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IISE Annual Conference and Expo 2022

作者： Abdelatty, Mohamed Y. Umar, Ashraf Khinda, Gurvinder S. Poliks, Mark D. Dept. of Systems Science and Industrial Engineering State University of New York at Binghamton BinghamtonNY United States Dept. of Electrical and Computer Engineering State University of New York at Binghamton BinghamtonNY United States

ISBN: (纸本)9781713858072

Minor changes between the designed Flexible hybrid electronic (FHE) devices and the actual realized printed devices are very crucial for the development and maturity of this technology. The quality of printed electronics depends on mechanical and chemical factors such as ink properties and the printing technique [1], [2]. Fluctuations in the quality of printed electronic devices, such as thickness, width, waviness, and surface roughness, affect their radio frequency (RF) performance [2]. In this paper, we investigate statistically, using design of experiments (DoE) techniques, the significance of surface roughness of screen-printed silver ink traces on the RF performance. Ninety-six microstrip lines were printed;we measured the average thickness, average width, average roughness, and average waviness, for all the samples. Also, we measured the S-parameters for all the samples;then, we extracted Insertion-loss at four different frequencies. We performed simple statistical analysis and checked independence and normality assumptions for our data. We considered the thickness, width, roughness, and waviness to be our independent variables. We performed regression analysis and best subsets regression four times each time we consider the insertion-loss as the dependent variable. We were able to conclude that surface roughness becomes more significant as the frequency increases for screen-printed FHEs. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

关键词： Surface roughness

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Latency Optimization in LEO Satellite Communications with Hybrid Beam Pattern and Interference Control

arXiv

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arXiv 2024年

作者： Zhang, Qianqian Hu, Ye Jung, Minchae Department of Electrical and Computer Engineering Rowan University NJ United States Department of Industrial and Systems Engineering University of Miami FL United States Department of Electronics and Information Engineering Sejong University Seoul Korea Republic of

The rapid advancement of low Earth orbit (LEO) satellite communication systems has significantly enhanced global connectivity, offering high-capacity, low-latency services crucial for next-generation applications. However, the dense configuration of LEO constellations poses challenges in resource allocation optimization and interference management, complicating coexistence with other communication systems. To address these limitations, this paper proposes a novel framework for optimizing the beam scheduling and resource allocation in multi-beam LEO systems. To satisfy the uneven terrestrial traffic demand, a hybrid beam pattern is employed to enhance the downlink quality of service and minimize the transmission latency from LEO satellites to ground user terminals. Additionally, a dynamic co-channel interference (CCI) control mechanism is developed to mitigate inter-beam interference within the LEO constellation and limit cross-system interference affecting protected users from other networks. The problem of user-beam-frequency allocation with power optimization is formulated as a mixed-integer dynamic programming model and solved using a low-complexity neural network-based graph generation algorithm. Simulation results show that the proposed approach outperforms the baseline methods of full frequency reuse and single-channel transmission, and highlights the potential for further performance improvement with multi-user transmissions. © 2024, CC BY.

关键词： Frequency allocation

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Machine Learning-Based Multivariate Classification of Cardiac Autonomic Neuropathy Via Imbalanced ECG Recordings

Machine Learning-Based Multivariate Classification of Cardia...

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Advances in Science and engineering Technology International Conferences (ASET)

作者： Muaz Al Radi Abdelfatah Ahmad Abdulrahman Ahmad Abderrahmene Boudiaf Nouf Alkaabi Maher Malouf Herbert Jelinek Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Department of Industrial And Systems Engineering Khalifa University Abu Dhabi UAE Department of Biomedical Engineering Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350344134

ISBN: (纸本)9798350344141

Cardiac Autonomic Neuropathy (CAN) is a frequent complication of diabetes that presents a diagnostic challenge in clinical settings. Ewing tests, comprising five assessments of blood pressure and heart rate, have traditionally been used to categorize the severity of CAN. This paper investigates the application of a machine learning (ML) approach that leverages patient demographic information and electrocardiogram (ECG) recordings to automatically classify CAN dysfunction without any of the Ewing tests. Additionally, the ML model being proposed addresses the challenges posed by imbalanced datasets both at the data level, through the utilization of synthetic minority over-sampling technique (SMOTE), and at the algorithmic level, by employing the focal loss function. The effectiveness of the proposed techniques is reflected in the ROC curve achieving an area under curve (AUC) value of 92%. Moreover, the model is able to produce 80% accurate prediction of the development of CAN. However, for enhancing system's classification performance, two Ewing tests are supported as additional features. A higher performance is achieved as indicated by accuracy and F1-score values of 88% and 88%, respectively.

关键词： Measurement Accuracy Prevention and mitigation Machine learning Artificial neural networks Electrocardiography Predictive models Prediction algorithms Recording Classification algorithms

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Decentralized and Uncoordinated Learning of Stable Matchings: A Game-Theoretic Approach

arXiv

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arXiv 2024年

作者： Etesami, S. Rasoul Srikant, R. Department of Industrial and Systems Engineering Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering and Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States

We consider the problem of learning stable matchings with unknown preferences in a decentralized and uncoordinated manner, where "decentralized" means that players make decisions individually without the influence of a central platform, and "uncoordinated" means that players do not need to synchronize their decisions using pre-specified rules. First, we provide a game formulation for this problem with known preferences, where the set of pure Nash equilibria (NE) coincides with the set of stable matchings, and mixed NE can be rounded to a stable matching. Then, we show that for hierarchical markets, applying the exponential weight (EXP) learning algorithm to the stable matching game achieves logarithmic regret in a fully decentralized and uncoordinated fashion. Moreover, we show that EXP converges locally and exponentially fast to a stable matching in general markets. We also introduce another decentralized and uncoordinated learning algorithm that globally converges to a stable matching with arbitrarily high probability. Finally, we provide stronger feedback conditions under which it is possible to drive the market faster toward an approximate stable matching. Our proposed game-theoretic framework bridges the discrete problem of learning stable matchings with the problem of learning NE in continuous-action games. Copyright © 2024, The Authors. All rights reserved.

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Implications of Solution Patterns on Adversarial Robustness

Implications of Solution Patterns on Adversarial Robustness

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Hengyue Liang Buyun Liang Ju Sun Ying Cui Tim Mitchell Department of Electrical & Computer Engineering University of Minnesota Department of Computer Science & Engineering University of Minnesota Department of Industrial & Systems Engineering University of Minnesota Department of Computer Science Queens College City University of New York

Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations involves solving non-trivial constrained optimization problems. Recent work has shown that these RE problems can be reliably solved by a general-purpose constrained-optimization solver, PyGRANSO with Constraint-Folding (PWCF). In this paper, we take advantage of PWCF and other existing numerical RE algorithms to explore distinct solution patterns in solving RE problems with various combinations of losses, perturbation models, and optimization algorithms. We then provide extensive discussions on the implications of these patterns on current robustness evaluation and adversarial training. A comprehensive version of this work can be found in [19].

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Upconversion Pumped Tm3+:KY3F10 Channeled Waveguide Laser at 2.3 μm and 1.9 μm

Upconversion Pumped Tm3+:KY3F10 Channeled Waveguide Laser at...

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2024 Advanced Solid State Lasers, ASSL 2024

作者： Ince, Faik Derya Morova, Yagiz Tonelli, Mauro Sennaroglu, Alphan Laser Research Laboratory Departments of Physics and Electrical-Electronics Engineering Koç University Istanbul34450 Turkey Department of Industrial & Systems Engineering Manufacturing and Automation Research Laboratory Rutgers University New BrunswickNJ United States Rumelifeneri Istanbul34450 Turkey MEGAMATERIALS s.r.l and Dipartimento di Fisica dell'Università di Pisa Largo B.Pontecorvo 3 Pisa56127 Italy

We demonstrate, for the first time, a femtosecond laser written Tm3+:KY3F10 waveguide laser at 2.3 μm in continuous-wave regime and at 1.9 µm in continuous-wave and passively Q-switched regimes by upconversion p... 详细信息

关键词： Femtosecond lasers

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Sensitivity Analysis on Green Hydrogen as Energy Storage: A Techno-Economic Case study

Sensitivity Analysis on Green Hydrogen as Energy Storage: A ...

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IEEE General Meeting Power& Energy Society

作者： Rahul R Urs Assia Chadly Ahmad Mayyas Ameena Al Sumaiti Department of Industrial Systems and Engineering Khalifa university Abu Dhabi UAE Department of Electrical & Computer Science Engineering Khalifa university Abu Dhabi UAE

Production of green Hydrogen is increasingly helping the world achieve its energy transition goals. Compared to conventional methods, producing Hydrogen using green energy produces fewer carbon emissions. Furthermore, green Hydrogen can be produced from several renewable sources depending on the region’s potential. Photovoltaic systems are considered in this study to estimate green hydrogen production in the United Arab Emirates. The impact of electrolyzer and hydrogen tank capital expenses on green Hydrogen production has been extensively researched in the literature. However, the impact of the varying capital cost is often overlooked. In this work, we analyze the sensitivity of renewable systems’ costs to changes in the costs of producing Hydrogen, and employing it as energy storage.

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NFTs for Trusted Traceability and Management of Digital Twins for Shipping Containers

NFTs for Trusted Traceability and Management of Digital Twin...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Feruz K. Elmay Mohammad Madine Khaled Salah Raja Jayaraman Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Department of Industrial and Systems Engineering Khalifa University Abu Dhabi UAE

The distribution, traceability, and management of shipping container logistics require secure data flow and trusted transactions. Digital Twins (DTs) can realize these features by offering shipping tracking and traceability, process flow and status monitoring, and management of the physical containersall in a remote manner. However, the data of a DT itself is typically stored, controlled, and managed by a centralized entity, which is often the original creator of the physical container. Having a centralized entity can cause mistrust. The centralized entity may alter, tamper, or delete the digital twin data. To overcome this problem, this paper proposes trusted sharing and management of DTs for shipping containers by using Non-Fungible Tokens (NFTs). NFTs are digital tokens that hold unique data stored, controlled, and managed in a decentralized and immutable blockchain ledger. We extend in this paper the use of NFTs to tokenize shipping container DTs and their metadata. The proposed solution uses NFTs and Ethereum blockchain smart contracts to offer decentralization, security, transparency, traceability, and immutability to the data and processes involved in the creation, storage, and management of DTs of shipping containers. To demonstrate our solution, we create a DT of a shipping container using Microsoft Azure Digital Twins services and showed how to tokenize it using NFT. We assess the system using various test cases to evaluate its main functionalities. Furthermore, we analyze the cost of transactions and the security of the smart contracts code. We have made the code of our smart contracts publicly available on GitHub.

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Smart Fibers for Energy Transformation and Storing

Smart Fibers for Energy Transformation and Storing

引用

2023 Advances in Science and engineering Technology International Conferences, ASET 2023

作者： Al Jaghoub, Haya Rami Almallahi, Maryam Nooman Obaideen, Khaled Moustafa, Asmaa Al Nassan, Wafaa Ramadan, Omar H. Bonny, Talal University of Sharjah Industrial Engineering and Engineering Management Sharjah United Arab Emirates United Arab Emirates University Department of Mechanical and Aerospace Engineering Al Ain United Arab Emirates Rise University of Sharjah Sustainable Energy & Power Systems Research Centre Sharjah United Arab Emirates Ahram Canadian University Faculty of Engineering Giza Egypt University of Sharjah Department of Computer Engineering Sharjah United Arab Emirates University of Sharjah Department of Electrical and Electronic Engineering Sharjah United Arab Emirates

ISBN: (纸本)9781665454742

Fibers have been a crucial element in the development of textiles. This is attributed to the numerous advantages fibers provide, including the production of stronger and long-lasting cloths. Moreover, synthetic fibers are perceived as highly flexible and cost-effective materials. Due to the expansion in portable and wearable devices, the creation of intelligent and versatile fibers has been significantly boosted. Additionally, due to the intrinsic materials of fibers, such as semiconductors and electroactive materials, it is feasible to create fiber electronics. As a result, this paper focuses on fiber electronics and especially the sources of energy supply for these fibers. This paper initially starts to introduce the development of functional fibers in energy conversion and storage, with an emphasis on supercapacitors, solar cells, and nanogenerator batteries. Thereafter, the significance of fiber-shaped energy conversion and smart structural design for storage devices is examined. © 2023 IEEE.

关键词： Supercapacitor

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Adaptive Oracle-Efficient Online Learning

arXiv

引用

arXiv 2022年

作者： Wang, Guanghui Hu, Zihao Muthukumar, Vidya Abernethy, Jacob College of Computing United States School of Electrical and Computer Engineering School of Industrial and Systems Engineering

The classical algorithms for online learning and decision-making have the benefit of achieving the optimal performance guarantees, but suffer from computational complexity limitations when implemented at scale. More recent sophisticated techniques, which we refer to as oracle-efficient methods, address this problem by dispatching to an offline optimization oracle that can search through an exponentially-large (or even infinite) space of decisions and select that which performed the best on any dataset. But despite the benefits of computational feasibility, oracle-efficient algorithms exhibit one major limitation: while performing well in worst-case settings, they do not adapt well to friendly environments. In this paper we consider two such friendly scenarios, (a) "small-loss" problems and (b) IID data. We provide a new framework for designing follow-the-perturbed-leader algorithms that are oracle-efficient and adapt well to the small-loss environment, under a particular condition which we call approximability (which is spiritually related to sufficient conditions provided in (Dudík et al., 2020)). We identify a series of real-world settings, including online auctions and transductive online classification, for which approximability holds. We also extend the algorithm to an IID data setting and establish a "best-of-both-worlds" bound in the oracle-efficient setting. © 2022, CC0.

关键词： Decision making

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