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SSRN 2024年

作者： Abrha, Selamawit Fthanegest Shiferaw, Helen Negash Kanakubo, Toshiyuki Degree Program in Engineering Mechanics and Energy University of Tsukuba Tsukuba305-8577 Japan Division of Engineering Mechanics and Energy University of Tsukuba Tsukuba305-8577 Japan

This study investigates the mechanical properties and behavior of palm fiber-reinforced cementitious composite (FRCC) through an experimental program. Palm fibers were utilized as reinforcement, with their physical and mechanical properties examined. Density calculations and scanning electron microscope analysis were conducted to characterize fiber morphology and distribution. Single fiber pullout tests were performed under various conditions, expressing pullout load, slip behavior, and failure modes. A trilinear model was developed to describe the pullout-slip curve of single fibers. A bridging model for palm fiber was constructed using the bridging law calculation derived from the single-fiber pullout model. A tensile stress–crack width relationship model for palm-FRCC was created using the bridging law calculation based on the trilinear model. Section analysis was conducted to assess the adaptability of the modeled bridging law calculations. The analysis result of the bending moment–curvature relationship shows good agreement with the experimental results obtained from the 4-point bending test of palm-FRCC. © 2024, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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Reconstruction of Rough Surface Profiles using a CNN-enhanced RTM-based Inversion Scheme

Reconstruction of Rough Surface Profiles using a CNN-enhance...

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International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Ahmet Sefer Guang Ooi Shehab Ahmed Hakan Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9781733509671

ISBN: (纸本)9798350362978

Numerical methods that can accurately reconstruct rough surface profiles are used in various fields of engineering such as remote sensing, microwave imaging, optics, nondestructive testing, etc. These methods express the electromagnetic scattered fields measured away from the surface itself as an integral function of the surface profile. This mapping is highly nonlinear and ill-posed (D. Colton and R. Kress, 1998, SpringerVerlag, Berlin), and therefore its inversion for reconstruction of the surface profile from measured scattered fields is challenging. This inversion can done using semi-analytic asymptotic approaches such as the small perturbation and the Rytov approximation methods (A.G. Voronovich, 2013, Springer-Verlag, Berlin), however the range of applicability of these approaches is rather limited. Fully numerical methods that rely on Newton-type iterative linearization techniques and regularization schemes such as those in (S. Arhab, et al., PIERS, pp. 3495–3500, 2017) and (A. Sefer, A. Yapar, IEEE Trans. Geosci Remote Sens., vol. 59, pp. 1041–1051, 2021) have a wider range of applicability but they suffer from convergence and accuracy issues.

关键词： Microwave measurement Surface reconstruction Perturbation methods Nondestructive testing Optical variables measurement Optics Surface roughness

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Natural Language Processing Meets Hardware Trojan Detection: Automating Security of FPGAs

Natural Language Processing Meets Hardware Trojan Detection:...

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IEEE Computer Society Annual Symposium on VLSI

作者： Vaishnavi More Aaditya Chaudhari Barnaboss Puli Vasavi Vuppala Jaya Dofe Wafi Danesh Department of Electrical and Computer Engineering California State University Fullerton CA USA Division of Engineering Program The State University of New York New Paltz NY USA

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

The inherent runtime reconfiguration capability of field programmable gate array (FPGA) has been a key feature for deployment in various application scenarios, such as data centers, cloud computing, and edge computing, among others. In such applications, reconfiguration is achieved via remote access, which allows multiple users to utilize FPGA resources concurrently and modify the configuration bitstream. An adversary can exploit the accessibility of the configuration bitstream to insert a hardware Trojan (HT) into the FPGA, thereby creating a critical security vulnerability. Since the HT is designed to remain dormant to avoid detection, it can bypass conventional verification and vali-dation techniques. However, any HT inserted in a configuration bitstream must leave a trace even if it is dormant. This paper proposes a supervised learning method using a deep, recurrent neural network (RNN) algorithm to identify such malicious configuration bitstreams in FPGAs. By analysing the patterns present in the bitstream, the proposed method is able to identify any anomalies present in the implemented design. Our method is applied to three ISCAS 85 benchmark circuits of various sizes and topology, implemented on a Xilinx Artix-7 FPGA. Our experimental results showed a maximum accuracy of 93% in detecting HT in bitstreams.

关键词： Recurrent neural networks Accuracy Benchmark testing Very large scale integration Hardware Topology Trojan horses

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A Local Time-Stepping Discontinuous Galerkin Time-Domain Scheme for Simulation of Electromagnetic Problems Involving Resistive Boundary Condition

A Local Time-Stepping Discontinuous Galerkin Time-Domain Sch...

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USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)

作者： Ruitao Sun Ming Dong Liang Chen Hakan Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9789463968119

ISBN: (纸本)9798350359497

Thin layers with high conductivity values, such as metal sheets, conductive paint, graphene, and other two-dimensional (2D) materials, are commonly used in various electromagnetic applications. One of the fundamental challenges in numerical modeling of these thin conductive layers is the requirement for an extremely fine mesh that can accurately capture field variations and account for the intricate geometrical features of the structure (H. Chen, A. J. Taylor and N. Yu, Rep. Prog. Phy., 79,10-35,2016). A dense mesh translates into high computational cost since the number of unknowns is increased and the time step size must be reduced for an explicit time marching scheme (to ensure that the Courant-Friedrichs-Lewy (CFL) condition is satisfied). One can replace the thin conductive layer with an infinitesimally thin sheet on which the resistive boundary condition (RBC) is enforced (T. B. A. Senior and J. L. Volakis, London, UK: IET, 1995). This approach completely avoids the dense mesh and the high computational cost that comes with it. However, RBC has to be incorporated into the electromagnetic solver.

关键词： Two-dimensional displays Graphene Metals Boundary conditions Computational efficiency Numerical models Electromagnetics

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Thermal Management of Photovoltaic Module Using Affordable Organic Phase Change Material Combined with Nano Metal Oxide Particles Enhancer

SSRN

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SSRN 2024年

作者： Phukaokaew, Wisanu Suksri, Amnart Punyawudho, Konlayutt Wongwuttanasatian, Tanakorn Energy Engineering Program Faculty of Engineering Khon Kaen University Thailand Electrical Engineering Division Faculty of Engineering Khon Kaen University Thailand Department of Mechanical Engineering Faculty of Engineering Chiang Mai University Thailand Mechanical Engineering Division Faculty of Engineering Khon Kaen University Thailand Centre for Alternative Energy Research and Development Khon Kaen University Thailand

The utilization of cooling systems based on phase change materials has experienced a surge in popularity due to their ability to effectively reduce the temperature of photovoltaic (PV) modules. Phase change materials (PCMs) have been widely recognized as a highly efficient medium for thermal energy storage due to their ability to harness latent heat capacity. The low thermal conductivity of the material has been identified in many studies. In the current investigation, we have blended PCM with nanoparticles to enhance its thermal conductivity and electrical efficiency. We produce nanoparticle composited phase change materials (nc-PCMs) by combining lauric acid (LA) with three different nanoparticles: aluminum oxide (Al2O3), copper oxide (CuO), and magnesium oxide (MgO), in an optimal weight proportion of 94: 6. Lauric acid and these nanoparticles are chosen based on their ability to provide a significantly lower cost compared to commercial nanoparticles. Research has demonstrated that the incorporation of nanoparticles increases thermal conductivity, resulting in a decrease in the PV module's temperature. The findings indicate that a combination of LA and MgO in a proportion of 94: 6 constitutes the optimal nc-PCM. This composition demonstrated a 5.2 °C reduction in PV module temperature, which resulted in a notable enhancement in the average electrical efficiency of 14.11%. Furthermore, the power output increased significantly to 14.18%. © 2024, The Authors. All rights reserved.

关键词： Aluminum oxide

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Intelligent reflux and suction detection system for ventricular assist devices: in silico study

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Research on Biomedical engineering 2025年第1期41卷 1-23页

作者： Santos, Bruno J. Cestari, Idágene A. Biomedical Engineering Graduate Program Polythecnic School of Engineering University of Sao Paulo Av. Prof. Luciano Gualberto Travessa 3 nº380 Sao Paulo05508-010 Brazil Department of Information Technology of Universidade São Judas Tadeu Rua Taquari nº546 Sao Paulo03166-000 Brazil Faculdade de Medicina Bioengineering Division - Heart Institute Hospital das Clinicas HCFMUSP Universidade de Sao Paulo LIM Bioengenharia Sao Paulo Sao Paulo Brazil

Introduction: Left ventricular assist devices (LVADs) serve both as a bridge to transplantation and as destination therapy for the treatment of congestive heart failure (CHF). However, the inability of the existing control strategies to automatically adapt LVAD flow according to hemodynamic changes can significantly impact patient survival and quality of life. Physiological control strategies for LVAD show promising results, with reflux and suction detection (RSD) increasing device safety. Methods: This study presents in silico results of an RSD system based on measurements of inlet and outlet pressures in continuous-flow LVAD. Two strategies were used to investigate control feasibility, safety, and adjustments to nonlinear variations and comprehensively assess the system’s state considering a structured algorithm (SA) and ensembles of AI models (eAIm): K-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). Results: The SA submodule achieved an accuracy of 99.66% in suction detection but showed limitations in reflux events, with 80.04% accuracy and an F1-Score of 70.4%. The KNN and SVM models demonstrated performance exceeding 96% for both events, exhibiting more excellent stability than the SA submodule. The ANN excelled with low variability and an RMSE of 0.07 in R1, though its suction accuracy (96.7%) was slightly lower than for reflux (99.48%). The KNN was the most effective model, achieving 99.66% accuracy in suction and 98.44% in reflux. The SVM also produced competitive results but with variability across evaluations. The eAIm model showed satisfactory precision (97.78% for suction and 97.14% for reflux), with variations depending on the scenario. The eAIm is recommended for optimization in precision-critical situations. Discussion: These strategies are designed to fulfill the proposal’s feasibility, flexibility, and safety requirements. They address the challenges of achieving consistent reproduction using an SA and the

关键词： Left ventricular assist devices

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A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMS

arXiv

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arXiv 2023年

作者： Gahman, Nicholas Elangovan, Vinayak Computer Science Program Division of Science and Engineering Penn State University AbingtonPA United States

Document similarity is an important part of Natural Language Processing and is most commonly used for plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity algorithm could have a major positive impact on the field of Natural Language Processing. This report sets out to examine the numerous document similarity algorithms, and determine which ones are the most useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based algorithms. The most effective algorithms in each category are also compared in our work using a series of benchmark datasets and evaluations that test every possible area that each algorithm could be used in. © 2023, CC BY-NC-ND.

关键词： Natural language processing systems

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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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Conversion of twisted light to twisted excitons using carbon nanotubes

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npj Computational Materials 2022年第1期8卷 392-402页

作者： Xiaoning Zang Nirpendra Singh Mark T.Lusk Udo Schwingenschlögl Applied Physics Program Physical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal23955-6900Saudi Arabia Department of Physics Khalifa University of Science and TechnologyAbu Dhabi127788United Arab Emirates Department of Physics Colorado School of MinesGoldenCO80401USA

Carbon nanotubes are explored as a means of coherently converting the orbital angular momentum of light to an excitonic form that is more amenable to quantum information *** analytical analysis,based on dynamical conductivity,is used to show that orbital angular momentum is conserved,modulo N,for a carbon nanotube illuminated by radially polarized,twisted *** result is numerically demonstrated using real-time time-dependent density functional theory which captures the absorption of twisted light and the subsequent transfer of twisted *** results suggest that carbon nanotubes are promising candidates for constructing optoelectronic circuits in which quantum information is more readily processed while manifested in excitonic form.

关键词： twisted carbon light

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Neural Bayes estimators for censored inference with peaks-over-threshold models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 19070-19118页

作者： Jordan Richards Matthew Sainsbury-Dale Andrew Zammit-Mangion Raphaël Huser School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK and Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia and School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.

关键词： censored data convolutional neural network likelihood-free inference neural estimation spatial extremes threshold exceedance

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