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2024 China Automation Congress, CAC 2024

作者： Li, Wen Zhang, Xinhao He, Weitian Zou, Chaobin Yang, Qinchen Zhang, Fukai Cheng, Hong Wang, Cong School of Control Science and Engineering Shandong University Jinan China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368604

With the intensification of population aging and the increasing number of patients with lower limb dysfunction, the research and precise control of lower limb rehabilitation robots have become increasingly important. In this study, a sitting and lying lower limb exoskeleton robot model is developed based on the CoppeliaSim platform. For the nonlinear system of a two-degree-of-freedom single-leg robot with uncertainty, an improved PID control method based on deterministic learning is proposed. Under the condition of persistent excitation, this control method accurately identifies the unknown dynamic characteristics of the system by introducing a neural network, ensuring precise control of lower limb movements. Learning-based PID controllers are constructed using the gained knowledge to achieve fast, smooth tracking and greatly improve the safety of rehabilitation control. The results of simulation experiments show that the proposed control strategy in this paper is effective. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Real-time Detection of Safety Helmet and Workwear Based on YOLOv7 12

Real-time Detection of Safety Helmet and Workwear Based on Y...

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12th International Conference on Intelligent control and Information Processing, ICICIP 2024

作者： Tang, Piqi Xing, Jun Wang, Xinzhe Fan, Jianchao School of Information Science and Engineering Dalian Polytechnic University Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350308020

Industrial safety has become a topic of great concern, especially in the oil and gas industry, where employee health and safety are of paramount importance. The correct wearing of work clothes and helmets is vital to employee safety, but there are still employees who do not comply. To solve this problem, many companies have adopted manual supervision and strengthened safety education, but the results are not ideal. Therefore, researchers developed an automatic helmet detection system. The development of target detection algorithms has gone through the era of manual feature construction and deep learning, in which single-stage target detection algorithms perform well in terms of speed and performance. However, existing methods are difficult to balance between detection efficiency and accuracy. This research aims to develop an all-weather real-time safety helmet and work clothes monitoring system, using automatic data collection methods to complete the collection of on-site data, saving a lot of manual screening time. The produced data set contains scenes in various time periods, and then the YOLOv7 network is used to train the data set, and Tensorrt is used to optimize the model. Experimental results show that the YOLOv7-based safety helmet and work clothes detection method MAP@O.5:0.95 has an accuracy of 80.42% and a YOLOv7 + Tensorrt detection speed of 90.39 FPS. YOLOv3 and YOLOv5 were used as comparative experiments to prove that our proposed helmet and work clothes detection method can meet the requirements of real-time detection environment while also having high detection accuracy. © 2024 IEEE.

关键词： Accident prevention

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A Distributed Algorithm for Large-Scale Multi-Agent MINLPs 63

A Distributed Algorithm for Large-Scale Multi-Agent MINLPs

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Dong, Sheng Xia, Weiguo Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering China

ISBN: (纸本)9798350316339

In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collaboratively optimize the sum of local objective functions through their own continuous and/or discrete decision variables, subject to global coupling constraints and local constraints. The resulting Mixed-Integer Nonlinear Programmings (MINLPs) are NP-hard, non-convex, and large-scale. Therefore, this paper aims to design distributed algorithms to find feasible suboptimal solutions with a guaranteed bound. To this end, considering dual decomposition as an effective method to decompose large-scale constraint-coupled optimization problems, we first show, based on the convexification effects of large-scale MINLPs, that the primal solutions from the dual are near-optimal under certain conditions. This expands recent results in Mixed-Integer Linear Programmings (MILPs) to the nonlinear case but requires additional efforts on the proof. Utilizing this result to tighten the coupling constraints, we develop a fully distributed algorithm for the tightened problem, based on dual decomposition and consensus protocols. The algorithm is guaranteed to provide feasible solutions for the original MINLP. Moreover, asymptotic suboptimality bounds are established for the obtained solution. Finally, the efficacy of the method is verified through numerical simulations. © 2024 IEEE.

关键词： Mixed-integer linear programming

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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High strength and high work hardening rate in oxygen gradient Ti-15Mo alloy

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Journal of Materials science & Technology 2024年第31期198卷 56-62页

作者： Zhixin Wang Kai Yao Binkai Du Suyun He Xiaohua Min Shewei Xin Shijian Zheng Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology School of Materials Science and EngineeringHebei University of TechnologyTianjin 300130China School of Materials Science and Engineering Dalian University of TechnologyDalian 116024China Northwest Institute for Non-Ferrous Metal Research Xi'an 710016China

The low work hardening is a prominent deficiency for high-strength titanium(Ti)*** gradient design of oxygen content was adopted to realize the coupling deformation of{332}twinning and dislocation slip in the Ti-15Mo ... 详细信息

The low work hardening is a prominent deficiency for high-strength titanium(Ti)*** gradient design of oxygen content was adopted to realize the coupling deformation of{332}<113>twinning and dislocation slip in the Ti-15Mo *** oxygen gradient alloy exhibited an optimal balance of yield/tensile strength(700 and 848 MPa)and elongation(25%),with remarkable work hardening *** dominated dislocation slip deformation and the solution strengthening of oxygen atoms in the oxygen-rich region resulted in a remarkable increase in yield *** successive formation of{332}<113>twins and piled-up geometrically necessary dislocations around the twin boundaries in the oxygen-free region induced remarkable back stress strengthening,maintaining the high work hardening rate,which resulted in a stable increase in *** twins and dislocations formed at the crack tips effectively hindered the cracking behavior,avoiding premature *** present study provides a novel idea for designing oxygen layer-distributed Ti alloys,which further improves the strength-ductility tradeoff.

关键词： Titanium alloys Oxygen gradient Work hardening Heterogeneous material Ductility

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Mold breakout prediction based on computer vision and machine learning

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Journal of Iron and Steel Research International 2024年第8期31卷 1947-1959页

作者： Yan-yu Wang Qi-can Wang Yong-chang Zhang Yong-hui Cheng Man Yao Xu-dong Wang School of Materials Science and Engineering Dalian University of TechnologyDalian 116024LiaoningChina Key Laboratory of Solidification Control and Digital Preparation Technology Dalian University of TechnologyDalian 116024LiaoningChina

Breakout is the most serious production accident in continuous casting and must be detected and predicted by stable and reliable *** sticking region,which forms on the local copper plate and expanded into a"V"shape,is the typical precursor of ***,computer vision technology was exploited to visualize the temperature change rate of the copper plate based on the temperature signals from thermocouples;then,the static and dynamic features of the abnormal sticking region were ***,logistic regression and Adaboost models were used to study and identify these features,resulting in the development of a mold breakout prediction model based on computer vision and machine *** test results demonstrate that the proposed model can effectively distinguish anomalous temperature patterns and considerably reduce false alarms without any missing *** a result,the proposed method could offer valuable insights into the realm of abnormality detection and prediction during continuous casting process.

关键词： Abnormality detection Image processing Adaboost Logistic regression Continuous casting

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A Multi-scale Spatial Attention Network for Aquaculture Extraction Based on Time Series SAR Image

A Multi-scale Spatial Attention Network for Aquaculture Extr...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Wang, Pingzhuo Wang, Xinzhe Fan, Jianchao School of Information Science and Engineering Dalian Polytechnic University Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350384185

Synthetic aperture radar (SAR) is a kind of radar system with high resolution remote sensing imaging capability, which can be used to obtain remote sensing images at different times and under complex weather *** this study, a multi-scale spatial attention network based on time series aquaculture extraction (MSATUnet) is *** model uses multiple time series SAR images to extract sequence features, and uses a encoder decoder network design to extract spatial features of different *** the encoder part, the network calculates the spatial attention weight of the multi-scale feature map to improve the feature expression ability of the convolutional layer for the *** the design of convolutional long short-term memory (ConvLSTM) and self-attention mechanism in the deep layer of the network, the network can capture the temporal features of sequence images and improve the ability of global semantic information of the *** addition, in order to solve the problem of target adhesion, a edge loss function is introduced to adjust the edge *** experiment uses the time-series images taken by GF-3 satellite as the data input, and compares with other *** results show that MSATUnet outperforms traditional single-input networks and multiple time series models, which verifies the effectiveness of multiple time series in marine aquaculture extraction. © 2024 IEEE.

关键词： Aquaculture

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Multisource Remote Sensing Fusion for Marine Aquaculture Information Extraction 16

Multisource Remote Sensing Fusion for Marine Aquaculture Inf...

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16th International Conference on Advanced Computational Intelligence, ICACI 2024

作者： Wang, Zhuo Dong, Jie Wang, Xinzhe Fan, Jianchao Dalian Polytechnic University School of Information Science and Engineering Dalian China Dalian University of Technology School of Control Science and Engineering Dalian China

ISBN: (纸本)9798350308501

Floating raft aquaculture is an important part of mariculture, and the use of single source data is easy to inhibits the extraction of floating raft aquaculture, so the advantages of integrating multisource data are particularly important. However, multisource remote sensing fusion still has the problem of data information imbalance and feature redundancy. In this paper, a channel exchanging bottleneck attention network (CEBANet) is proposed to extract features from multisource remote sensing data, determine whether features are redundant by using the scaling factor of batch normalization (BN) layer, replace the current redundant features with another modal feature. Bottleneck attention module (BAM) and feature calibration module are added to improve the capability of feature extraction and receptive field processing. The CEBANet is optimized by the sparse constraint on channel exchanging condition and cross-entropy loss and consistency loss. Using GF-5 and GF-3 data from Jinzhou District of Dalian City, it is proved that the proposed model can realize the complementary advantages of multisource remote sensing data and improve the accuracy of extracting floating raft aquaculture areas. © 2024 IEEE.

关键词： Remote sensing

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Adaptive Trajectory Tracking control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1007-1021页

作者： Yunjun Zheng Jinchuan Zheng Ke Shao Han Zhao Hao Xie Hai Wang the School of Mechanical Engineering Hefei University of TechnologyHefei 230009China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthorn VIC 3122Australia the School of Civil Aviation Northwestern Polytechnical UniversityXi’an 710072China the Shanghai Aerospace Control Technology Institute Shanghai 201109 the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China the College of Science HealthEngineering and EducationMurdoch UniversityPerth WA6150Australia

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external *** paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for *** with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances *** advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier *** benefit is that the overestimation of control gain can be eliminated,resulting in chattering ***,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the *** stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

关键词： Adaptive sliding mode barrier function nonholonomic wheeled mobile robot(NWMR) trajectory tracking control

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Robust control for Interval Type-2 T-S Fuzzy Discrete Systems with Input Delays and Cyber Attacks

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Journal of Systems science & Complexity 2023年第4期36卷 1443-1462页

作者： LIU Chuang WU Jinxia YANG Weidong School of Automation University of Science and Technology BeijingBeijing 100083China College of Control Science and Engineering BoHai UniversityJinzhou 121001China College of Science Liaoning University of TechnologyJinzhou 121001China

This paper focuses on the robust control issue for interval type-2 Takagi-Sugeno(IT2 T-S)fuzzy discrete systems with input delays and cyber *** lower and upper membership functions are first utilized to IT2 fuzzy discrete systems to capture parameter *** considering the influences of input delays and stochastic cyber attacks,a newly fuzzy robust controller is ***,the asymptotic stability sufficient conditions in form of LMIs for the IT2 closed-loop systems are given via establishing a Lyapunov-Krasovskii ***,a solving algorithm for obtaining the controller gains is ***,the effectiveness of the developed IT2 fuzzy method is verified by a numerical example.

关键词： Asymptotic stability fuzzy robust control input delays IT2 T-S fuzzy discrete systems stochastic cyber attacks

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