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Probing intrinsic magnetization dynamics of the Y3Fe5O12/bi2Te3 interface at low temperature

Physical Review Materials 2024年第7期8卷 074409-074409页

作者： A. R. Will-Cole Valeria Lauter Alexander Grutter Carsten Dubs David A. Lidsky Morris Lindner Timmy Reimann Nirjhar bhattacharjee Tzu-Ming Lu Peter Sharma Nichole R. Valdez Charles J. Pearce Todd C. Monson Matthew Matzelle Arun bansil Don Heiman Nian X. Sun Department of Electrical and Computer Engineering Albuquerque New Mexico 87123 USA Neutron Scattering Division Neutron Sciences Directorate Center for High Resolution Neutron Scattering e.V. Technologieentwicklung Prüssingstrasse 27B 07745 Jena Germany Department of Physics Plasma Science and Fusion Center

Topological insulator–magnetic insulator (TI–MI) heterostructures hold significant promise in the field of spintronics, offering the potential for manipulating magnetization through topological surface state–enab.ed spin-orbit torque. However, many TI–MI interfaces are plagued by issues such as contamination within the magnetic insulator layer and the presence of a low-density transitional region of the topological insulator. These interfacial challenges often obscure the intrinsic behavior of the TI–MI system. In this study, we addressed these challenges by depositing sputtered bi2Te3 on liquid phase epitaxy grown Y3Fe5O12/Gd3Ga5O12. The liquid phase epitaxy grown Y3Fe5O12 has b.en previously shown to have exceptional interface quality, without an extended transient layer derived from interdiffusion processes of the substrate or impurity ions, thereby eliminating rare-earth impurity-related losses in the MI at low temperatures. At the TI–MI interface, high-resolution depth-sensitive polarized neutron reflectometry confirmed the ab.ence of a low-density transitional growth region of the TI. by overcoming these undesirab.e interfacial effects, we isolate and probe the intrinsic low-temperature magnetization dynamics and transport properties of the TI–MI interface. Our findings revealed strong spin pumping at low temperatures, accompanied by an additional in-plane anisotropy. The enhanced spin pumping at low temperatures is correlated with the ob.erved suppression of bulk conduction and the weak antilocalization in the TI film, highlighting the interplay between the transport and spin pumping behavior in the TI–MI system.

关键词： Magnetism Magnetization dynamics Topological insulators Magnetic insulators Solid-solid interfaces Neutron scattering

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A Thermo-Mechanical, Viscoelasto-Plastic Model for Semi-Crystalline Polymers exhibiting One-Way and Two-Way Shape Memory effects Under Phase Change

SSRN

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SSRN 2023年

作者： Gülaşik, Hasan Houb.en, Maxime Sànchez, Clara Pereira Calleja Vázquez, Juan Manuel Vanderbemden, Philippe Jérôme, Christine Noels, Ludovic Allée de la découverte 9 LiègeB-4000 Belgium Allée du six août LiègeB-4000 Belgium University of Liege Department of Electrical Engineering and Computer Science Allée de la découverte 10 LiègeB-4000 Belgium

A finite strain phenomenological model is developed to simulate the shape memory behavior of semi-crystalline polymers under thermo-mechanical loading. The polymer is considered to be a composite of crystalline and amorphous phases with constant volume fractions. While the amorphous phase is stab.e, the crystalline phase is considered to change phase with temperature. Therefore, the crystalline phase is considered further to be composed of two phases, whose volume fractions are controlled by a temperature and strain dependent function: the melted phase which is soft, and the crystallized phasewhich is stiff.A pressure dependent viscoelasto-plastic behavior is considered for the constitutive model of the different phases. In addition to pressure dependent plasticity, additional deformation measures are applied to the crystalline phase to model temporary (imperfect shape fixity) and permanent (imperfect shape recovery) deformations in a thermomechanical loading cycle. Formulating a compressib.e plastic flow during the phase changes yields the possibility to capture both one-way and two-way shape memory *** a consequence, the load-dependent and anisotropic thermal expansion ob.erved experimentally in semi-crystalline polymers during phase change is naturally captured. The model is validated within a test campaign performed on nano-composite having a semi-crystalline polymer as a base material. It is shown that the model gives close results with the tests and it is ab.e to capture the shape fixity and shape recovery behaviors of the polymer, for both one-way and two-way shape memory effects. © 2023, The Authors. All rights reserved.

关键词： Volume fraction

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Data security and privacy computing in artificial intelligence

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Journal of Information and Intelligence 2024年第2期2卷 99-101页

作者： Dengguo Feng Hui Li Rongxing Lu Zheli Liu Jianbing Ni Hui Zhu Institute of Software Academy of SciencesBeijing 100190China State Key Laboratory of Integrated Services Networks School of Cyber EngineeringXidian UniversityXi’an 710071China Faculty of Computer Science University of New BrunswickFredericton E3B 5A3Canada College of Computer Science Nankai UniversityTianjin 300071China Department of Electrical and Computer Engineering Queen’s UniversityKingston K7L 3N6Canada

Artificial intelligence(AI)is a field of computer science dedicated to creating systems and algorithms that can perform tasks typically requiring human intelligence,such as learning,prob.em-solving,language understanding,and decision-making,contributing to a wide array of applications across diverse *** development of AI,such as machine learning and deep learning,has revolutionized data processing and *** transformation is rapidly changing human life and has allowed for many practical AI based applications,including biometric recognition,text/sentimental analysis,and attack detection in the fields of health care,finance,autonomous vehicles,personalized ***,the potential benefits of AI are hindered by issues,such as insecurity and privacy violations in data processing and communication.

关键词： such has finance

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Deep Learning-based Feature Recognition for enhancing Mobile Robot Vision

SSRN

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SSRN 2023年

作者： Aoun, Muhammad Nadeem, Muhammad Asgher Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Ali, Syeda Rija Hamam, Habib Department of Computer Science Ghazi University Dera Ghazi Khan32100 Pakistan Department of Computer Science University of Sargodha Sargodha40100 Pakistan Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Information Technology University of Malakand Pakistan Department of Computer Science NCAB&E Multan Pakistan College of Computer Science and Engineering University of Ha’il Ha’il55476 Saudi Arabia Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada Commune d'Akanda B.P. Libreville1989 Gabon

To improve the efficiency and accuracy of mobile robot visual image feature recognition, a mobile robot visual image feature recognition method based on machine vision is proposed in this paper. Firstly, the development of mobile robot vision is analyzed, and the specific functions of the robot visual feature recognition method are designed;Then, the Fourier series method is used to collect the mobile robot visual image, and the matrix associated with the autocorrelation function is calculated according to the Harris algorithm to complete the edge feature extraction of the mobile robot visual image;SIFT feature points of mobile robot visual image are classified, and mobile robot visual image feature recognition is realized through machine vision. The experimental results show that when the number of images is 600, the accuracy of image feature recognition and the loss value of image edge feature extraction of this method are 96.98% and 6.38%, respectively, and the number of iterations is 500. The time of visual image feature recognition of this method is only 3 minutes;The technique has the lowest error mean and error variance under different noise conditions. This method can effectively improve the efficiency and accuracy of image feature recognition. © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids

arXiv

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arXiv 2023年

作者： Hassani, Hossein Hallaji, ehsan Razavi-Far, Roozbeh Saif, Mehrdad The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada The Faculty of Computer Science Canadian Institute for Cybersecurity University of New Brunswick FrederictonNBE3B 5A3 Canada

The performance of fault diagnosis systems are highly affected by data quality in cyber-physical power systems. These systems generate massive amounts of data that overburden the system with excessive computational costs. Another issue is the presence of noise in recorded measurements, which prevents building a precise decision model. Furthermore, the diagnostic model is often provided with a mixture of redundant measurements that may deviate it from learning normal and fault distributions. This paper presents the effect of feature engineering on mitigating the aforementioned challenges in cyber-physical systems. Feature selection and dimensionality reduction methods are combined with decision models to simulate data-driven fault diagnosis in a 118-bus power system. A comparative study is enab.ed accordingly to compare several advanced techniques in both domains. Dimensionality reduction and feature selection methods are compared both jointly and separately. Finally, experiments are concluded, and a setting is suggested that enhances data quality for fault diagnosis. © 2023, CC bY.

关键词： Failure analysis

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Time-delay Robust Safety Control of Connected Vehicles under Stochastic DoS Attack

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Ieee Internet of Things Journal 2025年

作者： Song, Xiulan Liu, Xueyang Shan, Hangguan Lu, Rongxing Zhejiang University of Technology College of Information Engineering Hangzhou310023 China University of Birmingham School of Engineering BirminghamB15 2TT United Kingdom Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China University of New Brunswick Faculty of Computer Science FrederictonE3B 5A3 Canada

Connected vehicles utilize vehicle-to-vehicle (V2V) wireless communication networks to gather vital information for platoon tracking control, not only enhancing road traffic efficiency but also preventing collisions. This paper introduces a robust safety platooning control method for connected vehicles, which addresses safety challenges in heterogeneous vehicle platoon systems, including time delays, dynamic uncertainties, and stochastic denial-of-service (DoS) attacks. Specifically, we present an uncertain longitudinal dynamic model for heterogeneous vehicles to account for variab.e driving conditions. Then, a robust safety platooning controller, based on linear matrix inequality (LMI) and Markov jump system theory, is designed. This controller considers communication delays to ensure vehicle-following stability under dynamic uncertainties and stochastic DoS attacks. Finally, the effectiveness of the proposed algorithm is validated through co-simulations using MatLab and CarSim, as well as actual vehicle experiments, demonstrating its capability to mitigate the impact of communication delays, dynamic uncertainties, and stochastic DoS attacks. © 2014 Ieee.

关键词： Markov processes

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Deep Learning for Precision Grade-Quality Classification of Pomegranates

Deep Learning for Precision Grade-Quality Classification of ...

引用

Innovation and Novelty in engineering and Technology (INNOVA), International Conference on

作者： RajinderKumar M. Math Nagaraj V. Dharwadkar Vijaykumar Sajjanar M. N. Deshmukh Department of Electronics and Communication Engineering B.L.D.E.A's V.P Dr. P.G Halakatti College of Engineering and Technology Vijayapur Karnataka India Department of Computer Science Central University of Karnataka Kadaganchi Kalaburgi India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

The conventional methods of manual grading of fruits after agricultural harvest have always b.en laborious, time-consuming, and prone to human errors. In order to speed up the grading process without human intervention, an automatic system is the need of the hour. This research has led to the development a Convolutional Neural Network (CNN) based deep learning technique to provide real-time grading of pomegranate based on its visual features. This automatic grading technique would help farmers in minimizing their efforts and time spent on manual sorting, at the same time ensuring error-free and uniform sorting and packaging of quality fruits that would, in turn, enhance the storage time post packaging. The methodology uses nine pre-trained models to train on a custom pomegranate dataset with twelve grade-quality classes of pomegranates. The grade-quality grading is obtained using the fine-tuning approach in transfer learning, reducing the computational resource requirements and drastically improving inference speed, which is necessary for real-time model deployment scenarios. The results reveal that the fine-tuning of DenseNet-169 model outperformed the other models by achieving highest values of 99.20% and 1 for (Accuracy, Precision, Recall, and Fl-score) and AUC, respectively.

关键词： Deep learning Visualization Technological innovation Computational modeling Transfer learning Manuals Packaging Real-time systems Convolutional neural networks Sorting

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Vision based umpire detection method for event extraction in cricket video based on HOG and color image segmentation 1

Vision based umpire detection method for event extraction in...

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1st Ieee International Conference on Advances in electrical, Computing, Communications and Sustainab.e Technologies, ICAeCT 2021

作者： Nandyal, Suvarna Kattimani, Suvarna L. Department of Computer Science and Engineering P. D. A. College of Engineering Karnataka Kalaburgi585102 India Department of Computer Science and Engineering B.L.D.E.A's V.P. Dr.P.G.Halakatti College of Engg. and Tech. Karnataka Vijayapur586103 India

ISBN: (纸本)9781728157900

As of late, there has b.en expanded interest in Video Summarization and Automatic Highlights age. One of the most watched and a played sport is cricket, especially in South Asian Countries. In cricket sport video, Umpire has the force for settling on huge choices about functions in the field. In this work, the manually chosen Recorded Video of Some International Twenty-Twenty cricket match video (T20*20) is used as dataset for Umpire identification inside the sport of cricket. The proposed dataset is assessed as a starter help for creating frameworks to consequently produce cricket features. With the growing increase of the utilization of technology in sports, this paper points basically at the Umpire Identification utilizing Area-Of-interest based Histogram of Oriented Gradients (AOI-HOG) Feature extraction strategy for Human Identification. It expects to give Color Image Segmentation technique utilizing pixel wise color information to support vector machine (SVM) classification for Umpire Identification while protecting the arrangement of highlights. Test proof shows that the proposed technique accomplishes the Accuracy of 94.57%. Proposed one efforts towards successful segmentation results and computational conduct, and lessens the time and expands the norm of color image segmentation. © 2021 Ieee

关键词： Support vector machines

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evaluating Feature Selection Methods to enhance Diabetes Prediction with Random Forest 23

Evaluating Feature Selection Methods to Enhance Diabetes Pre...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Pushpa Negi AnishKumar Dhablia Hrishikesh bhanudas Vanjari Jayashree Tamkhade Sharayu Ikhar Shrinivas T. Shirkande Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) India Engineering Manager Altimetrik India Pvt Ltd India E&TC Engineering Dept BVCOE Lavale SPPU Pune India E&TC VIIT India Department of Information technology Vishwakarma Institute of Information technology India Computer Science & Engineering S.B.Patil College of Engineering Indapur India

ISBN: (纸本)9798400709418

Abstract - The issue of diabetes is a significant and urgent matter in global health, necessitating the development of precise predictive models to facilitate early intervention and effective management. The ob.ective of this study is to assess the effectiveness of different feature selection techniques in improving the accuracy of diabetes prediction when employing the Random Forest algorithm. The present study evaluates the impact of three feature selection methods, namely Recursive Feature elimination (RFe), Mutual Information, and L1 Regularization (Lasso), on the performance of the model. This study employs a comprehensive dataset obtained from Kaggle, which includes various attributes such as age, blood pressure, and laboratory measurements. The primary ob.ective of this research is to systematically evaluate and compare the predictive performance of Random Forest models when integrated with different feature selection techniques. The evaluated metrics encompass accuracy, precision, recall, and F1-Score. The findings demonstrate significant enhancements in the accuracy of predictions when employing feature selection techniques. Specifically, the utilization of L1 Regularization (Lasso) resulted in the highest level of accuracy, reaching 98.45%. Furthermore, the features chosen by each method provide insight into the variab.es that have the greatest impact on predicting diabetes. This study offers significant findings regarding the significance of feature selection in healthcare applications, thereby enabling more accurate and effective diagnosis and treatment of diabetes. The results of this study not only make a valuab.e contribution to improving the accuracy of diabetes prediction, but also highlight the importance of carefully selecting relevant features when developing predictive models for healthcare datasets. This study promotes the need for additional investigation into feature selection methodologies and their implementation in the field of healthcare

关键词： Diabetes Prediction

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Medical Image Denoising and brain Tumor Detection Using CNN and U-Net

Medical Image Denoising and Brain Tumor Detection Using CNN ...

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International Conference on Innovative Sustainab.e Computational Technologies (CISCT)

作者： basu Dev Shivahare S. K. Gupta Alok Nath Katiyar Punitha S Prabhishek Singh Manoj Diwakar Dr. APJ Abdul Kalam Technical University Lucknow India Deptt. of CSE B.I.E.T Jhansi India Amazon web services India Department of Computer Science Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

The process of medical imaging, which incorporates images produced by X-rays, ultrasound imaging, angiography, etc., is intricate. During the imaging process, image noise is also captured, some of which are very corrosive, causing a disturbance that impairs the quality of the image. The challenge of removing destructive Gaussian additive white noise from medical images while maintaining the fine features is addressed by the suggested approach. The concept of method noise is combined with a deep learning-based framework for a convolutional neural network (CNN), using method noise as a post-processing operation, to create the proposed technique. brain tumor detection over noisy images is much difficult with better accuracy. Hence in this paper we proposed a method for denoising as well as for brain tumor detection using CNN and U-Net. by explicitly adding Gaussian additive white noise at various noise variance values (= 10, 15, 20, 25, 30), distorted images are produced. The generated denoised images' aesthetic appeal and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), are next assessed. The results are also contrasted with those of other non-traditional methods to evaluate the effectiveness of the suggested methodology further. The findings of the critical examination demonstrate the proposed methodology's admirab.e effectiveness in degaussing the medical images that had b.en tainted by Gaussian noise. This method can be applied in a variety of real-world contexts in the realm of medical image processing.

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