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IEEE SENSORS

作者： Rylan Gomes Ehsan Mohseni S.G. Pierce Kenneth Burnham Amine Hifi Charles N Macleod Vincent Maes Matthew Chandler Andy Barnes Gavin Munro Department of Electronic & Electrical Engineering SEARCH: Sensor Enabled Automation Robotics & Control Hub Centre for Ultrasonic Engineering (CUE) University of Strathclyde Royal College Building Glasgow Department of Mechanical Engineering University of Bristol Bristol UK AdaptixNDE Eynsham Spirit AeroSystems Aerospace Innovation Centre Glasgow Prestwick Airport Monkton

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

The detection of fibre wrinkles (out-of-plane waviness) is investigated using Eddy Current (ECT), Ultrasonic Testing (UT) and X-ray and is compared on 9 Carbon Fibre Reinforced Plastic (CFRP) samples with varying wrinkle heights. All three Non-Destructive Evaluation (NDE) modalities demonstrated detection capabilities for wrinkle heights 0.13 - 1.31 mm, with UT being able to map the interior structure of the composite material, which was further used to measure the distortion of plies caused due to the presence of wrinkles using an image processing technique.

关键词： Ultrasonic imaging Costs Sensor phenomena and characterization Fiber reinforced plastics Feature extraction Distortion Distortion measurement Eddy currents Carbon Testing

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A novel robotic visual perception framework for underwater operation

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Frontiers of Information Technology & Electronic engineering 2022年第11期23卷 1602-1619页

作者： Yue LU Xingyu CHEN Zhengxing WU Junzhi YU Li WEN State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Ytech Kuaishou TechnologyBeijing 100085China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China

Underwater robotic operation usually requires visual perception(e.g.,object detection and tracking),but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual *** addition,detection continuity and stability are important for robotic perception,but the commonly used static accuracy based evaluation(i.e.,average precision)is insufficient to reflect detector performance across *** response to these two problems,we present a design for a novel robotic visual perception ***,we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection *** a result,although domain quality has an ignorable effect on within-domain detection accuracy,visual restoration is beneficial to detection in real sea scenarios by reducing the domain ***,non-reference assessments are proposed for detection continuity and stability based on object ***,online tracklet refinement is developed to improve the temporal performance of ***,combined with visual restoration,an accurate and stable underwater robotic visual perception framework is ***-overlap suppression is proposed to extend video object detection(VID)methods to a single-object tracking task,leading to the flexibility to switch between detection and *** experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed *** codes are available at https://***/yrqs/VisPerception.

关键词： Underwater operation Robotic perception Visual restoration Video object detection

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Towards Designing a Medication Assistive Intelligent System with Automated Insulin-dose Prediction for Elderlies

Towards Designing a Medication Assistive Intelligent System ...

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International Conference on Electrical, Computer and Communication engineering (ECCE)

作者： Abrar Ahmed Minhazul Islam Mahi M. Akhtaruzzaman Jamal Uddin Tanvin Fahim Shahryer Sachitra Halder Muhammad Towfiqur Rahman Md Shofiqul Islam Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh Department of Computer Science & Engineering University Asia Pacific (UAP) Dhaka Bangladesh Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Victoria

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Medication management poses significant challenges for many patients, particularly the elderlies, who often struggle with keeping track of their medication schedules and taking the correct dosages. To address this issue, this study aims to design an AI-integrated Medication Management and Assistive Unit (AMMAU) for elderlies with some important features like, schedule reminder, medicine recognition and count, early indication of shortage, sorted slots, and so on. Automated insulin-dose prediction and alert system makes the system unique and more demanding at these current scenarios. For this study, a suitable machine learning model is designed, analyzed, verified, and embedded in the proposed system so that the elderly diabetic patients can get alert, further compare with the current insulin-doses. Though, the proposed system is currently focusing on only the Basal insulin doses prediction, the system will definitely reduce the risks associated with wrong management of medication by degrading the chances of missed doses or taking wrong pills for elderlies at home.

关键词： Training Schedules Analytical models Prototypes Machine learning Predictive models Internet of Things Older adults Insulin Physical design

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Human Robot Interaction: Identifying Resembling Emotions Using Dynamic Body Gestures of Robot

Human Robot Interaction: Identifying Resembling Emotions Usi...

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Artificial Intelligence (ICAI), International Conference on

作者： Sara Ali Faisal Mehmood Khawaja Fahad Iqbal Yasar Ayaz Muhammad Sajid Muhammad Baber Sial Muhammad Faiq Malik Kashif Javed Department of Robotics and Artificial Intelligence Human Robot Interaction Lab (SINES) Intelligent Field Robotics Lab (NCAI) National University of Sciences and Technology H-12 Islamabad Pakistan Department of Robotics and Artificial Intelligence National University of Sciences and Technology H-12 Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Beijing China

Natural social interaction between a human and robot requires perception ability of robot for complex social behaviors and in turn displaying the learnt emotional behavior during interaction. The emotions expressed with body language are one of the key parameters during Human-Robot Interaction (HRI). This research focuses on extending the concept of affective non-verbal human-robot interaction using full body gestures for closely resembling emotion in absence of intricate facial expressions. The movements of head, torso, legs, and arms are used to link body movements and gestures of a bi-pedal humanoid robot with 28 closely resembling emotions. Variation of speed, frequency, and joint angles of the robot are the key features used for expressing a specific emotion. This research uses Russell's circumplex model to define 8 primary emotional categories which are further grouped into closely associated emotions resembling each other. 33 participants were involved in experimentation to authenticate different emotions using body gestures of robot. Each participant performed 28 trials in which emotions were displayed at random to recognize the state of robot. The results showed an average accuracy of 79.69%. The study authenticates that perceived emotions are highly dependent on body movements, postures, and selected features. This design model can therefore be used for even conveying the closely resembling emotions.

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SparseGTN: Human Trajectory Forecasting with Sparsely Represented Scene and Incomplete Trajectories

SparseGTN: Human Trajectory Forecasting with Sparsely Repres...

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

作者： Jianbang Liu Guangyang Li Xinyu Mao Fei Meng Jie Mei Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen Key Laboratory of Robotics Perception and Intelligence and the Department of Electronic and Electrical Engineering Southern University of Science and Technology in Shenzhen China Professor Emeritus in the Department of Electronic Engineering The Chinese University of Hong Kong in Hong Kong Professor in the Department of Electrical and Computer Engineering University of Alberta in Canada

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, great progress has been made in forecasting human motion in crowded scenes. However, current methods are far from practical applications due to the unbearable high computation costs, especially for encoding scene context. In addition, neglecting the partially detected trajectories makes the predicted outcome deviate from the real trajectory distribution. To handle the aforementioned concerns, we propose to represent the scene context and partially observed trajectories with sparse graphs. Customized for this special data structure, we design a hierarchical Graph Transformer Network model SparseGTN to predict multiple possible future trajectories of the target pedestrian by digesting the sparsely represented inputs. Our approach exhibits superiority over the state-of-the-art (SOTA) methods, utilizing a mere 3.42% of the number of floating point operations (FLOPs) and 0.53% of the number of model parameters. The code will be available online ⋆ .

关键词： Pedestrians Switches Predictive models Transformers Encoding Trajectory Mobile robots Forecasting Collision avoidance Intelligent robots

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Improving Referring Expression Comprehension by Suppressing Expression-unrelated Proposals 3

Improving Referring Expression Comprehension by Suppressing ...

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3rd International Conference on Electronic Information engineering and Computer, EIECT 2023

作者： Wang, Qun Zhu, Feng Li, Xiang Ye, Ruida Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Northeastern University Faculty of Computer Science and Engineering Shenyang110016 China Space Engineering University Department of Aerospace Engineering and Technology Beijing101416 China

ISBN: (纸本)9798350357707

The prevailing referring expression comprehension (REC) method is based on two stages: 1) detecting multiple candidate proposals using an object detector and 2) aligning proposals with text descriptions. Existing two-stage models have achieved significant improvements with excellent alignment modules. However, their alignment modules treat each proposal equally without filtering expression-unrelated proposals. These expression-unrelated proposals may interfere with the alignment modules' ability to accurately reason about the target proposal. These proposals also increase the difficulty of training alignment modules. To solve this problem, we propose a new framework, that can suppress expression-unrelated proposals for alignment modules. Specifically, we first design a suppression module, which calculates the relevance scores between the proposals and the expression by using the semantics in the expression. Then, these scores are integrated into three basic alignment modules, including subject, location, and relationship modules, to guide each module to focus on the expression-related proposals. We conduct extensive experiments on three benchmark datasets, and the results show the effectiveness of our suppression module and performance improvement. © 2023 IEEE.

关键词： Alignment

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Problems in Control Systems of Electric Drives of Metalworking Machines

Problems in Control Systems of Electric Drives of Metalworki...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Nikolay V. Lazovskiy Oleg A. Kundyukov Kirill K. Ershov Department of Robotics and Automation of Production Systems St. Petersburg state electrotechnical University “LETI” Saint Petersburg Russia Department of Automated Electric Drive and Electrical Engineering Saint Petersburg state University of industrial technologies and design Saint Petersburg Russia

The types of errors of machine tools are considered. Methods for increasing the productivity of machine tools and the accuracy of processing workpieces are analyzed. Promising directions for the development of electri...

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Robust Control of An Aerial Manipulator Based on A Variable Inertia Parameters Model

arXiv

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arXiv 2024年

作者： Zhang, Guangyu He, Yuqing Dai, Bo Gu, Feng Han, Jianda Liu, Guangjun The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institute for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China The Department of Aerospace Engineering Ryerson University TorontoONM5B 2K3 Canada

Aerial manipulator, which is composed of an UAV (Unmanned Aerial Vehicle) and a multi-link manipulator and can perform aerial manipulation, has shown great potential of applications. However, dynamic coupling between the UAV and the manipulator makes it difficult to control the aerial manipulator with high performance. In this paper, system modeling and control problem of the aerial manipulator are studied. Firstly, an UAV dynamic model is proposed with consideration of the dynamic coupling from an attached manipulator, which is treated as disturbance for the UAV. In the dynamic model, the disturbance is affected by the variable inertia parameters of the aerial manipulator system. Then, based on the proposed dynamic model, a disturbance compensation robust H∞ controller is designed to stabilize flight of the UAV while the manipulator is in operation. Finally, experiments are conducted and the experimental results demonstrate the feasibility and validity of the proposed control scheme. © 2024, CC BY.

关键词： Antennas

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Fault Diagnostic Opportunities for Electromagnetic Coils of Active Magnetic Bearings using Physics-of-Failure Analysis 5

Fault Diagnostic Opportunities for Electromagnetic Coils of ...

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5th International Conference on System Reliability and Safety engineering, SRSE 2023

作者： Li, Ruiqi Wang, Kai Zeng, Peng Cao, Jiwei Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Harbin Institute of Technology Department of Electrical Engineering China

ISBN: (纸本)9798350305944

Active magnetic bearings (AMBs) have many advantages over traditional oil bearings due to their non-contact characteristics. They are environmentally friendly solution and have been proven to be highly reliable and available. Electromagnetic coils are vital components of AMB and they are failure prone. In this paper, the failure mode of magnetic bearing coil and its corresponding failure mechanism are reviewed by FMMEA method, and the existing coil fault diagnosis methods are introduced. Future directions for coil insulation health monitoring aiming at the application characteristics of AMBs are also discussed. © 2023 IEEE.

关键词： Failure analysis

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Supervision policies can shape long-term risk management in general-purpose AI models

arXiv

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arXiv 2025年

作者： Cebrian, Manuel Gómez, Emilia Llorca, David Fernández Department of Statistics Universidad Carlos III de Madrid Spain Center for Automation and Robotics Spanish National Research Council Madrid Spain European Commission Joint Research Centre Seville Spain Computer Engineering Department University of Alcalá Alcalá de Henares Spain

The rapid proliferation and deployment of General-Purpose AI (GPAI) models, including large language models (LLMs), present unprecedented challenges for AI supervisory entities. We hypothesise that these entities will need to navigate an emergent ecosystem of risk and incident reporting, likely to exceed their supervision capacity. To investigate this, we develop a simulation framework parametrised by features extracted from the diverse landscape of risk, incident or hazard reporting ecosystems—including community-driven platforms, crowdsourcing initiatives, and expert assessments. We evaluate four supervision policies: non-prioritised (first-come, first-served), random selection, priority-based (addressing the highest-priority risks first), and diversity-prioritised (balancing high-priority risks with comprehensive coverage across risk types). Our results indicate that while priority-based and diversity-prioritised policies are more effective at mitigating high-impact risks—particularly those identified by experts—they may inadvertently neglect systemic issues reported by the broader community. This oversight can create feedback loops that amplify certain types of reporting while discouraging others, leading to a skewed perception of the overall risk landscape. We validate our simulation results with several real-world datasets, including one with over a million ChatGPT interactions, including more than 150K conversations identified as risky. This validation underscores the complex trade-offs inherent in AI risk supervision and highlights how the choice of risk management policies can shape the future landscape of AI risks across diverse GPAI models used in society. © 2025, CC BY.

关键词： Digital elevation model

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