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arXiv 2021年

作者： Akyol, Gamze Sariel, Sanem Aksoy, Eren Erdal Artificial Intelligence and Robotics Laboratory Faculty of Computer and Informatics Engineering Istanbul Technical University Maslak Turkey School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Halmstad Sweden

Despite decades of research, understanding human manipulation activities is, and has always been, one of the most attractive and challenging research topics in computer vision and robotics. Recognition and prediction of observed human manipulation actions have their roots in the applications related to, for instance, human-robot interaction and robot learning from demonstration. The current research trend heavily relies on advanced convolutional neural networks to process the structured Euclidean data, such as RGB camera images. These networks, however, come with immense computational complexity to be able to process high dimensional raw data. Different from the related works, we here introduce a deep graph autoencoder to jointly learn recognition and prediction of manipulation tasks from symbolic scene graphs, instead of relying on the structured Euclidean data. Our network has a variational autoencoder structure with two branches: one for identifying the input graph type and one for predicting the future graphs. The input of the proposed network is a set of semantic graphs which store the spatial relations between subjects and objects in the scene. The network output is a label set representing the detected and predicted class types. We benchmark our new model against different state-of-the-art methods on two different datasets, MANIAC and MSRC-9, and show that our proposed model can achieve better performance. We also release our source code https://***/gamzeakyol/GNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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A Variational Graph Autoencoder for Manipulation Action Recognition and Prediction

A Variational Graph Autoencoder for Manipulation Action Reco...

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International Conference on Advanced robotics (ICAR)

作者： Gamze Akyol Sanem Sariel Eren Erdal Aksoy Faculty of Computer and Informatics Engineering Artificial Intelligence and Robotics Laboratory Istanbul Technical University Maslak Turkey School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Halmstad Sweden

ISBN: (纸本)9781665436854

关键词： Computer vision Semantics Robot vision systems Human-robot interaction Predictive models Benchmark testing Market research

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The Cervical Spine Evaluation and Rehabilitation System

The Cervical Spine Evaluation and Rehabilitation System

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IEEE International Conference on Intelligence and Safety for robotics (ISR)

作者： Zhichen Pei Tianchen Li Hangping Yu Meimei Han João Paulo Ferreira Tao Liu State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China School of Medicine Zhejiang University Hangzhou China Zhejiang Fuzhi-Kechuang Inc. Hangzhou China Coimbra Institute of Engineering - Portugal Institute of Systems and Robotics of Coimbra Portugal

The present examination methods to neck musde have high cost, low accuracy, or high examination requirements. This paper establishes a cervical spine evaluation and rehabilitation system (CERS) based on biomechanical analysis, which can help doctor estimate the situation of the patient's neck muscles by comparing various kinds of patient's data with normal range. This paper introduces the present solutions to exam neck musde, including their advantages and disadvantages, and the characteristics of neck muscle and biomechanical analysis for muscle are described. Then this paper will mainly analysis the mechanical design and result processing of CERS. The CERS will provide more convenient options for both doctors and patients.

关键词： Biomechanics Conferences Medical services Muscles Neck Safety Robots

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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

SSRN

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SSRN 2023年

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 Science and Technology Kraków30-059 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 significant attention, owing to its straightforward and highly adaptable algorithm. It has been adopted in many 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. © 2023, The Authors. All rights reserved.

关键词： MATLAB

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Assembly process planning and dynamic evaluation method based on digital twin

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International Journal of Advanced Robotic systems 2020年第3期17卷

作者： Zhang, Xiangyu Liu, Lilan Wan, Xiang Li, Xiaolong Zou, Wei Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China Aerospace Systems Engineering Shanghai Shanghai China

In the dynamic product assembly environment, assembly process evaluation has become the key problem to improve process execution and assembly efficiency in intelligent manufacturing mode. For this reason, the assembly process planning and dynamic evaluation method driven by digital twin is proposed. Based on the five-dimensional model, the architecture of assembly process digital twin system is established, facing with ergonomics, the digital twin modeling and optimization of all elements of "human-machine-object-environment" in workshop assembly process was completed, the expression model and perception framework of assembly process real-time data are created, and the real-time mapping logic structure of assembly workshop operation is put forward, and the twin data of assembly process evaluation are created. The dynamic evaluation process of assembly process driven by twin data is described and then the validity of the proposed method is verified by an example of a car rear axle assembly process. © The Author(s) 2020.

关键词： Assembly

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Social Tasks in a Spatial Augmented Training for the Embodiment of Prosthetic Lower Limbs

Social Tasks in a Spatial Augmented Training for the Embodim...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Giulia Mariani Chiara Dominici Federico Tessari Marco Freddolini Simone Traverso Samuele De Giuseppe Andrea Cherubini Emanuele Gruppioni Lorenzo De Michieli Carlo Ferraresi Matteo Laffranchi Giacinto Barresi Department of Informatics Bioengineering Robotics and Systems Engineering Università degli Studi di Genova Genoa Italy Rehab Technologies Lab at Istituto Italiano di Tecnologia Genova Italy Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy Newman Laboratory for Biomechanics Human Rehabilitation Massachusetts Institute of Technology Cambridge MA USA Open University Affiliated Research Center at Istituto Italiano di Tecnologia (ARC@IIT) Genova Italy Centro Protesi INAIL Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro Bologna Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Despite the proven advantages of prostheses, a significant number of amputees discontinue their usage for multiple reasons. Enhancing the prosthetic embodiment could be a strategy to improve the acceptance of such devices. Current literature suggests that virtual and augmented reality technologies offer promising solutions to enhance the embodiment of artificial limbs. This study delves into the potential of a Spatial Augmented Reality (SAR) system in fostering prosthesis integration and engaging individuals with transfemoral amputations. In this paper, participants use electromyography (EMG) to control a virtual leg. By contracting the quadriceps, they are tasked to break a virtual wall. Each collision generates vibrational feedback on the thigh. The study incorporates three conditions, featuring both social (cooperative and competitive) and non-social interaction. The underlying hypothesis posits that alternating between these conditions can concurrently stimulate embodiment through sustained engagement, particularly when social elements are introduced. Data collection encompasses EMG signals, heart rate variability, and skin conductance. Comprehensive examination, involving subjective questionnaires and assessments of proprioceptive drift, seeks to clarify the intricacies of embodiment and engagement throughout the tests. Results from tests alternating social and non-social conditions prove satisfactory, underscoring the approach's potential to enhance subject engagement and facilitate the prostheses' embodiment. This study thus provides insights for future investigations on prosthetic integration for amputees.

关键词： Training Legged locomotion Electric potential Spatial augmented reality Propioception Thigh Electromyography Skin Heart rate variability Robots

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Polarization-independent resonant lattice Kerker effect in phase-change metasurface

arXiv

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arXiv 2022年

作者： Xiong, Lei Luo, Xiaoqing Ding, Hongwei Lu, Yuanfu Li, Guangyuan School of Information Science and Engineering Yunnan University Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Siat Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

Resonant lattice Kerker effect in periodic resonators is one of the most interesting generalizations of the Kerker effect that relates to various fascinating functionalities such as scattering management and Huygens metasurfaces. However, so far this effect has been shown to be sensitive to the incident polarization, restricting its applications. Here, we report, for the first time, polarization-independent resonant lattice Kerker effect in metasurfaces composed of periodic Ge2Se2Te5(GST) disks. For such a metasurface of square lattice, the spectrally overlap of the electric dipole and magnetic dipole surface lattice resonances can be realized by choosing an appropriate GST crystalline fraction regardless of the incident polarization. The operation wavelength and the required GST crystalline fraction can be conveniently tuned over large ranges since these parameters scale linearly with the disk size and the lattice period, greatly facilitating the design. Making use of the obtained resonant lattice Kerker effect, we realize a reconfigurable and polarization-independent lattice Huygens' metasurface with a dynamic phase modulation of close to 2π and high transmittance. This work will advance the engineering of the resonant lattice Kerker effect and promote its applications in phase modulation and wavefront control. Copyright © 2022, The Authors. All rights reserved.

关键词： Tellurium compounds

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Neural Augmentation Based Panoramic High Dynamic Range Stitching

arXiv

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arXiv 2024年

作者： Zheng, Chaobing Xu, Yilun Chen, Weihai Wu, Shiqian Zhang, Sen Li, Zhengguo Institute of Robotics and Intelligent Systems School of Electronic Information Wuhan University of Science and Technology 947 Heping Avenue Wuhan430081 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Automation and Electrical Engineering University of Science and Technology Beiing Beijing100083 China Key Laboratory of Knowledge Auomalion for indusrial Proceses of Mimistry ofEducation University of Science and Techmology Beijing Beijing1083 China Shunde lnnovation School University of Science and Technology Beijing Foshan 528300 China VI Department Institute for Infocomm Research A*STAR 138632 Singapore

Due to saturated regions of inputting low dynamic range (LDR) images and large intensity changes among the LDR images caused by different exposures, it is challenging to produce an information enriched panoramic LDR image without visual artifacts for a high dynamic range (HDR) scene through stitching multiple geometrically synchronized LDR images with different exposures and pairwise overlapping fields of views (OFOVs). Fortunately, the stitching of such images is innately a perfect scenario for the fusion of a physics-driven approach and a data-driven approach due to their OFOVs. Based on this new insight, a novel neural augmentation based panoramic HDR stitching algorithm is proposed in this paper. The physics-driven approach is built up using the OFOVs. Different exposed images of each view are initially generated by using the physics-driven approach, are then refined by a data-driven approach, and are finally used to produce panoramic LDR images with different exposures. All the panoramic LDR images with different exposures are combined together via a multi-scale exposure fusion algorithm to produce the final panoramic LDR image. Experimental results demonstrate the proposed algorithm outperforms existing panoramic stitching algorithms. © 2024, CC0.

关键词： Image fusion

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Micro-pores formation mechanism and porosity on the fatigue performance of dual laser-powder bed fusion of Ti6Al4V

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International Journal of Fatigue 2025年 199卷

作者： Li, Guotai Hu, Peichen Chen, Gang Chen, Mingjun Yu, Tianyu State Key Laboratory of Robotics and System Harbin Institute of Technology Heilongjiang Harbin150001 China Key Laboratory of Micro-systems and Micro-structures Manufacturing Ministry of Education Heilongjiang Harbin150001 China Institute of Electronic Engineering China Academy of Engineering Physics Sichuan Mianyang621000 China

Porosity significantly impacts the fatigue performance and stability of dual laser additively manufactured metallic materials. This study presents a comparative analysis of the fatigue behavior of samples fabricated by single-laser powder bed fusion (SL-PBF) and dual-laser PBF (DL-PBF). It shows that porosity defects in the overlap region reduced the fatigue strength of DL-PBF samples by nearly 20 %. Micro-CT analysis revealed that micro-pores in the SL-PBF samples were predominantly located in the contour regions. Machining with a removal depth of 0.5 mm has effectively eliminated over 90 % of internal porosity defects. For monolithic specimens, increasing the sample size and reducing the layer-wise slice area can improve the sphericity of the internal defects (0.71 to 0.85) and reduce the porosity density (0.19 % to 0.011 %). Molecular dynamics simulations further examined the influence of pore size, spacing, and quantity on fatigue behavior. Larger pore sizes promote the formation of {11 2¯ 1} twin variants, as the pore diameter increased from 20 Å to 80 Å, the proportion of variants structure increased from 3.6 % to 11.5 %. Fatigue failure initiated from a single pore, with stress propagating towards a distant pore, forming crack paths. Stress was relieved along these crack trajectories, leading to secondary crack branches that spread into low-stress zones around other defects, which resulted in fatigue cracks with vein-like morphologies. This work provides systematic insights into the fatigue degradation mechanisms induced by porosity and offers actionable strategies for improving the fatigue resistance of multi-laser PBF components through process control. © 2025 Elsevier Ltd

关键词： High-cycle fatigue

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Multiscale Convolutional Transformer with Diverse-aware Feature Learning for Motor Imagery EEG Decoding

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Hang, Wenlong Wang, Junliang Liang, Shuang Lei, Baiying Wang, Qiong Li, Guanglin Chen, Badong Qin, Jing Nanjing Tech University College of Computer Nanjing211816 China Nanjing Tech University Information Engineering Nanjing211816 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210023 China Shenzhen University School of Biomedical Engineering Shenzhen518060 China Guangdong Provincial Key Laboratory of Computer Vision Shenzhen518055 China Virtual Reality Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences Shenzhen518055 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hong Kong Polytechnic University School of Nursing Hung Hom Hong Kong

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs) have significant potential in improving motor function for neurorehabilitation. Despite recent advancements, learning diversified EEG features across different frequency ranges remains a significant challenge, as the homogenization of feature representations often limits the generalization capabilities of EEG decoding models for BCIs. In this paper, we propose a novel multiscale convolutional transformer framework for EEG decoding that integrates multiscale convolution, transformer, and diverse-aware feature learning scheme (MCTD) to tackle the above challenge. Specifically, we first capture multiple frequency features using dynamic one-dimensional temporal convolution with different kernel lengths. Subsequently, we incorporate convolutional layers and transformers with a contrastive learning scheme to extract discriminative local and global EEG features within a single frequency range. To mitigate the homogenization of features extracted from different frequency ranges, we propose a novel decorrelation regularization. It enables multiscale convolutional transformers produce less correlated features with each other, thereby enhancing the overall expressiveness of EEG decoding model. The performance of MCTD is evaluated on four public MI-based EEG datasets, including the BCI competition III 3a and IV 2a, the BNCI 2015- 001, and the OpenBMI. For the average Kappa/Accuracy scores, MCTD obtains improvements of 3.58%/2.68%, 3.09%/2.20%, 2.33%/1.54%, and 4.44%/2.22%, over the state-of-the-art method on four EEG datasets, respectively. Experimental results demonstrate that our method exhibits superior performance. © 2016 IEEE.

关键词： Brain computer interface

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