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Effect of radiofrequency coil and primary magnetic field on radiolucent composite plates

Noise and Vibration Worldwide 2023年第1期54卷 44-58页

作者： Gawande, Atul S Mukka Ramachanra, Shreyas Kamyab, Hesam Trung, Nguyen-Thoi Kattimani, Subhaschandra Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Laboratory for Applied and Industrial Mathematics Institute of Computational Science and Artificial Intelligence Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam

Magnetic Resonance Imaging (MRI) systems need a material compatible with the imaging technique with lesser attenuation and provide accurate images without distortion. Carbon fibers are the best-suited materials for x-ray applications because of their radiolucent properties and reduced attenuation characteristics. However, carbon fiber produces images with distortion in the MRI system by absorbing electromagnetic energy because of its conductive nature. In the present study, five different fiber-reinforced radiolucent composite plates are analyzed to predict their suitability for a radio frequency coil and the effect of a primary magnetic field of 1.5 T and 3.0 T on mechanical responses. Simulation models are built to explore the impact of electromagnetic waves and birdcage coil configurations on composite material and quantify the temperature changes caused due to energy absorption. A multiphysics coupling simulation is being used to understand the effect of stacking sequence, ply orientation, and boundary conditions on the response of composite plates under an electro-magneto-mechanical environment. © The Author(s) 2022.

关键词： Magnetic resonance imaging

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Bayesian Perspective for Orientation Estimation in Cryo-EM and Cryo-ET

arXiv

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arXiv 2024年

作者： Xu, Sheng Balanov, Amnon Bendory, Tamir Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States School of Electrical Engineering Tel Aviv University Tel Aviv69978 Israel

Accurate orientation estimation is a crucial component of 3D molecular structure reconstruction, both in single-particle cryo-electron microscopy (cryo-EM) and in the increasingly popular field of cryo-electron tomography (cryo-ET). The dominant method, which involves searching for an orientation with maximum cross-correlation relative to given templates, falls short, particularly in low signal-to-noise environments. In this work, we propose a Bayesian framework to develop a more accurate and flexible orientation estimation approach, with the minimum mean square error (MMSE) estimator as a key example. This method effectively accommodates varying structural conformations and arbitrary rotational distributions. Through simulations, we demonstrate that our estimator consistently outperforms the cross-correlation-based method, especially in challenging conditions with low signal-to-noise ratios, and offer a theoretical framework to support these improvements. We further show that integrating our estimator into the iterative refinement in the 3D reconstruction pipeline markedly enhances overall accuracy, revealing substantial benefits across the algorithmic workflow. Finally, we show empirically that the proposed Bayesian approach enhances robustness against the "Einstein from Noise" phenomenon, reducing model bias and improving reconstruction reliability. These findings indicate that the proposed Bayesian framework could substantially advance cryo-EM and cryo-ET by enhancing the accuracy, robustness, and reliability of 3D molecular structure reconstruction, thereby facilitating deeper insights into complex biological systems. © 2024, CC BY.

关键词： Electron microscopy

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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Computer Modeling in engineering & Sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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Defect-engineered localized exciton in monolayer WSe2

Defect-engineered localized exciton in monolayer WSe2

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CLEO: Science and Innovations, S and I 2022

作者： Maiti, Rishi Chen, Cecilia Rajendran, Anjaly Yao, Kaiyuan Bhatt, Gaurang Hone, James Lipson, Michal Gaeta, Alex L. Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States Department of Electrical Engineering Columbia University New YorkNY10027 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States

Here, we demonstrate controlled defect engineering in monolayer WSe2 via UV-ozone treatment. The creation of Tungsten oxyselenide (TOS) shows broad red-shifted PL emission centered at ~770 nm at room temperature. ... 详细信息

ISBN: (纸本)9781557528209

关键词： Monolayers

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On the design of scalable networks rejecting first order disturbances

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IFAC-PapersOnLine 2022年第13期55卷 216-221页

作者： Shihao Xie Giovanni Russo School of Electrical and Electronic Engineering University College Dublin Ireland Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Italy

This paper is concerned with the problem of designing distributed control protocols for network systems affected by delays and disturbances consisting of a first-order polynomial component and a residual signal. Specifically, we propose the use of a multiplex architecture to design distributed control protocols to reject polynomial disturbances up to ramps and guarantee a scalability property that prohibits the amplification of residual disturbances. For this architecture, we give a sufficient condition on the control protocols to guarantee scalability and ramps rejection. The effectiveness of the result, which can be used to study networks of nonlinearly coupled nonlinear agents, is illustrated via a robot formation control problem.

关键词： Network systems delay systems scalability of networks stability of nonlinear systems disturbance rejection

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Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset

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Intelligent Automation & Soft Computing 2023年第3期35卷 2821-2835页

作者： Riaz Muhammad Ahmed Ali M.Abid Anwar Toufique Ahmed Soomro Omar AlShorman Adel Alshahrani Mahmoud Masadeh Ghulam Md Ashraf Naif H.Ali Muhammad Irfan Athanasios Alexiou Department of Electrical Engineering Sukkur IBA University65200SukkurPakistan Department of Electronic Engineering Quaid-e-Awam University of EngineeringScience and TechnologyLarkana77150Pakistan Faculty of Engineering and AlShrouk Trading Company Najran UniversityNajran61441Saudi Arabia Department of Medical Rehabilitation Sciences-Physiotherapy Program College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Computer Engineering Department Yarmouk UniversityIrbid21163Jordan Pre-Clinical Research Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Internal Medicine Medical CollegeNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Novel Global Community Educational Foundation HebershamNSW 2770AustraliaAFNP Med AustriaWien2770Austria

Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the *** EEG headset is a wearable device that records electrophysiological data from the *** paper presents the design and fab-rication of a customized low-cost Electroencephalogram(EEG)headset based on the open-source OpenBCI Ultracortex Mark IV *** electrode placement locations are modiﬁed under a 10–20 standard *** fabricated headset is then compared to commercially available headsets based on the following para-meters:affordability,accessibility,noise,signal quality,and ***,the data is recorded from 20 subjects who used the EEG Headset,and signals were ***,the participants marked the accuracy,set up time,participant comfort,and participant perceived ease of set-up on a scale of 1 to 7(7 being excellent).Thirdly,the self-designed EEG headband is used by 5 participants for slide *** raw EEG signal is decomposed into a series of band sig-nals using discrete wavelet transform(DWT).Lastly,theseﬁndings have been compared to previously reported *** concluded that when used for slide-changing control,our self-designed EEG headband had an accuracy of 82.0 *** also concluded from the results that our headset performed well on the cost-effectiveness scale,had a reduced setup time of 2±0.5 min(the short-est among all being compared),and demonstrated greater ease of use.

关键词： Brain-computer interface EEG consumer-grade accessibility headset

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Developing a Robotic Solar-Powered Fire Detection and Avoidance System With Machine Learning-Based Sensing 2

Developing a Robotic Solar-Powered Fire Detection and Avoida...

引用

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Verma, Preeti Gangwar, Arun Singh, Bharat Singh, Shubhendra Pratap Vats, Pooja Ganesh, Reddy School of Electronic and Electrical Engineering Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Invertis University Department of Electrical Engineering Uttar Pradesh Bareilly243123 India School of Computer Science and Applications Iimt University Meerut India Tmu Department of Ee Moradabad India School of Basic & Applied Sciences K. R. Mangalam University Department of Mathematics Haryana Gurugram122103 India UTAS-IBRI Engineering Department Oman

ISBN: (纸本)9798350369960

This research addresses the challenge of ensuring a consistent energy supply for forest fire surveillance systems, which is critical in the field of Computer Science, AI, ML, Data Mining, NLP, Computer Vision, and Robotics. Traditional solar-powered solutions rely on fixed-mounted rooftop panels, which are often inadequate due to their limited electricity production capacity, especially under variable weather conditions. This study introduces an innovative, all-weather, autonomous solar tracking system that adapts to the changing position of the sun throughout the seasons, ensuring optimal energy collection for the surveillance equipment. To properly navigate the sun motion, the cautioned gadget merges sun's trajectory monitoring modalities with pv sensing. It begins via determining the season with a sensor that measures the volume of sunshine. The sun function detector & a customized PV itinerary algorithms then estimate the solar's course. The gadget makes use of specific tracking algorithms for sunny, gloomy, and moist instances, using artificial intelligence algorithms to forecast versions in the weather & adjust the panels as it should be. The approach has been proved in simulated experiments to be able to mitigating the influence of versions in the climate at the same time as keeping a steady and specific alignment facing the sun in all climates. The technique will increase the ability of photovoltaic cells utilized for wooded area fire tracking to produce electricity, improving the dependability and effectiveness of such vital device. This precise use of AI and gadget getting to know in environmental surveillance represents a step towards stronger and longer-lasting fire caution systems, which might be important for woodland preservation and management. © 2023 IEEE.

关键词： Autonomous Fire Detection Fire Avoidance Robotics Integrated Sensing Solutions Safety Automation Technology Solar-Powered Systems

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Research on Federated Learning's Contribution to Trustworthy and Responsible Artificial Intelligence 24

Research on Federated Learning's Contribution to Trustworthy...

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3rd International Symposium on Robotics, Artificial Intelligence and Information engineering, RAIIE 2024

作者： Huang, Shijia Liang, Yaxin Shen, Fangzhou Gao, Fanru Fu Foundation School of Engineering and Applied Science Columbia University New York10027 United States Viterbi School of Engineering Electrical and Electronic Engineering University of Southern California Los Angeles90007 United States Department of Mathematics and Statistics San Jose State University San Jose95129 United States Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9798400718311

As an emerging machine learning paradigm, federated learning allows multiple partners to work together to complete specific machine learning tasks while ensuring that the original data of participants is not leaked. This method effectively solves the problem of"data islands" in traditional centralized machine learning, and has been widely used in many fields such as smart healthcare, smart cities, and finance. Federated learning (FL), as a key technology to drive the development of trustworthy and responsible AI, provides a mechanism for sharing learning experiences without directly exchanging data. Federated learning, as a key technology to drive the development of trustworthy and responsible AI, provides a mechanism to share learning experiences without the need to directly exchange data. This study explores the contribution of federated learning in building trustworthy AI systems, reducing the risk of data leakage by designing privacy protection mechanisms and enhancing data security measures, and enhancing the generalizability and robustness of models by using multi-party participation in the updating and validation of models. In addition, we have introduced a new way to assess the fairness of the model, aiming to achieve more fair and unbiased decision-making in a variety of use cases. In experimental analysis, our results show that by implementing these strategies, federated learning can significantly improve the transparency and fairness of AI systems, while achieving high model performance and low privacy risk on multiple cross-domain datasets. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Control of Magnetic Skyrmions in an Exchange Biased van der Waals Ferromagnet

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1710-1711页

作者： McCray, Arthur R C Lebedev, Dmitry Arpaci, Sevdenur Te Velthuis, Suzanne G E Lopez-Dominguez, Victor Amiri, Pedram Khalili Hersam, Mark C. Petford-Long, Amanda K. Phatak, Charudatta Materials Science Division Argonne National Laboratory Lemont IL United States Applied Physics Program Northwestern University Evanston IL United States Department of Materials Science and Engineering Northwestern University Evanston IL United States Department of Electrical and Computer Engineering Northwestern University Evanston IL United States

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Fabric4show:real-time vision system for fabric defect detection and post-processing

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Visual Intelligence 2024年第1期2卷 146-158页

作者： Huaizhou Lin Dan Cai Zengmin Xu Jinsong Wu Lixian Sun Haibin Jia School of Materials Science and Engineering Guangxi Key Laboratory of InformationGuilin University of Electronic TechnologyGuilinChina School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuilin University of Electronic TechnologyCenter for Applied Mathematics of Guangxi(GUET)GuilinChina Anview.ai GuilinChina School of Artificial Intelligence Guilin University of Electronic TechnologyGuilinChina Department of Electrical Engineering University of ChileSantiagoChile

The exploration of computer vision applications for fabric defect detection has immense potential ***,current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency,while neglecting the practical industrial production ***,we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network(CNN)features(i.e.,Faster R-CNN)for defect detection,defect localization and detection model *** addition,the proposed intelligent system incorporates novel approaches,such as a rearranged fabric dataset,anomaly detection,recommended clipping region division,and a replenishment *** study illustrates an example of artificial intelligence(AI)-driven automated technology in fabric *** accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related *** results demonstrate that the proposed approach achieves comparable performance to other models,while significantly reducing computational resource *** potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted,which is likely to have relevant implications for the textile industry.

关键词： Computer vision Fabric manufacturing Automation Defect detection Faster R-CNN

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