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arXiv 2023年

作者： Bavle, Hriday Sanchez-Lopez, Jose Luis Civera, Javier Voos, Holger The Automation and Robotics Research Group Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg The Faculty of Science Technology and Medicine University of Luxembourg Luxembourg I3A Universidad de Zaragoza Spain

3D scene graphs hierarchically represent the environment appropriately organizing different environmental entities in various layers. Our previous work on situational graphs [1], [2] extends the concept of 3D scene graph to SLAM by tightly coupling the robot poses with the scene graph entities, achieving state-of-the-art results. Though, one of the limitations of S-Graphs is scalability in really large environments due to the increased graph size over time, increasing the computational complexity. To overcome this limitation in this work we present an initial research of an improved version of S-Graphs exploiting the hierarchy to reduce the graph size by marginalizing redundant robot poses and their connections to the observations of the same structural entities. Firstly, we propose the generation and optimization of room-local graphs encompassing all graph entities within a room-like structure. These room-local graphs are used to compress the S-Graphs marginalizing the redundant robot keyframes within the given room. We then perform windowed local optimization of the compressed graph at regular time-distance intervals. A global optimization of the compressed graph is performed every time a loop closure is detected. We show similar accuracy compared to the baseline while showing a 39.81% reduction in the computation time with respect to the baseline. © 2023, CC BY.

关键词： Global optimization

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A Sentiment Analysis Study of Twitter Users' Reactions to the COVID-19 Vaccine

A Sentiment Analysis Study of Twitter Users' Reactions to th...

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International Conference on Information and Communication Systems (ICICS)

作者： Suboh Alkhushayni Zakaria Alomari Du'a Al-Zaleq Dept. Information Systems Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan Dept. Computer Science New York Institute of Technology Vancouver BC Canada Dept. Robotics and Artificial Intelligence Engineering Al-Ahliyya Amman University Amman Jordan

This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution.

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Dual-arm Motion Generation for Repositioning Care based on Deep Predictive Learning with Somatosensory Attention Mechanism

arXiv

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arXiv 2024年

作者： Miyake, Tamon Saito, Namiko Ogata, Tetsuya Wang, Yushi Sugano, Shigeki Future Robotics Organization Waseda University Waseda Univ. Tokyo Japan School of Informatics The University of Edinburgh Edinburgh United Kingdom Alan Turing Institute London United Kingdom Faculty of Science and Engineering Waseda University Tokyo Japan National Institute of Advanced Science and Technology Tokyo Japan

A versatile robot working in a domestic environment based on a deep neural network (DNN) is currently attracting attention. One of the roles expected for domestic robots is caregiving for a human. In particular, we focus on repositioning care because repositioning plays a fundamental role in supporting the health and quality of life of individuals with limited mobility. However, generating motions of the repositioning care, avoiding applying force to non-target parts and applying appropriate force to target parts, remains challenging. In this study, we proposed a DNN-based architecture using visual and somatosensory attention mechanisms that can generate dual-arm repositioning motions which involve different sequential policies of interaction force;contact-less reaching and contact-based assisting motions. We used the humanoid robot Dry-AIREC, which features the capability to adjust joint impedance dynamically. In the experiment, the repositioning assistance from the supine position to the sitting position was conducted by Dry-AIREC. The trained model, utilizing the proposed architecture, successfully guided the robot’s hand to the back of the mannequin without excessive contact force on the mannequin and provided adequate support and appropriate contact for postural adjustment. Copyright © 2024, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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Enhanced Local Attention with Deep Neural Networks for EEG Decoding 4th

Enhanced Local Attention with Deep Neural Networks for EEG...

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4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Tao, Wei Ye, Jiawo Ieong, Chio-In Wu, Haiyan Wan, Feng Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Taipa China Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China Guangdong Institute of Intelligence Science and Technology Zhuhai China Department of Psychology Faculty of Social Sciences University of Macau Taipa China

ISBN: (纸本)9789819640003

Channel-wise attention mechanisms have significantly improved deep learning-based decoding in brain-computer interfaces (BCIs). However, these methods often fail to fully utilize spatial and temporal dynamics, focusing instead on individual channel enhancements to the detriment of broader EEG signal dynamics. To address these limitations, we introduce the Enhanced Local Attention (ELA) module, seamlessly integrated into deep neural networks that enhances EEG decoding performance. It captures long-range dependencies from deep features across two dimensions through a streamlined architecture. The ELA module employs adaptive 1D convolution for precise localization of spatial and temporal information without reducing dimensions. Additionally, it utilizes group normalization to enhance feature representation by normalizing features across groups. Its lightweight design enables easy integration into existing deep learning frameworks. Comprehensive evaluations on the BCI-IV2b dataset highlight the ELA module’s superior performance. Specifically, it increases the decoding accuracy of EEGNet in the motor imagery task from 81.89% to 85.97%. Furthermore, when integrated with ADFCNN, the ELA module achieves an accuracy of 88.13%, consistently outperforming other state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Implementation of EMG Signal Measurement for the Control of the 3D Hand Prototype

Implementation of EMG Signal Measurement for the Control of ...

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International Carpathian Control Conference (ICCC)

作者： Agata Nawrocka Marcin Nawrocki Andrzej Kot Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow Poland

The presented work focuses on the development of a measuring stand for the electromyographic signal and the hand clamp force signal. Two approaches to the implementation of the topic have been described - the first one uses a specially prepared prototype board and the second one uses the Arduino Uno module for active synchronization of measurements. The methodology of work on both versions was presented along with the measurement procedure used. The idea was to make a special EMG measurement system with parallel transmission of the clamp force value from the dynamometer. The data obtained in this way are to be used to train a machine learning model determining the value of the clamping force of the hand prosthesis. The paper deals with the issues of prosthesis construction, measurement and data storage methods, as well as the course of further planned works on the project. The presented solution has a chance to introduce methods that realistically reduce the development costs of a hand prosthesis, while improving its functioning. To achieve this, the following assumptions were adopted: the acquisition of the EMG signal using a single MyoWare Muscle Sensor (Sparkfun®) sensor on the superficial flexor of the fingers (Latin Musculus flexor digitorum superficialis), a constant ratio of the number of force samples to the number of electromyogram samples, the use of commonly available electronic components and limitations construction costs.

关键词： Electrodes Three-dimensional displays Force Prototypes Electromyography Time measurement Delays

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Self-sensing Capability of Electromagnetic Harvester Engineered for MR Damper: Experimental Evaluation

Self-sensing Capability of Electromagnetic Harvester Enginee...

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International Carpathian Control Conference (ICCC)

作者： Paweł Orkisz Bogdan Sapiński Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow Poland

The study investigates the self-sensing capability of an electromagnetic harvester designed for a commercial MR damper. The objective of the work is to demonstrate that the harvester can also serve as a velocity sensor. For this purpose, the relative velocity of the MR damper shaft (harvester rod) was reconstructed from the electromotive force induced in the harvester coil. To demonstrate the adequacy of the approach, experimental tests were conducted under harmonic input excitations. The experimental data revealed the self-sensing potential of the investigated harvester. This feature seems to be useful in vibration control systems with MR damper and energy regeneration.

关键词： Vibrations Time-frequency analysis Switches Control systems Shock absorbers Vibration control Electromagnetics

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Adaptation of an Energy Harvester Working in the Bending Mode to Utilize Dielectric Elastomers

Adaptation of an Energy Harvester Working in the Bending Mod...

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International Carpathian Control Conference (ICCC)

作者： Wojciech Sikora Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Cracow Poland

The bending mode in energy harvesting applications is generally used in combination with piezoelectric materials. This results from the fact that the mechanical strains appearing directly in the beam are very small but in most cases sufficient to induce piezoelectric effects in ceramic materials like PZT. The mechanism behind energy harvesting using dielectric elastomers is different as it relies on changing their electrical capacitance through changing dimensions. To obtain significant differences in elastomer shape due to beam tip deflection it is necessary to introduce construction modifications to the classic energy harvester based on cantilever beam. The scope of this work covers numerical simulations performed to assess the feasibility of such a structure.

关键词： Shape Piezoelectric materials Piezoelectric effect Bending Dielectric elastomers Numerical simulation Dielectrics

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Designing an Ultrasonic System Used to Reduce the Cross-Linking Time of Epoxy Resins Used in the Construction of Brake Cylinders

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Macromolecular Symposia 2024年第3期413卷 2300206-2300206页

作者： Pasare, Vili Nitoi, Dan Opran, Constantin G. Chivu, Oana Semenescu, Augustin Milicescu, Stefan Faculty of Industrial Engineering and Robotics Department of Engineering Graphics and Industrial Design National University of Science and Technology Politehnica of Bucharest 313 Splaiul Independentei Bucharest060042 Romania University of Medicine and Pharmacy Carol Davila Bucharest Bulevardul Eroii Sanitari Nr. 8 Sector 5 Bucuresti Romania

Ultrasounds and their applications constitute fields of theoretical and practical research with applicability starting with the military, medicine, automotive industry, and ending with household appliances. The use of ultrasonic field in the cross-linking of epoxy resins reduces the time required to produce composite structures, independent of the type of reinforcing components used. Brake cylinders, made by successive deposition of two layers of epoxy resin and quartz sand used in applications where frictional forces are very high, constitute a challenge for composite structures. The article presents the analytical calculation and the design of the ultrasonic system used in making a sample made of metallic base material and two layers of epoxy resin reinforced with quartz sand. Using this new proposed technology, there is a substantial reduction in the time required to solidify the structure and the elimination of the need for a high temperature necessary to carry out the traditional technological process. © 2024 Wiley-VCH GmbH.

关键词： Quartz

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A Feed-forward Switched Adaptive Filtering configuration for Underwater Acoustic Signal Denoising Technique with low-complexity

A Feed-forward Switched Adaptive Filtering configuration for...

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OCEANS

作者： Deekshitha Adusumalli Swapnil Maiti S. Hannah Pauline Gerard Dooly Samiappan Dhanalakshmi Department of Electronics and Communication Engg. College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Tech. SRM Nagar Kattankulathur Kanchipuram Chennai India Center for Robotics and Intelligent Systems (CRIS) University of Limerick Limerick Ireland

The Underwater acoustic communication is difficult by virtue of the noise that exists undersea. This can be caused by both natural phenomena, like waves and currents, as well as artificial sources such as ships and boats. In order to reduce or eliminate this interference, it is necessary to process the signal before further processing. One way of doing this is through denoising. However, because noise constantly fluctuates in intensity, predicting its future behavior becomes almost impossible. To address this problem, we propose a new method for filtering underwater communications using an superlative feedfoward switched adaptive filter model using Signed Error variant of Least Mean Square (SELMS) and Signed Data variant of Least Mean Square (SDLMS) algorithm that takes into account signed form LMS values. The effectiveness of the filter is tested using a clean fish sound that has been tainted by noises from underwater vessels from the ShipsEar database.

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Pallet and Pocket Detection Based on Deep Learning Techniques

Pallet and Pocket Detection Based on Deep Learning Technique...

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Iberian robotics Conference (ROBOT)

作者： Daniele Caldana Artur Cordeiro João Pedro Souza Ricardo B. Sousa Paulo M. Rebelo António Joaquim Silva Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Portugal Faculty of Engineering University of Porto (FEUP) Portugal School of Science and Technology University of Trás-os-Montes e Alto Douro (UTAD) Portugal Seúibal Industrial Complex The Navigator Company Portugal ISEP Polytechnic of Porto Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

The high level of precision and consistency required for pallet detection in industrial environments and logistics tasks is a critical challenge that has been the subject of extensive research. This paper proposes a system for detecting pallets and its pockets using the You Only Look Once (YOLO) v8 Open Neural Network Exchange (ONNX) model, followed by the segmentation of the pallet surface. On the basis of the system a pipeline built on the ROS Action Server whose structure promotes modularity and ease of implementation of heuristics. Additionally, is presented a comparison between the YOLOv5 and YOLOv8 models in the detection task, trained with a customised dataset from a factory environment. The results demonstrate that the pipeline can consistently perform pallet and pocket detection, even when tested in the laboratory and with successive 3D pallet segmentation. When comparing the models, YOLOv8 achieved higher average metric values, with YOLOv8m providing better detection performance in the laboratory setting.

关键词： YOLO Measurement Three-dimensional displays Pipelines Neural networks Production facilities Servers Pallets Robots Logistics

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