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SSRN 2023年

作者： Kim, Dong-Seog Ahemad, Mohammad Jamir Le, Thanh Duc Lee, Hu-Jun Yu, Yeon-Tae Division of Advanced Materials Engineering Research Center for Advanced Materials Development Jeonbuk National University Jeonju54896 Korea Republic of Department of Materials Science and Engineering Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan

In view of the explosive nature of hydrogen gas, the demand for detection is increasing. Although Pd-based chemiresistive sensors are gaining much attention in hydrogen detection, it is easily oxidized at high temperatures, leading to the deterioration of sensing performance. To address this issue, we synthesized bimetallic Pd-Au alloy nanoparticles (NPs) and mixed them with SnO2 powder for improving the hydrogen sensing performance. The response of the bimetallic Pd-Au(alloy)/SnO2 nanocomposites (NCs) sensor toward 100 ppm hydrogen was 72.78 which is 7 times higher than that of the pure SnO2 sensor at a relatively lower working temperature of 150°C. Furthermore, when compared to other interfering gases, the sensor revealed high selectivity for hydrogen gas. The enhanced hydrogen sensing performance was attributed to the synergistic effect arising from the formation of a heterojunction interface between Pd-Au alloy and SnO2 NPs and the decomposition and absorption of hydrogen molecules by Pd-Au alloy NPs. © 2023, The Authors. All rights reserved.

关键词： Binary alloys

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Robust globally divergence-free HDG finite element method for steady thermally coupled incompressible MHD flow

arXiv

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arXiv 2024年

作者： Zhang, Min Zhu, Zimo Zhai, Qijia Xie, Xiaoping Institute of Mathematics Henan Academy of Sciences Zhengzhou450046 China College of Mathematies and Information Science Henan Normal University Xinxiang453007 China Chengdu aircraft design and Research Institute Chengdu610041 China School of Mathematics Sichuan University Chengdu610064 China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

This paper develops an hybridizable discontinuous Galerkin (HDG) finite element method of arbitrary order for the steady thermally coupled incompressible Magnetohydrodynamics (MHD) flow. The HDG scheme uses piecewise polynomials of degrees k(k ≥ 1), k, k − 1, k − 1 and k respectively for the approximations of the velocity, the magnetic field, the pressure, the magnetic pseudo-pressure, and the temperature in the interior of elements, and uses piecewise polynomials of degree k for their numerical traces on the interfaces of elements. The method is shown to yield globally divergence-free approximations of the velocity and magnetic fields. Existence and uniqueness results for the discrete scheme are given and optimal a priori error estimates are derived. Numerical experiments are provided to verify the obtained theoretical results. © 2024, CC BY.

关键词： Galerkin methods

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Observation of Photonic Chiral Flatbands

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Physical Review Letters 2025年第10期134卷 103801-103801页

作者： Minho Choi Andrea Alù Arka Majumdar Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA and Physics Program Graduate Center City University of New York New York New York 10031 USA Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA and Department of Physics University of Washington Seattle Washington 98195 USA

Distinct selectivity to the spin angular momenta of photons has garnered significant attention in recent years, for its relevance in basic science and for imaging and sensing applications. While nonlocal metasurfaces with strong chiral responses to the incident light have been reported, these responses are typically limited to a narrow range of incident angles. In this study, we demonstrate a nonlocal metasurface that showcases strong chirality, characterized by circular dichroism (∼0.6), over a wide range of incident angles ±5°. Its quality factor, circular dichroism and resonant frequency can be optimized by design. These findings pave the way to further advance the development of valley-selective optical cavities and augmented reality applications.

关键词： Natural frequencies

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Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

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computer Systems science & engineering 2023年第10期47卷 553-574页

作者： Adnan Hasan Bdair Aighuraibawi Selvakumar Manickam Rosni Abdullah Zaid Abdi Alkareem Alyasseri Ayman Khallel Dilovan Asaad Zebari Hussam Mohammed Jasim Mazin Mohammed Abed Zainb Hussein Arif National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaPenang11800Malaysia Baghdad College of Economic Sciences University BaghdadIraq School of Computer Sciences Universiti Sains MalaysPenang11800Malaysia Information Technology Research and Development Center University of KufaNajafIraq College of Engineering University of Warith Al-AnbiyaaKarbalaIraq Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Business Administration College of Administration and Financial SciencesImam Ja’afar Al-Sadiq UniversityBaghdad10001Iraq College of Computer Science and Information Technology University of AnbarRamadi AnbarIraq Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq

Internet Protocol version 6(IPv6)is the latest version of IP that goal to host 3.4×10^(38)unique IP addresses of devices in the ***6 has introduced new features like Neighbour Discovery Protocol(NDP)and Address Auto-configuration ***6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol(ICMPv6).IPv6 is vulnerable to numerous attacks like Denial of Service(DoS)and Distributed Denial of Service(DDoS)which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial ***,an Intrusion Detection System(IDS)is a monitoring system of the security of a network that detects suspicious activities and deals with amassive amount of data comprised of repetitive and inappropriate features which affect the detection rate.A feature selection(FS)technique helps to reduce the computation time and complexity by selecting the optimum subset of *** paper proposes a method for detecting DDoS flooding attacks(FA)based on ICMPv6 messages using a Binary Flower PollinationAlgorithm(BFPA-FA).The proposed method(BFPA-FA)employs FS technology with a support vector machine(SVM)to identify the most relevant,influential ***,The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack *** results show that the proposed method BFPAFA achieved the best accuracy rate(97.96%)for the ICMPv6 DDoS detection with a reduced number of features(9)to half the total(19)*** proven proposed method BFPA-FAis effective in the ICMPv6 DDoS attacks via IDS.

关键词： IPv6 ICMPv6 DDoS feature selection flower pollination algorithm anomaly detection

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Controllable multi-level switching of optical phase change materials via microheater temperature profile engineering

Controllable multi-level switching of optical phase change m...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Sun, Hongyi Lian, Chuanyu Vásquez-Aza, Francis Huang, Yi-Siou Vitale, Steven A. Takeuchi, Ichiro Hu, Juejun Youngblood, Nathan Pavlidis, Georges Ríos Ocampo, Carlos A. Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Department of Mechanical Engineering University of Connecticut StorrsCT06269 United States Advanced Materials and Microsystems Group MIT Lincoln Laboratory LexingtonMA02139 United States Quantum Materials Center Department of Physics University of Maryland College ParkMD20742 United States Department of Materials Science & Engineering MIT CambridgeMA02139 United States Electrical & Computer Engineering Department The University of Pittsburgh PittsburghPA15213 United States

ISBN: (纸本)9781957171395

We demonstrate doped-silicon-on-insulator microheaters with triangle-like temperature profiles. Such devices control the hotspot size and temperature and, thus, the area that undergoes amorphization or crystallization in optical phase-change materials, leading to controllable multilevel, nonvolatile response. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Optical materials

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advanced wind turbine blade inspection with hyperspectral imaging and 3D convolutional neural networks for damage detection

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Energy and AI 2024年第2期16卷 528-537页

作者： Patrick Rizk Frederic Rizk Sasan Sattarpanah Karganroudi Adrian Ilinca Rafic Younes Jihan Khoder Department of Mechanical Engineering Universitédu QuébecàTrois-RivièresDrummondvilleQCCanada Centre national intégrédu manufacturier intelligent(CNIMI) Universitédu QuébecàTrois-RivièresDrummondvilleQCCanada Department of Mathematics Computer Science and EngineeringUniversitédu QuébecàRimouski300 allée des UrsulinesRimouskiQCG5L3A1Canada The Center for Advanced Computer Studies University of Louisiana at LafayetteLafayetteLAUSA T3E Industrial Research Group Mechanical Engineering DepartmentÉcole de Technologie Supérieure1100 Notre-Dame St WMontréalQC H3C 1K3Canada Faculty of Engineering Third BranchLebanese UniversityHadathBeirutLebanon LISV Laboratory University of Versailles Saint-Quentin-en-Yvelines10-12 Avenue de l'Europe78140VélizyFrance

In the context of global efforts to mitigate climate change by pursuing sustainable energy sources, wind energy has emerged as a critical contributor. However, the wind energy industry faces substantial challenges in maintaining and preserving the integrity of wind turbine blades. Timely and accurate detection and classification of blade faults, encompassing issues such as cracks, erosion, and ice buildup, are imperative to uphold wind turbines' ongoing efficiency and safety. This study introduces an inventive approach that amalgamates hyperspectral imaging and 3D Convolutional Neural Networks (CNNs) to augment the precision and efficiency of wind turbine blade fault detection and classification. Hyperspectral imaging is harnessed to capture comprehensive spectral information from blade surfaces, facilitating exact fault identification. The process is streamlined through Incremental Principal Component Analysis (IPCA), reducing data dimensions while maintaining integrity. The 3D CNN model demonstrates remarkable performance, achieving high accuracy in detecting all fault categories in full-band hyperspectral images. The model retains high accuracy even with dimensionality reduction to 20 spectral bands. The reduced processing time of the 20-band image enhances the practicality of real-world applications, thereby reducing downtime and maintenance expenditures. This research represents a significant advancement in wind turbine blade inspection, contributing to the sustainability and dependability of wind energy systems and furthering the cause of a cleaner and more sustainable energy future as part of the broader fight against climate change.

关键词： blade thereby turbine

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Intelligent cloud computing platform for three-dimensional sound reproduction

Intelligent cloud computing platform for three-dimensional s...

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作者： Zhang, Maosheng Hu, Ruimin Jiang, Lin Wang, Xiaochen School of Mathematics and Statistics Yulin Normal University Yulin China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

Three-dimensional (3D) audio reproduction techniques reproduce realistic sound sources and spatial perception for listeners. However, it is a rather difficult work to configure all the parameters in the previous 3D reproduction systems since ordinary users hardly possess professional acoustic knowledge. In this article, we developed a sound source distance reproduction model and design an online parameter computing framework for users to generate reproduction parameters based on a cloud computing platform. The proposed model reproduces sound direction and distance cues under specific conditions. Subjective experiments are executed to assess the spatial reproduction performance in a real environment and objective experiments are performed in simulated scenarios. Both the subjective and objective experiments show that the proposed method improves the spatial perception of sound events. Since the reproduction model depends on environment and loudspeaker array configuration, a cloud parameter generation framework is developed for users to generate acoustic parameters for the reproduction system. © 2021 John Wiley & Sons Ltd

关键词： Parameter estimation

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Crypt-OR: A privacy-preserving distributed cloud computing framework for object-removal in the encrypted images

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Journal of Network and computer Applications 2022年 208卷

作者： Tanwar, Vishesh Kumar Raman, Balasubramanian Bhargava, Rama Center for Research in Computer Vision University of Central Florida United States Department of Computer Science & Engineering India Department of Mathematics India Indian Institute of Technology Roorkee India

Image inpainting is a technique to modify an image by removing/fill-up the undesired region(s) in a visually plausible manner. With the advancement of cloud applications, the cloud service providers (CSPs) provide image modifying services such as inpainting and undesired object removal to their users. However, these inpainting services require the user's original image containing sensitive information that an adversary can misuse. In this paper, we address these privacy concerns in image enhancement techniques by proposing a novel privacy-preserving distributed-cloud-based framework for object removal in the encrypted images, namely Crypt-OR. The unknown pixels intensity value, obtained by removing the specified object(S), is approximated by incorporating the merits of the exemplar-based search–copy–paste approach and diffusion equation on the Shamir's secret shares. The qualitative and quantitative analysis of Crypt-OR is evaluated over different real-world images with varying shaped and sized objects in the encrypted domain (ED). Crypt-OR outperforms the traditional exemplar-based object-removal schemes and is comparable with generative network-based inpainting schemes in the plain domain (PD). Further, Crypt-OR is proved information-theoretically secure in probabilistic and entropy viewpoints with standard cryptographic adversary attacks. © 2022 Elsevier Ltd

关键词： Distributed cloud

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Decoding White Matter Fiber ODFs: A Mixture Learning Framework in x-q Space

Decoding White Matter Fiber ODFs: A Mixture Learning Framewo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiquan Ma Chengdong Deng Geng Chen Haotian Jiang Shijie Huang Jaeil Kim Xuyun Wen Dinggang Shen Department of Computer Science and Technology Heilongjiang University Harbin China School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Computer Science and Engineering Kyungpook National University Daegu South Korea School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Diffusion magnetic resonance imaging (dMRI), as a powerful non-invasive white matter imaging technology, plays an important role in studying brain white matter. The fiber orientation distribution functions (fODFs) derived from dMRI data provide the key directional information of fiber tracts for revealing the 3D geometric structure of brain white matter. The estimation of fODFs faces two challenges, including (i) the demand for dMRI data densely sampled in q-space and (ii) the joint consideration of x-q space. To address these challenges, we propose a mixture learning framework with q-space sparely sampled dMRI data as input. Specifically, we propose an x-space learning module based on 3D U-Net to learn x-space features and a q-space learning module based on spherical convolutional neural networks to learn q-space features. Two kinds of features are then fused with a mixture learning fusion module for fODFs estimation. The whole framework is supervised with an x-q space loss function. Our framework makes full use of joint x-q space information for fODFs estimation with clinically available q-space sparsely sampled dMRI data. Extensive experiments on three public datasets show that our framework is effective in fODFs estimation and outperforms cutting-edge models.

关键词： Three-dimensional displays Convolution Fuses Magnetic resonance imaging Estimation Optical fiber networks Robustness White matter Faces Distribution functions

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Object Identification and Localization of Visual Explanation for Weapon Detection

Object Identification and Localization of Visual Explanation...

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2022 research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Yuenyong, Sumeth Wongpatikaseree, Konlakorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand Mahidol University Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Weapon detection is a difficult task that requires accurate identification of weapon objects in images. The object localization approach is mostly used because it combines a gradient with a convolutional layer to create a map of key locations on the image. This paper presents an Eigen-CAM method to localize and detect objects in an image for a Faster Region-based Convolutional Neural Network (Faster R-CNN) residual neural network (ResNet 50) model, giving a visual explanation. The Internet Movie Firearms Database (IMFDB) was used to train a deep learning model with the Faster R-CNN ResNet 50 model of the pre-trained PyTorch framework. Experimental results indicated that the Faster R-CNN ResNet 50 model achieved the highest mAP of 0.497 with 0.5 IoU. The Eigen-CAM method performed effectively for visual image representation. © 2022 IEEE.

关键词： Convolutional neural networks

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