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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： robotics

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Fabrication of Translucent Graded Dental Crown Using Zirconia-Yttrium Multi-Slurry Tape Casting 3d Printer

SSRN

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SSRN 2024年

作者： Romario, Yulius Shan Bhat, Chinmai Ramezani, Maziar Pasang, Tim Chen, Zhangwei Jiang, Cho-Pei Graduate Institute of Manufacturing Technology National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan High-value Biomaterials Research and Commercialization Center National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan Department of Mechanical Engineering Auckland University of Technology Auckland1010 New Zealand Department of Engineering Design Manufacturing and Management Systems Western Michigan University KalamazooMI4900-5200 United States Additive Manufacturing Institute Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robotics College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Department of Mechanical Engineering National Taipei University of Technology Taiwan 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan

This paper aims to fabricate functionally graded dental crown using a multi-slurry tape casting additive manufacturing technology. The different luminescence of the dental crown was obtained with different composition of zirconia and yttria. Zirconia with tunable mechanical properties and translucency are obtained by adding 3, 3.5, 4, 4.5, and 5 mol% of yttrium oxide to zirconia powder. After obtaining the printable slurry with maximum solid loading, the green bodies are prepared using the in-house built high speed multi-ceramic tape casting technology. They are later sintered with two-stage sintering method. After the successful fabrication, the mechanical properties and translucency of the specimens were evaluated with Vickers hardness, three-point bending and translucency parameter tests. Finally, an FGM tooth crown with five photocurable slurries is proposed to demonstrate the translucent gradient effect of sintered part. The solid loading of 80% zirconia and 20% resin delivered samples without any surface cracks. The shrinkage ratio analysis showed that the sintered sample dimension was reduced by 20%, 20%, and 23% along X, Y, and Z directions. The samples fabricated with 3% yttrium oxide to zirconia delivered excellent hardness (1687 HV) and flexural strength (650.6 MPa). However, the relative luminescence increased with increasing the yttrium oxide for 3 – 5 mol%. With the optimized process parameters, the proposed dental crown is fabricated and analyzed for their shrinkage ratio, mechanical, and translucency properties. The study proposes the potential of fabricating customized dental crown with gradient translucent appearance. © 2024, The Authors. All rights reserved.

关键词： Zirconia

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Propagating Asymptotic-Estimated Gradients for Low Bitwidth Quantized Neural Networks

arXiv

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arXiv 2020年

作者： Chen, Jun Liu, Yong Zhang, Hao Hou, Shengnan Yang, Jian Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China China Research and Development Academy of Machinery Equipment Beijing100089 China

—The quantized neural networks (QNNs) can be useful for neural network acceleration and compression, but during the training process they pose a challenge: how to propagate the gradient of loss function through the graph flow with a derivative of 0 almost everywhere. In response to this non-differentiable situation, we propose a novel Asymptotic-Quantized Estimator (AQE) to estimate the gradient. In particular, during back-propagation, the graph that relates inputs to output remains smoothness and differentiability. At the end of training, the weights and activations have been quantized to low-precision because of the asymptotic behaviour of AQE. Meanwhile, we propose a M-bit Inputs and N-bit Weights Network (MINW-Net) trained by AQE, a quantized neural network with 1-3 bits weights and activations. In the inference phase, we can use XNOR or SHIFT operations instead of convolution operations to accelerate the MINW-Net. Our experiments on CIFAR datasets demonstrate that our AQE is well defined, and the QNNs with AQE perform better than that with Straight-Through Estimator (STE). For example, in the case of the same ConvNet that has 1-bit weights and activations, our MINW-Net with AQE can achieve a prediction accuracy 1.5% higher than the Binarized Neural Network (BNN) with STE. The MINW-Net, which is trained from scratch by AQE, can achieve comparable classification accuracy as 32-bit counterparts on CIFAR test sets. Extensive experimental results on ImageNet dataset show great superiority of the proposed AQE and our MINW-Net achieves comparable results with other state-of-the-art QNNs. Copyright © 2020, The Authors. All rights reserved.

关键词： Backpropagation

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Reference Aware Model Predictive control for Autonomous Vehicles

Reference Aware Model Predictive Control for Autonomous Vehi...

引用

IEEE Symposium on Intelligent Vehicle

作者： Gonçalo Collares Pereira Pedro F. Lima Bo Wahlberg Henrik Pettersson Jonas Mårtensson Research and Development Scania CV AB Sodertalje Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Research and Development Scania CV AB Södertälie Sweden

ISBN: (数字)9781728166735

ISBN: (纸本)9781728166742

This paper presents a path following controller for autonomous vehicles, making use of the linear time-varying model predictive control (LTV-MPC) framework. The controller takes into consideration control input rates and accelerations, not only to account for limitations in the steering dynamics, but also to provide a safe and comfortable ride while minimizing wear and tear of the vehicle components. Furthermore, it introduces a method to handle model references generated by motion planning algorithms that can consider different vehicle models from the controller. The proposed controller is verified by simulations and through experiments in a Scania construction truck, and is shown to have better performance than the state-of-the-art smooth and accurate MPC.

关键词： Autonomous vehicles Vehicle dynamics Acceleration Kinematics Actuators Safety Predictive models

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Classification of Einstein spaces with Stackel metric of type (3.0)

arXiv

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arXiv 2025年

作者： Obukhov, V.V. Tomsk State Pedagogical University Institute of Scietific Research and Development 60 Kievskaya St. Tomsk634041 Russia Tomsk State University of Control Systems and Radio Electronics Laboratory for Theoretical Cosmology International Center of Gravity and Cosmos 36 Lenin Avenue Tomsk634050 Russia

The classification of the Einstein spaces with the Stackel metric of the(3.0) has been done. These spaces are invariant under the action of thethree-parameter abelian group of motions and belong to the first type Bianchispaces. Thus the classification of vacuum and electrovacuum Stackel spaces ofall types is completed and the complete list of metrics of such spaces inprivileged coordinate systems is *** Codes 70H20, 83C05, 83C15, 83C20 © 2025, CC BY-NC-ND.

关键词： Hilbert spaces

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Time synchronization between parietal–frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks

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Journal of NeuroEngineering and Rehabilitation 2024年第1期21卷 1-16页

作者： Phang, Chun-Ren Su, Kai-Hsiang Cheng, Yuan-Yang Chen, Chia-Hsin Ko, Li-Wei International Ph.D. Program in Interdisciplinary Neuroscience (UST) College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Physical Medicine and Rehabilitation Taichung Veterans General Hospital Taichung Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Physical Medicine and Rehabilitation Kaohsiung Medical University Hospital Kaohsiung Taiwan School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Regenerative Medicine and Cell Therapy Research Center Kaohsiung Medical University Kaohsiung Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Science and Environment Biology Kaohsiung Medical University Kaohsiung Taiwan Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung Taiwan

Background: In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated. Methods: Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson’s correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification. Results: Parietal–frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients. Conclusions: In this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation. © The Author(s) 2024.

关键词： Brain–computer interface Functional connectivity Motor execution Motor preparation Post-stroke

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Switching function-based optimal active fault-tolerant bipartite consensus control for UAV swarm

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Automatica 2025年 179卷

作者： Pei-Ming Liu Xiang-Gui Guo Jian-Liang Wang Hao Lu Zheng-Guang Wu Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China Shunde Innovation School University of Science and Technology Beijing Foshan 528000 China Collaborative Flying Robotics Lab Hangzhou Innovation Institute of Beihang University Hangzhou 310051 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China

This paper studies the problem of optimal active fault-tolerant bipartite consensus control for flying wing unmanned aerial vehicle (UAV) swarm with nonidentical and unknown direction faults (NUDFs) and disturbances by integrating switching and saturation functions. An observer–controller framework is employed to prevent error propagation among follower UAVs. Based on this framework, an active fault-tolerant control strategy is proposed to improve the system’s transient performance. This strategy prevents sudden shocks from excessive reverse inputs on the system and avoids state chattering caused by excessive adjustment inputs, which often occur in the Nussbaum function-based fault-tolerant control method. Additionally, the newly proposed switching criterion directly matches the desired operating mode, even in the presence of disturbances and multiple fault direction changes, thus avoiding ineffective switching and enhancing the robustness of the designed controller. To further improve the UAVs’ transient performance, a reinforcement learning (RL)-based preset performance backstepping control method is introduced. This method optimizes output regulation, saves energy consumption, and reduces the impact of soft saturation on system performance. Finally, simulation results validate the effectiveness and superiority of the proposed scheme.

关键词：

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Mapping Surgeons Hand/Finger Movements to Surgical Tool Motion during Conventional Microsurgery Using Machine Learning

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Journal of Medical robotics research 2021年第3-4期6卷

作者： Fattahi Sani, Mohammad Ascione, Raimondo Dogramadzi, Sanja Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Bristol Heart Institute and Translational Biomedical Research Centre Faculty of Health Science University of Bristol Bristol United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffiled United Kingdom

Purpose: Recent developments in robotics and artificial intelligence (AI) have led to significant advances in healthcare technologies enhancing robot-Assisted minimally invasive surgery (RAMIS) in some surgical specialties. However, current human-robot interfaces lack intuitive teleoperation and cannot mimic surgeon's hand/finger sensing required for fine motion micro-surgeries. These limitations make teleoperated robotic surgery not less suitable for, e.g. cardiac surgery and it can be difficult to learn for established surgeons. We report a pilot study showing an intuitive way of recording and mapping surgeon's gross hand motion and the fine synergic motion during cardiac micro-surgery as a way to enhance future intuitive teleoperation. Methods: We set to develop a prototype system able to train a Deep Neural Network (DNN) by mapping wrist, hand and surgical tool real-Time data acquisition (RTDA) inputs during mock-up heart micro-surgery procedures. The trained network was used to estimate the tools poses from refined hand joint angles. Outputs of the network were surgical tool orientation and jaw angle acquired by an optical motion capture system. Results: Based on surgeon's feedback during mock micro-surgery, the developed wearable system with light-weight sensors for motion tracking did not interfere with the surgery and instrument handling. The wearable motion tracking system used 12 finger/thumb/wrist joint angle sensors to generate meaningful datasets representing inputs of the DNN network with new hand joint angles added as necessary based on comparing the estimated tool poses against measured tool pose. The DNN architecture was optimized for the highest estimation accuracy and the ability to determine the tool pose with the least mean squared error. This novel approach showed that the surgical instrument's pose, an essential requirement for teleoperation, can be accurately estimated from recorded surgeon's hand/finger movements with a mean squared error (M

关键词： feature extraction hand tracking machine learning minimally invasive surgery real-Time low-cost hand tracking Robot-Assisted surgery

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Preface

Lecture Notes in Networks and Systems

引用

Lecture Notes in Networks and systems 2024年 1076 LNNS卷 v-vii页

作者： Biele, Cezary Kacprzyk, Janusz Kopeć, Wieslaw Możaryn, Jakub Owsiński, Jan W. Romanowski, Andrzej Sikorski, Marcin National Information Processing Institute Warszaw Poland Polish Academy of Sciences Systems Research Institute Warsaw Poland Polish-Japanese Academy of Information Technology Warsaw Poland Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Institute of Applied Computer Science Faculty of Electrical Electronic Computer and Control Engineering Lódż University of Technology LODZ Poland Department of Applied Informatics in Management Faculty of Management and Economics Gdańsk University of Technology Gdańsk Poland

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Weakly Aligned Feature Fusion for Multi-modal Object Detection

arXiv

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arXiv 2022年

作者： Zhang, Lu Liu, Zhiyong Zhu, Xiangyu Song, Zhan Yang, Xu Lei, Zhen Qiao, Hong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100086 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China The Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong The Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China

To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, depth, etc. However, multi-modal data often suffer from the position shift problem, i.e. the image pair is not strictly aligned, making one object has different positions in different modalities. For the deep learning method, this problem makes it difficult to fuse multi-modal features and puzzles the CNN training. In this paper, we propose a general multi-modal detector named Aligned Region CNN (AR-CNN) to tackle the position shift problem. Firstly, a region feature alignment module with adjacent similarity constraint is designed to consistently predict the position shift between two modalities and adaptively align the cross-modal region features. Secondly, we propose a novel RoI jitter strategy to improve the robustness to unexpected shift patterns. Thirdly, we present a new multi-modal feature fusion method, which selects the more reliable feature and suppresses the less useful one via feature re-weighting. Additionally, by locating bounding boxes in both modalities and building their relationships, we provide novel multi-modal labelling named KAIST-Paired. Extensive experiments on 2D and 3D object detection, RGB-T and RGB-D datasets demonstrate the effectiveness and robustness of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

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