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第42届中国控制会议

作者： Dapeng Xu Xiaobo Lu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University

Fall events have unique dynamic features,which are not fully utilized by existing fall detection *** on video understanding,we propose Fall-LSTM to learn such features pertinently without additional ***-LSTM is composed of CNN-LSTM framework and two excitation modules i.e.,Spatial Attention Module(SAM) and Temporal Location Module(TLM).SAM provides spatial constraints on motion for feature layers through foreground extraction and spatial *** emphasizes frames with high probability of fall events to LSTM by inferring the rate and trend of motion in *** results show that our proposed modules significantly improve the performance of LSTM model,outperforming the state-of-theart methods on two public Fall Detection Datasets i.e.,Le2 i and UR.

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Hmdo: Markerless Multi-View Hand Manipulation Capture with Deformable Objects

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SSRN 2023年

作者： Xie, Wei Yu, Zhipeng Zhao, Zimeng Zuo, Binghui Wang, Yangang Southeast University The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

We construct the first markerless deformable interaction dataset recording interactive motions of the hands and deformable objects, called HMDO (Hand Manipulation with Deformable Objects). Our motivation is the current lack of hand and deformable object interaction datasets, as 3D hand and deformable object reconstruction is challenging. Mainly due to mutual occlusion, the interaction area is difficult to observe, and the visual features between hands and objects are entangled. To tackle this challenge, we propose a method to annotate our captured data. Our key idea is to collaborate with estimated hand features to guide the object pose estimation, and then optimize the deformation process of the object by analyzing the relationship between the hand and the object. Through comprehensive evaluation, the proposed method can reconstruct interactive motions of hands and deformable objects with high quality. In the future, this dataset could boost the research of learning-based reconstruction of deformable interaction scenes. © 2023, The Authors. All rights reserved.

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Sensorless control of SPMSM Based on Generalized Super-twisting Observer and Non-smooth controller

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

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第42届中国控制会议

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

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 nonsmooth controller is ***,a GSTO for back electromotive force(back-EMF) estimation is *** with the conventional super-twisting observer,the GSTO has a faster convergence rate and stronger robustness due to the additional *** a linear extended state observer(LESO) is adopted to estimate the position,speed,and lumped disturbance at the same ***,a non-smooth composite speed controller is designed by combining the disturbance feed-forward *** with the conventional PI speed controller,the non-smooth controller has a shorter settling time and a better disturbance rejection ***,the effectiveness of the proposed method is verified by simulation results.

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BPH-YOLOv5: Improved yolov5 based on biformer prediction head for small target cigatette detection

BPH-YOLOv5: Improved yolov5 based on biformer prediction hea...

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2023 Jiangsu Annual Conference on Automation, JACA 2023

作者： Zheng, Xinyi Lu, Xiaobo College of Software Engineering Southeast University Suzhou215123 China School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

ISBN: (纸本)9781839539848

Small object detection in the cigarette detection field is a recent popular task. As cigarette targets in surveillance are tiny, the object scale leads to a great challenge of object detection. To solve the issue mentioned above, we propose BPH-YOLOv5 based on YOLOv5. We add one more prediction head to detect different-scale objects, which is more sensitive to tiny objects using multi-scale features. Then we replace the original prediction heads with BiFormer Prediction Heads (BPH) to explore the prediction potential with an attention mechanism. Experiments showed that the mean average precision (mAP) of BPHYOLOv5 in the cigarette dataset reached 90.34%, which was 6.94% higher than that of YOLOv5-s prototype, 7.20% higher than that of YOLOv7, which are encouraging and competitive. Tests on the actual scenarios verified that the accuracy of smallscale target detection was significantly improved. © The Institution of Engineering & Technology 2023.

关键词： Forecasting

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A Nonsmooth Observer Based on Friction Model Compensation for Sensorless Permanent Magnet Synchronous Motor 42

A Nonsmooth Observer Based on Friction Model Compensation fo...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Zaishan Zhu, Wenwu Yu, Ke 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 Engineering and Automation Hefei University of Technology Hefei230009 China

ISBN: (纸本)9789887581543

In this paper, a nonsmooth observer considering friction compensation is proposed for the sensorless permanent magnet synchronous motor (PMSM) to estimate the rotor position and speed. Friction is one of the main factors affecting the tracking accuracy of the motor at low speed, especially in the back and forth motion of the motor. Firstly, based on the Stribeck friction model, a nonlinear friction model of the motor is established. Secondly, a nonsmooth observer with friction compensation is designed to estimate the position and speed of the rotor, and the lumped disturbance of the system at the same time. Further, the stability analysis of this nonsmooth observer is given. Finally, the effectiveness of the proposed method is verified by the simulation results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Sensorless control

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A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1188-1198页

作者： Chuan Sun Hongpeng Yin Yanxia Li Yi Chai School of Big Data&Software Engineering Chongqing UniversityChongqing 401331China Key Laboratory of Complex System Safety and Control Ministry of EducationChongqing UniversityChongqing 400044China

The localized faults of rolling bearings can be diagnosed by its vibration impulsive ***,it is always a challenge to extract the impulsive feature under background noise and non-stationary *** paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary *** overcome the effects harmonic and noise components,we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission *** address the shortcomings of conventional sparse representation under the changeable operation environment,we propose an approach that combines K-clustering with singular value decomposition(K-SVD)and split-Bregman to extract impulsive components *** experiments on synthetic signals and real run-to-failure signals,the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed ***,a comparison with the state-of-the-art methods is illustrated,which shows that the proposed approach can provide more accurate detected impulsive signals.

关键词： Dictionary learning impulsive signals detection Kclustering with singular value decomposition(K-SVD) minimum entropy deconvolution rolling bearing signal processing

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Non-smooth Speed control of Switched Reluctance Motor Based on Generalized Proportional Integral Observer

Non-smooth Speed Control of Switched Reluctance Motor Based ...

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第42届中国控制会议

作者： Qingzhao Yin Zheng Tian Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper investigates the speed regulation control of switched reluctance motor(SRM) *** improve the antidisturbance performance of SRM,a composite non-smooth control strategy is ***,the structure of SRM is analyzed,and a simplified nonlinear model is obtained based on a segmented representation of the varying phase inductance.A virtual control function is introduced to represent the nonlinear part of the torque equation,whose inverse function is cascaded to linearized the nonlinear ***,a generalized proportional integral observer(GPIO) is constructed to estimate the lumped disturbance of the system,which is used for feedforward compensation ***,a composite speed controller is designed based on a combination of finite time proportional feedback and feed-forward compensation based on GPIO(FTP+GPIO).The speed error closed-loop system can be regarded as a first-order finite time control system with bounded *** analysis shows that the proposed scheme can improve the anti-disturbance performance of the closed-loop *** effectiveness of the proposed method is verified by simulation ***,it is compared with proportional feedback combining feed-forward compensation(P+GPIO) method and proportional integral(PI) control method.

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Pylon Reconstruction Based on NeRF

Pylon Reconstruction Based on NeRF

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International Conference on Electronics Information and Emergency Communication

作者： Zijian Hu Chenxing Wang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

In this paper, we propose a pylon reconstruction method based on Neural Radiance Fields (NeRF) technology. The advantage of this method lies in its ability to reconstruct pylons from images, with a multi-view model maintaining good performance even under complex lighting and occlusion conditions. We demonstrate the process of using NeRF to reconstruct electric power towers through experiments. During the experimental process, we collect images of electric power towers from different environments and angles, then apply NeRF to process these images, using deep learning to learn and generate a network model capable of expressing the scene. The experimental results show that our method can complete the reconstruction of scenes with only image inputs, and the reconstructed models have a certain accuracy in terms of geometric details and textures.

关键词： Surface reconstruction Image segmentation Image resolution Shape Poles and towers Lighting Reconstruction algorithms

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An online drilling rate of penetration(ROP) optimization method 41

An online drilling rate of penetration(ROP) optimization met...

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第41届中国控制会议

作者： Tuo Wang Chao Gan Weihua Cao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Drilling rate of penetration(ROP) optimization is crucial due to its vital role in increasing the drilling efficiency and decreasing the non-productive *** existing methods seldom pay attention to updating model parameter during drilling,which is actually important for ROP optimization under dynamical and uncertain downhole *** this paper,an online drilling ROP optimization method is proposed via three stages(drilling data pre-processing,online ROP modelling and online optimal operational parameters optimization).In the first stage,drilling data is pre-processed by resampling and Savitzky-Golay *** this,support vector regression and moving-window strategy are introduced together to establish an online ROP model in the next ***,particle swarm optimization algorithm is applied in the online ROP optimization framework to obtain the optimal operational parameters in the last *** results indicated the effectiveness of the proposed method.

关键词： Rate of penetration Drilling optimization Online optimization Moving-window strategy

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Traffic Signal control for Large-Scale Road Networks Based on Deep Reinforcement with PSR

Traffic Signal Control for Large-Scale Road Networks Based o...

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Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Zhicheng Zhou Hui Zhang Ya Zhang School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper investigates the problem of Traffic Signal control (TSC) in large-scale road networks. In extensive road networks, it is customary to define each intersection as an agent, however, the issue of partial observability is particularly prominent. In this paper, Predictive State Representation (PSR) is employed to address the challenge of partial observability in large-scale multi-agent systems. A Multi-agent Deep Reinforcement Learning (DRL) model based on PSR called PSR-XLight is proposed in Large-Scale TSC systems. Multi-agent PSR is conducted with centralized training and independent filtering which overcome the challenge of prohibitive computations when the number of agents is large. Parameters sharing is adopted between each agent’s PSR model to enhance learning efficiency and facilitate utilization in large-scale multi-agent environments. Each agent undergoes independent DRL training and execution while parameters sharing is adopted. Experiments are conducted on real-world road networks and a large-scale road network comprising 1000 intersections.

关键词： Training Filtering Roads Computational modeling Deep reinforcement learning control systems Observability

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