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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Galina Demidova Hao Chen Yibin Li Dmitry Lukichev Alecksey Anuchin Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China Institute of Marine Science and Technology Shandong University Shandong China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

This paper provides a review of popular fault diagnosis methods in the power subsystem and electrical machine of electric drives. The review aims to explore the different approaches used to detect faults in these components and the most common types of faults encountered in electric drives. The paper first provides an overview of the power subsystem and electrical machine and their respective functions within the electric drive system. It then proceeds to discuss the different types of faults that can occur in these components, including overvoltage, undervoltage, overcurrent, and short circuits. The review evaluates the various fault diagnosis methods available for these components, including model-based, signal-based, and artificial intelligence-based approaches. Model-based approaches use mathematical models of the system to detect and diagnose faults. Signal-based approaches rely on the analysis of measured signals to detect changes in system behavior that indicate a fault. Artificial intelligence-based approaches use machine learning algorithms to identify patterns in data and detect faults. Model-based approaches are highly accurate but can be computationally intensive. Signal-based approaches are less computationally intensive but may be less accurate in certain situations. Artificial intelligence-based approaches are versatile but require significant amounts of data for training. This review provides a comprehensive overview of the current state-of-the-art in fault diagnosis methods for the power subsystem and electrical machine of electric drives. The review concludes by suggesting potential areas for future research to further improve the accuracy and efficiency of fault diagnosis methods for electric drives.

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Modern Manufacturing for Alloy Wheel Defect Detection using Image Processing and Application

Modern Manufacturing for Alloy Wheel Defect Detection using ...

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IEEE International Conference on Computer Communication and the Internet (ICCCI)

作者： Tuaniai Archevapanich Woranidtha Krungseanmuang Vasutorn Chaowalittawin Posathip Sathaporn Punyisa Chaowalittawin Boonchana Purahong Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Indusstrail Physics School of Science Bangkok Thailand Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents an innovative approach to identifying defects in alloy wheel production by integrating image processing techniques with a mobile application platform. The system receives X-ray alloy images from the factory via mobile phone, processes them using image processing techniques to enhance clarity and readiness for defect detection, and then transmits the processed images to a Django framework via a uniform resource locator (URL). Subsequently, the system detects defects in the images, encodes them in Base64 format, and sends them to the mobile application through an API (Application Program Interface) for display on the user interface. This well-designed system architecture offers manufacturers a comprehensive solution to ensure product quality, reduce costs, and enhance customer satisfaction.

关键词： Uniform resource locators Image processing Metals Wheels Systems architecture User interfaces Mobile applications

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Rayleigh-Based Distributed Strain Measurements with Monolithic Sensors: Thermal Compensation

SSRN

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SSRN 2024年

作者： Lukasz, Bednarski Sieńko, Rafal Howiacki, Tomasz Marek Badura, Kamil Department of Mechanics and Vibroacoustics Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology in Krakow Mickiewicza 30 Kraków30-059 Poland Faculty of Civil Engineering Cracow University of Technology ul. Warszawska 24 Kraków31-155 Poland SHM System / Nerve-Sensors Libertów ul. Jana Pawla II 82A Kraków30-444 Poland

Distributed fibre optic sensing (DFOS) is gaining popularity as a diagnostic solution for non-destructive testing (NDT) and structural health monitoring (SHM). However, there are still challenges to be overcome, particularly with regard to long-term measurements under real field conditions, taking into account temperature changes. These changes affect strain readings and appropriate correction depends on the internal design of the sensor and the installation methods. The novelty of the presented research lies in 1) the theoretical explanation on thermal performance of monolithic DFOS sensors in comparison to the layered sensing cables;2) the analysis and correction for strain data measured with monolithic sensors placed in a thermal chamber;3) the verification of the laboratory results and the proposed approach in real field conditions: in a concrete footbridge deck and in a steel underground gas pipeline. All the measurements presented hereafter were performed with high spatial resolution (mm order) Rayleigh-based devices. © 2024, The Authors. All rights reserved.

关键词： Steel fibers

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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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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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Weisfeiler and leman go loopy: a new hierarchy for graph representational learning 24

Weisfeiler and leman go loopy: a new hierarchy for graph rep...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Raffaele Paolino Sohir Maskey Pascal Welke Gitta Kutyniok Department of Mathematics LMU Munich and Munich Center for Machine Learning (MCML) Department of Mathematics LMU Munich Faculty of Computer Science TU Wien Department of Mathematics LMU Munich and Munich Center for Machine Learning (MCML) and Institute for Robotics and Mechatronics DLR-German Aerospace Center and Department of Physics and Technology University of Tromsø

ISBN: (纸本)9798331314385

We introduce r-loopy Weisfeiler-Leman (r-ℓWL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, r-ℓMPNN, that can count cycles up to length r + 2. Most notably, we show that r-ℓWL can count homomorphisms of cactus graphs. This extends 1-WL, which can only count homomorphisms of trees and, in fact, we prove that r-ℓWL is incomparable to k-WL for any fixed k. We empirically validate the expressive and counting power of r-ℓMPNN on several synthetic datasets and demonstrate the scalability and strong performance on various real-world datasets, particularly on sparse graphs. Our code is available on https://***/RPaolino/loopy.

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Robust Co-Design of Canonical Underactuated Systems for Increased Certifiable Stability

Robust Co-Design of Canonical Underactuated Systems for Incr...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Federico Girlanda Lasse Shala Shivesh Kumar Frank Kirchner Robotics Innovation Center German Research Center for Artificial Intelligence (DFKI GmbH) Bremen Germany Department of Information Engineering University of Padova Italy Department of Mechanics & Maritime Sciences Dynamics Division Chalmers University of Technology Gotheburg Sweden Faculty of Mathematics and Computer Science University of Bremen Bremen Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Optimal behaviours of a system to perform a specific task can be achieved by leveraging the coupling between trajectory optimization, stabilization, and design optimization. This approach is particularly advantageous for underactuated systems, which are systems that have fewer actuators than degrees of freedom and thus require for more elaborate control systems. This paper proposes a novel co-design algorithm, namely Robust Trajectory Control with Design optimization (RTC-D). An inner optimization layer (RTC) simultaneously performs direct transcription (DIRTRAN) to find a nominal trajectory while computing optimal hyperparameters for a stabilizing time-varying linear quadratic regulator (TVLQR). RTC-D augments RTC with a design optimization layer, maximizing the system’s robustness through a time-varying Lyapunov-based region of attraction (ROA) analysis. This analysis provides a formal guarantee of stability for a set of off-nominal states. The proposed algorithm has been tested on two different underactuated systems: the torque-limited simple pendulum and the cart-pole. Extensive simulations of off-nominal initial conditions demonstrate improved robustness, while real-system experiments show increased insensitivity to torque disturbances.

关键词： Couplings Torque Regulators Stability analysis Robustness Task analysis robotics and automation

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Adaptive Fourier Decomposition for Multi-Channel Signal Analysis

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IEEE Transactions on Applied Superconductivity 2022年第4期32卷

作者： Wang, Ze Wong, Chi Man Rosa, Agostinho Qian, Tao Wan, Feng Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Taipa999078 China Department of Bioengineering LaSEEB-System and Robotics Institute Instituto Superior Tecnico University of Lisbon Lisbon1049-001 Portugal Macao Center for Mathematical Sciences Macau University of Science and Technology Taipa China

Evolved from the conventional Fourier decomposition based on a pre-defined basis, Adaptive Fourier decomposition (AFD) uses adaptive basis to achieve the fast energy convergence. This paper extends the AFD to the multi-channel case, which finds common adaptive basis across all channels. The proposed multi-channel AFD (MAFD) scheme includes the multi-channel core AFD for general signals and the multi-channel unwinding AFD for specific signals that have common inner functions. Owing to the merits of the original AFD, the MAFD can provide sparse joint time-frequency distribution by computing the transient time frequency distribution (TTFD) across channels. Simulations on synthetic and real-world signals demonstrate that the proposed scheme can find and apply the common adaptive basis with desired properties maintained by the AFD, showing high potentials in real-world applications. © 1991-2012 IEEE.

关键词： Signal analysis

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Thermal-to-Color Image Translation for Enhancing Visual Odometry of Thermal Vision

Thermal-to-Color Image Translation for Enhancing Visual Odom...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Liyun Zhang Photchara Ratsamee Yuki Uranishi Manabu Higashida Haruo Takemura Graduate School of Information Science and Technology Osaka University Osaka Japan Department of System Design Faculty of Robotics and Design Osaka Institute of Technology Osaka Japan Cyber Media Center Osaka University Osaka Japan

ISBN: (纸本)9781665456814

A panoptic perception-based generative adversarial network for thermal-to-color image translation is proposed to demonstrate its potential as an image sequence enhancement for monocular visual odometry in blurry and low-resolution thermal vision. The pre-trained panoptic segmentation model is utilized to obtain the panoptic perception (i.e., bounding boxes, categories, and masks) of the image scene to guide the alignment between the object content codes of the original thermal domain and panoptic-level style codes sampled from the target color style space. A feature masking module further refines the style-aligned object representations for sharpening object boundaries to synthesize higher fidelity translated color image sequences. The extensive experimental evaluation shows that our method outperforms other thermal-to-color image translation methods in the image quality of translated color images. We demonstrate that the enhanced image sequences significantly improve the performance of monocular visual odometry compared with dif-ferent competing methods including thermal image sequences.

关键词： Image quality Night vision Image segmentation Codes Color Image sequences Safety

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Dual-loop joint torque control based on lower limb exoskeleton robots

Dual-loop joint torque control based on lower limb exoskelet...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Meitong Zhang Chi Zhang Feng Yi Xindong Shu Ting Yuan Zheng Li School of Electrical Engineering Hebei University of Science and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Science Ningbo China School of Electrical Engineering Hebei University of Science and Technology Shijiazhuang China

In the framework of impedance control, the torque trajectory of the joint is unpredictable due to nonlinear factors during the movement of the exoskeleton. To precisely predict command torque trajectory and compensate for the disturbances, we propose a dual-loop joint torque control system including predictive feedforward and a disturbance observer. A predictive feedforward module is adopted as the control outer loop based on the feedback control, and a general compensation module is used to compensate for the prediction error. The inner loop adopts a disturbance observer to estimate and compensate for low-frequency external disturbances. The simulation, which utilizes the actual torque of hip and knee joints as a reference, is conducted to prove the effectiveness of the proposed method. The comparison results demonstrate that the proposed control method can realize high bandwidth for exoskeleton joint control.

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