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Seamless Interaction Design with Coexistence and Cooperation Modes for Robust Human-Robot Collaboration

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arXiv 2022年

作者： Huang, Zhe Mun, Ye-Ji Li, Xiang Xie, Yiqing Zhong, Ninghan Liang, Weihang Geng, Junyi Chen, Tan Driggs-Campbell, Katherine The Department of Electrical and Computer Engineering The University of Illinois Urbana-Champaign United States The Department of Computer Science The University of Illinois Urbana-Champaign United States The Robotics Institute Carnegie Mellon University United States The Coordinated Science Laboratory The University of Illinois Urbana-Champaign United States

A robot needs multiple interaction modes to robustly collaborate with a human in complicated industrial tasks. We develop a Coexistence-and-Cooperation (CoCo) human-robot collaboration system. Coexistence mode enables the robot to work with the human on different sub-tasks independently in a shared space. Cooperation mode enables the robot to follow human guidance and recover failures. A human intention tracking algorithm takes in both human and robot motion measurements as input and provides a switch on the interaction modes. We demonstrate the effectiveness of CoCo system in a use case analogous to a real world multi-step assembly task. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine design

EFaR 2023: Efficient Face Recognition Competition

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arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well. © 2023, CC BY-NC-SA.

关键词： Face recognition

InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation

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arXiv 2023年

作者： Dong, Jiahua Cong, Yang Sun, Gan Wang, Lixu Lyu, Lingjuan Li, Jun Konukoglu, Ender The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The University of Chinese Academy of Sciences Beijing100049 China The Computer Science Department Northwestern University Evanston United States The Sony AI Tokyo108-0075 Japan The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Computer Vision Lab ETH Zurich Zürich8092 Switzerland

3D object recognition has successfully become an appealing research topic in the real-world. However, most existing recognition models unreasonably assume that the categories of 3D objects cannot change over time in the real-world. This unrealistic assumption may result in significant performance degradation for them to learn new classes of 3D objects consecutively, due to the catastrophic forgetting on old learned classes. Moreover, they cannot explore which 3D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3D objects. To tackle the above challenges, we develop a novel Incremental 3D Object Recognition Network (i.e., InOR-Net), which could recognize new classes of 3D objects continuously via overcoming the catastrophic forgetting on old classes. Specifically, a category-guided geometric reasoning is proposed to reason local geometric structures with distinctive 3D characteristics of each class by leveraging intrinsic category information. We then propose a novel critic-induced geometric attention mechanism to distinguish which 3D geometric characteristics within each class are beneficial to overcome the catastrophic forgetting on old classes of 3D objects, while preventing the negative influence of useless 3D characteristics. In addition, a dual adaptive fairness compensations strategy is designed to overcome the forgetting brought by class imbalance, by compensating biased weights and predictions of the classifier. Comparison experiments verify the state-of-the-art performance of the proposed InOR-Net model on several public point cloud datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Object recognition

SVM-based Extended Kalman Filter State Estimation of Biped Robot

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SVM-based Extended Kalman Filter State Estimation of Biped R...

IEEE International Conference on robotics and Biomimetics

作者： Chengzhi Gao Ye Xie Shiqiang Zhu Guanyu Huang Lingyu Kong Anhuan Xie Jianjun Gu Dan Zhang Zhejiang Laboratory Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Zhejiang University Hangzhou Zhejiang China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada York University North York Toronto Canada

ISBN: (纸本)9781665481106

At present, more and more attention has been paid to the exploration of biped robots. As the precondition of biped stable walking, the state estimation of robot has also received the attention of many experts. Biped robot has special motion characteristics, has a hybrid dynamic system and belongs to a floating base, so it must obtain the contact information of two feet before realizing the motion state estimation of the robot. The contribution of this paper is to introduce soft support vector machine to detect contact phase. This method based on support vector machine (SVM) eliminates the need for force sensors on two feet because they are vulnerable to the impact between the foot and the contact surface. In addition, precise dynamic modeling is not required. Furthermore, this paper uses SVM to obtain the touchdown information, and proposes a state estimation method using extended Kalman filter to combine motion estimation with IMU data. Finally, the touchdown detection algorithm and the state estimation algorithm are verified through experiments, and the accuracy and feasibility of the SVM method are proved through the touchdown detection experiments. Based on this, the full state estimation of the robot is realized through the state estimation experiments.

关键词： Support vector machines Legged locomotion Technological innovation Motion estimation Switches Robot sensing systems Kalman filters

Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions

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Digital Twin Definition for Detecting Abnormal Perspiration ...

Roedunet International Conference (RoEduNet)

作者： Cristian Vizitiu Ioan Turcin Kevin Alexander Dominey Selver Softic Adrian-Cătălin Dinculescu Vlad I. Bocăneţ Cosmin Dugan Sorin-Aurel Moraru The Space Applications and Technologies Laboratory Institute of Space Science -Subsidiary of INFLPR Magurele Romania Department of Automatics and Information Technology Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania Department Automation Technology CAMPUS 02 University of Applied Sciences Graz Austria Department IT & Business Informatics CAMPUS 02 University of Applied Sciences Graz Austria Department of Manufacturing Engineering Faculty of Industrial Engineering Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation.

关键词： Adaptation models Generative AI Psychology Human factors Machine learning Predictive models Physiology Digital twins Modeling Biomedical monitoring

An Explainable Convolutional Neural Networks for Automatic Segmentation of the Left Ventricle in Cardiac MRI 11

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An Explainable Convolutional Neural Networks for Automatic S...

11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Liu, Jun Deng, Feng Yuan, Geng Lin, Xue Song, Houbing Wang, Yanzhi Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science and Technology University Beijing China Department of Electrical and Computer Engineering College of Engineering Northeastern University BostonMA United States Embry-Riddle Aeronautical University Daytona BeachFL United States

ISBN: (纸本)9781643682402

Recently, the study on model interpretability has become a hot topic in deep learning research area. Especially in the field of medical imaging, the requirements for safety are extremely high;Moreover, it is very important for the model to be able to explain. However, the existing solutions for left ventricular segmentation by convolutional neural networks are black boxes;explainable CNNs remains a challenge;explainable deep learning models has always been a task often overlooked in the entire data science lifecycle by data scientists or deep learning engineers. Because of very limited medical imaging data, most solutions currently use transfer learning methods to transfer the model which used on large-scale benchmark data sets (such as ImageNet) to fine tune medical imaging models. Consequently, a large amount of useless parameters are generated, resulting in further barrier for the model to provide a convincing explanation. This paper presents a novel method to automatically segment the Left Ventricle in Cardiac MRI by explainable convolutional neural networks with optimized size and parameters by our enhanced Deep Learning GPU Training System. It is very suitable for deployment on mobile devices. We simplify deep learning tasks on DIGITS systems, monitoring performance, and displaying the heat map of each layer of the network with advanced visualizations in real time. Our experiment results demonstrated that the proposed method is feasible and efficient. © 2022 The authors and IOS Press.

关键词： Image segmentation

Kombucha electronics: electronic circuits on kombucha mats (vol 13, 9367, 2023)

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SCIENTIFIC REPORTS 2023年第1期13卷 1-10页

作者： Adamatzky, Andrew Tarabella, Giuseppe Phillips, Neil Chiolerio, Alessandro D'Angelo, Pasquale Nikolaidou, Anna Sirakoulis, Georgios Ch. Unconventional Computing Laboratory University of the West of England Bristol UK Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Istituto Italiano di Tecnologia Center for Converging Technologies Soft Bioinspired Robotics Via Morego 30 16165 Genova Italy Institute of Materials for Electronic and Magnetism National Research Council (IMEM-CNR) Parma Italy

A kombucha is a tea and sugar fermented by over sixty kinds of yeasts and bacteria. This symbiotic community produces kombucha mats, which are cellulose-based hydrogels. The kombucha mats can be used as an alternative to animal leather in industry and fashion once they have been dried and cured. Prior to this study, we demonstrated that living kombucha mats display dynamic electrical activity and distinct stimulating responses. For use in organic textiles, cured mats of kombucha are inert. To make kombucha wearables functional, it is necessary to incorporate electrical circuits. We demonstrate that creating electrical conductors on kombucha mats is possible. After repeated bending and stretching, the circuits maintain their functionality. In addition, the abilities and electronic properties of the proposed kombucha, such as being lighter, less expensive, and more flexible than conventional electronic systems, pave the way for their use in a diverse range of applications.

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Frequency multiplexed photothermal correlation tomography for non-destructive evaluation of manufactured materials

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International Journal of Extreme Manufacturing 2025年第03期 538-552页

作者： Pengfei Zhu Rongbang Wang Koneswaran Sivagurunathan Stefano Sfarra Fabrizio Sarasini Clemente Ibarra-Castanedo Xavier Maldague Hai Zhang Andreas Mandelis Department of Electrical and Computer Engineering Computer Vision and Systems Laboratory(CVSL)Laval University Centre for Composite Materials and Structures (CCMS) Harbin Institute of Technology Center for Advanced Diffusion-Wave and Photoacoustic Technologies (CADIPT) and Institute for Advanced Non-Destructive and Non-Invasive Diagnostic Technologies (IANDIT) Department of Mechanical and Industrial EngineeringUniversity of Toronto Department of Industrial and Information Engineering and Economics (DIIIE) University of L'Aquila Department of Chemical Engineering Materials Environment&UDR INSTM Sapienza University of Rome

Infrared thermography has been widely applied in real industrial inspection of aerospace,energy management systems,engines,and electric ***,two-dimensional imaging modality limits its ***,a technique named frequency multiplexed photothermal correlation tomography (FM-PCT) was developed to enable non-destructive and contactless cross-sectional imaging for manufactured material evaluation and *** combining advantages of photothermal tomography and pulsed thermography,FM-PCT facilitates the generation of three-dimensional thermal images through temporal superposition(stacking) of two-dimensional images from sequential subsurface ***-PCT image processing involves pulsed excitation signals to which frequency delay and matched filtering techniques are *** features of FM-PCT are high-resolution three-dimensional tomographic imaging under low camera frame-rate conditions with self-correcting capability for diffusion (blurring) correction of subsurface images due to cross-correlation processing of individual frequencies in the Fourier decomposition spectrum of the excitation ***,FM-PCT extends truncated-correlation photothermal coherence tomography from chirp and pulsed signals to more general linear heating ***-in thermography and x-ray computed tomography validation demonstrate that 3D FM-PCT imaging accurately reveals subsurface discontinuities/defects in solids despite the diffusive nature of thermal-wave imaging.

关键词：

Object Detection, Recognition, Deep Learning, and the Universal Law of Generalization

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arXiv 2022年

作者： Rustom, Faris B. Öğmen, Haluk Yazdanbakhsh, Arash Department of Psychological and Brain Sciences Computational Neuroscience and Vision Lab Center for Systems Neuroscience Boston University BostonMA United States Department of Electrical & Computer Engineering Laboratory of Perceptual and Cognitive Dynamics University of Denver DenverCO United States

Object detection and recognition are fundamental functions underlying the success of species. Because the appearance of an object exhibits a large variability, the brain has to group these different stimuli under the same object identity, a process of generalization. Does the process of generalization follow some general principles or is it an ad-hoc "bag-of-tricks"? The Universal Law of Generalization provided evidence that generalization follows similar properties across a variety of species and tasks. Here we test the hypothesis that the internal representations underlying generalization reflect the natural properties of object detection and recognition in our environment rather than the specifics of the system solving these problems. By training a deep-neural-network with images of "clear" and "camouflaged" animals, we found that, with a proper choice of category prototypes, the generalization functions are monotone decreasing, similar to the generalization functions of biological systems. Our findings support the hypothesis of the study. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection