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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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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Event-Triggered Sampled-Data Iterative Learning Control for Linear Time-Varying Switched Systems

Event-Triggered Sampled-Data Iterative Learning Control for ...

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2023 China automation Congress, CAC 2023

作者： Ma, Xin Dai, Xisheng Zhou, Rusheng School of Automation Guangxi University of Science and Technology Liuzhou China School of Automation Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou China School of Computer Science and Technology Guangxi University of Science and Technology Liuzhou China

ISBN: (纸本)9798350303759

Consider a class of continues linear time-varying switched systems and study its sampled-data iterative learning control output tracking problem under event triggering. Firstly, in order to reduce the update batch of the control input in the direction of the iteration axis, an event-triggered sampled-data iterative learning control law is designed and the event-triggered conditions are derived by the Lyapunov function method. Secondly, a fixed threshold method is proposed to accelerate the convergence of the system. Then, according to the activation intervals of the switched subsystem,triggered and untriggered are analyzed for convergence, and the sufficient conditions for consistent convergence of the system are derived by the compression mapping method. Finally, the effectiveness of the algorithm is verified by the numerical simulation. © 2023 IEEE.

关键词： Learning algorithms

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Approximation Schemes for Geometric Knapsack for Packing Spheres and Fat Objects 51

Approximation Schemes for Geometric Knapsack for Packing Sph...

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Acharya, Pritam Bhore, Sujoy Gupta, Aaryan Khan, Arindam Mondal, Bratin Wiese, Andreas Department of Mathematics Indian Institute of Science Education and Research Pune India Department of Computer Science and Engineering Indian Institute of Technology Bombay India Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Mathematics Technical University of Munich Germany

ISBN: (纸本)9783959773225

We study the geometric knapsack problem in which we are given a set of d-dimensional objects (each with associated profits) and the goal is to find the maximum profit subset that can be packed non-overlappingly into a given d-dimensional (unit hypercube) knapsack. Even if d = 2 and all input objects are disks, this problem is known to be NP-hard [Demaine, Fekete, Lang, 2010]. In this paper, we give polynomial time (1 + Ε)-approximation algorithms for the following types of input objects in any constant dimension d: disks and hyperspheres, a class of fat convex polygons that generalizes regular k-gons for k ≥ 5 (formally, polygons with a constant number of edges, whose lengths are in a bounded range, and in which each angle is strictly larger than π/2), arbitrary fat convex objects that are sufficiently small compared to the knapsack. We remark that in our PTAS for disks and hyperspheres, we output the computed set of objects, but for a OΕ(1) of them we determine their coordinates only up to an exponentially small error. However, it is not clear whether there always exists a (1 + Ε)-approximate solution that uses only rational coordinates for the disks’ centers. We leave this as an open problem which is related to well-studied geometric questions in the realm of circle packing. © Pritam Acharya, Sujoy Bhore, Aaryan Gupta, Arindam Khan, Bratin Mondal, and Andreas Wiese.

关键词： Approximation algorithms

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FRCD: Future Ready Career-Duck a Machine Learning-Driven Career Guidance for Students

FRCD: Future Ready Career-Duck a Machine Learning-Driven Car...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Paritosh, Priyanshu Yadav, Vedant Gupta, Dev Biswas, Ujjwal Indian Institute of Information Technology Bhagalpur Department of Mechatronics and Automation Engineering Bhagalpur India Indian Institute of Information Technology Bhagalpur Department of Computer Science and Engineering Bhagalpur India

ISBN: (纸本)9798331540821

In today's society, finding a suitable career is crucial. High school students frequently need to fully understand the notion of labor due to a lack of experience and practical knowledge in various disciplines. This renders students particularly vulnerable to external influences while deciding on a career path. As a result, students must carefully consider high-paying careers to ensure that they align with their interests and skills. Otherwise, students may end up in jobs they dislike or are not suited to. In response to this issue, we intend to develop a machine-learning framework capable of predicting career pathways. Students will receive career recommendation from our model based on their performance and potential. Students can confidently follow their chosen fields if they do well academically;if not, they can choose to change to other fields that better suit them. There are challenges in determining the metrics and improving prediction accuracy to quantify students' career paths and performance in teaching-learning. Best on the test results, the proposed model has a higher prediction accuracy. Also, this method is both reliable and portable, and it may be used to predict career paths in real time. © 2024 IEEE.

关键词： Students

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A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning

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ACM Transactions on Graphics 2023年第5期42卷 1-16页

作者： Zhang, Yuxin Tang, Fan Dong, Weiming Huang, Haibin Ma, Chongyang Lee, Tong-Yee Xu, Changsheng MAIS Institute of Automation China and School of Artificial Intelligence CAS UCAS China Institute Of Computing Technology CAS China Kuaishou Technology China Department of Computer Science and Information Engineering National Cheng-Kung University Taiwan

This work presents Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, that can fit in most existing arbitrary image style transfer models, such as CNN-based, ViT-based, and flow-based methods. As the key component in image style transfer tasks, a suitable style representation is essential to achieve satisfactory results. Existing approaches based on deep neural networks typically use second-order statistics to generate the output. However, these hand-crafted features computed from a single image cannot leverage style information sufficiently, which leads to artifacts such as local distortions and style inconsistency. To address these issues, we learn style representation directly from a large number of images based on contrastive learning by considering the relationships between specific styles and the holistic style distribution. Specifically, we present an adaptive contrastive learning scheme for style transfer by introducing an input-dependent temperature. Our framework consists of three key components: a parallel contrastive learning scheme for style representation and transfer, a domain enhancement (DE) module for effective learning of style distribution, and a generative network for style transfer. Qualitative and quantitative evaluations show the results of our approach are superior to those obtained via state-of-the-art methods. The code is available at https://***/zyxElsa/CAST_pytorch. © 2023 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots

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Tsinghua science and technology 2024年第5期29卷 1481-1494页

作者： Xu Zhou Benlian Xu Zhengqiang Jiang Jun Li Brett Nener School of Mechanical Engineering Changshu Institute of TechnologyChangshu 215500China School of Automation Nanjing University of Science and TechnologyNanjing 210094China School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Faculty of Medicine and Health The University of SydneySydney 2006Australia Department of Electrical Electronic and Computer EngineeringThe University of Western AustraliaPerth 6009Australia

Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.

关键词： reinforcement learning manual reset multi-state recovery failure prediction

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Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud

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International Journal of Intelligent Networks 2023年第1期4卷 301-309页

作者： Mageshkumar, Nagappan Swapna, J. Pandiaraj, A. Rajakumar, R. Krichen, Moez Ravi, Vinayakumar Department of Computer Science and Technology Madanapalle Institute of Technology and Science Madanapalle India Department of Computer Science & Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur chennai India Al-Baha University Alaqiq 65779-7738 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar34754 Saudi Arabia Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

Data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the Diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the Diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search. © 2023 The Author(s)

关键词： Cryptography

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Dynamic Wind Turbine Blade 3D Model Reconstruction with Event Camera

Dynamic Wind Turbine Blade 3D Model Reconstruction with Even...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Li, Qiuxian Wang, Zheng Jie, Leiping Hu, Yiyang Deng, Rongfeng Zhang, Hui Department of Computer Science and Technology BNU-HKBU United International College Zhuhai China School of Industrial Automation Beijing Institute of Technology Zhuhai519088 China

ISBN: (纸本)9783031494208

In this paper, we proposed E2DSNeRF, a new image-based method that is highly suitable for 3D reconstruction of wind turbines. Specifically, considering the smooth and textureless characteristics of wind turbine blades that can lead to blurring and ghosting, we use event cameras with high dynamic range and low latency instead of regular RGB-D cameras to capture images of wind turbines. Then, compared to the dozens of images required by the vanilla neural radiation field method NeRF, we used very sparse input views, which greatly reduced the burden of image acquisition and preprocessing. In addition, the use of deep supervision not only improves the rendering quality of sparse input views, but also eliminates blurring and ghosting caused by low textures on wind turbine blades. At the same time, in order to obtain a more accurate camera pose, we added necessary feature points to the background when collecting the wind turbine dataset to ensure that the camera pose obtained by COLMAP is sufficiently accurate. Due to sparse input views, our method has significantly improved training speed compared to NeRF. The experimental results demonstrate the feasibility and effectiveness of this method, which has lower cost and clearer reconstruction results compared to traditional methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cameras

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