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作者： Shuai Kang Zhiduo Tan Xinle Liu College of Mechanical and Electrical Engineering Beijing University of Chemical Technology Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

The seafloor-resident autonomous underwater vehicle AUV (SRAUV) is a new concept underwater vehicle for long-term observation of the seafloor. Monitoring the SRAUV bottom-sitting state is essential for its safety. In this paper, the dynamic model of the SRAUV during bottom-sitting operations is established at full and non-full bottom-sitting states respectively. The judgment basis for determining the SRAUV state is derived based on the established dynamic model. The CFD simulations of the SRAUV during bottom-sitting operations are used for evaluation.

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Organic Heterojunction Enabled Near 1% H2o2 Photoproduction with Ultrafast Charge Separation

SSRN

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SSRN 2024年

作者： Liu, Jingjing Hu, Qiushi Liu, Shang Lin, Meng Zhang, Xiaosa Xie, Ying Chen, Xihan Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Key Laboratory for Rare Earth Chemistry and Application of Liaoning Province College of Science Shenyang University of Chemical Technology Liaoning Shenyang China

Hydrogen peroxide (H2O2) finds wide application in disinfection, chemical production, and bleaching owing to its sustainability, stability, and ease of storage compared to hydrogen. However, the current industrial engineering practices for producing photocatalysts used in H2O2 generation face challenges such as low yields with organic catalysts and high costs associated with inorganic catalysts. In light of these challenges, we investigate a viable method utilizing a simple condensation reflux technique to synthesize pure organic heterojunction photocatalysts featuring a π-π stacked structure derived from graphitized carbon nitride (melem) and meso-tetrakis (4-carboxyphenyl) porphyrin. This approach enables efficient H2O2 production from water, seawater, and oxygen without the necessity of sacrificial agents. In this study, the synthesized catalyst demonstrates an impressive H2O2 production rate of 106 mmol/h.g.L under seawater conditions and 77.67 mmol/h.g.L under pure water conditions in ambient air. Following a reaction time of 4.5 hours, the H2O2 concentration reaches 0.88 wt% with a volume of 5.0 mL. Transient absorption spectroscopy validates the formation of a heterojunction between melem and porphyrin. Notably, this heterogeneous catalyst efficiently facilitates water oxidation and oxygen reduction while suppressing both H2O2 decomposition and subsequent photodecomposition reactions, thereby addressing the challenges associated with organic catalyst yield. © 2024, The Authors. All rights reserved.

关键词： Absorption spectroscopy

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key Parameters Prediction for intelligent Control and Energy Optimization Based on MIC-BWO-XGBoost 2

Key Parameters Prediction for Intelligent Control and Energy...

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2nd International Conference on Cyber-Energy Systems and intelligent Energy, ICCSIE 2024

作者： Lv, Changyao Wang, Yaqi Chen, Yudong Yu, Zhuo Hu, Guoliang Wu, Hongliang Li, Shuai Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai201324 China

ISBN: (纸本)9798350373318

In the aviation industry, anodic oxidation serves as a core process for surface modification of aluminum alloy components, albeit accompanied by high energy consumption. Accurate prediction of key parameters can offer decision support for the intelligent control and energy optimization of anodic oxidation. However, the limited sample size resulting from the high cost of data collection and the complex nonlinear patterns caused by the synergy of multiple factors pose a challenge in accurately predicting key parameters. Therefore, we propose a key parameters prediction method based on the Maximum Information Coefficient (MIC), Beluga Whale Optimization algorithm (BWO), and XGBoost algorithm. Considering the adverse impact of low correlation parameters on prediction accuracy, we first use MIC to extract features from the original data. Then, XGBoost is used to integrate multiple decision trees to ensure the strong fitting ability for nonlinear relationships. Compared to deep learning models, XGBoost performs well even in a limited sample size. In addition, considering XGBoost is prone to overfitting training data as an ensemble learning algorithm, we employ the BWO to optimize four key parameters of the model that are difficult to choose, thereby enhancing model's generalization. The model's performance is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with values of 0.04388, 0.09410, and 0.20730%, respectively. The experimental results demonstrate that the proposed method outperforms traditional approaches, enabling precise prediction of key parameters and holding significant value for the intelligent control and energy optimization of anode oxidation processes. © 2024 IEEE.

关键词： Anodic oxidation

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An Attention-LSTM-based Fault Diagnosis Method for Satellite Attitude Control System

An Attention-LSTM-based Fault Diagnosis Method for Satellite...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Siyuan Ma Sheng Gao Xiaozhong Zuo Zhengyang Lv Chenyang Zou State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China China-Portugal Belt and Road Joint Laboratory on Space & Sea Technology Advanced Research Shanghai China College of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This study proposes a fault diagnosis method based on attention-LSTM for the satellite attitude control system. First, the hidden correlation information between high-dimensional variables is exploited through the variable attention layer. Then, deep temporal features are extracted with using the LSTM network layer. Finally, the time attention layer is introduced to increase the weight of important temporal information, effectively avoiding the problem of being forgotten due to too long input sequence and too many deep temporal features. The identification accuracy of slow fault in satellite attitude control system can be improved. The method is validated using data from the satellite semi-physical simulation platform, which demonstrates the effectiveness of this method.

关键词： Fault diagnosis Analytical models Visualization Satellites Recurrent neural networks Correlation Attitude control

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Safe and Reliable Distributed Fault-Tolerant Formation Control for Quadrotor Swarms

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Hu, Yang Miao, Zhiqiang Wang, Yaonan Zhang, Hui Wang, Xiangke He, Wei Hunan University College of Electrical and Information Engineering Changsha410082 China Hunan University School of Robotics Changsha410082 China National University of Defense Technology College of Intelligence Science and Technology Changsha410073 China Beijing Information Science and Technology University School of Automation Institute of Artificial Intelligence China University of Science and Technology Beijing Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education Beijing100083 China

This paper addresses formation control of quadrotor swarms subject to external time-varying disturbances and actuator failures, while ensuring collision avoidance. Firstly, a complete dynamics model incorporating translational movement, rotation, motor, and actuator failures was developed for each quadrotor. Then, nominal controllers for the position and attitude subsystems are designed separately to achieve formation control under ideal conditions. A higher-order sliding mode (HOSM) observer is implemented to effectively estimate and compensate for external disturbances and actuator bias faults, thereby improving the stability of the system. Next, a potential energy function-based collision avoidance mechanism is implemented to ensure inter-quadrotor collision avoidance. Furthermore, a fault detection and fault-tolerant control(FTC) method is designed to estimate the lift coefficient of actuator partial failures online in real time by employing Newtons correction method, which ensures the safe and reliable flight of the system. The stability of the closed-loop system is analyzed using Lyapunov stability theory. Finally, numerical simulations and hardware-in-the-loop (HIL) experiment results are performed to validate the approach's effectiveness and performance in quadrotor swarm formation control. © 2025 IEEE. All rights reserved.

关键词： Potential energy functions

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Towards intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

arXiv

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arXiv 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin The Xiangjiang Laboratory Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Changsha410205 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China The Department of Electrical and Computer Engineering University of Concordia Montreal Canada The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada

In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However, there is falls short of simulating complex real-world ITS scenarios, primarily due to the lack of a digital twin implementation framework for characterizing interactions between pedestrians and vehicles at different locations in different traffic environments. In this article, we propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop. Specifically, SVFDT builds comprehensive pedestrian-vehicle interaction models by leveraging multi-source traffic surveillance videos. Its architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources;(ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions;and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime. We analyze key design requirements and challenges and present core guidelines for SVFDT’s system implementation. A testbed evaluation demonstrates its effectiveness in optimizing traffic management. Comparisons with traditional terminal-server frameworks highlight SV-FDT’s advantages in mirroring delays, recognition accuracy, and subjective evaluation. Finally, we identify some open challenges and discuss future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Event-triggered impulsive synchronization of heterogeneous neural networks

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Science China(Information Sciences) 2024年第2期67卷 331-332页

作者： Zhengxin WANG Chongfang JIN Wangli HE Min XIAO Guo-Ping JIANG Jinde CAO College of Science Nanjing University of Posts and Telecommunications Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing University of Posts and Telecommunications Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology School of Automation Nanjing University of Posts and Telecommunications School of Mathematics Southeast University Yonsei Frontier Lab Yonsei University

Synchronization is one of the most important dynamics in neural networks. Several scholars have studied the quasi-synchronization of heterogeneous neural networks [1],wherein external controllers play a significant role. Impulsive control, a kind of energy-saving control, is activated instantaneously only at specific discrete moments.

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Type Synthesis of Single-DOF Multi-Loop-Coupled Planar Continuum Morphing Mechanisms

Type Synthesis of Single-DOF Multi-Loop-Coupled Planar Conti...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Jieyu Wang Yuxiang Li Yingzhong Tian Fengfeng Xi Guangbo Hao Yinjun Zhao Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Aerospace Engineering Toronto Metropolitan University Toronto ON Canada School of Engineering Electrical and Electronic Engineering University College Cork Cork Ireland School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

This paper investigates a single degree-of-freedom (DOF) multi-loop-coupled planar continuum morphing mechanism (MPCMM), which can be used in broad application prospects in fields such as robotics, aerospace, and biomedical devices. The mechanism is proposed based on the generalized kinematic chains. The design process mainly includes determining the design requirements, selecting the generalized kinematic chains based on the design requirements, modifying the chains, and choosing the most appropriate chain to construct MPCMM. In addition, the vector equation method is used to analyze the mechanisms. Finally, the MPCMM is applied to a variable camber wing trailing edge.

关键词： Couplings Kinematics Vectors Robots

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Unsupervised Domain Adaptive Object Detection Based on Frequency Domain Adjustment and Pixel-Level Feature Fusion 12

Unsupervised Domain Adaptive Object Detection Based on Frequ...

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12th International Conference on CYBER technology in Automation, Control, and intelligent Systems, CYBER 2022

作者： Xu, Yanlong Fan, Huijie Pan, Hao Wu, Lianquan Tang, Yandong College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Department of Police Skills and Tactics Training Criminal Investigation Police University of China Shenyang110854 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

It is very risky to apply the knowledge learned by the model on the labeled dataset directly to a completely new dataset, especially the unlabeled data, as the gap between these two domains can lead to a severe drop in model performance, However, the difference in illumination, texture and background of the image is one of the difficult problems to solve. In this paper, We proposed a Frequency domain Adjustment module (FA) and Pixel-Level feature Fusion (PLF) block for unsupervised domain adaptation of different types of features. From a frequency domain perspective, we first transfer the style of unlabeled target domain data to labeled source domain data before training using a Fourier transform. The benefit of this strategy can narrow the illumination gap between the source domain and the target domain. Secondly, due to the lack of labels in the target domain data, the feature extraction effect is poor. We consider proposing an improved multi-scale feature fusion mechanism to integrate information. Our method is experimentally validated on multiple public adaptive public datasets, The experimental results show that this method can more accurately detect and recognize images in domain adaptive scenes, and it outperforms the mainstream methods of domain adaptive object detection. © 2022 IEEE.

关键词： Object detection

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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