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Landé gJ factors of the even-parity electronic levels of the terbium atom

Journal of Quantitative Spectroscopy and Radiative Transfer 2022年 288卷

作者： Suski, M. Chomski, M. Furmann, B. Ruczkowski, J. Stefańska, D. Institute of Materials Research and Quantum Engineering Faculty of Materials Engineering and Technical Physics Poznan University of Technology Piotrowo 3A Poznan60-965 Poland Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Piotrowo 3A Poznan60-965 Poland

In the present work Landé gJ factors for 17 even-parity electronic levels of the terbium atom are presented. The spectra of the Zeeman splitting of the hyperfine structure were recorded with the method of laser spectroscopy in a hollow cathode discharge lamp in a constant magnetic field, with laser induced fluorescence detection. The analysis of 47 spectral lines of terbium in the spectral range between 490 and 609 nm enabled the determination of the gJ factors for the presented even-parity levels for the first time. Moreover, verification of the gJ factor values for 17 odd-parity levels, previously reported by other authors from the analysis of Fourier transform spectra. © 2022 Elsevier Ltd

关键词： Atoms

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Evaluation and Results of Research Ranking Platform for Academic Staff and Universities in The Republic of North Macedonia

Evaluation and Results of Research Ranking Platform for Acad...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Arbnor Rushiti Artan Luma Ylber Januzaj Azir Aliu Halil Snopçe Mitko Trpkoski Anis Sefidanoski Faculty of Contemporary Sciences and Technologies SEEU University Tetovo Republic of North Macedonia Faculty of Contemporary Sciences and Technologies University "Haxhi Zeka" Peya Kosovo Faculty of Information and Communication Technologies University "St. Kliment Ohridski" Bitola Republic of North Macedonia Faculty of Applied IT Machine Intelligence and Robotics University of Information Science and Technology (UIST) "St. Paul the Apostle" Ohrid Republic of North Macedonia

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Based on research and preliminary work for the Ranking Platform, the main objective is to provide recommendations in the form of informative reports for interested institutions and to rank the scientific works of university professors on an open data platform. By automating the evaluation of research publications, universities can better track and increase their academic impact, ultimately fostering a culture of excellence in research and *** this research paper, the evaluation and results obtained from our model are presented with the reports generated in the form of graphs depending on the requests of interested third *** research not only contributes to the advancement of educational evaluation methodologies, but also responds to the growing importance of open data and machine learning in modern higher education.

关键词： Human computer interaction Education machine learning Data models Open data Robots Optimization

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Correction to: Integrating the Perspectives of Relevant Stakeholders into the Development of a Service Robot for Nursing Homes: Needs Analysis and Scenario Development Using the International Classification of Functioning, Disability and Health (ICF) (International Journal of Social robotics, (2025), 10.1007/s12369-024-01205-2)

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International Journal of Social robotics 2025年 1-1页

作者： Horstmannshoff, Caren Jahn, Eva Theresa Müller, Martin Centre for Research Development and Technology Transfer Rosenheim Technical University of Applied Sciences Rosenheim Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany Department of Primary Care and Health Services Research Nursing Science and Interprofessional Care Medical Faculty Heidelberg Heidelberg University Heidelberg Germany

The original version of this online publication has been updated as the corresponding author's email address was published incorrectly as: ***@*** The correct email address is: ***@*** Springer wishes to apologize...

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OCTOPUS: Optimized Cross-border TeleOperated Medicine Pouring Using NextGen Seamless Communication Networks

OCTOPUS: Optimized Cross-border TeleOperated Medicine Pourin...

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IEEE International Conference on Communications (ICC)

作者： Edwin Babaians Praveen Gorla Serkut Ayvaşık Jan Plachý Zdenek Becvar Wolfgang Kellerer Eckehard Steinbach Chair of Media Technology School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Dept. of Telecommunication Engineering Faculty of Electrical Engineering Czech Technical University Prague Chair of Communication Networks School of Computation Information and Technology Technical University of Munich Germany

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities - Prague and Munich. We focus on the robot's ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system's holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial.

关键词： 5G mobile communication Urban areas Medical services Streaming media User experience Haptic interfaces Task analysis

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A Passivity-based Approach on Relocating High-Frequency Robot Controller to the Edge Cloud

A Passivity-based Approach on Relocating High-Frequency Robo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Xiao Chen Hamid Sadeghian Lingyun Chen Mario Tröbinger Abadalla Swirkir Abdeldjallil Naceri Sami Haddadin Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany Faculty of Engineering University of Isfahan Isfahan Iran Centre for Tactile Internet with Human-in-the-Loop (CeTI) Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Albaida Libya

As robots become more and more intelligent, the complexity of the algorithms behind them is increasing. Since these algorithms require high computation power from the onboard robot controller, the weight of the robot and energy consumption increases. A promising solution to tackle this issue is to relocate the expensive computation to the cloud. In this pioneering work, the possibility of relocating a state-of-the-art nonlinear control is investigated. To this end, the Unified Force-Impedance Controller (UFIC) is relocated to a remote location and high frequency feedback loop is established by including the remote controller in the loop. Passivity analysis is used to ensure the stability of the whole system, comprising the robot in interaction with the environment, the communication channel, as well as the remote controller. The instability associated with the communication channel is resolved by Time Domain Passivity Approach (TDPA). The performance of the proposed framework is experimentally evaluated on a robot arm in interaction with the environment. The results illustrate the stability of the system to a time-varying delay of up to 50 ± 10ms.

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Spatial and Temporal Analysis of Normal and Shear Forces During Grasping, Manipulation and Social Activities

Spatial and Temporal Analysis of Normal and Shear Forces Dur...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Theophil Spiegeler Castañeda Joana Matos Patricia Capsi-Morales Cristina Piazza Department of Computer Engineering School of Computation Information and Technology Technical University of Munich (TUM) Munich Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich (TUM) Munich Germany Faculty of Engineering University of Porto (U.Porto) Porto Portugal

Extensive research has established and widely acknowledged the important contribution of human hand sensory receptors in providing tactile feedback. The absence of these receptors results in a poor perception of the environment, impairing our efficient manipulation skills. Although the literature emphasizes the significance of normal forces in human grasping, further investigations should point toward the role of shear forces in this process. This paper presents an analysis of human everyday grasping activities through the use of 20 three-axis magnetic soft skin force sensors, in the form of rings and bands, that measure both normal and shear forces. Our study includes twelve tasks that cover various grasping requirements. Results highlight the importance of spatial information and the usefulness of shear forces in the prediction of unexpected changes that can not be always observed in normal forces. Tactile sensing can ultimately be integrated into prosthetic and rehabilitation devices for improved control and potentially provide sensory feedback to the user.

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GSA-Gaze: Generative Self-adversarial Learning for Domain Generalized Driver Gaze Estimation

GSA-Gaze: Generative Self-adversarial Learning for Domain Ge...

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International Conference on Intelligent Transportation

作者： Hongcheng Han Zhiqiang Tian Yuying Liu Shengpeng Li Dong Zhang Shaoyi Du National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China Faculty of Electronic and Information Engineering School of Software Engineering Xi'an Jiaotong University Xi'an China Faculty of Electronic and Information Engineering School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China

Estimating driver gaze accurately is critical for the human-machine cooperative driving, but the significant facial appearance diversions caused by background, illumination, personal characteristics, etc. pose a challenge to the generalizability of gaze estimation models. In this paper, we propose the generative self-adversarial learning mechanism for generalized gaze estimation that aims to learn general gaze features while eliminating sample-specific features and preventing cross-domain feature over-fitting. Firstly, to reduce information redundancy, the feature encoder is designed based on pyramid-grouped convolution to extract a sparse feature representation from the facial appearance. Secondly, the gaze regression module supervises the model to learn as many gaze-relevant features as possible. Thirdly, the adversarial image reconstruction task prompts the model to eliminate the domain-specific features. The adversarial learning of the gaze regression and the image reconstruction tasks guides the model to learn only general gaze features across domains, preventing cross-domain feature over-fitting, enhancing the domain generalization capability. The results of cross-domain testing of four active gaze datasets prove the effectiveness of the proposed method. The code is available at https://***/HongchengHan/GSA-Gaze

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OCTOPUS: Optimized Cross-border TeleOperated Medicine Pouring Using NextGen Seamless Communication Networks

TechRxiv

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TechRxiv 2023年

作者： Babaians, Edwin Gorla, Praveen Ayvaşık, Serkut Plachý, Jan Becvar, Zdenek Kellerer, Wolfgang Steinbach, Eckehard School of Computation Information and Technology Technical University of Munich Germany School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Dept. of Telecommunication Engineering Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities – Prague and Munich. We focus on the robot’s ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system’s holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial. © 2023, CC BY-NC-SA.

关键词： 5G mobile communication systems

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Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks

arXiv

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arXiv 2024年

作者： Shokry, Ahmed Gomaa, Walid Zaenker, Tobias Dawood, Murad Maged, Shady A. Awad, Mohammed I. Bennewitz, Maren Humanoid Robots Lab University of Bonn the Center for Robotics Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany Cyber Physical Systems Lab Egypt Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Alexandria University Alexandria Egypt Mechatronics Department Ain Shams University Cairo Egypt

Peg-in-hole assembly in unknown environments is a challenging task due to onboard sensor errors, which result in uncertainty and variations in task parameters such as the hole position and orientation. Meta Reinforcement Learning (Meta RL) has been proposed to mitigate this problem as it learns how to quickly adapt to new tasks with different parameters. However, previous approaches either depend on a sample-inefficient procedure or human demonstrations to perform the task in the real world. Our work modifies the data used by the Meta RL agent and uses simple features that can be easily measured in the real world even with an uncalibrated camera. We further adapt the Meta RL agent to use data from a force/torque sensor, instead of the camera, to perform the assembly, using a small amount of training data. Finally, we propose a fine-tuning method that consistently and safely adapts to out-of-distribution tasks with parameters that differ by a factor of 10 from the training tasks. Our results demonstrate that the proposed data modification significantly enhances the training and adaptation efficiency and enables the agent to achieve 100% success in tasks with different hole positions and orientations. Experiments on a real robot confirm that both camera- and force/torque sensor-equipped agents achieve 100% success in tasks with unknown hole positions, matching their simulation performance and validating the approach’s robustness and applicability. Compared to the previous work with sample-inefficient adaptation, our proposed methods are 10 times more sample-efficient in the real-world tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions, spherical coordinates, and intensity

arXiv

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arXiv 2021年

作者： Zywanowski, Kamil Banaszczyk, Adam Nowicki, Michal R. Komorowski, Jacek Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznan Poland Faculty of Electronics and Information Technology Warsaw University of Technology Warsaw Poland

The 3D LiDAR place recognition aims to estimate a coarse localization in a previously seen environment based on a single scan from a rotating 3D LiDAR sensor. The existing solutions to this problem include hand-crafted point cloud descriptors (e.g., ScanContext, M2DP, LiDAR IRIS) and deep learning-based solutions (e.g., PointNetVLAD, PCAN, LPD-Net, DAGC, MinkLoc3D), which are often only evaluated on accumulated 2D scans from the Oxford RobotCar dataset. We introduce MinkLoc3D-SI, a sparse convolution-based solution that utilizes spherical coordinates of 3D points and processes the intensity of 3D LiDAR measurements, improving the performance when a single 3D LiDAR scan is used. Our method integrates the improvements typical for hand-crafted descriptors (like ScanContext) with the most efficient 3D sparse convolutions (MinkLoc3D). Our experiments show improved results on single scans from 3D LiDARs (USyd Campus dataset) and great generalization ability (KITTI dataset). Using intensity information on accumulated 2D scans (RobotCar Intensity dataset) improves the performance, even though spherical representation doesn’t produce a noticeable improvement. As a result, MinkLoc3D-SI is suited for single scans obtained from a 3D LiDAR, making it applicable in autonomous vehicles. © 2021, CC BY-SA.

关键词： Convolution

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