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arXiv 2021年

作者： Wang, Chongzhi Shao, Haibin Li, Dewei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

The high-dimensional generalization of the one-dimensional Kuramoto paradigm has been an essential step in bringing about a more faithful depiction of the dynamics of real-world systems. Despite the multi-dimensional nature of the oscillators in these generalized models, the interacting schemes so far have been dominated by a scalar factor unanimously between any pair of oscillators that leads eventually to synchronization on all dimensions. As a natural extension of the scalar coupling befitting for the one-dimensional case, we take a tentative step in studying numerically and theoretically the coupling mechanism of 2 × 2 real matrices on two-dimensional Kuramoto oscillators. One of the features stemmed from this new mechanism is that the matrix coupling enables the two dimensions of the oscillators to separate their transitions to either synchronization or desynchronization which has not been seen in other high-dimensional generalizations. Under various matrix configurations, the synchronization and desynchronization of the two dimensions combine into four qualitatively distinct modes of position and motion of the system. We demonstrate that as one matrix is morphed into another in a specific manner, the system mode also switches correspondingly either through continuous or explosive transitions of the order parameters, thus mimicking a range of behaviors in information science and biology. Copyright © 2021, The Authors. All rights reserved.

关键词： Synchronization

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Volume Measuring Method for Forming Additive Manufacturing Based on SGBM

Volume Measuring Method for Forming Additive Manufacturing B...

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Chinese Automation Congress (CAC)

作者： Ruidong Xie Haichuan Yang Yingmin Yi Qimeng Shi keying Wang Xiaolin Fan Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

For volume measurement problems in the processes of additive manufacturing, a volume measurement method is proposed through using the SGBM algorithm in this paper. In this method, the internal and external parameters of the camera are calibrated by the binocular vision system. And the image processes by SGBM are performed for the image acquired by binocular stereo vision system. The processes are including filtering, correction and stereo matching to obtain the disparity information of the object to be tested in the left and right cameras. The volume of the work-piece to be tested can be obtained by the calculation that measured the three-dimensional coordinate information through the disparity information. The experimental results illustrate that the method is of certain reliability and accuracy for volume measurement.

关键词： Cameras Calibration Volume measurement Three-dimensional printing Mathematical model Measurement by laser beam Three-dimensional displays

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The Laser Point Detecting Method Based on Faster R-CNN

The Laser Point Detecting Method Based on Faster R-CNN

引用

Chinese Automation Congress (CAC)

作者： Ruidong Xie Haichuan Yang Yingmin Yi Qimeng Shi keying Wang Chaoyang Zheng Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Aiming at issues of low accuracy and poor anti-interference ability in laser point detecting, a target detection method based on Faster R-CNN was proposed. The laser point position detecting method is realized by calibrating the laser point data set, initializing the weight coefficient and the training network model. Experiments illustrate that the proposed method is more accurate in cases of strong noise such as the light changing, the background changing and the smoke filling the air. The accuracy of the method is 98.1%, as a better detection effect and generalization ability.

关键词： Feature extraction Laser modes Training Proposals Laser noise Convolution Three-dimensional printing

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Topology Inference for Network systems: Causality Perspective and Non-asymptotic Performance

arXiv

引用

arXiv 2021年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Topology inference for network systems (NSs) plays a crucial role in many areas. This paper advocates a causality-based method based on noisy observations from a single trajectory of a NS, which is represented by the state-space model with general directed topology. Specifically, we first prove its close relationships with the ideal Granger estimator for multiple trajectories and the traditional ordinary least squares (OLS) estimator for a single trajectory. Along with this line, we analyze the non-asymptotic inference performance of the proposed method by taking the OLS estimator as a reference, covering both asymptotically and marginally stable systems. The derived convergence rates and accuracy results suggest the proposed method has better performance in addressing potentially correlated observations and achieves zero inference error asymptotically. Besides, an online/recursive version of our method is established for efficient computation or time-varying cases. Extensions on NSs with nonlinear dynamics are also discussed. Comprehensive tests corroborate the theoretical findings and comparisons with other algorithms highlight the superiority of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Trajectories

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An Approximated Domination Relationship based on Binary Classifiers for Evolutionary Multiobjective Optimization

An Approximated Domination Relationship based on Binary Clas...

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Congress on Evolutionary Computation

作者： Hao Hao Aimin Zhou Hu Zhang Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China

Preselection is an important strategy to improve evolutionary algorithms’ performance by filtering out unpromising solutions before fitness evaluations. This paper introduces a pre-selection strategy based on an approximated Pareto domination relationship for multiobjective evolutionary optimization. For each objective, a binary relation between each pair of solutions is constructed based on the current population, and a binary classifier is built based on the binary relation pairs. In this way, an approximated Pareto domination relationship can be defined. When new trial solutions are generated, the approximated Pareto domination is used to select promising solutions, which shall be evaluated by the real objective functions. The new preselection is integrated into two algorithms. The experimental results on two benchmark test suites suggest that the algorithms with preselection outperform their original ones.

关键词： Filtering Computational modeling Sociology Training data Evolutionary computation Approximation algorithms Linear programming

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Distributed Stabilization of Signed Networks via Self-loop Compensation

arXiv

引用

arXiv 2021年

作者： Shao, Haibin Pan, Lulu The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

This paper examines the stability and distributed stabilization of signed multi-agent networks. Here, positive semidefiniteness is not inherent for signed Laplacians, which renders the stability and consensus of this category of networks intricate. First, we examine the stability of signed networks by introducing a novel graph-theoretic objective negative cut set, which implies that manipulating negative edge weights cannot change a unstable network into a stable one. Then, inspired by the diagonal dominance and stability of matrices, a local state damping mechanism is introduced using self-loop compensation. The self-loop compensation is only active for those agents who are incident to negative edges and can stabilize signed networks in a fully distributed manner. Quantitative connections between selfloop compensation and the stability of the compensated signed network are established for a tradeoff between compensation efforts and network stability. Necessary and/or sufficient conditions for predictable cluster consensus of compensated signed networks are provided. The optimality of self-loop compensation is discussed. Furthermore, we extend our results to directed signed networks where the symmetry of signed Laplacian is not free. The correlation between the stability of the compensated dynamics obtained by self-loop compensation and eventually positivity is further discussed. Novel insights into the stability of multi-agent systems on signed networks in terms of self-loop compensation are offered. Simulation examples are provided to demonstrate the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Feedback

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Procedure Recognition by Knowledge-Driven Segmentation in Robotic-Assisted Vitreoretinal Surgery

Procedure Recognition by Knowledge-Driven Segmentation in Ro...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhen Li Yawen Deng Qiang Ye Weihong Yu Haoxiang Qi Yaliang Liu Zhangguo Yu Gui-Bin Bian School of Electronic and Information Engineering Tongji University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China School of Mechatronic Engineering Beijing Institute of Technology Beijing China Department of Ophthalmology Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Internal limiting membrane (ILM) peeling is a vital vitreoretinal surgery procedure. However, due to the thickness of just 1-2 micrometers and the intricacies associated with its varying density and adhesion, the difficulty of manipulation exceeds the physiological limits of human perception and operation. Surgical robot is characterized by high precision and stability. However, navigating intricate intraocular environments and handling minuscule high-precision areas remain enormous challenges. These include issues of uneven lighting, field-of-view loss, and motion blur. This paper proposed a perception method named ‘Multimodal Surgical Process Recognition based on Domain Knowledge and Segmentation (MSPR-DKS),’ designed to address these challenges and provide input for the precise control of robots. Moreover, a comprehensive dataset focused on ILM peeling during macular hole surgeries was established. Experimental results underscore the efficacy of this approach, with segmentation accuracies exceeding 99.27% for instruments and macular holes and an average accuracy of 98.97% in recognizing surgical processes. This study paves the way for leveraging domain knowledge and image segmentation to improve robot-assisted manipulation of soft tissues in ophthalmology.

关键词： Image segmentation Visualization Accuracy Medical robotics Motion segmentation Surgery Lighting

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Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations

arXiv

引用

arXiv 2022年

作者： Liu, Qi Yang, Bo Wang, Zhaojian Zhu, Dafeng Wang, Xinyi Ma, Kai Guan, Xinping The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

Due to the different losses caused by various photovoltaic (PV) array faults, accurate diagnosis of fault types is becoming increasingly important. Compared with a single one, multiple PV stations collect sufficient fault samples, but their data is not allowed to be shared directly due to potential conflicts of interest. Therefore, federated learning can be exploited to train a collaborative fault diagnosis model. However, the modeling efficiency is seriously affected by the model update mechanism since each PV station has a different computing capability and amount of data. Moreover, for the safe and stable operation of the PV system, the robustness of collaborative modeling must be guaranteed rather than simply being processed on a central server. To address these challenges, a novel asynchronous decentralized federated learning (ADFL) framework is proposed. Each PV station not only trains its local model but also participates in collaborative fault diagnosis by exchanging model parameters to improve the generalization without losing accuracy. The global model is aggregated distributedly to avoid central node failure. By designing the asynchronous update scheme, the communication overhead and training time are greatly reduced. Both the experiments and numerical simulations are carried out to verify the effectiveness of the proposed method. © 2022, CC BY.

关键词： Failure analysis

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An Integrated Neural Unmanned system for Multi-Domain Perception and Decision-Making

An Integrated Neural Unmanned System for Multi-Domain Percep...

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IEEE International Conference on Robotics and Biomimetics

作者： Qiming Liu Neng Xu Jinpeng Zhang Hesheng Wang Department of Automation Shanghai Jiao Tong University Shanghai China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai China Second Academy of China Aerospace Science and Industry Corporation (CASIC) Beijing China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665481106

Most recent intelligent systems study the perception or policy modules independently but ignore the inter-connection between them, which results in low information efficiency and high network redundancy, in the end, leads to the incomplete utilization of network potential and poor system-level end-to-end intelligence. This paper proposes an integrated end-to-end network that adapts to multi-domain inputs and multi-function outputs. “Integration” is defined in this paper in mainly three aspects: network structure level, optimization level, and functional level. The integrated network architecture is capable of fusing multiple raw sensory inputs and outputting multiple perceptions and decisions simultaneously. We first implement the integrated system in a virtual simulation environ-ment and investigate the domain transformation methods to real world. Comprehensive experiment results in both virtual and real environments show that our proposed system can behave normally under exploration, target-driven navigation and target shooting tasks.

关键词： Training Point cloud compression Target tracking Navigation Robot kinematics Redundancy Robot sensing systems

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A method of laser point temperature detection based on CNN

A method of laser point temperature detection based on CNN

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Chinese Automation Congress (CAC)

作者： Ruidong Xie Haichuan Yang Yingmin Yi Qimeng Shi keying Wang Jiabin Huang Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

There has been a direct relationship between the temperature of the laser point and the quality of the casting in the process of the 3D printing. In the paper, a method based on convolutional neural network (CNN) was proposed to estimate the temperature of the laser point. The collected temperature data used were trained by the deep-learning method. A new structure of the model was proposed on the part of the CNN model, which improved from original LeNet. The process of the prediction for the testing set was carried out through the new model. The unknown temperature in the testing set can be estimated. The experimental result illustrates that the proposed method is satisfactory.

关键词： Laser modes Temperature measurement Training Temperature distribution Surface emitting lasers Testing Measurement by laser beam

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