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Disagreement and Antagonism in Signed Networks: A Survey

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1166-1187页

作者： Yuxin Wu Deyuan Meng Zheng-Guang Wu Seventh Research Division Beihang University(BUAA)and also with the School of Automation Science and Electrical EngineeringBeihang University(BUAA)Beijing 100191China National Laboratory of Industrial Control Techno-logy Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China IEEE

Signed networks refer to a class of network systems including not only cooperative but also antagonistic interactions among *** to the existence of antagonistic interactions in signed networks,the agreement of nodes may not be established,instead of which disagreement behaviors generally *** paper reviews several different disagreement behaviors in signed networks under the single-integrator linear dynamics,where two classes of topologies,namely,the static topology and the dynamic topology,are *** the static signed networks with the adjacency weights as(time-varying)scalars,we investigate the convergence behaviors and the fluctuation behaviors with respect to fixed topologies and switching topologies,respectively,and give some brief introductions on the disagreement behaviors of general time-varying signed ***,several classes of behavior analysis approaches are also *** the dynamic signed networks with the adjacency weights as transfer functions or linear time-invariant systems,we show the specific descriptions and characteristics of them such that the disagreement behaviors can be obtained by resorting to the derived static signed ***,we give their applications to the behavior analysis of static signed networks in the presence of high-order dynamics or communication delays.

关键词： Antagonistic interaction disagreement behavior dynamic topology signed network static topology

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Description and fault diagnosis in autonomous underwater vehicles based on ontologies

Description and fault diagnosis in autonomous underwater veh...

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2022 International Conference on Ocean Studies, ICOS 2022

作者： Alexander, Timoshenko Alexander, Zuev Eduard, Mursalimov Valeria, Gribova Alexander, Inzartsev Far Eastern Federal University Institute of Marine Technology Problems Feb Ras Laboratory of Intelligent Systems for Marine Robots Department of Automation and Robotics Vladivostok Russia Institute of Automation and Control Feb Ras Laboratory of Intelligent Systems Vladivostok Russia Institute of Marine Technology Problems Feb Ras Laboratory of Control Systems Vladivostok Russia

ISBN: (纸本)9781665473729

The paper proposes new ontologies for describing and diagnosing malfunctions of subsystems of autonomous underwater vehicles (AUV) as a part of the development of a previously created approach to the intelligent diagnosis of these vehicles using knowledge bases. To implement this approach, it is proposed to use two knowledge bases, one of which describes malfunctions, features for their identification and actions necessary to adapt to the negative consequences caused by the appearance of these malfunctions. The other database contains information about the main subsystems of the AUV, devices and replaceable modules, as well as the states they may be in. Knowledge bases are used in the work of the intelligent control-emergency system (ICES) on board the AUV, which allows for comprehensive functional diagnostics of all subsystems of the device, including payload modules, as well as adaptation to the consequences of emerging malfunctions, taking into account the actual cause-and-effect relationships between them. To test the system's operability, a prototype was developed. The results of the prototype simulation showed that ICES performs the tasks and can be implemented on the onboard computers of existing AUV using well-known programming technologies. © 2022 IEEE.

关键词： Autonomous underwater vehicles

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Generalized Zero-Shot Learning via Implicit Attribute Composition

Generalized Zero-Shot Learning via Implicit Attribute Compos...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Zhou, Lei Liu, Yang Li, Qiang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9798350337020

Zero-shot learning (ZSL) is an important but challenging task in computer vision that aims to identify unseen classes without matching training samples. Current cutting-edge ZSL methods based on locality focus on acquiring the explicit locality of distinguishing characteristics, which could face a lack of adequate supervision at the class attribute level. This paper introduces a novel approach called IAC, which aims to learn Implicit Attribute Composition for ZSL. This method is more comprehensive compared to attribute localization that solely focuses on class-level attribute supervision. IAC utilizes subspace representations that efficiently capture the inherent structure of high-dimensional image features. Then, we learn implicit attribute composition through subspace representation learning. The superiority of the proposed IAC compared to the state-of-the-art is demonstrated through sufficient experiments conducted on three commonly used ZSL datasets, CUB, SUN, and AwA2. © 2023 IEEE.

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PCB Defect Detection Method Based on G-YOLOv8

PCB Defect Detection Method Based on G-YOLOv8

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Gao, Chao Bai, Mengting Lian, Xiaoqin Hu, Xiangyu Han, Shuangshuang Chen, Shichao Beijing Technology and Business University School of Computer and Artificial Intelligence Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Chinese Academy of Sciences Beijing Huairou Academy of Parallel Sensing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China

ISBN: (纸本)9798350376739

In the electronics industry, PCB is crucial for ensuring the product quality. However, these defects of PCB generally have the characteristics of complex backgrounds, small sizes and similar features, resulting in the issues of high computational complexity or low detection precision of PCB defect detection algorithms based on deep learning. To resolve the aforementioned concerns, this paper proposed a lightweight PCB defect detection model named G-YOLOv8 which was based on the YOLOv8. The G-YOLOv8 can effectively reduce the computational complexity by integrating the Ghost Conv into the Backbone and Neck networks. Furthermore, the G-C2f module was introduced into Neck network to strengthen the capability of G-YOLOv8 to perceive the feature information at different scales and semantic levels, thus improving the detection performance of the model for the PCB defect. Meanwhile, the performance of G-YOLOv8 model was evaluated on the public data set. The experimental results show that the parameter number of G-YOLOv8 is 2.6 million, which is decreased by 89.7% and 13.3% compared with SSD and YOLOv8, respectively. The computational load of G-YOLOv8 is 7.5 GFLOPs, which is decreased by 93.5% and 7.4% compared with SSD and YOLOv8, respectively. The mAP@0.5 of G-YOLOv8 is 94.4%, which is increased by 4.8% and 1.3% compared with SSD and YOLOv8, respectively. Hence, in the task of PCB defect detection, the G-YOLOv8 model can reduce the computational complexity while preserving a high detection precision, which can meet the requirements of PCB defect detection in industrial scenarios. © 2024 IEEE.

关键词： Electronics industry

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The Prospects of Multi-modal Pre-trained Models in Epidemic Forecasting 9th

The Prospects of Multi-modal Pre-trained Models in Epidemi...

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9th China National Conference on Big Data and Social Computing, BDSC 2024

作者： Fei, Jiaqiang Zhao, Pengfei Luo, Tianyi Wang, Jiaojiao Cao, Zhidong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819758029

Accurate epidemic forecasting is imperative in light of the increasing global threat posed by epidemic diseases. Presently, researchers predominantly approach this challenge as a time-series prediction task and have proposed a variety of effective methodologies. However, two critical issues deserve thorough consideration. Firstly, the transmission of infectious diseases is a profoundly intricate process influenced by a multitude of factors, encompassing complex dynamics and propagation mechanisms. In contrast to the multifaceted nature of real-world scenarios, prevailing modeling approaches often exhibit undue simplification, resulting in imprecision and a deficit of interpretability. Secondly, existing methodologies excessively rely on data and require substantial adaptation for new infectious disease scenarios. The endeavor to conduct rapid and precise simulations and predictions during the early stages of transmission is challenging due to limited data availability. Thus, we aim to explore the feasibility and effective strategies of leveraging multi-modal pre-trained technology for epidemic forecasting. This approach offers the potential to comprehensively grasp the transmission process from multi-view perspectives and acquire richer representations by integrating multi-modal data. Moreover, through pre-trained, this methodology captures overarching patterns and principles of infectious disease transmission, facilitating swift and accurate predictions via fine-tuning, particularly in scenarios of sparse early-stage transmission data. We discuss the prospects of multi-modal pre-trained models in epidemic forecasting and provide feasible strategies for constructing the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Development of a Method for Generating Extremely High Reference Motion Speeds of Mechatronic Objects Moving Alone or in a Group

Development of a Method for Generating Extremely High Refere...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Gubankov, Anton Gornostaev, Igor Institute of Automation and Control Processes Feb Ras Robotics Laboratory Vladivostok Russia M.D. Ageev Institute of Marine Technology Problems Feb Ras Laboratory of Robotic Systems Vladivostok Russia Admiral G.I. Nevelskoy Maritime State University Laboratory of Marine Unmanned Aviation and Marine Aviation Systems Vladivostok Russia

ISBN: (纸本)9798350395044

The paper presents the results of developing a method for generating reference control signals that describe the desired law of movement of various mechatronic objects (unmanned aerial vehicles, mobile land and underwater robots, etc.) along smooth trajectories specified by parametric splines. In order to maintain high dynamic accuracy of control of these mechatronic objects, including when solving group control problems, it is proposed to adjust their reference movement speed, taking into account the specified restrictions on the developed accelerations. Taking into account these restrictions is necessary to reduce the reference speed in sections of the trajectory with increased curvature. To implement the proposed approach, a method was developed and a system was synthesized, the performance of which was confirmed by the results of numerical simulation. The presented system is designed to operate in conditions where the reference trajectory of a mechatronic object is unknown in advance and is formed (or adjusted) directly during the mission without the possibility of preliminary calculations. Also, this system does not require complex calculations and can be implemented on the basis of low-power on-board computers. © 2024 IEEE.

关键词： Motion planning

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Decomposing Visual and Semantic Correlations for Both Fully Supervised and Few-Shot Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1658-1668页

作者： Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Most image classification methods are designed to either boost the classification accuracies with abundant supervision, or cope with the shortage of supervision information. This is often achieved by using the visual and semantic information of other sources. However, these methods use the visual and semantic information within the same class independently, leaving their intrinsic correlations unconsidered. Objects and disturbing components as well as noise exist on the same image. Besides, semantic representations also contain noisy information. To solve the problems mentioned above, we propose a novel method for both fully supervised image classification and few-shot image classification by decomposing visual and semantic correlations. We jointly explore the intrinsic correlations of visual and semantic information of images within the same class. For each class, we decompose its visual and semantic correlations using low-rank and sparse constraint respectively. The decomposed low-rank parts character the intrinsic correlations of images can be used in a linear transformation way. Using the decomposed parts of each class, Classification can be achieved by reconstruction error minimization. We conduct experiments on several datasets for both fully supervised image classification and few-shot image classification. Experimental results well show the effectiveness of the proposed method. © 2020 IEEE.

关键词： Semantics

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Calculation of Compositional Maps of Sea Surface Temperature from IR Measurements of US Satellites

Calculation of Compositional Maps of Sea Surface Temperature...

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2024 International Conference on Ocean Studies, ICOS 2024

作者： Aleksanina, Marina Dyakov, Sergey Ershova, Anastasia Far Eastern Branch The Russian Academy of Sciences Satellite Monitoring Laboratory of IACP FEB RAS Institute of Automation and Control Processes Vladivostok Russia Institute of Mathematics and Computer Technologies Far Eastern Federal University Vladivostok Russia

ISBN: (纸本)9798331532307

The work is devoted to approbation of a methodology for constructing and verifying composite maps of sea surface temperature (SST) with the preservation of thermal fronts. The equatorial part of the Northwestern Pacific Ocean region, characterized by weak temperature contrasts, significant clouds and high concentrations of water vapor, was chosen as the area for testing. Satellite and sub-satellite measurements were extracted from international databases. It is shown that the use of map provided by international databases based on a single satellite session requires additional cloud filtering. The developed composite maps are compared with their analogues. The advantage of the proposed approach is shown. A comparison of the temperatures of composite one-day maps and buoy measurements shows the accuracy of calculations within 0.6°C. The linear relationship has a slope coefficient close to one and a free term that does not exceed the accuracy of calculations. © 2024 IEEE.

关键词： Surface temperature

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Distributed Tracking control of Nonlinear Multi-agent systems Against False Data Injection Attacks 41

Distributed Tracking Control of Nonlinear Multi-agent System...

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第41届中国控制会议

作者： Yanhui Zhang Jian Sun Gang Wang Yong Xu the School of Automation and the Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology the Beijing Institute of Technology Chongqing Innovation Center IEEE

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,the distributed resilient tracking control problem is investigated for nonlinear multi-agent systems with false data injection(FDI) attacks and unknown *** FDI attacks are considered in communication network between ***,the neighbor's information received by the agent may be wrong due to the attacks,which will affect tracking consensus ***,the approximate property of fuzzy logic systems are utilized to estimate unknown nonlinear *** backstepping framework,we design a distributed resilient control scheme under asymmetric *** asymmetric saturation nonlinearity,an auxiliary system is constructed to simplify designed progress of the *** on Lyapunov stability theory,it is proved that all signals in the closed-loop system are semi-globally uniformly bounded,and the followers' outputs are driven to a neighborhood near the output of ***,simulation results are presented to illustrate the effectiveness of the proposed method.

关键词： Adaptive resilient control nonlinear MASs FDI attacks asymmetric saturation

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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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