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IEEE International Conference on Intelligent engineering Systems (INES)

作者： Homayoun Safarpour Motealegh Mahalegi Amirfarhad Farhadi György Molnár Enikő Nagy Institute of Mechatronics and Vehicle Engineering Óbuda University Budapest Hungary Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9798350367591

ISBN: (纸本)9798350367607

Domain adaptation, a subset of transfer learning, involves generating examples from two related but differently distributed source and target domains. This paper introduces an incremental adversarial learning method for unsupervised domain adaptation, where the source data is labeled, and the target data remains unlabeled. We employ a discriminative adversarial strategy as the primary technique for unsupervised domain adaptation to minimize the significant distributional variance between the source and target domains. Traditional domain adaptation methods typically train the source domain to align with a fixed target domain distribution. However, in many real-world scenarios, the target domain's distribution continuously evolves, leading to a lack of generalizability in the trained model for subsequent domains. To tackle this issue, we suggest using a continuous sequential domain adaptation method. This involves including instances from several related target domains in the training process in a sequential and continuous manner. This process ensures that all receiving domains are correctly aligned with their respective sources, and to maintain sequence continuity, the source domain is incrementally updated by incorporating the most reliable data from the current target domain. This enhancement aims to improve the network's training for forthcoming domain data. Leveraging the effectiveness of deep neural networks, our method utilizes deep GANs for domain adaptation. The approach enhances domain matching by augmenting the source with selected target data and introduces a novel strategy for continuous sequential domain matching. This involves a progressive update of source domain samples with reliable data from the current target domain, facilitating seamless adaptation to successive domain shifts. The proposed method demonstrated a high classification accuracy, achieving an impressive 99% and 95% in different settings. Furthermore, it outperforms previous studies rega

关键词： Training Adaptation models Machine learning algorithms Accuracy Transfer learning Clustering algorithms Sensitivity and specificity

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A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection

arXiv

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arXiv 2024年

作者： Lin, Yuxuan Chang, Yang Tong, Xuan Yu, Jiawen Liotta, Antonio Huang, Guofan Song, Wei Zeng, Deyu Wu, Zongze Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200438 China Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy College of Information Technology Shanghai Ocean University Shanghai201306 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://***/Sunny5250/Awesome-Multi-Setting-UIAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

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Preliminary Assessment of Two Simultaneous and Proportional Myocontrol Methods for 3-DoFs Prostheses Using Incremental Learning

Preliminary Assessment of Two Simultaneous and Proportional ...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Fabio Egle Dario Di Domenico Andrea Marinelli Nicolò Boccardo Michele Canepa Matteo Laffranchi Lorenzo De Michieli Claudio Castellini Department Artificial Intelligence in Biomedical Engineering Assistive Intelligent Robotics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Rehab Technologies Lab Italian Institute of Technology Genova Italy Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Bioengineering Lab University of Genova DIBRIS Genova Italy Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT) Genova Italy German Aerospace Center Institute of Robotics and Mechatronics Wessling Germany

Despite progressive developments over the last decades, current upper limb prostheses still lack a suitable control able to fully restore the functionalities of the lost arm. Traditional control approaches for prostheses fail when simultaneously actuating multiple Degrees of Freedom (DoFs), thus limiting their usability in daily-life scenarios. Machine learning, on the one hand, offers a solution to this issue through a promising approach for decoding user intentions but fails when input signals change. Incremental learning, on the other hand, reduces sources of error by quickly updating the model on new data rather than training the control model from scratch. In this study, we present an initial evaluation of a position and a velocity control strategy for simultaneous and proportional control over 3-DoFs based on incremental learning. The proposed controls are tested using a virtual Hannes prosthesis on two healthy participants. The performances are evaluated over eight sessions by performing the Target Achievement Control test and administering SUS and NASA-TLX questionnaires. Overall, this preliminary study demonstrates that both control strategies are promising approaches for prosthetic control, offering the potential to improve the usability of prostheses for individuals with limb loss. Further research extended to a wider population of both healthy subjects and amputees will be essential to thoroughly assess these control paradigms.

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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Experimental Validation of Sensitivity-Aware Trajectory Planning for a Quadrotor UAV Under Parametric Uncertainty

Experimental Validation of Sensitivity-Aware Trajectory Plan...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Ali Srour Salvatore Marcellini Tommaso Belvedere Marco Cognetti Antonio Franchi Paolo Robuffo Giordano CNRS Univ Rennes Inria IRISA Rennes Cedex France Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy LAAS-CNRS Université de Toulouse CNRS UPS Toulouse France LAAS-CNRS Robotics and Mechatronics Department Electrical Engineering Mathematics Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

In this work, we provide an experimental vali-dation of the recent concepts of closed-loop state and input sensitivity in the context of robust flight control for a quadrotor (UAV) equipped with the popular PX4 1 1 https://***/ controller. Our objective is to experimentally assess how the optimization of the reference trajectory w.r.t. these sensitivity metrics can improve the closed-loop system performance against model uncertainties commonly affecting the quadrotor systems. To accomplish this, we present a series of experiments designed to validate our optimization approach on two distinct trajectories, with the primary aim of assessing its precision in guiding the quadrotor through the center of a window at relatively high speeds. This approach provides some interesting insights for increasing the closed-loop robustness of the robot state and inputs against physical parametric uncertainties that may degrade the system's performance.

关键词： Uncertainty Sensitivity Target tracking Trajectory planning Perturbation methods Autonomous aerial vehicles Robustness

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New High Boost DC-DC Converter for Renewable Energy Conversion

New High Boost DC-DC Converter for Renewable Energy Conversi...

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International Conference on Communications and Electronics, ICCE

作者： Dinh-Tuyen Nguyen Truong-Khang Duong Truong-Duy Duong Trong-Nha Quang Ton Duc Do Faculty of Electrical and Electronics Engineering Ho Chi Minh City University of TechnologyVNU-HCM Ho Chi Minh City Vietnam Dept. Electrical and Computer Engineering Wayne State University Detroit MI USA High Volume Power Design Advanced Energy Industries Inc Eden Prairie MN USA Dept. Robotics and Mechatronics School of Science and Technology Nazarbayev University Kazakhstan

ISBN: (数字)9798350379792

ISBN: (纸本)9798350379808

This paper proposes a high-boost power converter for renewable energy systems. The suggested converter exhibits a simplified structure by replacing a diode in the switched-inductor cell with an active power switch to reduce the total voltage stress on the semiconductor device, get cost reduction, and improve efficiency. Additionally, the common ground feature inherent in the introduced topology expands its potential for renewable energy applications. Finally, the simulation verifications are performed using PSIM software to confirm the effectiveness of the suggested approach, and a detailed comparison study between the basic switched-inductor converter and the suggested converter is also presented.

关键词： Renewable energy sources Costs Simulation Prototypes Voltage Switches Software

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Tooth contact analysis of spur and helical gears with crowned profile for pinion

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IOP Conference Series: Materials Science and engineering 2020年第1期724卷

作者： S Cazan C Dănilă S Creţu Department of Mechanical Engineering Mechatronics and Robotics Technical University of Iaşi Romania Continental Automotive Romania Iaşi Romania

The paper presents tooth contact analysis (TCA) of modified gears using semi-analytical methods (SAMs). The semi-analytical methods use Love's analytical formulation for Boussinesq equation of linear half-space theory and Hartnett approach for numerical formulation. The linear contact between two teeth has a finite length and during meshing process the edge effect appears, with a harmful influence on the gear reliability. To eliminate this, some tooth profile modifications are needed. To turn a line contact into a point contact, the crowned lead profile surface is considered. The ways used to obtain the matrices of separation are discussed for both spur and helical cylindrical gears. The large pressures' systems of equations were solved using the method of conjugate gradients and fast Fourier transform. For validation, in cases of hertzian geometries the SAM results have been compared with results obtained analytically, while for non-hertzian geometries, the SAM results have been compared with FEM results as presented in the specific literature. The developed SAM proved to be an accurate, robust and very fast tool for TCA of spur and helical cylindrical gear.

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Determining the probability of failure for a diesel electric locomotive 060-DA using fault tree analysis

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IOP Conference Series: Materials Science and engineering 2018年第6期444卷

作者： V Goanţă M Alexandrov D Apostol I Blanari Mechanical Engineering Mechatronics and Robotics Department 'Gheorghe Asachi' Technical University of Iasi Iasi Romania CFR Company Iasi branch

The 'fault tree' analysis is one of the most commonly used methods for determining reliability, calculating the probability of failure and thereby determining risk. The primary purpose of this analysis is to evaluate, using analytical and statistical methods, the likelihood of an unfavorable event occurring. These calculations involve the knowledge of system reliability data such as probability of failure, failure rate, time to failure, repair rate etc. Building a 'fault tree' model can provide insight into how to pinpoint potential deficiencies within a functional system. In this paper, a defective tree structure was made for the main components of the 060-DA electric diesel locomotive. Five types of equipment part of the locomotive were taken into consideration: mechanical, pneumatic, thermal, power and auxiliary electrical. Each of these contains, in turn, other subassemblies and components. For the latter, some parameters needed to draw the fault tree diagrams were calculated on a statistical basis. In this way, the probability of the locomotive exiting operation was determined as a result of the defects that occurred in the component parts.

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Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration

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Computers in Biology and Medicine 2024年 183卷 109309-109309页

作者： Ha, Ho-Gun Jeung, Deokgi Ullah, Ihsan Tokuda, Junichi Hong, Jaesung Lee, Hyunki Division of Intelligent Robot DGIST 333 Techno Jungang-daero Hyeonpung-myeon Dalseong-gun Daegu42988 Korea Republic of Department of Robotics and Mechatronics Engineering DGIST 333 Techno Jungang-daero Hyeonpung-myeon Dalseong-gun Daegu42988 Korea Republic of Department of Radiology Brigham and Women's Hospital and Harvard Medical School 75 Francis Street BostonMA02115 United States

Background and objective: For the augmented reality of surgical navigation, which overlays a 3D model of the surgical target on an image, accurate camera calibration is imperative. However, when the checkerboard images for calibration are captured using a surgical microscope having high magnification, blur owing to the narrow depth of focus and blocking artifacts caused by limited resolution around the fine edges occur. These artifacts strongly affect the localization of corner points of the checkerboard in these images, resulting in inaccurate calibration, which leads to a large displacement in augmented reality. To solve this problem, in this study, we proposed a novel target-specific deep learning network that simultaneously enhances both the blur and spatial resolution of an image for surgical zoom lens camera calibration. Methods: As a scheme of an end-to-end convolutional deep neural network, the proposed network is specifically intended for the checkerboard image enhancement used in camera calibration. Through the symmetric architecture of the network, which consists of encoding and decoding layers, the distinctive spatial features of the encoding layers are transferred and merged with the output of the decoding layers. Additionally, by integrating a multi-frame framework including subpixel motion estimation and ideal reference image with the symmetric architecture, joint image deblurring and enhanced resolution were efficiently achieved. Results: From experimental comparisons, we verified the capability of the proposed method to improve the subjective and objective performances of surgical microscope calibration. Furthermore, we confirmed that the augmented reality overlap ratio, which quantitatively indicates augmented reality accuracy, from calibration with the enhanced image of the proposed method is higher than that of the previous methods. Conclusions: These findings suggest that the proposed network provides sharp high-resolution images from blurry low

关键词： Image resolution

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Haptic interfaces for the rescue walking robots motion in the disaster areas

Haptic interfaces for the rescue walking robots motion in th...

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UKACC International Conference on Control (CONTROL)

作者： Luige Vladareanu Octavian Melinte Adrian Bruja Hongbo Wang Xiaojie Wang Shuang Cang Hongnian Yu Zeng-Guang Hou Xiao-Liang Xie Robotics and Mechatronics Department Institute of Solid Mechanics C-tin Mille 15 Bucharest 1 ROMANIA School of Design Engineering and Computing Boumemouth University Poole Dorset UK Parallel Robot and Mechatronic System Laboratory of Hebei Province University Qinhuangdao CHINA State Key Laboratory of Management and Control for Complex Systems Beijing P.R.CHINA

In the recent years haptic interfaces became a reliable solution in order to solve problems which arise when humans interact with the environment. If in the research area of the haptic interaction between human and environment there are important researches, a innovative approach for the interaction between the robot and the environment using haptic interfaces and virtual projection method is presented in this paper. In order to control this interaction we used the Virtual Projection Method where haptic control interfaces of impedance and admittance will be embedded. The obtained results, validated by simulations assure stability, stiffness, high maneuverability and adaptability for rescue walking robots in order to move in disaster, dangerous and hazardous areas.

关键词： Haptic interfaces Legged locomotion Robot sensing systems Impedance Force

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