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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xu, Zhihui Wei, Tanyong Li, Zongze Huang, Ding Liu, Shoubin Hu, Chengzhi Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen China Southern University of Science and Technology Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen China

ISBN: (纸本)9798350325706

Capsule robots have emerged as a promising solution for non-invasive and painless screening of gastrointestinal diseases, offering significant advantages in patient comfort and reduced risk of cross-infection. Despite these advantages, existing capsule robots fall short in their ability to conduct targeted biopsies and deliver drugs to specific gastric lesions. To address this limitation, this paper presents the design and development of a magnetically driven capsule robot that leverages visual feedback to achieve accurate targeting for biopsy and drug administration in gastric lesions. Internal magnets, responsive to an external gradient magnetic field, enable the controlled propulsion of the capsule robot. A kinetic model of the capsule robot is analyzed, allowing it to recognize lesion contours through the endoscopic camera. This recognition determines the rotation angle or small adjustments required for the capsule robot's attitude and positioning. Experimental results in a simplified in vitro stomach model showcase the capability of the capsule robot to achieve targeted biopsy and drug delivery. These results highlight the potential feasibility of the proposed capsule robot in enhancing diagnostic accuracy and treatment efficacy with broader applications within the realm of gastric healthcare. © 2023 IEEE.

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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STAPointGNN: Spatial-Temporal Attention Graph Neural Network for Gesture Recognition Using Millimeter-Wave Radar 19th

STAPointGNN: Spatial-Temporal Attention Graph Neural Network...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zhang, Jun Wang, Chunyu Wang, Shunli Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China Engineering Research Center of AI and Unmanned Vehicle Systems of Jilin Province Changchun China

ISBN: (纸本)9783031545276

Gesture recognition plays a pivotal role in enabling natural and intuitive human-computer interaction (HCI), finding applications in diverse domains such as smart homes, robot control, and virtual reality. Thanks to advances in computer vision, the most popular method currently is to use the camera for gesture recognition. However, the camera struggles to function properly in poor lighting and inclement weather, and risks invading privacy. Due to the robust and non-invasive features of millimeter-wave radar, gesture recognition based on millimeter-wave radar has received extensive attention from researchers in recent years. In this paper, we propose a novel graph neural network named STAPointGNN for gesture recognition using millimeter-wave radar. In order to better extract features in the spatial and temporal dimensions of point clouds collected by millimeter-wave radar, we designed a spatial-temporal attention mechanism based on graph neural network. We also propose a novel point flow embedding method to capture the motion features of the point clouds in adjacent frames. To verify the superiority of our method, we conduct experiments on two public millimeter-wave radar gesture recognition datasets. The results show that our model outperforms existing mainstream algorithms. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Gesture recognition

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Robust Tracking for Electromagnetic-Actuated Microrobot Via Sliding Mode Control 49

Robust Tracking for Electromagnetic-Actuated Microrobot Via ...

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

作者： Wu, Yihang Qu, Ziyao Chang, Xiaocong Hu, Zhijian Yang, Rongni Ma, Renjie Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9798350331820

Microrobots can work in small, enclosed spaces and complete complex tasks. It has potential applications in the field of biomedical and life sciences. This paper focuses on investigating robust dynamic tracking for electromagnetic-actuated microrobot via sliding mode control. First, we generalize the planar motion to the case of spatial trajectory tracking and then establish the three-dimensional tracking error model of microrobot. Then, we deploy a novel sliding control strategy and ensure the stability of sliding mode dynamics, once the robotic state reaches the sliding surface and sustains there in the sequel. Finally, the numerical simulation is conducted, compared to the traditional proportion-integration-differentiation control strategy, to verify the effectiveness and advantages of our proposed control scheme. © 2023 IEEE.

关键词： Sliding mode control

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A Fault Detection Method Based on Bearing Dynamics with Multi-frequency Sinusoidal Signal 41

A Fault Detection Method Based on Bearing Dynamics with Mult...

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第41届中国控制会议

作者： Xiaohong Li Zhicheng Li Zaiyue Yang Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineeringand the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in UniversitiesSouthern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Bearing is a common and important component in the industry,and it is crucial to detect and diagnose its working *** to the characteristic the motion process of the bearing that is a periodic nonlinear system with sinusoidal input,a bearing fault detection method based on the nonlinear system with multi-frequency sinusoidal input is *** the dynamics modeling of the motion for the bearing,it is found that its input and output have the characteristics of almost periodic *** the periodogram-based asymptotic local fault detection method,the fault detection problem is transformed into a hypothesis test problem,and a threshold is obtained by the given confidence and the degree of freedom of a specific frequency *** method can effectively and quickly monitor the early minor faults in strong noise *** realization and performance of this bearing detection method are illustrated by the simulated vibration signal of the bearing.

关键词： Bearing dynamics model fault detection sinusoidal signal statistical methods

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DROPLET-BASED MICROFLUIDIC SYNTHESIS OF FUNCTIONAL VASCULARIZED HYDROGEL MICROSPHERES 26

DROPLET-BASED MICROFLUIDIC SYNTHESIS OF FUNCTIONAL VASCULARI...

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26th International Conference on Miniaturized systems for Chemistry and Life sciences, MicroTAS 2022

作者： Zhan, Zhen Liu, Zeyang Li, Jianjie Zhang, Wenxuan Hu, Chengzhi Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology China

ISBN: (纸本)9781733419048

This paper proposes a droplet-based microfluidic method to generate hydrogel microspheres with interior undulated wavy or spiral canals for 3D cell culture. Human umbilical vein endothelial cells (HUVECs) were cultured in the interior canals of the hydrogel microspheres and formed monolayers on the surface of the spiral canals, mimicking native blood vessel configurations. The fabricated heterogeneous spheroids with spiral blood vessel lumen were also demonstrated to build spirally vascularized bone tissue. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life sciences. All rights reserved.

关键词： Microfluidics

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Predict Hip Joint Moment Using CNN for Hip Exoskeleton Control 1

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

作者： Zhang, Yuanwen Xiong, Jingfeng Qian, Yuepeng Chen, Xinxing Guo, Yixuan Fu, Chenglong Leng, Yuquan Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China National University of Singapore Singapore117583 Singapore

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

The assistance of the hip exoskeleton is closely related to hip joint moment, and the corresponding assistance can be generated by hip joint moment directly. However, it is difficult to obtain the hip joint moment directly from the sensor during walking. An effective method is to use deep learning models to predict hip joint moment using hip joint angle information. But there are still many challenges in applying this method to practical control systems. This study designs specific parameters to train a deep neural network for predicting hip joint moments and discusses the effects of the number of convolutional layers (N) and the length of sampling time (SL) on the prediction accuracy and the computation time in a real control system. Experiments show that for N ≤ 5, using angle information with SL ≤ 1.0 s achieves the highest prediction accuracy. However, for larger values of N (N > 5), the optimal SL becomes to be longer, where it increases to 2.0 s and the corresponding RMSE is 0.06260 Nm/kg and 0.05977 Nm/kg, respectively. These results indicate the importance of SL for prediction accuracy. Furthermore, a comparative analysis of the computation time with models using different N and SL is performed, and the relationship between computation time and prediction accuracy is discussed. This study provides specific model training parameters and detailed comparative analysis, which facilitates further application of deep learning models on hip exoskeletons. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Forecasting

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Artificial neural network-based direct power control to enhance the performance of a PMSG-wind energy conversion system under real wind speed and parameter uncertainties: An experimental validation

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Energy Reports 2024年 11卷 4356-4378页

作者： Majout, Btissam Bossoufi, Badre Karim, Mohammed Skruch, Pawel Mobayen, Saleh El Mourabit, Youness El Zair Laggoun, Zakaria Laboratory of Engineering Modelling and Systems Analysis SMBA University Fez Morocco Department of Automatic Control and Robotics AGH University of Krakow Poland Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco Laboratory of Electrotechnique Constantine Mentouri Brothers Constantine 1 University Constantine Algeria

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power Control (DPC) approach has garnered considerable attention due to its simple and highly adaptable algorithm. This approach has been increasingly recognized in numerous applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system's performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board. © 2024 The Authors

关键词： MATLAB

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Data-based Stabilization of Unknown Discrete-time 2-D Roesser systems 23

Data-based Stabilization of Unknown Discrete-time 2-D Roesse...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Yang, Runmin Yang, Rongni Ma, Renjie Zhu, Yanzheng Shi, Peng School of Control Science and Engineering Shandong University Jinan250061 China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150001 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China School of Electrical and Mechanical Engineering University of Adelaide AdelaideSA5005 Australia

ISBN: (纸本)9798331528041

This paper studies the stabilization problem of discrete-time two-dimensional (2-D) systems represented by Roesser based on available data. First of all, based on the pre-collected input-state data, the original system is transformed into a data-based form. Then a set is established to represent all systems capable of generating the given data, thereby indirectly stabilizing the original system by stabilizing all systems within this set. Next, by utilizing the S-procedure, sufficient conditions are established for the resultant closed-loop systems that incorporate a state feedback controller based on collected data. Finally, an example is given to demonstrate the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Discrete time control systems

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Impact of Employing Weather Forecast Data as Input to the Estimation of Evapotranspiration by Deep Neural Network Models**

arXiv

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arXiv 2024年

作者： Vaz, Pedro J. Schütz, Gabriela Guerrero, Carlos Cardoso, Pedro J.S. Universidade do Algarve Portugal CEOT – Centre for Electronics Optoelectronics and Telecommunications Portugal MED – Mediterranean Institute for Agriculture Environment and Development Portugal LARSyS Laboratory of Robotics and Engineering Systems Portugal

Reference Evapotranspiration (ET!) is a key parameter for designing smart irrigation scheduling, since it is related by a coefficient to the water needs of a crop. The United Nations Food and Agriculture Organization, proposed a standard method for ET! computation (FAO56PM), based on the parameterization of the Penman-Monteith equation, that is widely adopted in the literature. To computeET ! using the FAO56-PM method, four main weather parameters are needed: temperature, humidity, wind, and solar radiation (SR). One way to make daily ET! estimations for future days is to use freely available weather forecast services (WFSs), where many meteorological parameters are estimated up to the next 15 days. A problem with this method is that currently, SR is not provided as a free forecast parameter on most of those online services or, normally, such forecasts present a financial cost penalty. For this reason, several ET! estimation models using machine and deep learning were developed and presented in the literature, that use as input features a reduced set of carefully selected weather parameters, that are compatible with common freely available WFSs. However, most studies on this topic have only evaluated model performance using data from weather stations (WSs), without considering the effect of using weather forecast data. In this study, the performance of authors’ previous models is evaluated when using weather forecast data from two online WFSs, in the following scenarios: (i) direct ET! estimation by an Artificial Neural Network (ANN) model, and (ii) estimate SR by (another) ANN model, and then use that estimation for ET! computation, using the FAO56-PM method. Employing data collected from two WFSs and a WS located in Vale do Lobo, Portugal, the latter approach achieved the best result, with a coefficient of determination (R") ranging between 0.893 and 0.667, when considering forecasts up to 15 days. © 2024, CC BY.

关键词： Irrigation

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