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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Action recognition through fusion of sEMG and skeletal data in feature level

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Journal of Ambient Intelligence and Humanized Computing 2022年第9期13卷 4125-4134页

作者： Wang, Xinming Ding, Weili Bian, Shuangshuang Liu, Honghai Department of Automation Institute of Electrical Engineering Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Yanshan University 438 West of Hebei Avenue Haigang District Qinhuangdao066004 China School of Mechanical Engineering and Automation State Key Laboratory of Robotics and Systems Harbin Institute of Technology Shenzhen Nanshan District Shenzhen518055 China

Human action can be recognized through a unimodal way. However, the information obtained from a single mode is limited due to the fact that a single mode contains only one type of physical attribute. Therefore, it is motivational to improve the accuracy of actions through fusion of two different complementary modality, which are the surface electromyography (sEMG) and the skeletal data. In this paper, we propose a general framework of fusion of sEMG signals and skeletal data. Firstly, vector of locally aggregated descriptor (VLAD) was extracted from sEMG sequences and skeletal sequences, respectively. Secondly, features obtained from sEMG and skeletal data are mapped through different weighted kernels using multiple kernel learning. Finally, the classification results are obtained through the model of multiple kernel learning. A dataset of 18 types of human actions is collected via KinectV2 and Thalmic Myo armband to verify our ideas. The experimental results show that the accuracy of human action recognition are improved by combining skeletal data with sEMG signals. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Musculoskeletal system

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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A Manta Ray-Inspired Biosyncretic Robot with Stable Controllability by Dynamic Electric Stimulation

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Cyborg and Bionic Systems 2022年第1期2022卷 314-326页

作者： Chuang Zhang Yiwei Zhang Wenxue Wang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Inst itute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang 110159China Emerging Technologies Institute Department of Industrial Manufacturing Systems Engineering University of Hong KongPokfulamChina

Biosyncretic robots,which are new nature-based robots in addition to bionic robots,that utilize biological materials to realize their core function,have been supposed to further promote the progress in *** as the main operation mechanism relates to the robotic overall ***,biosyncretic robots actuated by living biological actuators have attracted increasing ***,innovative propelling modes and control methods are still necessary for the further development of controllable motion performance of biosyncretic *** this work,a muscle tissue-based biosyncretic swimmer with a manta *** propelling mode has been *** is more,to improve the stable controllability of the biosyncretic swimmer,a dynamic control method based on circularly distributed multiple electrodes(CDME)has been *** this method,the direction of the electric field generated by the CDME could be real-time controlled to be parallel with the adtuation tissue of the dynamic ***,the instability of the tissue actuation induced by the dynamic included angle between the tissue axis and electric field direction could be ***,the biosyncretic robot has demonstrated stable,controllable,and efective swimming,by adjusting the electric stimulation pulse direction,amplitude,and *** work may be beneficial for not only the development of biosyncretic robots but also other related studies including bionic design of soft robots and muscle tissue engineering.

关键词： Robot robot Dynamic

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Kinetostatic Model of Redundant Actuation Modes in a Lockable Variable Geometry Truss Manipulator Driven by Flexible Limbs 24th

Kinetostatic Model of Redundant Actuation Modes in a Lockabl...

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24th IFToMM China International Conference on Mechanism and Machine Science and engineering, IFToMM CCMMS 2024

作者： Zhao, Yinjun Xi, Fengfeng Hao, Guangbo Tian, Yingzhong Li, Long Wang, Jieyu School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China Department of Aerospace Engineering Toronto Metropolitan University TorontoON Canada School of Engineering and Architecture-Electrical and Electronic Engineering University College Cork Cork Ireland

ISBN: (纸本)9789819614639

Manipulators consisting of multiple large deformation limbs can flexibly change their shapes and separate actuation systems from mechanism body, making them particularly suitable for requirements of compact design space, and continuous and versatile deformation. However, the low structural stiffness restricts their practical applications. To enhance the stiffness, we have integrated lockable variable geometry truss (LVGT) supports within the manipulator design. Depending on the number of locked branches within the LVGT, the active flexible limbs may exhibit redundant actuation modes, meaning that the number of working actuators exceeds the DOF of the internal LVGT. The redundant actuation modes, while beneficial for enabling heavier load manipulation, poses challenges in mechanical analysis, necessitating a comprehensive consideration of factors such as motion constraints from rigid joints, nonlinear coupling deformations in the flexible limbs, and control strategies for the redundant actuators. In this paper, we construct a mechanical analysis framework tailored for such operating modes. This framework enables the kinetostatic analyses under the consideration of distributing redundant actuation forces. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Geometry

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Double Deep Q Network Optimized Linear Active Disturbance Rejection Control for Ship Course Keeping 17th

Double Deep Q Network Optimized Linear Active Disturbance Re...

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17th Chinese intelligent Systems Conference, CISC 2021

作者： Zheng, Yuemin Liu, Zhongxin Tao, Jin Sun, Qinglin Sun, Hao Sun, Mingwei Chen, Zengqiang College of Artificial Intelligence Nankai University Tianjin300350 China Department of Electrical Engineering and Automation Aalto University Espoo02500 Finland Key Laboratory of Intelligent Robotics of Tianjin Nankai University Tianjin300350 China

ISBN: (纸本)9789811663277

This paper mainly proposes a parameter-optimized linear active disturbance rejection controller (LADRC) based on a double deep Q network (DDQN) and applies it to ship course control. Firstly, based on the separate mathematical models’ equation, a ship’s dynamic model is established. Then, a LADRC based course keeping controller is designed to overcome the ship’s environmental disturbances and internal uncertainty during navigation. Furthermore, to facilitate LADRC parameter adjustment and obtain a better performance of ship course keeping control, the DDQN is applied to tune the adaptive parameters of LADRC. Finally, simulation results and comparisons on ship course keeping show that the proposed DDQN optimized LADRC can control the ship’s heading angle to track the planned course, and the control performance outperforms the traditional LADRC. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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UAV Forest Fire Detection based on RepVGG-YOLOv5

UAV Forest Fire Detection based on RepVGG-YOLOv5

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Song, Kang Zhang, Yueyuan Lu, Bo Chi, Wenzheng Sun, Lining Department of Automation Engineering School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Ningbo Institute of Materi-als Technology & Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Soochow University Robotics and Microsystems Center School of Mechanical and Electrical Engineering Suzhou215137 China

ISBN: (纸本)9781665481090

The safety of forest resources is of great im-portance to natural and public safety, and the efficient and accurate detection of forest fires is an issue of close concern. Considering the limitations of traditional forest fire detection methods, such as the limited range of view from watchtowers and insufficient resolution of satellite images, a deep learning-based Unmanned Aerial Vehicle (UAV) fire detection system has been proposed. With its high mobility, images of forest fires can be captured by camera-equipped UAV and transmitted to the ground station in real time. The ground station uses a deep learning object detection algorithm to achieve the recognition of fire. In this paper, we choose the state-of-the-art object detection algorithm YOLOv5 for the UAV forest fire detection scenario, and to further improve the detection performance of YOLOv5, we propose RepVGG-YOLOv5 by drawing on the RepVGG network, a training-time multi-branch topology with an inference-time plain architecture. Then we test the models on a self-built dataset, and the experimental results show that the improved RepVGG-YOLOv5 model outperforms the YOLOv5 model in terms of detection performance and detection speed, which proves the feasibility of this paper's model in the UAV forest fire detection systems and the effectiveness of the model improvement. Furthermore, the great potential of this paper's object detection model for real-time applications of accurate forest fire detection has also been demonstrated. © 2022 IEEE.

关键词： Object detection

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Fault Diagnostic Opportunities for Electromagnetic Coils of Active Magnetic Bearings using Physics-of-Failure Analysis 5

Fault Diagnostic Opportunities for Electromagnetic Coils of ...

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5th International Conference on System Reliability and Safety engineering, SRSE 2023

作者： Li, Ruiqi Wang, Kai Zeng, Peng Cao, Jiwei Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Harbin Institute of Technology Department of Electrical Engineering China

ISBN: (纸本)9798350305944

Active magnetic bearings (AMBs) have many advantages over traditional oil bearings due to their non-contact characteristics. They are environmentally friendly solution and have been proven to be highly reliable and available. Electromagnetic coils are vital components of AMB and they are failure prone. In this paper, the failure mode of magnetic bearing coil and its corresponding failure mechanism are reviewed by FMMEA method, and the existing coil fault diagnosis methods are introduced. Future directions for coil insulation health monitoring aiming at the application characteristics of AMBs are also discussed. © 2023 IEEE.

关键词： Failure analysis

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Rock-Climbing Fish Inspired Skeleton-Embedded Rigid-Flexible Coupling Suction Disc Design for Adhesion Enhancement 16th

Rock-Climbing Fish Inspired Skeleton-Embedded Rigid-Flexible...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Tan, Wenjun Qin, Hengshen Zhang, Chuang Wang, Ruiqian Zhang, Yiwei Yang, Lianchao Chen, Qin Wang, Feifei Xi, Ning Liu, Lianqing State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chengdu Institute of Biology Chinese Academy of Sciences Sichuan Chengdu610042 China Department of Electrical and Electronic Engineering University of Hong Kong 999077 Hong Kong Emerging Technologies Institute Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Pokfulam999077 Hong Kong

ISBN: (纸本)9789819964888

Underwater high-performance biological adhesion systems have attracted increasing research interest to inspire engineered adhesion systems. Switchable underwater adhesion systems typically employ suction adhesion. However, achieving low pre-pressure negative pressure adhesion is still a challenge. An inspiring nature mode is provided by the rock-climbing fish, which needs little preload and can resist a pull-off force of 1000 times its own body weight. The pectoral and abdominal fins of rock-climbing fish have evolved to almost a flat suction cup. Through micro CT, we found that a unique skeletal network is embedded in the suction cup. This unique structure increases the stiffness of the suction cup and enhances its adhesion performance. Through numerical simulation, it was revealed that this rigid, flexible coupling structure enhances the ability of the suction cup to resist lateral pressure and increases the energy dissipation of the edge detachment of the suction cup. This work can shed light on the design of novel underwater adhesion systems and inspire underwater adhesion crawling robots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Adhesion

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