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15th International Conference on Advanced Computer Theory and Engineering, ICACTE 2022

作者： Wang, Jingyang Zou, Tao Yang, Zhijia Wang, Hongrui Key Laboratory Of Networked Control System Institutes For Robotics And Intelligent Manufacturing Shenyang Institute Of Automation Chinese Academy Of Sciences Shenyang China Guangzhou University College Of Mechanical And Electrical Engineering Guangzhou China State Key Laboratory Of Robotics Key Laboratory Of Networked Control System Institutes For Robotics And Intelligent Manufacturing Shenyang Institute Of Automation Chinese Academy Of Sciences Shenyang China University Of Chinese Academy Of Sciences Beijing China

ISBN: (数字)9781665481809

ISBN: (纸本)9781665481809

This paper illustrates the application of an autonomous decentralized system introducing edge nodes in a smart factory. The nodes include atomic nodes, the most fundamental autonomic units constituting an autonomous decentralized system that are capable of independently extracting information from the data field and processing that information internally and simultaneously transmitting the processing results and other internal information to the data field proactively in a broadcast mode, where the transmitted information circulates in the data field. The group-based data field enables the sharing of information by atomic nodes. Grouped management nodes are the managers of the whole group, and edge nodes are the terminal intelligent control units of an intelligent autonomous decentralized system. Improvements in system decentralization, fault tolerance characteristics and flexibility of the system can lead to homogenization, autonomous control and autonomous coordination in complex manufacturing environments, as well as excellent online fault tolerance, online expansion and online maintenance of the system. In addition, it enhances the ever-changing and evolving control requirements by deconstructing the complexity in the system. © 2022 IEEE.

关键词： Fault tolerance

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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On Trajectory Tracking for an Under-actuated Helicopter 35

On Trajectory Tracking for an Under-actuated Helicopter

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第35届中国控制会议

作者： LI Junfang LI Yi Gao Qiang SUN Mingwei CHEN Zengqiang Tianjin Key Laboratory of Intelligent Robotics Nankai University Dept of Automation College of Computer and Control Engineering Nankai University Dept of Automation College of Automation Tianjin University of Technology

ISBN: (纸本)9781509009107

This paper tackles the problem of the attitude tracking control for the 3-DOF helicopter. A robust tracking control scheme is proposed based on active disturbance rejection control(ADRC). Aiming at the 3-DOF helicopter manufactured by Quanser Inc, a high order nonlinear ESO is designed to implement the real-time estimation and online compensation of the external disturbance. Simulation results show high performance and good robustness in the presence of the external disturbance.

关键词： helicopter active disturbance rejection controller high order ESO trajectory tracking control

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Determination of circular waits in multiple-reentrant flowlines based on machine-job incidence matrix 10

Determination of circular waits in multiple-reentrant flowli...

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2009 10th European control Conference, ECC 2009

作者： Petrovic, Tamara Bogdan, Stjepan Sindicic, Ivica Laboratory for Robotics and Intelligent Control Systems Department of Control and Computer Engineering University of Zagreb Zagreb Croatia Exor D.o.o Bani 72 Zagreb10000 Croatia

ISBN: (纸本)9783952417393

As the first step in resolving stability of a manufacturing system in the sense of deadlock, calculation of circular waits might involve time and memory consuming calculations. In this paper we propose an efficient method for determination of circular waits in multiple-reentrant flowline class (MRF) of manufacturing systems. We introduce machine-job incidence (MJI) matrix that can be obtained from Steward sequencing matrix and Kusiak machine-part incidence matrix. The method uses index vector and MJI matrix in order to allocate circular wait auxiliary vector. Resources involved in the circular wait are directly computed from the auxiliary vector. Although efficiency of the proposed method has been demonstrated on examples involving single- and multi- part type manufacturing workcells, the method can be used for other discrete event systems as well, as long as the system under study belongs to MRF class. © 2009 EUCA.

关键词： Manufacture

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Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well *** learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis ***,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational ***,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different ***,there is heterogeneity in field data,which can dramatically impair the diagnostic *** solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this ***,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC ***,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data ***,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the *** on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational *** the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix

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Event-triggered control for multi-agent system with a smart leader

Event-triggered control for multi-agent system with a smart ...

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Chinese intelligent Systems Conference, CISC 2017

作者： Zhao, Bin Liu, Zhongxin Chen, Zengqiang College of Computer and Control Engineering Nankai University Tianjin300350 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China

ISBN: (纸本)9789811064951

This paper studies the consensus problem of the leader-following system. First of all, we propose a new kind of leader, smart leader, by adding the velocity states of the followers to the leader’s control algorithm. We also use the event-triggered control to make the leader only use the feedback at necessary time. Then a distributed control law for both the leader and followers is designed. Compared with the multi-agent system with traditional leader, the system with a smart leader can reach the consensus more quickly, smoothly and have a smaller control energy consumption. Moreover, we propose a suffcient condition which can ensure the system can reach the consensus. Finally, some simulation examples are presented for illustration. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Gait-based Emotion Recognition Using Spatial Temporal Graph Convolutional Networks 4

Gait-based Emotion Recognition Using Spatial Temporal Graph ...

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4th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2021

作者： Yu, Shiyi Li, Xinde Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Nanjing China Guangdong Intelligent Robotics Institute Dongguan China

ISBN: (纸本)9781665406925

Most of the existing gait emotion recognition methods use spatial convolution or temporal convolution separately. Without integrating these two dimensions, it is easy to loss valid information for emotion analysis. In order to solve this problem, we propose a supervised learning method using Spatial Temporal Graph Convolutional Networks (ST-GCN) to classify emotions into positive, neutral and negative patterns. In addition, we made a new gait dataset called EmoGait3d including 742 real gait skeleton sequences extracted from videos, also annotated with emotion labels. According to the experiment results, our method achieves the accuracy of 92.2% which outperforms current state-of-art algorithms for gait emotion recognition. © 2021 IEEE.

关键词： Speech recognition

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Distributed fixed-time consensus algorithm for multiple nonholonomic chained-form systems

Distributed fixed-time consensus algorithm for multiple nonh...

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Chinese intelligent Systems Conference, CISC 2018

作者： Jiang, Yutao Liu, Zhongxin Chen, Zengqiang College of Computer and Control Engineering Nankai University Tianjin300353 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300353 China

ISBN: (纸本)9789811322877

In this paper, the fixed-time control algorithm is used to address the consensus problem for multiple nonholonomic chained-form systems. For the sake of analysis, a switching control strategy is introduced to solve the fixed-time consensus problem. Compared with the finite-time control algorithm, the convergence time of the fixed-time consensus protocol, can be guaranteed regardless of the initial conditions. Rigorous proof using Lyapunov theory shows that the sates of multiple nonholonomic chained-form systems can reach a consensus in a fixed time. To further illustrate the effectiveness of the control algorithm, a numerical simulation is given. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Distributed parameter control systems

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Accurate modulation of photoprinting under stiffness imaging feedback for engineering ECMs with high-fidelity mechanical properties

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Microsystems & Nanoengineering 2022年第3期8卷 291-301页

作者： Xin Li Huaping Wang Xinyi Dong Qing Shi Tao Sun Shingo Shimoda Qiang Huang Toshio Fukuda Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China The Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China School of Medical Technology Beijing Institute of TechnologyBeijing 100081China Intelligent Behavior Control Collaboration Unit RIKEN Center of Brain Science463-0003 NagoyaJapan

Engineered extracellular matrices(ECMs)that replicate complex in-vivo features have shown great potential in tissue *** hydrogel microstructures have been widely used to replace these native ECMs for physiologically relevant ***,accurate reproduction of the 3D hierarchical and nonuniform mechanical stffness inside one integrated microstructure to mimic the complex mechanical properties of native ECMs presents a major ***,by using digital holographic microscopy(DHM)-based stffness imaging feedback,we propose a novel closed-loop control algorithm to achieve high-accuracy control of mechanical properties for hydrogel microstructures that recapitulate the physiological properties of native ECMs with high *** photoprinting,the photocuring area of the hydrogel is divided into microscale grid areas to locally control the photocuring *** the assistance of a motorized microfluidic channel,the curing thickness is controlled with layer-by-layer *** DHM-based stiffness imaging feedback allows accurate adjustment of the photocuring degree in every grid area to change the crosslinking network density of the hydrogel,thus enabling large-span and high-resolution modulation of mechanical ***,the gelatin methacrylate was used as a typical biomaterial to construct the highfidelity biomimetic *** Young's modulus could be flexibly modulated in the 10 kPa to 50 kPa ***,the modulus gradient was accurately controlled to within 2.9 *** engineering ECM with locally different mechanical properties,cell spreading along the stff areas was observed *** believe that this method can regenerate complex biomimetic ECMs that closely recapitulate in-vivo mechanical properties for further applications in tissue engineering and biomedical research.

关键词： microstructure ECM curing

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Research on the Application of Mask2Former for Segmenting Small Mushroom Caps of Seafood Mushrooms 4

Research on the Application of Mask2Former for Segmenting Sm...

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4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Ren, Mengchao Du, Wanhe Yang, Shuzhen Yu, Tao Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China School of Intelligent Manufacturing and Control Engineering Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798331528850

In the task of monitoring mushroom growth characteristics, the mushroom cap is one of the most important features. To address the challenges faced by traditional instance segmentation algorithms in segmenting the caps of seafood mushrooms during the early stages of growth, such as unclear edges and low success rates. This paper proposes the application of the Mask2Former algorithm for instance segmentation of the caps of seafood mushrooms in their early differentiation stage. First, camera equipment was set up to capture images of the mushroom house for dataset creation. Then, the collected images were augmented through techniques such as flipping and adding noise. Finally, the processed images were input into the model, allowing Mask2Former to be compared with three other instance segmentation algorithms. Experimental results demonstrated that Mask2Former performed better in segmenting small mushroom caps during the early growth stage of seafood mushrooms, achieving an average precision (AP) of 96.731%, which effectively meets practical production needs. © 2024 IEEE.

关键词： Meats

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