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Structural Optimization of Photoacoustic Transducer with Pdms/Csnps Nanocomposite for Laser-Induced Nonlinear Surface Wave and its Application in Fatigue Crack Detection

SSRN

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SSRN 2023年

作者： Yao, Zhijun Li, Yehai Lv, Gaolong Chen, Dan Yang, Jian Guo, Shifeng Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic Shenzhen518055 China Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Ningbo China Ningbo315100 China

Laser ultrasonics is a promising non-contact inspection technique but faces the challenges of low signal-to-noise ratio and low amplitude under the non-destructive thermoelastic regime. In this paper, an ultrasonic surface wave modulation method based on laser ultrasonics and photoacoustic transducer (PAT) is proposed and combined with nonlinear wave mixing technique to achieve non-contact detection of fatigue crack. The PAT comprised of candle soot nanoparticles and polydimethylsiloxane (PDMS) as well as a line-arrayed laser source modulated by a slit mask are firstly introduced to generate narrowband surface wave with high intensity and SNR under the thermoelastic regime. The thicknesses of the PDMS layers of PAT are thereafter optimized by analytical, numerical and experimental investigations to acquire narrowband surface wave with pure fundamental frequency component. Two modulated ultrasonic surface waves with frequencies of 2.1 MHz and 2.9 MHz are excited on a fatigued aluminum plate to generate nonlinear mixed components, and the ultrasonic responses over the fatigue crack regions are acquired by using a scanning laser Doppler vibrometer. The localization of fatigue crack with microscale is eventually acquired by mapping the nonlinear parameter of the mixed components, which proves it a reliable and nondestructive technique to inspect the fatigue crack. © 2023, The Authors. All rights reserved.

关键词： Transducers

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A framework for human-exoskeleton interaction based on sEMG interface and electrotactile feedback

A framework for human-exoskeleton interaction based on sEMG ...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Duan, Shengcai Wang, Can Li, Mengyao Su, Zhilong Liu, Jiaqing Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Department of Mechanical and Automation Engineering Chinese University of Hong Kong

ISBN: (纸本)9781665436786

The lower extremity rehabilitation exoskeleton provides a priceless opportunity for paraplegics to stand up and walk normally. In this paper, a framework for human-exoskeleton interaction with sEMG interface and electrotactile feedback is presented. Firstly, the exoskeleton recognises human motion intention through the Long Short Term Memory (LSTM) Neural Network based on surface electromyogram (sEMG). Secondly, electrotactile based on spatiotemporal coding scheme feeds back the exoskeleton states to the sensory skin, as a compensation for paraplegic's absent proprioception in lower limbs. Thirdly, physiological information, i.e. muscle fatigue, of arms is monitored and quantified with sEMG. Three healthy subjects participate the experiments including the motion intention recognition experiment, electrotactile perception experiment and online exoskeleton experiment. The online exoskeleton experiments demonstrate the feasibility and efficiency of the proposed framework. Furthermore, it is proved experimentally that the performance of collaborative control in rehabilitation human-exoskeleton interaction can be facilitated via the framework. © 2021 IEEE.

关键词： Long short-term memory

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Improving Incremental Encoder Measurement: Variable Acquisition Window and Quadrature Phase Compensation to Minimize Acquisition Errors

Improving Incremental Encoder Measurement: Variable Acquisit...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： José Lima Ví tor H. Pinto A. Paulo Moreira Paulo Costa Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Polit&#x00B4 ecnico de Braganca Campus de Santa Apolonia Braganca Portugal INESC Technology and Science Porto Portugal SYSTEC (DIGI2) - Research Center for Systems and Technologies (Digital and Intelligent Industry Lab) Porto Portugal Faculty of Engineering of University of Porto Portugal

Motion control is an important task in several areas, such as robotics where the angular position and speed should be acquired, usually with encoders. For slow angular speeds, an error is introduced spoiling the measurement. In this paper there will be proposed two new methodologies, that when combined allow to increase the precision whereas reducing the error, even on transient velocities. The two methodologies Variable Acquisition Window and a Quadrature Phase Compensation are addressed and combined simultaneously. A real implementation of the proposed algorithms is performed on a real hardware, with a DC motor and a low resolution encoder based on hall effect. The results validate the proposed approach since the errors are reduced compared with the standard Quadrature Encoder Reading.

关键词： Phase measurement Conferences Measurement uncertainty DC motors Hardware Hall effect Transient analysis

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Modeling sensory adaptation to peripheral nerve stimulation

Modeling sensory adaptation to peripheral nerve stimulation

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： J. Kljajic G. Valle S. Raspopovic Institute Mihajlo Pupin Belgrade Serbia and School of Electrical Engineering University of Belgrade Serbia Neuroengineering Lab Institute for Robotics and Intelligent Systems ETH Zürich Switzerland

Neural adaptation is a well-known phenomenon of the human sensory systems. Prolonged stimulation of the sensory system causes the adaptation (e.g. reduced neural activity) of the afferent sensory inputs. Interestingly, the use of direct nerve stimulation using implanted electrodes has been shown as the promise for providing sensory feedback to amputees. Although this technique is proven to be able to convey several sensory information, sensory adaptation to the neural stimulation can cause some unwanted behavior (e.g., disappearing of the artificial sensation after few minutes, changes in the perceptual intensity over time). Here we present a computational model of the sensory adaptation to direct electrical nerve stimulation. The model considers important biological parameters (i.e. synaptic processes) together with the different stimulation parameters (i.e. stimulation amplitude and frequency). The model was fitted on experimental data collected in two human transfemoral amputees, who were implanted with four transverse intrafascicular multichannel electrodes (TIME) in their residual tibial nerve. This model is not only used to better understand the mechanisms of sensory adaptation to different neural stimulation strategies, but it could also be exploited in future in neuroprosthetic applications to predict and control sensory adaptation.

关键词： Electrodes Adaptation models Biological system modeling Computational modeling Neural activity Neural engineering Predictive models

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A Sample Efficiency Improved Method via Hierarchical Reinforcement Learning Networks

A Sample Efficiency Improved Method via Hierarchical Reinfor...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Qinghua Chen Evan Dallas Pourya Shahverdi Jessica Korneder Osamah A. Rawashdeh Wing-Yue Geoffrey Louie Intelligent Robotics Laboratory Oakland University MI USA College of Electrical and Information Engineering Zhengzhou University of Light Industry Henan China Applied Behavior Analysis Clinic Oakland University MI USA Embedded Systems Research Lab Oakland University MI USA

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Learning from demonstration (LfD) approaches have garnered significant interest for teaching social robots a variety of tasks in healthcare, educational, and service domains after they have been deployed. These LfD approaches often require a significant number of demonstrations for a robot to learn a performant model from task demonstrations. However, requiring non-experts to provide numerous demonstrations for a social robot to learn a task is impractical in real-world applications. In this paper, we propose a method to improve the sample efficiency of existing learning from demonstration approaches via data augmentation, dynamic experience replay sizes, and hierarchical Deep Q-Networks (DQN). After validating our methods on two different datasets, results suggest that our proposed hierarchical DQN is effective for improving sample efficiency when learning tasks from demonstration. In the future, such a sample-efficient approach has the potential to improve our ability to apply LfD approaches for social robots to learn tasks in domains where demonstration data is limited, sparse, and imbalanced.

关键词： Social robots Education Reinforcement learning Medical services Task analysis

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Joint Counting, Detection and Re-Identification for Multi-Object Tracking

arXiv

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arXiv 2022年

作者： Ren, Weihong Wu, Denglu Cao, Hui Chen, Xi'ai Han, Zhi Liu, Honghai The School of Mechanical Engineering and Automation State Key Lab of Robotics and Systems Harbin Institute of Technology Shenzhen518055 China The Shenzhen Duorou Intelligent System Co. Ltd. Shenzhen518055 China The School of Electrical Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Beijing110169 China

The recent trend in 2D multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in crowded scenes, joint detection and tracking usually fail to find accurate object associations due to missed or false detections. In this paper, we jointly model counting, detection and re-identification in an end-to-end framework, named CountingMOT, tailored for crowded scenes. By imposing mutual object-count constraints between detection and counting, the CountingMOT tries to find a balance between object detection and crowd density map estimation, which can help it to recover missed detections or reject false detections. Our approach is an attempt to bridge the gap of object detection, counting, and re-Identification. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to failure in crowded scenes, or depend on local correlations to build a graphical relationship for matching targets. The proposed MOT tracker can perform online and real-time tracking, and achieves the state-of-the-art results on public benchmarks MOT16 (MOTA of 79.7%), MOT17 (MOTA of 81.3%) and MOT20 (MOTA of 78.9%). Source code is available at https://***/weihong9/CountingMOT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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252 - ON-DEMAND ENCRUSTATION-REPELLENT URETERAL STENT/CATHETER SURFACE: A NOVEL BIOINSPIRED ULTRASONIC APPROACH

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Continence 2024年 12卷 101594-101594页

作者： Dillinger, C Amado, P Obrist, D Burkhard, F Clavica, F Ahmed, D Acoustic Robotics Systems Lab Institute of Robotics and Intelligent Systems Department of Mechanical and Process Engineering ETH Zurich Zurich Switzerland ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Urology Inselspital Bern University Hospital University of Bern Bern Switzerland

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Safe Value Functions

arXiv

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arXiv 2021年

作者： Massiani, Pierre-François Heim, Steve Solowjow, Friedrich Trimpe, Sebastian The Institute for Data Science in Mechanical Engineering RWTH Aachen University Aachen52068 Germany The Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Stuttgart70569 Germany The Biomimetic Robotics Lab Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge02139 United States

Safety constraints and optimality are important but sometimes conflicting criteria for controllers. Although these criteria are often solved separately with different tools to maintain formal guarantees, it is also common practice in reinforcement learning to simply modify reward functions by penalizing failures, with the penalty treated as a mere heuristic. We rigorously examine the relationship of both safety and optimality to penalties, and formalize sufficient conditions for safe value functions (SVFs): value functions that are both optimal for a given task, and enforce safety constraints. We reveal this structure by examining when rewards preserve viability under optimal control, and show that there always exists a finite penalty that induces a safe value function. This penalty is not unique, but upper-unbounded: larger penalties do not harm optimality. Although it is often not possible to compute the minimum required penalty, we reveal clear structure of how the penalty, rewards, discount factor, and dynamics interact. This insight suggests practical, theory-guided heuristics to design reward functions for control problems where safety is important. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Transferability of a Sensing Mattress for Posture Classification from Research into Clinics

Transferability of a Sensing Mattress for Posture Classifica...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Oriella Gnarra Alexander Breuss Lorenzo Rossi Manuel Fujs Samuel E.J. Knobel Jan D. Warncke Stephan M. Gerber Claudio L.A. Bassetti Robert Riener Tobias Nef Markus H. Schmidt Department of Neurology Sleep-Wake Epilepsy Center University Hospital of Bern Switzerland Department of Health Sciences and Technology Sensory-Motor Systems Lab Institute of Robotics and Intelligent Systems ETH Zurich ETH Zurich Switzerland Gerontechnology Rehabilitation Group ARTORG Centre for Biomedical Engineering Research University of Bern Switzerland Spinal Cord Injury Center University Hospital Balgrist Zurich Switzerland Ohio Sleep Medicine Institute Dublin Ohio USA

Sleep is crucial in rehabilitation processes, promoting neural plasticity and immune functions. Nocturnal body postures can indicate sleep quality and frequent repositioning is required to prevent bedsores for bedridden patients after a stroke or spinal cord injury. Polysomnography (PSG) is considered the gold standard for sleep assessment. Unobtrusive methods for classifying sleep body postures have been presented with similar accuracy to PSG, but most evaluations have been done in research lab environments. To investigate the challenges in the usability of a previously validated device in a clinical setting, we recorded the sleep posture of 17 patients with a sensorized mattress. Ground-truth labels were collected automatically from a PSG device. In addition, we manually labeled the body postures using video data. This allowed us also to evaluate the quality of the PSG labels. We trained neural networks based on the VGG-3 architecture to classify lying postures and used a self-label correction method to account for noisy labels in the training data. The models trained with the video labels achieved a higher classification accuracy than those trained with the PSG labels (0.79 vs. 0.68). The self-label correction could further increase the models' scores based on video and PSG labels to 0.80 and 0.70, respectively. Unobtrusive sensors validated in clinics can, therefore, potentially improve the quality of care for bedridden patients and advance the field of rehabilitation.

关键词：

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Next-Generation Prosthetic Hand:from Biomimetic to Biorealistic

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Research 2021年第1期2021卷 222-225页

作者： Ning Lan Manzhao Hao Chuanxin MNiu He Cui Yu Wang Ting Zhang Peng Fang Chih-hong Chou Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghaiChina Laboratory of Neurorehabilitation Engineering School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina Department of Rehabilitation Medicine Ruijin HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghaiChina Center for Excellence in Brain Science and Intelligent Technology Chinese Academy of SciencesShanghaiChina School of Biological Science and Medical Engineering Beihang UniversityBeijingChina Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina i-lab Key Laboratory of Multifunctional Nanomaterials and Smart SystemsSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhouChina Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenChina

Integrating a prosthetic hand to amputees with seamless neural compatibility presents a grand challenge to neuroscientists and neural engineers for more than half *** anatomical structure or appearance of human hand does not lead to improved neural connectivity to the sensorimotor system of *** functions of modern prosthetic hands do not match the dexterity of human hand due primarily to lack of sensory awareness and compliant ***,progress in restoring sensory feedback has marked a significant step forward in improving neural continuity of sensory information from prosthetic hands to ***,little effort has been made to replicate the compliant property of biological muscle when actuating prosthetic ***,a full-fledged biorealistic approach to designing prosthetic hands has not been contemplated in neuroprosthetic *** this perspective article,we advance a novel view that a prosthetic hand can be integrated harmoniously with amputees only if neural compatibility to the sensorimotor system is *** ongoing research supports that the next-generation prosthetic hand must incorporate biologically realistic actuation,sensing,and reflex functions in order to fully attain neural compatibility.

关键词： neural compatibility primarily

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