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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Amir Ismail Maroua Mehri Anis Sahbani Najoua Essoukri Ben Amara Ecole Nationale d’Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Enova Robotics S.A. Novation City Technopôle de Sousse Sousse Tunisie CNRS Institute for Intelligent Systems and Robotics (ISIR) Sorbonne Université Paris France

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Automatic license plate recognition (ALPR) is a powerful tool for analyzing the growing number of vehicles in major cities worldwide. However, building datasets that accurately represent vehicles and license plates (LPs) is challenging, as most current datasets are specific to certain countries and do not provide a comprehensive representation. This remains particularly true for French LPs, where there is currently no publicly available dataset that covers a wide range of scenarios such as plate inclinations, varying lighting conditions, and changing weather. To address this issue, we present a benchmark suite called PGFLP 1.0, which includes more than 14k fully annotated LPs from several vehicle categories (e.g. cars, trucks, motorcycles). PGFLP 1.0 includes three annotation levels: rectangular boxes for the LP, four vertices of the LP region, and labels for the full LP sequence. Using PGFLP 1.0, we provide an experimental evaluation of the most recent ALPR approaches, assessing their ability to detect and recognize LPs. This work provides a useful benchmark in terms of accuracy and speed for current and future research efforts in ALPR.

关键词： Urban areas Buildings Motorcycles Lighting Benchmark testing Licenses Information technology License plate recognition Robots Meteorology

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Three-Dimensional Force Sensor Based on Deep Learning 1st

Three-Dimensional Force Sensor Based on Deep Learning

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1st International Conference on Cognitive Computation and systems, ICCCS 2022

作者： Duan, Qingling Zhang, Qi Luo, Dong Yang, Ruofan Zhu, Chi Liu, Zhiyuan Ou, Yongsheng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen China

ISBN: (纸本)9789819927883

Human skin can accurately sense subtle changes of both normal and shear forces. However, tactile sensors applied to robots are challenging in decoupling 3D forces due to the inability to develop adaptive models for complex soft materials. Therefore, a new soft tactile sensor has been designed in this paper to detect shear and normal forces, including a soft probe and image acquisition device. First, to capture the deformation of the sensor, colored silicone squares were embedded in the soft probe. Capcamera movement of the colored squares under external forces. The image dataset collected at different 3D forces is then input into a deep learning model. Finally, a custom miniature image device is acquired and embedded in the soft probe to miniaturize the sensor. Computing results obtained from experimental datasets show that the proposed method can accurately decouple the 3D forces. Robots can grap vulnerable objects with sensors prepared at the robot’s tip. The tactile sensors studied in this paper are expected to be applied in robotics fields such as adaptive grasping, dexterous manipulation and human-computer interaction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Probes

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On-Chip Automated Acoustofluidic Three-Dimensional Observation System for Oocytes*

On-Chip Automated Acoustofluidic Three-Dimensional Observati...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Zhuo Chen Xiaoming Liu Xiaoqing Tang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and the Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Materials Engineering Science Osaka UniversityToyonaka Osaka Japan

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

Single-cell manipulation techniques are an important topic in medical and biological research, especially in assisted reproductive technology. The traditional method of observing oocytes through contact manipulation using a capillary is inefficient, requires a high level of skill, and poses potential damage to the oocytes. Therefore, we propose an on-chip acoustofluidic system for the automated observation of oocytes. Multiple trap chambers allow for the simultaneous capture of multiple cells, increasing operational efficiency. Fluid-based trapping and acoustofluidic-driven rotation reduce the probability of oocytes encountering sharp ends, making them less susceptible to damage. Our system has been tested and proven capable of rotating the oocyte in both in-plane and out-of-plane directions, which enables the adjustment of the oocyte to an arbitrary orientation in space. The whole process does not involve the movement of the focal plane. By combining the proposed automated oocyte position recognition method with the pulse width modulation-based position control method, we achieved a maximum speed of 1.4 revolutions per second and a minimum control accuracy of less than 1.6°, making it user-friendly with almost no operational threshold.

关键词： In vitro fertilization Accuracy Human-machine systems Position control Pulse width modulation Biology System-on-chip Quality assessment Microfluidics Streams

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A Magnetically Steerable and Automatically Propulsion Guidewire Robot System for Vascular Interventional Surgery

A Magnetically Steerable and Automatically Propulsion Guidew...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Fu, Shixiong Zhang, Shanxiu Yin, Meng Wang, Shu Li, Dong Wu, Xinyu Du, Shiwei Xu, Tiantian Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced Technology CAS Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China South China Hospital of Shenzhen University Department of Neurosurgery Shenzhen518111 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Magnetically soft continuum robot with automatic navigation capability has great potential to improve the treatment conventional vascular interventional surgery, which exists many problems such as long procedure time and high radiation for doctors to manipulate the passive guidewire. This paper proposes a novel guidewire robot system with magnetically steering and automatically propulsion ability. Actuated by an external magnetic field, the system is able to greatly increase the autonomy of conventional guidewires through the efficient coordination of steering and propulsion. We model the kinematics of autonomous propulsion and magnetically actuation steering separately. Then, the steering performance of the guidewire robot is validated under different magnetic field magnitudes and directions. Finally, two scenario application experiments are implemented to demonstrate the automatic navigation capability of the proposed guidewire robot in a simulated clinical environment. The proposed guidewire robot system provides an effective solution for advancing the automation of vascular interventional surgery. © 2022 IEEE.

关键词： Propulsion

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Development of an in vitro perfusable neural interface model for sequential connection of nerve cells

Development of an in vitro perfusable neural interface model...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Menghua Liu Huaping Wang Qing Shi Yaozhen Hou Jiaxin Liu Tao Sun Qiang Huang Toshio Fukuda Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Neural interface models based on microfluidic chips are widely used in the regeneration of neural interfaces in vitro for their low cost, simple structure and accurate simulation of complex interactions between cells and the extracellular environment. However, due to the lack of influence of the dynamic flow environment, it is difficult to realize the reduction of cell culture and the cell growth environment for a long time while forming sequential connections between cells. Here,we propose an in vitro perfusable culture chip as a neural interface for neuroblastoma cells (SH-SY5Y cells) to realize sequential cell connection under dynamic perfusion and physical guidance. A microfluidic chip with physical guidance was fabricated based on soft lithography. The microfluidic chip can realize long-term cell culture under dynamic perfusion conditions by an automatic perfusion system in vitro. The interior of the microfluidic chip is optimized to provide physical cues for SH-SY5Y cells to construct nerve conduction microunits. The microfluidic chip has great potential in the research of in vitro neural interface regeneration models in dynamic complex environments.

关键词： Soft lithography Costs Biological system modeling Neurons In vitro Extracellular Microfluidics

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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第42届中国控制会议

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network(TL-DNN) was proposed in this paper. Its specific implementation steps include(1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor;(2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models(random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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A Weighting Factors Autotuning Strategy for Model Predictive Torque Control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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第42届中国控制会议

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor(PMSM) based on the adaptive multi-objective black hole algorithm(AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multiobjective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

关键词： Model Predictive Torque Control Weighting Factors Multi-objective Optimization Algorithm Permanent Magnet Synchronous Motor

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EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-LSTM Hybrid Model

EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-L...

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World Rehabilitation Robot Convention (WRRC)

作者： Wujing Cao Xinqiang Guo Yupeng Zou Shuo Zhang Mingxiang Luo Worawarit Kobsiriphat Xinyu Wu Meng Yin Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Mechanical Engineering China University of Petroleum East China National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350355123

ISBN: (纸本)9798350355130

Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance.

关键词： Support vector machines Deep learning Accuracy Noise Gesture recognition Stroke (medical condition) Feature extraction Electromyography Convolutional neural networks Long short term memory

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Mode-switching single-pixel imaging via a high-performance perovskite-Si dual-mode photodetector

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Photonics Research 2024年第12期12卷 2873-2880页

作者： YUJIN LIU DONGXU LIN JINGYANG XING HANYAN ZHAO HONGLING WAN HAO WANG ZHONG JI XUELI CHEN Innovation Center for Advanced Medical Imaging and Intelligent Medicine Guangzhou Institute of TechnologyXidian UniversityGuangzhou 510555China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Department of Mechanical and Energy EngineeringSUSTech Energy Institute for Carbon NeutralitySouthern University of Science and TechnologyShenzhen 518055China Center for Biomedical-Photonics and Molecular Imaging Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi ProvinceSchool of Life Science and TechnologyXidian UniversityXi’an 710126China

Visible–near-infrared(VIS-NIR)dual-mode imaging can expand the human perception ***,the development of dual-mode image sensors is still challenging due to complex fabrication processes and readout circuit ***,we design a simple-structured(perovskite-Au/Si/Ag)dual-mode *** unique asymmetric electrode design allows the device to support two operating modes at zero *** device exhibits a detection range covering 400–1100 nm under top illumination mode,with a peak specific detectivity of up to 5.56×1013 *** bottom illumination mode,the device demonstrates pronounced narrowband NIR response *** importantly,we develop a dual-mode single-pixel imaging system based on this device,bypassing the fabrication processes of high-density array image *** system exhibits excellent VISNIR dual-mode imaging results,effectively separating NIR and VIS information and enhancing infrared details in the fused ***,we discover that the system can effectively suppress ringing artifacts,achieving infrared information perception at a low sampling rate,which can accelerate the imaging speed by∼16 times(reduced from∼3.2 s to∼0.2 s).Our proposed dual-mode single-pixel imaging technology offers new means for material identification and intelligent perception.

关键词： mode illumination details

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Centimeter-Scale Submarine Robot for Monitoring Coral Reef Ecosystem

Centimeter-Scale Submarine Robot for Monitoring Coral Reef E...

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IEEE International Conference on Mechatronics and Automation

作者： Zongze Li Yi Zhang Shujing He Xiyu Zhu Tanyong Wei Chengzhi Hu Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Advanced Biomaterials Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Monitoring coral reef ecosystem is of great importance for assessing their health and promoting conservation efforts to protect these valuable and vulnerable ecosystems. Underwater robots provide a feasible approach for long-term monitoring due to their compact size for navigating through narrow passages. However, existing underwater robots face limitations due to their large size, preventing them from entering narrow coral reef caves to gather information. To address this challenge, this paper proposes a highly integrated propeller- driven centimeter-scale submarine robot with a length of 4.4 cm for monitoring coral reef ecosystem. There are two propellers on two sides of the robot, facilitating lift motion, while an additional two propellers are mounted at the tail to enable forward propulsion and turning maneuvers. This robot can realize three degrees of freedom movement, such as circular motion at specific heights or cruising in a spiral paths under the control of external infrared signals. A kinematic and dynamic model is formulated to develop the control strategy. For object monitoring by the proposed submarine robot, distance and displacement measurement algorithms are developed based on a monocular camera. These algorithms have been experimentally evaluated, demonstrating a calculation error of less than 5%.

关键词： Spirals Propellers Heuristic algorithms Ecosystems Robot vision systems Marine vegetation Propulsion

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