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Pattern Matching of Industrial Alarm Floods Using Word Embedding and Dynamic Time Warping

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1096-1098页

作者： Wenkai Hu Xiangxiang Zhang Jiandong Wang Guang Yang Yuxin Cai School of Automation China University of GeosciencesWuhan 430074Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhan 430074 Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China

Dear Editor,This letter proposes a new pattern matching method based on word embedding and dynamic time warping(DTW)to identify groups of similar alarm ***,alarm messages are transformed into numeric values that represent alarms and also reflect the relationships between alarm ***,similarities between numerically encoded alarm flood sequences are calculated by DTW and groups of similar floods are identified via *** effectiveness of the proposed method is demonstrated by a case study with alarm&event data obtained from a public industrial simulation model.

关键词： Word Alarm DTW

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Exploring Image Generation for UAV Change Detection

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1061-1072页

作者： Xuan Li Haibin Duan Yonglin Tian Fei-Yue Wang Peng Cheng Laboratory Shenzhen 518000China IEEE State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083 Department of Automation University of Science and Technology of ChinaHefei 230027 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense TechnologyChangsha 410073China

Change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and ***,even the most advanced models require large amounts of data for model training and ***,sufficient labeled images with different imaging conditions are *** by computer graphics,we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated *** simulated dataset consists of six challenges to test the effects of dynamic background,weather,and noise on change detection ***,we propose an image translation framework that translates simulated images to synthetic *** framework uses shared parameters(encoder and generator)and 22×22 receptive fields(discriminator)to generate realistic synthetic images as model training *** experimental results indicate that:1)different imaging challenges affect the performance of change detection models;2)compared with simulated images,synthetic images can effectively improve the accuracy of supervised models.

关键词： Change detection computer graphics image translation simulated images synthetic images unmanned aerial vehicles(UAVs)

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Multi-task Learning Based Single Temporal Remote Sensing Image Change Detection 23

Multi-task Learning Based Single Temporal Remote Sensing Ima...

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Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition

作者： Peixuan Yang Yichao Cao Xiaobo Lu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China

ISBN: (纸本)9798400707988

Change Detection is a classic and fundamental task in remote sensing(RS), and has accomplish remarkable achievement on bitemporal image based methods. However, bitemporal images are very expensive and needs professional researchers to align and label the paired images. Furthermore, most bitemporal image based methods are only semantic-aware not instance-aware, which indicates these methods only use paired images’ semantic information. Thus, we propose a method that only needs unpaired RS images with semantic labels. In this paper, we introduce multi-task learning into change detection progress, which comprises four major parts: Boundary-Aware Module, Contextual-Aware Module, Segmentation Module, Change Detection Module. At first, we construct Boundary-Aware Module and Contextual-Aware Module to extract sharp boundary and dense contextual features. Then we feed the feature maps to Change Detection Module and Segmentation Module to accomplish the subtasks. The comprehensive experiments on a single temporal image dataset demonstrate the efficiency of our approach over other bitemporal image based methods, and the experimental results shows our approach is competitive with other methods while all methods was trained on bitemporal image dataset.

关键词： Change Detection Multi-task Learning Single Temporal Image

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Recent progress of green sorbents-based technologies for low concentration CO_(2) capture

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Chinese Journal of Chemical engineering 2021年第3期34卷 113-125页

作者： Yuanyue Zhao Yihui Dong Yandong Guo Feng Huo Fang Yan Hongyan He College of Mathematics Science Bohai UniversityJinzhou 121013China Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and EngineeringState Key Laboratory of Multiphase Complex SystemsInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China

The increased concentration of CO_(2) due to continuous breathing and no discharge of human beings in the manned closed space,like spacecraft and submarines,can be a threat to health and *** removal of low concentration CO_(2) from the manned closed space is essential to meet the requirements of long-term space or deep-sea exploration,which is an international frontier and *** liquids(ILs),as a widespread and green solvent,already showed its excellent performance on CO_(2) capture and absorption,indicating its potential application in low concentration CO_(2) *** this review,we first summarized the current methods and strategies for direct capture from low concentration CO_(2) in both the atmosphere and manned closed ***,the multi-scale simulation methods of CO_(2) capture by ionic liquids are described in detail,including screening ionic liquids by COSMO-RS methods,capture mechanism by density functional theory and molecular dynamics simulation,and absorption process by computational fluid dynamics ***,some typical IL-based green technologies for low concentration CO_(2) capture,such as functionalized ILs,co-solvent systems with ILs,and supported materials based on ILs,are introduced,and analyzed the subtle possibility in manned closed ***,we look forward to the technology and development of low concentration CO_(2) capture,which can meet the needs of human survival in closed space and proposed that supported materials with ionic liquids have great advantages and infinite possibilities in the vital area.

关键词： Low concentration CO_(2)capture Ionic liquids Manned closed spaces

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Computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Liang, Tailin Yuan, Zhaolin School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Beijing China Research Institute of Advanced Manufacturing Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350378658

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications. © 2024 IEEE.

关键词： Radiation hazards

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Development of a hydrodynamic model and the corresponding virtual software for dual-loop circulating fluidized beds

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Frontiers of Chemical Science and engineering 2021年第3期15卷 579-590页

作者： Shanwei Hu Xinhua Liu State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Dalian National Laboratory for Clean Energy Dalian 116023China

Dual-loop circulating fluidized bed(CFB)reactors have been widely applied in industry because of their good heat and mass transfer characteristics and continuous handling ***,the design of such reactors is notoriously difficult owing to the poor understanding of the underlying mechanisms,meaning it has been heavily based on empiricism and stepwise *** the gas-solid CFB system requires a quantitative description of the multiscale heterogeneity in the sub-reactors and the strong coupling between *** article proposed a general method for modeling multiloop CFB systems by utilizing the energy minimization multiscale(EMMS)principle.A full-loop modeling scheme was implemented by using the EMMS model and/or its extension models to compute the hydrodynamic parameters of the sub-reactors,to achieve the mass conservation and pressure balance in each circulation *** on the modularization strategy,corresponding interactive simulation software was further developed to facilitate the flexible creation and fast modeling of a customized multi-loop CFB *** research can be expected to provide quantitative references for the design and scale-up of gas-solid CFB reactors and lay a solid foundation for the realization of virtual process engineering.

关键词： multi-loop circulating fluidized bed mathematical modeling energy minimization multiscale virtual fluidization mesoscale structure

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Inverse Reinforcement Learning Integrated Reinforcement Learning for Single Intersection Traffic Signal Control 5

Inverse Reinforcement Learning Integrated Reinforcement Lear...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Gu, Shiyi Zhang, Tingting Zhang, Ya Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China Army Engineering University College of Command and Control Engineering Nanjing210017 China

ISBN: (纸本)9798350325294

Traffic Signal Control (TSC) optimization is vital for efficient traffic flow in urban intersections. Recent research has explored the application of Reinforcement Learning (RL) agents to tackle TSC challenges. However, the selection of an appropriate reward function in RL agent training still relies on subjective judgment and experience. To address this issue, an Inverse Reinforcement Learning (IRL) integrated RL algorithm is proposed. In the IRL learning section, expert trajectory data are collected and analyzed by Relative Entropy IRL (REIRL). The latent reward of expert policy is reconstructed and utilized in RL agent training process. In the RL control section, a Double Dueling Deep Q Network is applied under a cycle control framework. As verified in simulations, the introduction of expert experience improves the performance of the RL agent to the expert level and concurrently enables robustness to expert policy noises. © 2023 IEEE.

关键词： Reinforcement learning

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Sensorless Control of SPMSM Based on Generalized Super-Twisting Observer and Non-Smooth Controller 42

Sensorless Control of SPMSM Based on Generalized Super-Twist...

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42nd Chinese Control Conference, CCC 2023

作者： Chen, Dejin Yu, Ke Ding, Shihong Wang, Xiangyu Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Electrical and Information Engineering Zhenjiang212013 China

ISBN: (纸本)9789887581543

To enhance the estimation accuracy and dynamic performance of sensorless surface-mounted permanent magnet synchronous motor (SPMSM) drives, a sensorless control scheme based on generalized super-twisting observer (GSTO) and non-smooth controller is proposed. Firstly, a GSTO for back electromotive force (back-EMF) estimation is proposed. Compared with the conventional super-twisting observer, the GSTO has a faster convergence rate and stronger robustness due to the additional terms. Then a linear extended state observer (LESO) is adopted to estimate the position, speed, and lumped disturbance at the same time. Moreover, a non-smooth composite speed controller is designed by combining the disturbance feed-forward compensation. Compared with the conventional PI speed controller, the non-smooth controller has a shorter settling time and a better disturbance rejection ability. Finally, the effectiveness of the proposed method is verified by simulation results. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Controllers

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