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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022

作者： He, Tianfang Xu, Aidong Wang, Kai Zeng, Peng Laboratory of Industrial Control Network and System State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Industrial Control Network and System State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781839538360

Blockages of pressure transmitter sensing line are a recurring problem in nuclear plants. The traditional solution is to purge them with nitrogen periodically, which is time consuming and radiation intensive. It is thus desirable to monitor the status of sensing lines on-site. The noise analysis method is a promising method for sensing line health monitoring. However, the existing noise analysis methods, which evaluate the sensing line status by analyzing time domain or frequency domain features of process noise signal, are susceptible to working conditions of pressure transmitters. Further, blockage degree of the sensing line can not be determined by existing methods. Therefore, this paper proposes a machine learning based noise analysis method for health monitoring of pressure transmitter sensing line, in which 1-D Convolutional Neural Network (CNN) is used to extract features from process noise signals. Experimental results show that the proposed method achieved satisfactory results in diagnosis accuracy, false alarm rate and robustness of the model under different working conditions. Further, blockage degree of sensing line can also be evaluated. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Frequency domain analysis

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A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy

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Plasma Science and Technology 2020年第7期22卷 11-20页

作者： Guodong WANG Lanxiang SUN Wei WANG Tong CHEN Meiting GUO Peng ZHANG State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016People’s Republic of China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016People’s Republic of China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People's Republic of China

In the spectral analysis of laser-induced breakdown spectroscopy,abundant characteristic spectral lines and severe interference information exist simultaneously in the original spectral ***,a feature selection method called recursive feature elimination based on ridge regression(Ridge-RFE)for the original spectral data is recommended to make full use of the valid information of *** the Ridge-RFE method,the absolute value of the ridge regression coefficient was used as a criterion to screen spectral characteristic,the feature with the absolute value of minimum weight in the input subset features was removed by recursive feature elimination(RFE),and the selected features were used as inputs of the partial least squares regression(PLS)*** Ridge-RFE method based PLS model was used to measure the Fe,Si,Mg,Cu,Zn and Mn for 51 aluminum alloy samples,and the results showed that the root mean square error of prediction decreased greatly compared to the PLS model with full spectrum as *** overall results demonstrate that the Ridge-RFE method is more efficient to extract the redundant features,make PLS model for better quantitative analysis results and improve model generalization ability.

关键词： laser-induced breakdown spectroscopy feature selection ridge regression recursive feature elimination quantitative analysis

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Effect of stagger angle on capillary performance of microgroove structures with reentrant cavities

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Science China(Technological Sciences) 2021年第7期64卷 1436-1446页

作者： SUN YaLong LIANG FuYe TANG Yong TANG Heng XI XiaoQian YANG Shu FU Ting Intelligent Manufacturing Engineering Laboratory of Functional Structure and Device in Guangdong School of Mechanical and Automotive EngineeringSouth China University of TechnologyGuangzhou510640China College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen518000China School of Machinery and Automation Wuhan University of Science and TechnologyWuhan430000China

Aluminum-based microgroove surfaces with reentrant cavities (MSRCs) were fabricated by two staggered ploughing/extrusion processes to meet the requirements of lightweight phase change heat transfer *** MSRCs with different stagger angles between cavities and microgrooves (MGs) were fabricated to study the effect of stagger angle on capillary *** rise and permeability tests were performed on all MSRCs and the results were compared with MGs having the same processing *** was found that MSRCs with smaller stagger angles have higher capillary height,and the maximum enhancement maintained by MSRC45 was about 54.84%.However,MSRCs with larger stagger angles were found to have higher ***,the capillary parameter K·ΔP_(cap)was used as a comprehensive index to evaluate these ***90 and MSRC75 obtained the largest K·ΔP_(cap)values without and with the effect of gravity considered,*** all MSRCs had a higher capillary rise height than MGs,smaller stagger angles (≤60°) seriously reduced the permeability of MSRCs and even resulted in smaller K·ΔP_(cap)value than that of MGs when calculated considering the effect of ***,MSRCs with larger stagger angles (≥75°) may be the optimum wicks due to the good balance between capillary pressure and permeability.

关键词： stagger angle capillary performance microgroove structure reentrant cavities

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Description and fault diagnosis in autonomous underwater vehicles based on ontologies

Description and fault diagnosis in autonomous underwater veh...

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2022 International Conference on Ocean Studies, ICOS 2022

作者： Alexander, Timoshenko Alexander, Zuev Eduard, Mursalimov Valeria, Gribova Alexander, Inzartsev Far Eastern Federal University Institute of Marine Technology Problems Feb Ras Laboratory of Intelligent Systems for Marine Robots Department of Automation and Robotics Vladivostok Russia Institute of Automation and Control Feb Ras Laboratory of Intelligent Systems Vladivostok Russia Institute of Marine Technology Problems Feb Ras Laboratory of Control Systems Vladivostok Russia

ISBN: (纸本)9781665473729

The paper proposes new ontologies for describing and diagnosing malfunctions of subsystems of autonomous underwater vehicles (AUV) as a part of the development of a previously created approach to the intelligent diagnosis of these vehicles using knowledge bases. To implement this approach, it is proposed to use two knowledge bases, one of which describes malfunctions, features for their identification and actions necessary to adapt to the negative consequences caused by the appearance of these malfunctions. The other database contains information about the main subsystems of the AUV, devices and replaceable modules, as well as the states they may be in. Knowledge bases are used in the work of the intelligent control-emergency system (ICES) on board the AUV, which allows for comprehensive functional diagnostics of all subsystems of the device, including payload modules, as well as adaptation to the consequences of emerging malfunctions, taking into account the actual cause-and-effect relationships between them. To test the system's operability, a prototype was developed. The results of the prototype simulation showed that ICES performs the tasks and can be implemented on the onboard computers of existing AUV using well-known programming technologies. © 2022 IEEE.

关键词： Autonomous underwater vehicles

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Joint Algorithm of Message Fragmentation and No-Wait Scheduling for Time-Sensitive Networks

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 478-490页

作者： Xi Jin Changqing Xia Nan Guan Peng Zeng Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016 Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China Department of Computing Hong Kong Polytechnic UniversityHong KongChina

Time-sensitive networks(TSNs)support not only traditional best-effort communications but also deterministic communications,which send each packet at a deterministic time so that the data transmissions of networked control systems can be precisely scheduled to guarantee hard real-time ***-wait scheduling is suitable for such TSNs and generates the schedules of deterministic communications with the minimal network resources so that all of the remaining resources can be used to improve the throughput of best-effort ***,due to inappropriate message fragmentation,the realtime performance of no-wait scheduling algorithms is ***,in this paper,joint algorithms of message fragmentation and no-wait scheduling are ***,a specification for the joint problem based on optimization modulo theories is proposed so that off-the-shelf solvers can be used to find optimal ***,to improve the scalability of our algorithm,the worst-case delay of messages is analyzed,and then,based on the analysis,a heuristic algorithm is proposed to construct low-delay ***,we conduct extensive test cases to evaluate our proposed *** evaluation results indicate that,compared to existing algorithms,the proposed joint algorithm improves schedulability by up to 50%.

关键词： Message fragmentation networked control system real-time scheduling time sensitive network

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Synthesis of Diagnostic Observers for Linear Systems Based on Optimal control Methods

Synthesis of Diagnostic Observers for Linear Systems Based o...

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2022 International Conference on control, automation and Diagnosis, ICCAD 2022

作者： Kabanov, Aleksey A. Zuev, Aleksandr V. Zhirabok, Aleksey N. Filaretov, Vladimir F. Department of Informatics and Control in Technical Systems Sevastopol State University Russia Laboratory of Intelligent Information Systems for Marine Robots Institute of Marine Technology Problems FEB RAS Russia Department of Automation and Robotics Far Eastern Federal University Russia Robotics Laboratory Institute of Automation and Control Processes FEB RAS Russia

ISBN: (数字)9781665497947

ISBN: (纸本)9781665497947

The paper considers the problem of fault identification in dynamic systems described by linear differential equations. To solve this problem, diagnostic observers constructed on the basis of optimal control methods are used. The proposed new method for the synthesis of such observers is based on the idea of introducing a new system with some auxiliary control which plays a role of an unknown vector function describing emerging faults. This control is proposed to be formed in such a way that it provides a minimum for the residual functional. Unlike popular approaches to solving the problem of fault identification based on observers operating in a sliding mode, the proposed method allows us to expand the class of systems for which the identification problem can be solved. It is known that the methods of sliding mode observers design impose certain restrictions on the systems under consideration. The suggested theory is illustrated by an example. The simulation results confirmed the operability of the diagnostic observers synthesized using the method proposed in the work. © 2022 IEEE.

关键词： Linear systems

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network 42

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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42nd Chinese control Conference, CCC 2023

作者： Wang, YiFei Wu, Tao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China System Integration and Industrial Robotics Laboratory Wuhan430074 China

ISBN: (纸本)9789887581543

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network (TL-DNN) was proposed in this paper. Its specific implementation steps include (1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor;(2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models (random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Finite element method

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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-Objective Black Hole Algorithm 42

A Weighting Factors Autotuning Strategy for Model Predictive...

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42nd Chinese control Conference, CCC 2023

作者： Zhou, Zixiang Wu, Tao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China System Integration and Industrial Robotics Laboratory Wuhan430074 China

ISBN: (纸本)9789887581543

The rational design of weighting factors in the cost function for finite control set model predictive torque control (FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor (PMSM) based on the adaptive multi-objective black hole algorithm (AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multi-objective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Cost functions

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An Optimization Method for SLAM Feature Point Extraction in Fuzzy Scenes 10

An Optimization Method for SLAM Feature Point Extraction in ...

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10th International Forum on Electrical Engineering and automation, IFEEA 2023

作者： Wang, Yunmeng Cui, Long Liu, Zaoming Liu, Nailong Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Beijing Institute of Control Engineering Science and Technology on Space Intelligent Control Laboratory Beijing China

ISBN: (纸本)9798350309379

In order to solve the problem that image blur is easily caused by camera shaking of micro-robots during surgical navigation, which leads to the accumulation of SLAM errors, this paper studies the feature point extraction optimization method based on deep learning in fuzzy scenes, proposes an improved defuzzification network, and finally applies the network to the front end of SLAM, and conducts experimental verification on the open data set TUM. The results show that the proposed algorithm significantly improves the accuracy of front-end feature extraction and matching. © 2023 IEEE.

关键词： environment perception feature extraction fuzzy detection Monocular vision SLAM Surgical navigation

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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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