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International Carpathian Control Conference (ICCC)

作者： Wolmar Araújo Neto Daniel Khede Dourado Villa Mário Sarcinelli-Filho Murillo Ferreira Dos Santos José Lima Ana Isabel Pereira Maurício Herche Fófano De Morais Paolo Mercorelli Department of Electrical Engineering Federal University of Vitória (UFES) Vitória Brazil Department of Electroelectronics CEFET-MG Leopoldina Brasil IPB Research Centre in Digitalization and Intelligent Robotics (CeDRI) Bragança Portugal Institute for Production Technology and Systems Leuphana University of Lüneburg Lüneburg Germany

ISBN: (数字)9798331501273

ISBN: (纸本)9798331501280

This study presents an approach to improving the localization of mobile robots in a controlled test environment through sensor fusion. Currently, the position of the ground robot (Unmanned Ground Vehicle (UGV) P3DX) can be estimated using an optimization algorithm or another technique based on a previously known map. In contrast, the aerial robot (Unmanned Aerial Vehicle (UAV) Bebop Parrot 2) can determine its relative position by detecting a marker on the ground robot using the Robot Operating System (ROS) WhyCon package. However, ambiguities or disturbances may compromise the accuracy of the robots’ localization. To mitigate these limitations, Ultra-WideBand (UWB) sensors were incorporated into the system, enabling a more robust data fusion by integrating information from multiple sensors. Additionally, a filter was applied to reduce the impact of high-frequency noise on position estimation. Accurate localization test data were used to construct a simulated scenario and analyze the performance of the proposed approach. The simulation results demonstrate that sensor fusion, combined with noise filtering, significantly improves localization accuracy, making the approach promising for applications in mobile robot navigation.

关键词： Location awareness Accuracy Three-dimensional displays Navigation Noise Data integration Sensor fusion Robot sensing systems Autonomous aerial vehicles Mobile robots

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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Functional Mode Switching for Safe and Efficient Human-Robot Interaction

Functional Mode Switching for Safe and Efficient Human-Robot...

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IEEE-RAS International Conference on Humanoid Robots

作者： Petr Svarny Mazin Hamad Alexander Kurdas Matej Hoffmann Saeed Abdolshah Sami Haddadin Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Engineering Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence School of Computation Information and Technology Technische Universität München (TUM)

ISBN: (纸本)9798350309805

Various approaches can ascertain the safety and efficiency of physical human-robot collaboration. This paper presents the concept of robot functional mode switching to efficiently ensure human safety during collaborative tasks based on biomechanical pain and injury data and task information. Besides the robot's reflected inertial properties summarizing its impact dynamics, our concept also integrates safe and smooth velocity shaping that respects human partner motion, interaction type, and task knowledge. We further discuss different approaches to safely shape the robot velocity without sacrificing the overall task execution time and motion smoothness. The experimental results showed that our proposed approaches could decrease jerk level during functional mode switching and limit the impact of safety measures on productivity, especially when guided with additional task knowledge.

关键词： Robot motion Service robots Human-robot interaction Humanoid robots Switches Interference Safety

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks

Swallowing Assessment using High-Resolution Cervical Auscult...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ayman Anwar Yassin Khalifa Erin Lucatorto James L. Coyle Ervin Sejdic Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto Toronto Canada Center for Research Computing and Information Technology Analytics University of Pittsburgh Pittsburgh PA USA Systems and Biomedical Engineering Department Faculty of Engineering Cairo University Giza Egypt Department of Communication Science and Disorders School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh PA USA Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto and North York General Hospital Toronto Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient’s neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics; specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers’ ability to generalize on unseen data.

关键词： Accuracy Sensitivity Recurrent neural networks Convolution Kinematics Transformers Feature extraction Solids Data models Signal resolution

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Vibrational Stabilization of Complex Network systems

Vibrational Stabilization of Complex Network Systems

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American Control Conference (ACC)

作者： Alberto Maria Nobili Yuzhen Qin Carlo Alberto Avizzano Danielle S. Bassett Fabio Pasqualetti Department of Mechanical Engineering University of California Riverside Department of Excellence in Robotics and A.I. Scuola Superiore Sant’Anna Perceptual Robotics Laboratory at the IIM Institute Pisa Italy Department of Bioengineering the Department of Electrical & Systems Engineering the Department of Physics & Astronomy the Department of Psychiatry and the Department of Neurology University of Pennsylvania and the Santa Fe Institute Collaborative Research Network Aligning Science Across Parkinson’s (ASAP) Chevy Chase MD

Many natural and man-made network systems need to maintain certain patterns, such as working at equilibria or limit cycles, to function properly. Thus, the ability to stabilize such patterns is crucial. Most of the existing studies on stabilization assume that network systems’ states can be measured online so that feedback control strategies can be used. However, in many real-world scenarios, systems’ states, e.g., neuronal activity in the brain, are often difficult to measure. In this paper, we take this situation into account and study the stabilization problem of linear network systems with an open-loop control strategy—vibrational control. We derive a graph-theoretic sufficient condition for structural vibrational stabilizability, under which network systems can always be stabilized. We further provide an approach to select the locations in the network for control placement and design corresponding vibrational inputs to stabilize systems that satisfy this condition. Finally, we provide some numerical results that demonstrate the validity of our theoretical findings.

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Design of Capsule Robot for Magnetic Gastric Biopsy and Drug Administration

Design of Capsule Robot for Magnetic Gastric Biopsy and Drug...

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IEEE International Conference on Mechatronics and Automation

作者： Zhihui Xu Zongze Li Shoubin Liu Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Harbin Institute of Technology Shenzhen China

Capsule robots have emerged as a promising tool for the early diagnosis of gastrointestinal diseases. Unlike conventional gastroscopy, they offer the potential for minimally invasive or even non-invasive treatment, and can alleviate discomfort and pain in patients. However, current capsule robots have some limitations. For instance, they lack stomach tissue biopsy and drug administration functions, and the capsule robot used for biopsy does not consider the tolerance of gastrointestinal tissue. Similarly, the capsule robot used for drug administration does not account for the outlet speed of drug administration. This paper proposes a new biopsy method using a magnetically manipulated capsule robot and analyzes the principles governing the robot's manipulation during the biopsy and drug administration processes. Furthermore, an ANSYS gastric model was established and used to simulate and experimentally analyze damage to the gastric tissue model from different types of biopsy needles and their moving speeds. The appropriate type of biopsy needle and moving speed were determined to prevent damage to the gastric tissue. The study also used FLUENT to determine the appropriate thrust at the entrance of the drug cavity and the impact of the drug density on the drug outlet velocity to ensure complete adherence of the drug to the lesion.

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Land & Localize: An Infrastructure-free and Scalable Nano-Drones Swarm with UWB-based Localization

Land & Localize: An Infrastructure-free and Scalable Nano-Dr...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Mahyar Pourjabar Ahmed AlKatheeri Manuele Rusci Agata Barcis Vlad Niculescu Eliseo Ferrante Daniele Palossi Luca Benini Department of Electrical Electronic and Information Engineering (DEI) University of Bologna Italy Autonomous Robotics Research Center (ARRC) Technology Innovation Institute UAE Department of Electrical Engineering KU Leuven Belgium Integrated Systems Laboratory (IIS) ETH Zürich Switzerland Dalle Molle Institute for Artificial Intelligence (IDSIA) USI-SUPSI Switzerland

Relative localization is a crucial functional block of any robotic swarm. We address it in a fleet of nano-drones characterized by a 10 cm-scale form factor, which makes them highly versatile but also strictly limited in their onboard power envelope. State-of-the-Art solutions leverage Ultra-WideBand (UWB) technology, allowing distance range measurements between peer nano-drones and a stationary infrastructure of multiple UWB anchors. Therefore, we propose an UWB-based infrastructure-free nanodrones swarm, where part of the fleet acts as dynamic anchors, i.e., anchor-drones (ADs), capable of automatic deployment and landing. By varying the ADs' position constraint, we develop three alternative solutions with different trade-offs between flexibility and localization accuracy. In-field results, with four flying mission- drones (MDs), show a localization root mean square error (RMSE) spanning from 15.3 cm to 27.8 cm, at most. Scaling the number of MDs from 4 to 8, the RMSE marginally increases, i.e., less than 10 cm at most. The power consumption of the MDs' UWB module amounts to 342mW. Ultimately, compared to a fixed-infrastructure commercial solution, our infrastructure-free system can be deployed anywhere and rapidly by taking 5.7 s to self-localize 4 ADs with a localization RMSE of up to 12.3% in the most challenging case with 8 MDs.

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Vibrational Stabilization of Complex Network systems

arXiv

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arXiv 2023年

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

关键词： Linear networks

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Methods for Verifying the Security of Available IoT Devices 21

Methods for Verifying the Security of Available IoT Devices

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21st IEEE International Symposium on Intelligent systems and Informatics, SISY 2023

作者： Francisti, Jan Balogh, Zoltan Molnar, Gyorgy Jozsef, Kopjak Gergely, Sebestyen Nagy, Eniko Constantine the Philosopher University in Nitra Faculty of Natural Sciences and Informatics Department of Informatics Nitra94901 Slovakia Óbuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Széchenyi University János Csere Apáczai Faculty Gyr Hungary Institute of Cyberphysical Systems Óbuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Budapest Hungary

ISBN: (纸本)9798350343366

In present, the field of Internet of Things (IoT) is experiencing significant development, as evidenced by the everincreasing number of connected devices equipped with various sensors and controllers. These devices have become an integral part of smart homes, offering numerous benefits. Paradoxically, their growing popularity also brings an increased risk of vulnerabilities that threaten the security and privacy of users. This paper focuses on analysing common IoT devices used in smart homes to identify weaknesses and propose measures for improving their protection. This will involve evaluating security tests and providing recommendations for securing the devices and safeguarding their users. The proposed solutions and recommendations will assist users in better understanding potential threats and protecting their devices from unauthorized access and the leakage of sensitive information. This research approach contributes to enhancing the security and privacy of IoT device users in smart homes. © 2023 IEEE.

关键词： Internet of things

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A Novel Single-Phase Seven-Level Boost Inverter with Simplified Control

A Novel Single-Phase Seven-Level Boost Inverter with Simplif...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Dai-Van Vo Nguyen Gia Minh Thao Minh-Khai Nguyen Truong-Duy Duong Van-Cuong Bui Tan-Tai Tran Young-Cheol Lim Joon-Ho Choi Department of Electrical Engineering Chonnam National University Gwangju South Korea Department of Mechanical Electrical and Electronic Engineering Shimane University Matsue Japan Global Electrification and Battery Systems General Motors Company Warren USA Department of Electrical and Computer Engineering Wayne State University Detroit USA Industrial University of Ho Chi Minh City Ho Chi Minh City Vietnam

This work proposes a new single-phase seven-level inverter (1P7LBI) for a single-phase system that uses only eight transistors to increase the output voltage three times. Out of these eight switches, two operate at low frequency to reduce loss with a simple control system. Besides, the capacitors used in the proposed structure are maintained at the input voltage and operate at high frequencies with low ripple. Furthermore, a comparison using the proposed 1P7LBI structure and other multi-level structures was performed to demonstrate the potential of the suggested structure. Finally, some simulation and experimental findings are given to confirm the feasibility and efficacy of this novel configuration.

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