost. A distributed synthesis approach is then introduced to handle the stability constraint locally within each local agent. To solve the underlying optimization problem for distributed MPC, a violation-free distributed optimization approach is developed, using constraint tightening to ensure feasibility throughout iterations. A numerical example demonstrates that the proposed distributed MPC approach ensures closed-loop stability for each feasible control sequence, with each agent computing its control input in parallel. © 2025, CC BY.

关键词： Predictive control systems

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Safe platooning control of connected and autonomous vehicles on curved multi-lane roads

arXiv

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arXiv 2025年

作者： Chen, Xiao Tang, Zhiqi Johansson, Karl H. Mårtensson, Jonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper investigates the safe platoon formation tracking and merging control problem of connected and automated vehicles (CAVs) on curved multi-lane roads. The first novelty is the separation of the control designs into two distinct parts: a lateral control law that ensures a geometrical convergence towards the reference path regardless of the translational velocity, and a longitudinal control design for each vehicle to achieve the desired relative arc length and velocity with respect to its neighboring vehicle. The second novelty is exploiting the constructive barrier feedback as an additive term to the nominal tracking control, ensuring both lateral and longitudinal collision avoidance. This constructive barrier feedback acts as a dissipative term, slowing down the relative velocity toward obstacles without affecting the nominal controller’s performance. Consequently, our proposed control method enables safe platoon formation of vehicles on curved multi-lane roads, with theoretical guarantees for safety invariance and stability analysis. Simulation and experimental results on connected vehicles are provided to further validate the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Feedback

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Locate N’ Rotate: Two-Stage Openable Part Detection with Foundation Model Priors 17th

Locate N’ Rotate: Two-Stage Openable Part Detection with F...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Li, Siqi Chen, Xiaoxue Cheng, Haoyu Zhou, Guyue Zhao, Hao Tian, Guanzhong Ningbo innovation Center Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Institute for AI Industry Research Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789819609628

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to unseen environments and objects. In this paper, we propose a Transformer-based Openable Part Detection (OPD) framework named Multi-feature Openable Part Detection (MOPD) that incorporates perceptual grouping and geometric priors, outperforming previous methods in performance. In the first stage of the framework, we introduce a perceptual grouping feature model that provides perceptual grouping feature priors for openable part detection, enhancing detection results through a cross-attention mechanism. In the second stage, a geometric understanding feature model offers geometric feature priors for predicting motion parameters. Compared to existing methods, our proposed approach shows better performance in both detection and motion parameter prediction. Codes and models are publicly available at https://***/lisiqi-zju/MOPD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Intelligent robots

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BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification 5th

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural ...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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TSQ-2024: A Categorized Dataset of 2D LiDAR Images of Moving Dump Trucks in Various Environment Conditions 17

TSQ-2024: A Categorized Dataset of 2D LiDAR Images of Moving...

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17th International Conference on Machine Vision, ICMV 2024

作者： Kokhan, V.V. Konyushenko, I.D. Bocharov, D.A. Seleznev, I.O. Nikolaev, I.P. Nikolaev, D.P. Institute for Information Transmission Problems Russian Academy of Sciences Moscow Russia Moscow Institute of Physics and Technology Dolgoprudny Russia GLAVKARIER LLC Borovsk Russia Federal Research Center "Computer Science and Control" Russian Academy of Sciences Moscow Russia Smart Engines Service LLC Moscow117312 Russia

ISBN: (纸本)9781510688278

LiDARs are powerful and common sensors used for complex 3D environment analysis tasks. They are widely utilized as data providers in industrial measurements, robotics and unmanned technologies. Due to physical properties of such sensors they suffer from environmental heterogenity resulting in distortions of obtained measurements because of natural clutter such as cloud dust, snow and fog. Nevertheless the problem of robust clutter filtering algorithms is of significant importance, we highlight the lack of LiDAR measurements datasets containing natural clutter. The current study presents a novel publicly available and expertly annotated 2D LiDAR measurements dataset TSQ-2024 that contains 120 measurements of passing trucks under natural conditions including sand dust clutter data. The proposed dataset was utilized in the task of truck body segmentation. The experiments section provides the performance evaluation of the proposed truck body segmentation algorithm on the TSQ-2024 dataset which is also published along with the data. © 2025 SPIE.

关键词： Radar clutter

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Distributed Prescribed-Time Observer for Nonlinear Systems in Block-Triangular Form

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IEEE control Systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs control systems Vectors Linear systems Electronic mail

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Enhancing the transferability of adversarial attacks via Scale Enriching

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Neural Networks 2025年 189卷 107549页

作者： Zhao, Yuhang Zheng, Jun Gao, Xianfeng Liu, Lu Zhang, Yaoyuan Zhang, Quanxin School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

Deep learning models are vulnerable to adversarial attacks. Transfer-based adversarial examples are crafted against surrogate models and transferred to victim models. However, under the black-box settings, most adversaries have poor transferability on models with different input sizes. In this work, we propose the Scale Enriching Method (SEM) to enhance the transferability of adversarial examples using an input scale-enriching framework. By scaling the surrogate model's input in a specific range, our method enriches the attention areas that the surrogate model perceives and enlarges the tolerance of the distinction among different models, significantly improving the transferability. Notably, SEM avoids introducing extraneous noise during perturbation generation, thereby preserving the inherent textural features corresponding to different scales within the original images. Experiments on ImageNet show that our method successfully mitigates the gap of transferability between models with different input sizes. Furthermore, we demonstrate that our method can integrate with existing methods and bypass a variety of defense methods with over 90% success rate. © 2025 Elsevier Ltd

关键词： Adversarial attack Adversarial example Transfer attack

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