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arXiv 2022年

作者： Salazar, Teresa Fernandes, Miguel Araújo, Helder Abreu, Pedro Henriques Centre for Informatics and Systems Department of Informatics Engineering of The University of Coimbra University of Coimbra Coimbra Portugal Institute of Systems and Robotics Department of Electrical and Computer Engineering of The University of Coimbra University of Coimbra Coimbra Portugal

While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has been on centralized machine learning, leaving decentralized methods underexplored. Federated Learning is a decentralized form of machine learning where clients train local models with a server aggregating them to obtain a shared global model. Data heterogeneity amongst clients is a common characteristic of Federated Learning, which may induce or exacerbate discrimination of unprivileged groups defined by sensitive attributes such as race or gender. In this work we propose FAIR-FATE: a novel FAIR FederATEd Learning algorithm that aims to achieve group fairness while maintaining high utility via a fairness-aware aggregation method that computes the global model by taking into account the fairness of the clients. To achieve that, the global model update is computed by estimating a fair model update using a Momentum term that helps to overcome the oscillations of non-fair gradients. To the best of our knowledge, this is the first approach in machine learning that aims to achieve fairness using a fair Momentum estimate. Experimental results on real-world datasets demonstrate that FAIR-FATE outperforms state-of-the-art fair Federated Learning algorithms under different levels of data *** Codes 68T07 © 2022, CC BY.

关键词： Momentum

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Selection of relevant mode of thin and soft parts measurements on machines with multisensor possibility 23

Selection of relevant mode of thin and soft parts measuremen...

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23rd International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2023

作者： Harmatys, Wiktor Gaska, Adam Gaska, Piotr Gruza, Maciej Marxer, Michael Cracow University of Technology Faculty of Mechanical Engineering Laboratory of Coordinate Metrology al. Jana Pawla II 37 Cracow31-864 Poland Department of Manufacturing Systems Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology al. Mickiewicza 30 Krakow30-059 Poland Eastern Switzerland University of Applied Sciences Institute for Production Metrology Materials and Optics Werdenbergstrasse 4 SG BuchsCH-9471 Switzerland

ISBN: (纸本)9781998999132

This paper presents measurements of thin and soft parts that are easily deformable under contact force related to tactile measurement. Few types of such elements, widely used in practice, were chosen and measured on multisensor CMM using low force tactile probe, video probe and chromatic white light sensor. Measurement procedure included aligning of the workpiece based on points measured using different sensors and performing measurements according to multiple-measurement strategy method. Values of repeatability determined for measurement results from different sensors were compared. Differences in obtained repeatability values are significant. Discussion of aftermaths related to use of various types of sensors is also included in the paper. © 2023 Euspen Headquarters.

关键词： Probes

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A Scalable Real-time Semantic Segmentation Network for Autonomous Driving 1

A Scalable Real-time Semantic Segmentation Network for Auton...

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1st Workshop on Advanced Multimedia Computing for Smart Manufacturing and engineering, AMC-SME 2023

作者： Liu, Jun Wu, Chao Yuan, Geng Niu, Wei Zhang, Wenbin Song, Houbing Herbert Robotics Institute of School of Computer Science Carnegie Mellon University Pittsburgh United States Department of Electrical & Computer Engineering Northeastern University Boston United States School of Computing University of Georgia Athens United States Department of Computer Science Michigan Technological University Houghton United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

ISBN: (纸本)9798400702730

Computational resources are limited on real-time embedded devices, so the available computing cost of deployment on the target platform must be considered. We develop a feature extraction module based on the MobileNet backbone that can be adjusted in terms of computational complexity and capacity using the depth multiplier parameter, classifier depth, and kernel depth. These three parameters allow us to control the count of channels within the network, effectively managing the model's capacity and computational requirements. To achieve semantic segmentation, we incorporate additional components, such as an extension module. This extension module typically includes 1x1 pointwise convolutional layers for pixel-level classification and a transposed convolutional layer for upsampling the output to the original input image size. By combining the feature extraction module with this extension module, we can create a complete architecture capable of performing semantic segmentation tasks. The feature extraction module provides the initial feature extraction and the extension module adds the necessary components for accurate pixel-wise classification and upsampling. Compared to Hardware-aware Neural Architecture Search (NAS), pruning, runtime pruning, and knowledge distillation methods, our model has several advantages in terms of modular design, structural controllability, ease of implementation, and cost-effectiveness. Our computational efficiency, as measured by FLOPS, is highly competitive. Our method is distinguished by solving the problem of MobileNet's inability to adjust the size and number of convolution kernels. It achieves this through adaptable parameter tuning, including MobileNet's depth multiplier, the kernel size in the FCN head's Separable Convolution layer, and the depth of the first Point-wise Convolution layer. These adjustments are customized to match hardware's max multiply-accumulates (MACs), optimizing network capacity and maximizing resource utilizat

关键词： Feature extraction

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Vibrational Stabilization of Cluster Synchronization in Oscillator Networks

arXiv

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arXiv 2023年

作者： Qin, Yuzhen Nobili, Alberto Maria Bassett, Danielle S. Pasqualetti, Fabio Department of Mechanical Engineering University of California RiversideCA United States Perceptual Robotics Laboratory at the IIM Institute Department of Excellence in Robotics and A.I. Scuola Superiore Sant’Anna Pisa Italy Department of Bioengineering Department of Electrical & Systems Engineering Department of Physics & Astronomy The Department of Psychiatry Department of Neurology University of Pennsylvania The Santa Fe Institute United States

Cluster synchronization is of paramount importance for the normal functioning of numerous technological and natural systems. Deviations from normal cluster synchronization patterns are closely associated with various malfunctions, such as neurological disorders in the brain. Therefore, it is crucial to restore normal system functions by stabilizing the appropriate cluster synchronization patterns. Most existing studies focus on designing controllers based on state measurements to achieve system stabilization. However, in many real-world scenarios, measuring system states, such as neuronal activity in the brain, poses significant challenges, rendering the stabilization of such systems difficult. To overcome this challenge, in this paper, we employ an open-loop control strategy, vibrational control, which does not requires any state measurements. We establish some sufficient conditions under which vibrational inputs stabilize cluster synchronization. Further, we provide a tractable approach to design vibrational control. Finally, numerical experiments are conducted to demonstrate our theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Brain

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Intention-Aware Planner for Robust and Safe Aerial Tracking

Intention-Aware Planner for Robust and Safe Aerial Tracking

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Qiuyu Ren Huan Yu Jiajun Dai Zhi Zheng Jun Meng Li Xu Chao Xu Fei Gao Yanjun Cao The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China The Robotics Institute of Zhejiang University Yuyao China Huzhou Institute Zhejiang University Huzhou China Huzhou Laboratory of Autonomous Intelligent Systems Huzhou China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to speci cally evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method.

关键词： Target tracking Semantics Prediction methods Turning Prediction algorithms Robustness Safety Planning Trajectory optimization Quadrotors

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EVEN SMALL CORRELATION AND DIVERSITY SHIFTS POSE DATASET-BIAS ISSUES

arXiv

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arXiv 2023年

作者： Bissoto, Alceu Barata, Catarina Valle, Eduardo Avila, Sandra Institute of Computing University of Campinas Brazil Institute for Systems and Robotics Instituto Superior Técnico Portugal School of Electrical and Computing Engineering University of Campinas Brazil Recod.ai Lab. University of Campinas Brazil

Distribution shifts are common in real-world datasets and can affect the performance and reliability of deep learning models. In this paper, we study two types of distribution shifts: diversity shifts, which occur when test samples exhibit patterns unseen during training, and correlation shifts, which occur when test data present a different correlation between seen invariant and spurious features. We propose an integrated protocol to analyze both types of shifts using datasets where they co-exist in a controllable manner. Finally, we apply our approach to a real-world classification problem of skin cancer analysis, using out-of-distribution datasets and specialized bias annotations. Our protocol reveals three findings: 1) Models learn and propagate correlation shifts even with low-bias training;this poses a risk of accumulating and combining unaccountable weak biases;2) Models learn robust features in high- and low-bias scenarios but use spurious ones if test samples have them;this suggests that spurious correlations do not impair the learning of robust features;3) Diversity shift can reduce the reliance on spurious correlations;this is counter-intuitive since we expect biased models to depend more on biases when invariant features are missing. Our work has implications for distribution shift research and practice, providing new insights into how models learn and rely on spurious correlations under different types of shifts. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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The Validity of a Machine Learning-Based Video Game in the Objective Screening of Attention Deficit Hyperactivity Disorder in Children Aged 5 to 12 Years

arXiv

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arXiv 2023年

作者： Zakani, Zeinab Moradi, Hadi Ghasemzadeh, Sogand Riazi, Maryam Mortazavi, Fatemeh Department of Psychology University of Tehran Iran Department of Robotics and Artificial Intelligence University of Tehran Iran Intelligent Systems Research Institute SKKU Iran Department of Electrical and Computer Engineering Boston University United States Department of Robotics and Artificial Intelligence University of Tehran Tehran Iran

Objective: Early identification of ADHD is necessary to provide the opportunity for timely treatment. However, screening the symptoms of ADHD on a large scale is not easy. This study aimed to validate a video game (FishFinder) for the screening of ADHD using objective measurement of the core symptoms of this disorder. Method: The FishFinder measures attention and impulsivity through in-game performance and evaluates the child's hyperactivity using smartphone motion sensors. This game was tested on 26 children with ADHD and 26 healthy children aged 5 to 12 years. A Support Vector Machine was employed to detect children with ADHD. results: This system showed 92.3% accuracy, 90% sensitivity, and 93.7% specificity using a combination of in-game and movement features. Conclusions: The FishFinder demonstrated a strong ability to identify ADHD in children. So, this game can be used as an affordable, accessible, and enjoyable method for the objective screening of ADHD. © 2023, CC BY-NC-ND.

关键词： Serious games

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Multiparametric atomic force microscopy imaging of single native exosomes

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生物化学与生物物理学报（英文版） 2021年第3期53卷 385-388页

作者： Mi Li Xinning Xu Ning Xi Wenxue Wang Xiaojing Xing Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China Cancer Hospital of China Medical University Shenyang 110042China Liaoning Cancer Hospital&Institute Shenyang 110042China Department of Industrial and Manufacturing Systems Engineering The University of Hong KongHong Kong 999077China

The advent of atomic force microscopy(AFM)provides a novel powerful tool for addressing biological issues in their native *** uses a sharp tip mounted at the end of a micro-fabricated cantilever to probe the specimens immobilized on the *** with other high-resolution imaging and mea-surement techniques(***-resolution optical microscopy,scan-ning electron microscopy,transmission electron microscopy,and magnetic/optical tweezers),the unique merit of AFM is that it is able to simultaneously acquire the structures and properties of living biological samples under aqueous conditions with unprece-dented spatiotemporal resolution(nanometer spatial resolution and millisecond temporal resolution)[1],making AFM an invalu-able platform for revealing the underlying mechanisms guiding life *** wide applications of AFM in molecular and cell biology provide considerable novel insights into the physi-ological/pathological processes[2].Particularly,in recent years,a new AFM imaging mode,which is called peak force tapping(PFT)mode[3],emerges as a promising way for correlating the topography of the specimens with the mechanics of *** PFT mode,the vibrating AFM tip indents the sample at each sam-pling point to record force curves,and the peak force of the force curve is used as the feedback[4].By analyzing the force curves,several types of mechanical properties of the specimens can be real-timely visualized,and thus PFT mode is also called multiparametric imaging AFM[5],offering novel possibilities for resolving the fine structures and properties of fragile biological specimens.

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基于自解耦三明治结构的横向运动栅场效应晶体管MEMS微力传感器

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engineering 2023年第2期21卷 61-74,M0004页

作者：高文迪乔智霞韩香广王小章 Adnan Shakoor 刘存朗卢德江杨萍赵立波王永录王久洪蒋庄德孙东 State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesOverseas Expertise Introduction Center for Micro/Nano Manufacturing and Nano Measurement Technologies Discipline InnovationXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150006China Eleventh Research Institute Sixth Academy of China Aerospace Science and Technology Co.Xi’an 710100China Department of Control and Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia Department of Biomedical Engineering City University of Hong KongHong Kong 999077China

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵... 详细信息

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵列层和两个软光阻层SU-8组成。通过所提出的双差分传感结构,LMGFET横向工作在垂直干扰下的输出电流被大大消除,所提出传感器的相对输出误差从4.53%(传统差分结构)降低到0.01%。提出了一种实用的传感器制造工艺,并对其进行了模拟。基于LMGFET的力传感器的灵敏度为4.65μA·nN^(-1),可与垂直可移动栅极场效应晶体管(VMGFET)器件相媲美,但非线性度提高了0.78%,测量范围扩大了±5.10μN。这些分析为LMGFET器件的电气和结构参数提供了全面的设计优化,并证明了所提出的传感器在生物医学显微操作应用中具有良好的力传感潜力。

关键词：力传感器场效应晶体管横向运动三明治结构电气模型显微操作非线性度双差分

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A New Workspace and Structural Parameter Optimization Method of Parallel Robot

A New Workspace and Structural Parameter Optimization Method...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Haiyun Zhang Weijun Hu Renge Zhang Jin Wang Guodong Lu School of Robotics Ningbo University of Technology Ningbo China Robotics Institute of Zhejiang University Hangzhou China Zhejiang Qiantang Robot and Intelligent Equipment Reserch Co. LTD Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Robotics Research Center of Yuyao City Ningbo China

The parallel robot workspace refers to the set of points and boundary that can be reached by the parallel mechanism. With the continuous development and industrial application of parallel robots, the workspace analysis and optimization becomes a critical link in this process. In this paper, a workspace solution approach based on seed space segmentation and principal component analysis is proposed. In order to improve the working performance of parallel robots, a flexible workspace evaluation method based on quadratic fourth moment is proposed, and a relationship model between flexible workspace and structural parameters is established. Furthermore, based on the multi-objective genetic algorithm, the structure parameters of the parallel robot are optimized by the workspace and flexible workspace indexes. Finally, the advantage and effectiveness of proposed method are verified by experiment and comparison with traditional methods.

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