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Dust Distribution Study at the Blast Furnace Top Based on k-Sε-u_(p)Model

IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 121-135页

作者： Zhipeng Chen Zhaohui Jiang Chunjie Yang Weihua Gui Youxian Sun the School of Automation Central South UniversityChangsha 410083China the School of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

The dust distribution law acting at the top of a blast fumace(BF)is of great significance for understanding gas flow distribution and mitigating the negative influence of dust particles on the accuracy and service life of detection *** harsh environment inside a BF makes it difficult to describe the dust *** paper adresses this problem by proposing a dust distribution k-Sε-u_(p)model based on interphase(gas-powder)*** proposed model is coupled with a k-Sεmodel(which describes gas flow movement)and a u_(p)model(which depicts dust movement).First,the kinetic energy equation and turbulent dissipation rate equation in the k-Sεmodel are established based on the modeling theory and single Green-function two scale direct interaction approximation(SGF-TSDIA)***,a dust particle mnovement u_(p)model is built based on a force analysis of the dust and Newton's laws of ***,a coupling factor that descibes the interphase interaction is proposed,and the k-Sε-u_(p)model,with clear physical *** mathematical logic,and adequate generality,is *** results and o-site verification show that the k-Sε-u_(p)model not only has high precision,but also reveals the aggregate distribution features of the dust,which are helpful in optimizing the installation position of the detection equipment and imnproving its accuracy and service life.

关键词： Blast furnace(BF) dust movement interphase interaction modeling theory turbulent flow two-scale direct interaction approximation(TSDIA)

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Design of a Set Prediction Based Oriented Detector for Specialty Paper Making Fiber Identification 5

Design of a Set Prediction Based Oriented Detector for Speci...

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5th International Conference on Artificial Intelligence and Electromechanical automation, AIEA 2024

作者： Hua, Citao Liu, Yiqi South China University of Technology School of Automation Science and Engineering Guangdong Guangzhou China South China University of Technology National Engineering Research Center of Papermaking and Pollution Control Guangdong Guangzhou China

ISBN: (纸本)9798350366174

Proper orientation detection of paper making fiber is an important task for quality monitoring and control in specialty paper production. However, the slender nature of the fiber objects and their dense and interlaced distribution in the paper production make it impossible for a general-purpose oriented detector to accurately detect all fibers accurately. In this paper, we propose a two-stage rotated object detection method, termed Set Oriented Detector, which is able to cope with oriented detection scenarios where the detection target is dense, slender and interlaced distribution. Firstly, we propose the Set Oriented Region Proposal Network, which can make set predictions by generating set proposal for a single anchor and uses Set Oriented Non-Maximum Suppression to obtain dense oriented proposals. Secondly, we employ a bounding box to enhance the edge offset method in the second stage to further improve the detection accuracy for slender targets. Finally, we conduct experiments and evaluations on a specialty paper fiber dataset, demonstrating that our method can effectively improve the detection accuracy of dense, slender and interlaced distributed fiber targets. © 2024 IEEE.

关键词： Computer vision Dense detection Oriented detector Set prediction Slender detection

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Study on Inertia-Resistant Disturbance Speed control of Permanent Magnet Synchronous Motor Based on Exponential Integral Time-Varying Sliding Mode 2nd

Study on Inertia-Resistant Disturbance Speed Control of Perm...

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Conference Proceedings of 2022 2nd International Joint Conference on Energy, Electrical and Power engineering

作者： Ni, Shilin Xie, Fang Wang, Houying School of Electrical Engineering and Automation Anhui University Hefei China National Engineering Laboratory of Energy-Saving Motor & Control Technology Anhui University Hefei China

ISBN: (纸本)9789819943333

To enhance the dynamic performance of speed control when the moment of inertia changes during the operation of permanent magnet synchronous motor (PMSM), this paper investigates an anti-inertia disturbance method based on exponential integral time-varying sliding mode control (EITSMC). First, the paper analyzes the relationship between moment of inertia and motor speed using the mathematical analytical model of PMSM. Secondly, to track the change of the inertia moment immediately, the model reference adaptive system (MRAS) is applied to estimate the inertia change online and improve its tracking accuracy. Thirdly, To solve the issues of unstable motor speed under the sudden change of inertia, the sliding mode control method of exponential integration time variable is proposed, and the introduction of this method can not only better enhance the stability of motor speed, but also improve the fast performance of motor speed. Then, the identifiable moment of inertia is refreshed in real time to the exponentially integrated time-varying SMC, which realizes the adaptive speed control of the PMSM and satisfies the dynamic speed performance of the motor when the inertia changes. Eventually, the feasibility and effectiveness of the proposed method are verified by experiments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Speed control

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Pantograph Structure Anomaly Detection Based on Computer Vision 6

Pantograph Structure Anomaly Detection Based on Computer Vis...

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6th International Conference on Electronics Technology, ICET 2023

作者： Liu, Libo Liu, Quanli Wang, Wei Yu, Zichen Zhao, Xiaoguang Dalian University of Technology School of Control Science and Engineering Dalian China Dalian Seasky Automation Co. Ltd Dalian China

ISBN: (纸本)9798350337693

Pantograph is the only equipment for trains to obtain power. As the core component of the train power supply system, real-time and accurate detection of the pantograph structure state is of great significance to ensure the safety of train operation. Aiming at the shortcomings of current pantograph structure anomaly detection method, such as high cost, narrow application range and strong dependence on abnormal data, this paper designs a pantograph structure anomaly detection method based on computer vision technology, which has a wide range of application and does not rely on abnormal data. Firstly, the improved YOLOv5s object detection model is used to locate the pantograph area in the vehicle pantograph surveillance camera in real time. Then, the semantic segmentation method based on UNet is used to accurately segment the pantograph structure. Finally, the state of the pantograph structure is judged by calculating the similarity with the normal state of the pantograph structure. The experimental results show that the detection method can realize the detection of pantograph structural anomalies with high accuracy and speed. © 2023 IEEE.

关键词： Object detection

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An Improved YOLOv5 Algorithm for Elderly Fall Detection 12

An Improved YOLOv5 Algorithm for Elderly Fall Detection

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12th International Conference on Innovation, Knowledge, and Management, ICIKM 2023

作者： Wu, Han Zhao, Xueling Qiao, Jianghe Che, Yanqiu School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9798350303315

Accidental falls are one of the major threats to the elderly population. Older adults who are not caught in time after a fall may miss the best time to be rescued. We propose an improved YOLOv5 fall detection algorithm in this paper. Firstly, we use SIoU Loss instead of CIoU Loss to improve the speed of training and the accuracy of inference. Secondly, the SimAM attention mechanism is introduced into the YOLOv5 model to further improve the model's performance. Experiments show that the mAP@0.5 of the improved YOLOv5 fall detection model reaches 96.7%, which is 2.4% higher than the original YOLOv5. The overall detection effect is better than the original YOLOv5 model and other network algorithms. The algorithm used in this paper can detect fall behavior promptly, which largely alleviates the risk of death or long-term treatment. © 2023 IEEE.

关键词： Fall detection

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Nonlinear Dynamic System Based on SAE-LDS Model for Fault Diagnosis 13

Nonlinear Dynamic System Based on SAE-LDS Model for Fault Di...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Tang, Xiaochu Zhang, Yi Zhang, Xinmin Song, Zhihuan School of Automation Shenyang Aerospace University Liaoning Province Shenyang110136 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Zhejiang Province Hangzhou310027 China

ISBN: (纸本)9798350361674

Dynamic, uncertainty and nonlinearity are three very important characteristics of complex industrial process data. In this paper, a method of fault detection and classification based on stacked autoencoder-linear dynamic system (SAE-LDS) model is proposed. This method aims to simultaneously consider multiple characteristics of industrial process data for fault detection and classification. Firstly, the deep learning algorithm SAE is used to mine the nonlinear characteristic of process data. On this basis, the SAE model is embedded into the LDS model. The LDS model is used to further extract the dynamics and uncertainty, so as to establish a SAE-LDS model that can comprehensively consider multiple characteristics of data. Finally, the SAE-LDS model is applied to the TE process, compared with the traditional method for fault detection and fault classification. The experimental results show that SAE- LDS model has good effectiveness. © 2024 IEEE.

关键词： data-driven fault classification fault detection linear dynamic system stacked autoencoder

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Optimal Design of Torque Ripple of External Rotor Permanent Magnet Synchronous Motor Based on Particle Swarm Optimization 2nd

Optimal Design of Torque Ripple of External Rotor Permanent ...

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Conference Proceedings of 2022 2nd International Joint Conference on Energy, Electrical and Power engineering

作者： Wang, Houying Xie, Fang Ni, Shilin School of Electrical Engineering and Automation Anhui University Hefei China National Engineering Laboratory of Energy-Saving Motor & Control Technology Anhui University Hefei China

ISBN: (纸本)9789819943333

In order to reduce the torque ripple of outer rotor frameless permanent magnet synchronous motor, this paper adopts the structure of skewed slots to optimize the design of PMSM (permanent magnet synchronous motor). First, the equivalent magnetization method is used to analyze the key parameters that affect the torque ripple of the motor. Secondly, in view of the problem that the torque caused by the skewed slots is greatly reduced, the angle of the skewed slots is studied in this paper, and the structural parameters of the skewed slots are determined, so that the torque ripple is greatly reduced while the torque is slightly reduced. Thirdly, this paper establishes a fitting model based on the sample data generated by the key parameters and introduces particle swarm optimization algorithm to optimize the key parameters of the fitting model. Finally, joint simulation is used to verify the effectiveness of the optimization results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Torque

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Automatic Schizophrenia Detection from Scalp EEG Using CNN with Wide Convolutional Kernel and ELM 5

Automatic Schizophrenia Detection from Scalp EEG Using CNN w...

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5th Asia Symposium on Image Processing, ASIP 2023

作者： Wei, Chun Qin, Yingmei Liu, Jing Jiang, Lijie Che, Yanqiu Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9798350323429

Detection of schizophrenia is of great significance in clinical practice. In conventional machine learning methods, manual feature extraction is required, which is a hard and time-consuming task. This paper proposes a hybrid model consisting of a convolutional neural network (CNN) with a wide convolution kernel and an extreme learning machine (ELM). By changing the size of the convolution kernel to form a suitable wide convolution kernel, and automatically extracting and optimizing the features of the scalp electroencephalogram (EEG), the performance of the model is effectively improved and accurate detection of schizophrenia is achieved. The input of this model is the original multi-channel EEG picture after short-time Fourier transform, and the output are two types of classification labels for schizophrenia and healthy people. In the end, the detection accuracy of schizophrenia reached 95.59%. This model has a powerful advantage in detecting schizophrenia. © 2023 IEEE.

关键词： Electroencephalography

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Torque Ripple Reduction of Permanent Magnet Synchronous Motor Based on Least Mean Square Algorithm 2nd

Torque Ripple Reduction of Permanent Magnet Synchronous Moto...

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Conference Proceedings of 2022 2nd International Joint Conference on Energy, Electrical and Power engineering

作者： Shen, Mengyuan Xie, Fang Zhang, Wenyu Zhang, Jinqiang School of Electrical Engineering and Automation Anhui University Hefei China National Engineering Laboratory of Energy-Saving Motor & Control Technology Anhui University Hefei China

ISBN: (纸本)9789819943333

Current harmonic is one of the main causes of torque ripple in permanent magnet synchronous motor PMSM). Considering this problem, an adaptive filter is designed based on least square algorithm (LSM). Firstly, in order to better analyze the problem, this paper establishes the mathematical model of current harmonics, and establishes the mathematical relationship between current harmonics and torque. Secondly, the principle of the adaptive algorithm and the influence of the convergence coefficient on the filter are analyzed. Thirdly, an adaptive filter for current loop LMS algorithm is designed in synchronous rotating coordinate system. Finally, an experimental platform for harmonic suppression of PMSM is built to verify the effectiveness and feasibility of this method through experimental comparison. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Adaptive filters

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Research on Multi-operating control Strategy of Vehicle Motor Based on ALO 2nd

Research on Multi-operating Control Strategy of Vehicle Moto...

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Conference Proceedings of 2022 2nd International Joint Conference on Energy, Electrical and Power engineering

作者： Zhang, Wenyu Xie, Fang Shen, Mengyuan Zhang, Jinqiang School of Electrical Engineering and Automation Anhui University Hefei China National Engineering Laboratory of Energy-Saving Motor & Control Technology Anhui University Hefei China

ISBN: (纸本)9789819943333

This paper studies an optimized control method of Ant lion algorithm (ALO) to optimize the dynamic response of vehicle permanent magnet synchronous motor under different working conditions, and then to improve the robust adaptability of the control system to disturbances and parameter changes in the range of full speed. Firstly, according to the current and torque matching, the correlation between the torque and the DQ axis current is analyzed, and the influence of the change of the DQ axis current on the controller is explained. Then, according to different errors, the ant lion algorithm is adopted to improve the controller parameters. Finally, simulation is set up to compare the control performance of different approaches to verify the effectiveness of this approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： controllers

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