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arXiv 2025年

作者： Xu, Xiangyu Zheng, Zhi Wang, Jin Chen, Yikai Huang, Jingyang Wu, Ruixin Yu, Huan Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Flapping-wing robots offer great versatility;however, achieving efficient multi-modal locomotion remains challenging. This paper presents the design, modeling, and experimentation of T3, a novel tailless flapping-wing robot with three pairs of independently actuated wings. Inspired by juvenile water striders, T3 incorporates bio-inspired elastic passive legs that effectively transmit vibrations generated during wing flapping, enabling ground movement without additional motors. This novel mechanism facilitates efficient multi-modal locomotion while minimizing actuator usage, reducing complexity, and enhancing performance. An SE(3)-based controller ensures precise trajectory tracking and seamless mode transition. To validate T3’s effectiveness, we developed a fully functional prototype and conducted targeted modeling, real-world experiments, and benchmark comparisons. The results demonstrate the robot’s and controller’s outstanding performance, underscoring the potential of multi-modal flapping-wing technologies for future aerial-ground robotic applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna grounds

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Traffic Sign Classification Using Deep and Quantum Neural Networks

arXiv

引用

arXiv 2022年

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2022, CC BY.

关键词： Traffic signs

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Traffic Sign Classification Using Deep and Quantum Neural Networks

TechRxiv

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TechRxiv 2022年

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

关键词： Traffic signs

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A Novel Hybrid Method for Fault Diagnosis of Industrial Equipment Based on Vibration Signals 14

A Novel Hybrid Method for Fault Diagnosis of Industrial Equi...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Tao, Sen Wang, Kai Zeng, Peng Yu, Tianda Wu, Hao Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110169 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Nuclear Power Institute of China Chengdu610213 China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China

ISBN: (纸本)9798350301359

Industrial equipment reliability is a critical issue that smart manufacturing systems must address. Implementation of online fault diagnosis for industrial equipment is a promising technology that can improve equipment reliability. Vibration analysis is a popular technique for equipment condition monitoring. For the non-linear and non-stationary features of industrial vibration signals, a hybrid model based on wavelet transform (WT), support vector machine (SVM), and grey wolf optimization algorithm (GWO) is proposed. The time-frequency domain features of fault signals are extracted by wavelet transform to solve the non-linearity of signals, and the obtained feature vectors are input into SVM for classification. Aiming at the problem of setting penalty coefficient and kernel function parameters, GWO is introduced to seek hyperparameters by using the hunting principle of wolves. In the experimental part, firstly, the comparison of the GWO-WT-SVM, GWO-SVM and SVM models was carried out. Secondly, Comparison with classical classification algorithms is made. Apparently, the GWO-WT-SVM model achieves higher accuracy than other models in bearing fault diagnosis. © 2023 IEEE.

关键词： component Fault diagnosis GWO PHM SVM

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Feedback-feedforward Signal control with Exogenous Demand Estimation in Congested Urban Road Networks

arXiv

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arXiv 2023年

作者： Pedroso, Leonardo Batista, Pedro Papageorgiou, Markos Control Systems Technology section Eindhoven University of Technology Netherlands Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal Dynamic Systems and Simulation Laboratory Technical University of Crete Chania Greece Faculty of Maritime and Transportation Ningbo University Ningbo China

To cope with uncertain traffic patterns and traffic models, traffic-responsive signal control strategies in the literature are designed to be robust to these uncertainties. These robust strategies still require sensing infrastructure to implement traffic-responsiveness. In this paper, we take a novel perspective and show that it is possible to use the already necessary sensing infrastructure to estimate the uncertain quantities in real time. Specifically, resorting to the store-and-forward model, we design a novel network-wide traffic-responsive strategy that estimates the occupancy and exogenous demand in each link, i.e., entering (exiting) vehicle flows at the origins (destinations) of the network or within links, in real time. Borrowing from optimal control theory, we design an optimal linear quadratic control scheme, consisting of a linear feedback term, of the occupancy of the road links, and a feedforward component, which accounts for the varying exogenous vehicle load on the network. Thereby, the resulting control scheme is a simple feedback-feedforward controller, which is fed with occupancy and exogenous demand estimates, and is suitable for real-time implementation. Numerical simulations for the urban traffic network of Chania, Greece, show that, for realistic surges in the exogenous demand, the proposed solution significantly outperforms tried-and-tested solutions that ignore the exogenous demand. © 2023, CC BY-NC-ND.

关键词： Traffic signals

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Event-Based Supervisor control for a Bascule Bridge

Event-Based Supervisor Control for a Bascule Bridge

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South Eastern European Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Dimitrios G. Fragkoulis Fotis N. Koumboulis Maria P. Tzamtzi Georgios L. Giannaris Department of Digital Industry Technologies School of Science Robotics Automatic Control and Cyber-Physical Systems Laboratory National and Kapodistrian University of Athens Euripus Campus Euboea Greece Core Department National and Kapodistrian University of Athens Euripus Campus Euboea Greece

ISBN: (数字)9798331504489

ISBN: (纸本)9798331504496

The automation of a bascule bridge, located in the Netherlands, is studied. The modeling of the system is worked out in the framework of Ramadge-Wonham and analyzed per automation component of the bridge. The desired performance of the bridge is expressed in the form of rules, that will be translated to five groups of sets of regular languages. Next, new groups of sets of regular languages will be introduced by the injection of appropriate uncontrollable events, in order to guarantee the controllability of the languages. Finally, an event-based supervisor scheme will be developed and the nonblockingness of the controlled automaton is proved.

关键词： Bridges Automation Design automation Event detection Social networking (online) Smart cities Programmable logic devices Automata SCADA systems Europe

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In Situ Measurement of the Kinetic Friction of Si Nanowires on SiC Substrates in SEM with a Microprobe 11

In Situ Measurement of the Kinetic Friction of Si Nanowires ...

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11th IEEE Annual International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2021

作者： Gao, Peng Zhou, Jie Zou, Tao Rong, Weibin State Key Laboratory of Robotics and Systems Harbin Institute of Technology HarbinHeilongjiang150080 China School of Electrical and Control Engineering Heilongjiang University of Science and Technology HarbinHeilongjiang150022 China

ISBN: (纸本)9781665425278

This research introduces a kinetic friction modeling and measurement method for silicon nanowires (NWs) and silicon carbide substrates. The NW was pushed to slide by the micro-force probe at a constant speed. Under the conditions of kinetic friction and deformation of the nanowires, the silicon nanowires are in equilibrium during the measurement. Under this condition, the value measured by the micro-force probe is the total kinetic friction force. The thermal insulation connector is used to isolate the heat conduction to the micro-force probe to avoid the influence of temperature rise on the measurement result. The Faraday cup was used to measure the surface current in the scanning electron microscope (SEM). The appropriate parameters of SEM were chosen according to the surface current to reduce the influence of the charging effect on the measurement results. The high sensitivity of the micro-force probe is used to ensure that there is no contact between the probe and the substrate, thereby avoiding the interference of the pre-load force. The entire experiment process was monitored in real-time to improve the stability of the measurement. The study in this paper found that the shear stress of silicon nanowires is 1.05 \pm 0.32\text{MPa}. © 2021 IEEE.

关键词： Silicon carbide

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED

A High-Precision Edge Detection Algorithm Based on Improved ...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Yeborui Yuan Wenyuan Chen Jie Tang Jia Yang School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology.

关键词： Automation Image edge detection Image processing Neural networks control systems Iterative algorithms Intelligent systems

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SAD-TIME: a Spatiotemporal-fused network for depression detection with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor

arXiv

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arXiv 2024年

作者： Wang, Han-Guang Hou, Hui-Rang Jin, Li-Cheng Xu, Chen-Yang Zhang, Zhong-Yi Meng, Qing-Hao Zhang, Yong-Hui Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China Department of Psychiatry Tianjin Anding Hospital Mental Health Center Tianjin Medical University China

Background and Objective: Depression is a severe mental disorder, and accurate diagnosis is pivotal to the cure and rehabilitation of people with depression. However, the current questionnaire-based diagnostic methods could bring subjective biases and may be denied by subjects. In search of a more objective means of diagnosis, researchers have begun to experiment with deep learning-based methods for identifying depressive disorders in recent years. Methods: In this study, a novel Spatiotemporal-fused network with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor (SAD-TIME) is proposed. SAD-TIME incorporates an automated nodes’ common features extractor (CFE), a spatial sector (SpS), a modified temporal sector (TeS), and a domain adversarial learner (DAL). The CFE includes a multiscale depth-wise 1D-convolutional neural network and a time-interval embedding generator, where the unique information of each channel is preserved. The SpS fuses the functional connectivity with the distance-based connectivity containing spatial position of EEG electrodes. A multi-head-attention graph convolutional network is also applied in the SpS to fuse the features from different EEG channels. The TeS is based on long short-term memory and graph transformer networks, where the temporal information of different time-windows is fused. Moreover, the DAL is used after the SpS to obtain the domain-invariant feature. Results: Experimental results under tenfold cross-validation show that the proposed SAD-TIME method achieves 92.00% and 94.00% depression classification accuracies on two datasets, respectively, in cross-subject mode. Conclusion: SAD-TIME is a robust depression detection model, where the automatedly-generated features, the SpS and the TeS assist the classification performance with the fusion of the innate spatiotemporal information in the EEG signals. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Robust Finite-Time Containment of Networked Heterogeneous Nonlinear systems with Intermittent Measurement Only

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IEEE Transactions on Network Science and Engineering 2025年

作者： Tian, Biao Zhang, Hao Cui, Peiyu Wang, Zhuping Yan, Huaicheng Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai201804 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China

The robust finite-time containment problem of fully heterogeneous multiagent systems with uncertainties is often challenging, especially when only intermittent output measurement is used. To address the issue posed by the nonidentical system dynamics of multiple leaders, which makes the traditional distributed estimator form for the convex hull inapplicable, a distributed finite-time estimator is constructed for each follower to extract the system matrices and states of leaders. Then, in the output-triggering setting, a finite-time extended state observer driven by intermittent output measurement is developed to reconstruct the uncertainties and unmeasurable states of followers. Meanwhile, the non-continuous output measurement will lead to the differentiation of virtual control laws undefined. To solve this issue, a filtering compensation scheme-based finitetime controller via the backstepping technique is developed for nonlinear followers, ensuring practical finite-time stability (PFTS) of the closed-loop system. It is shown that the proposed algorithm steers each follower into the preset convex combination spanned by the positions of multiple leaders. The capability of the exploited control protocol is verified through simulations and experiments. 2013 IEEE.

关键词： Backstepping

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