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Chinese Control and Decision Conference, CCDC

作者： Zheng Yao Huaiyu Wu Yang Chen Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665478977

When designing a Wi-Fi indoor positioning system in large, real-world environments, the problem we have to face is that available access points of indoor location scenes are very extensive, and the signal interference is very serious, leading to weak positioning accuracy. Besides, too many access points also increase the computation complexity and the storage size of the fingerprint database. To remedy those problems, we propose a new ensemble model consisting of Gaussian Mixture Model (GMM) region classifier and Random Forest feature learner using best-discriminating access points optimization to enhance Wi-Fi indoor positioning real application. The GMM classifier is applied to get better regional classification, and for the same area, the Random Forest feature learner is trained to obtain best-discriminating access points optimization. To fully reflect the performance of the proposed method, experiments have been carried out in the real environment of indoor parking lots and the performance of the proposed algorithm was compared with the other three existing access points optimization methods. The results show that the proposed access points optimization ensemble model successfully enhances Wi-Fi indoor positioning real application of indoor parking lots.

关键词： Optimization methods Estimation Interference Fingerprint recognition Planning Reliability Wireless fidelity

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The generalized synchronization for double-layer delayed coupling complex networks by dynamic soft variable structure control method

The generalized synchronization for double-layer delayed cou...

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Chinese automation Congress (CAC)

作者： Kang Qian Zhihuan Chen Yanqin Sun Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

This study focuses on the synchronization problem of double-layer complex networks with time delayed coupling. Distinguish from most of previous works, a more general network model is introduced, where the number and the dimension of the nodes can be different between the drive network and the response network. This network model is more close to the practice. In order to obtain the synchronization of double-layer complex networks, an ingenious dynamic soft variable structure controller is proposed, where three sub-controllers and one adaptive observers are designed, and the stability of the controller is demonstrated by a strict mathematical proof. Numerical simulations verify the effectiveness of proposed strategy.

关键词： Couplings Complex networks Observers Drives Numerical simulation Stability analysis Mathematical models

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Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

arXiv

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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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Optimization of input shaping parameters based on genetic algorithm 4

Optimization of input shaping parameters based on genetic al...

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4th International Symposium on Power Electronics and Control Engineering, ISPECE 2021

作者： Lu, Xu Jia, Danping Zhang, Hualiang Zhao, Bingjie Zhang, Tao Liu, Yang Yan, Hongyu School of Artificial Intelligence Shenyang University of Technology Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781510650374

ISBN: (纸本)9781510650367

In view of the position control vibration problem of permanent magnet synchronous motor, the control model of permanent magnet synchronous motor is simulated by using Simulink software. At the same time, according to the structure of the system model, an input shaper is added to the input end of the system to suppress the vibration of the system. However, the input parameters of the shaper are highly dependent on the parameters of the system model, so it is usually necessary to accurately identify the system model. However, the identification of permanent magnet synchronous motor is often not accurate enough, which makes it difficult to determine the input parameters of the shaper. Aiming at the problem that the input shaper is too dependent on the system, a method of input shaper parameter optimization based on genetic algorithm is designed and verified by experiments. The results show that the parameters of the input shaper with better effect can be selected by genetic optimization, and the dependence of the input shaper on the system model is reduced. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer control systems

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The measuring ZMP of self-balancing exoskeleton robot is calibrated by using the neural network

The measuring ZMP of self-balancing exoskeleton robot is cal...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Xu, Yang Xiao, Yang Ma, Yue Zheng, Liangsheng He, Yong Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665436786

The exoskeleton robot is an auxiliary device to help the disabled people walk, and the self-balancing exoskeleton robot is one which is to keep balance without the assistance of external crutches. In order to keep the balance of the self-balancing exoskeleton robot, it is necessary to get the position of the Zero Moment Point by measuring the pressure of the footplate, and make the position of ZMP in range of supporting area. In this experiment, the footplate is used with the double-deck structure, this structure is compared with the single-deck structure, the double-dack structure will not lose the information of the collected ZMP without direct touch with the sensor, and it is lighter than another structure with dozens of sensors. But there is an inevitable structural coupling in the double-deck structure, which makes the ZMP have a large measurement error. In order to solve this problem, a novel idea is proposed, with the help of the powerful processing and learning capabilities of the neural network, four kinds of neural networks are used to calibrate measured position of ZMP so that reducing error of the measured ZMP. By comparing position of the actual ZMP before and after the calibration with the ideal position of ZMP and computing the errors to judge the effect of the calibration. Through experimental comparison, it is concluded that the different neural networks eliminate error of the measured ZMP in different extent. When the GRNN neural network is used to calibrate position of ZMP, the effect is the most ideal. © 2021 IEEE.

关键词： Errors

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Curved path planning based on 3D vision water immersion ultrasonic nondestructive testing 2

Curved path planning based on 3D vision water immersion ultr...

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2nd International Conference on Computer Vision and Data Mining, ICVDM 2021

作者： Chen, Zheng Wang, Jian Zhang, Hualiang School of Information Engineering Shenyang University of Chemical Technology Liaoning Shenyang110142 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Liaoning Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110169 China

Compared with traditional 2D image processing, 3D point cloud processing has become a hot technology in the industry, but there are few researches applied to nondestructive testing of curved workpieces. This article aims at the non-destructive testing of curved workpieces, using 3D point cloud and robotic arm for water immersion ultrasonic nondestructive testing. Aiming at the 3D point cloud with many outliers, using the commonly used filter in two-dimensional images-the guide filter, the algorithm is improved and used for the filtering and downsampling of the 3D point cloud, and the adjustment of the curved surface workpiece and the robot arm's pose is introduced. Finally, the workpiece was scanned by a robotic arm with a water immersion ultrasonic nondestructive testing system to prove the feasibility of the experiment and improve the detection efficiency of traditional nondestructive testing technology. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Robotic arms

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Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industrial Process

Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industria...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Yang, Shaojie Li, Peng Li, Shuai Zhou, Xiaofeng Jiang, Shanghong 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 IntelligentManufacturing Chinese Academy OfSciences Shenyang110169 China University of Chinese Academy OfSciences Beijing100049 China Information Center of Ansteel Mining Limited Company Anshan114031 China

ISBN: (纸本)9781665401159

Aiming at the characteristics of multi-scale, time series and nonlinear of the complex industrial process data, in order to extract process information more efficiently for improving the diagnosibility, a novel fault diagnosis method based on multi-scale long short term memory- fully convolutional networks (LSTM-FCNs) for industrial process is proposed. Firstly, variational mode decomposition (VMD) is used to obtain the multi-scale components of the process data. Then a parallel LSTM and FCNs optimized by batch normalization and random inactivation is built to extract features for each component. Finally, based on the fusion of multi-scale features, a fully connected layer is used for diagnosis. The LSTM-FCNs method is evaluated by the Tennessee Eastman (TE) benchmark industrial process, and compared with other methods, its effectiveness and superiority are verified. © 2021 IEEE.

关键词： Deep neural networks

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A Camera Calibration Method for Temperature Measurements of Incandescent Objects Based on Quantum Efficiency Estimation

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Sensors 2025年第10期25卷 3094-3094页

作者： Sala, Vittorio Vandone, Ambra Banfi, Michele Mazzucato, Federico Baraldo, Stefano Valente, Anna Automation Robotics and Machines Institute of Systems and Technology for Sustainable Production Department of Innovative Technologies University of Applied Science and Arts of Southern Switzerland via La Santa 1 Lugano6962 Switzerland

Highlights: What are the main findings? We present a novel method for calibrating the temperature-to-color relation (Planckian locus), enabling the measurement of incandescent object temperatures using a standard Bayer-pattern CMOS camera. Our method estimates the quantum efficiency of the entire optical system—including the camera, lens, and filters—using images of a black body furnace. What is the implication of the main finding? Our method enables high-resolution, high-frame-rate temperature measurement of glowing hot materials in the range of 700–2500 °C with standard cameras and lenses improving process control in metal, semiconductor, and ceramic industries, and accelerating the development of additive manufacturing. Our technique is designed for standard cameras and lenses, potentially enhancing environmental monitoring by estimating the temperature in wildfires and volcanic activity, improving predictive modeling and hazard assessment. High-temperature thermal images enable monitoring and controlling processes in metal, semiconductors, and ceramic manufacturing but also monitor activities of volcanoes or contrasting wildfires. Infrared thermal cameras require knowledge of the emissivity coefficient, while multispectral pyrometers provide fast and accurate temperature measurements with limited spatial resolution. Bayer-pattern cameras offer a compromise by capturing multiple spectral bands with high spatial resolution. However, temperature estimation from color remains challenging due to spectral overlaps among the color filters in the Bayer pattern, and a widely accepted calibration method is still missing. In this paper, the quantum efficiency of an imaging system including the camera sensor, lens, and filters is inferred from a sequence of images acquired by looking at a black body source between 700 °C and 1100 °C. The physical model of the camera, based on the Planck law and the optimized quantum efficiency, allows the calculation of the Planckian locus

关键词： Thermography (imaging)

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Research on Industrial Control Network Security Measurement Method Based on GRU-SVM

Research on Industrial Control Network Security Measurement ...

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Chinese automation Congress (CAC)

作者： Hengfei Chen Xianda Liu Chuan Sheng Hao Pan College of Information Engineering Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

Since the industrial Internet entered people's field of vision, industrial control network security has gradually received attention. Traditional network security measurement methods rely on the accuracy of situation sequences and historical situation values, and various factors in the network have a great influence on the results. However, compared with industrial control networks, the number and types of affected factors are less than those of the modern Internet. Therefore, based on the research on network security situational awareness, this paper proposes an industrial control network security measurement method model based on GRU-SVM. First, the data sets with threat type labels are classified, and then the similarity scoring mechanism and softmax function are introduced, to process the classification results. For the GRU-SVM classification module, the analysis of the simulation results shows that the convergence is better and the robustness is stronger. For the safety prediction measurement part, by comparing with different models, the mean absolute error and root mean square error are smaller and the reliability is higher.

关键词： Industrial control Simulation Measurement uncertainty Network security Predictive models Logic gates Robustness

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

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

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Prognostics and System Health Management Conference (PHM-Qingdao)

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

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.

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