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SSRN 2023年

作者： Omar, Ilhaam A. Hasan, Haya Jayaraman, Raja Salah, Khaled Omar, Mohammad Department of Industrial and Systems Engineering Khalifa University Abu Dhabi United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

The hospitality industry generates significant food waste leading to adverse environmental and economic impacts, social and ethical concerns, legal and regulatory issues, and operational challenges. In particular, the lack of traceability and trust among stakeholders in the hospitality industry makes it expensive and difficult to manage food waste. Blockchain technology can revolutionize how food waste is managed within the hospitality industry. This work proposes a blockchain-based solution that encourages a sustainable food ecosystem by minimizing food waste generated in hospitality outlets. With the proposed solution, businesses can track and verify the origin of food items, movement, and disposal in real-time, allowing them to plan and purchase inventory, reduce waste, and increase efficiency. Additionally, our proposed system can provide transparency and accountability, ensuring food waste is properly disposed of. Using a permissioned Ethereum network, our solution connects food and beverage outlets with food waste organizations. We present a generalized system for securely sharing information, including algorithms to capture stakeholder interactions to enhance trust among participating stakeholders. Additionally, we analyze the solution for various security vulnerabilities. The proposed blockchain-based solution is commercially viable, the hospitality industry can operate with enhanced efficiency and provides better information connectivity for various stakeholders in the food supply chain. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Model Predictive Control in Optimal Intervention of Covid-19 with Mixed Epistemic-Aleatoric Uncertainty

Model Predictive Control in Optimal Intervention of Covid-19...

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2023 Winter Simulation Conference, WSC 2023

作者： Wan, Jinming Mirghorbani, Saeideh Wu, N. Eva Cheng, Changqing Binghamton University Department of Systems Science and Industrial Engineering 4400 Vestal Parkway East BinghamtonNY13902 United States Binghamton University Department of Business Analytics and Operations 4400 Vestal Parkway East BinghamtonNY13902 United States Binghamton University Department of Electrical and Computer Engineering 4400 Vestal Parkway East BinghamtonNY13902 United States

ISBN: (纸本)9798350369663

Non-pharmaceutical interventions (NPI) have been proven vital in the fight against the COVID-19 pandemic before the massive rollout of vaccinations. Considering the inherent epistemic-aleatoric uncertainty of parameters, accurate simulation and modeling of the interplay between the NPI and contagion dynamics are critical to the optimal design of intervention policies. We propose a modified SIRD-MPC model that combines a modified stochastic Susceptible-Infected-Recovered-Deceased (SIRD) compartment model with mixed epistemic-aleatoric parameters and Model Predictive Control (MPC), to develop robust NPI control policies to contain the infection of the COVID-19 pandemic with minimum economic impact. The simulation result indicates that our proposed model can significantly decrease the infection rate compared to the practical results under the same initial conditions. © 2023 IEEE.

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Invertibility of Discrete-Time Linear systems with Sparse Inputs

arXiv

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arXiv 2024年

作者： Poe, Kyle Mallada, Enrique Vidal, René Applied Mathematics and Computational Science Group University of Pennsylvania PA19104 United States Department of Electrical and Computer Engineering Johns Hopkins University MD21218 United States Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA19104 United States

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparse-input setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024, CC BY.

关键词： Linear systems

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

TechRxiv

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TechRxiv 2023年

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

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 containers–all 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. © 2023, CC BY.

关键词： Supply chains

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Locally Differentially Private Online Federated Learning With Correlated Noise

arXiv

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arXiv 2024年

作者： Zhang, Jiaojiao Zhu, Linglingzhi Fay, Dominik Johansson, Mikael Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States

We introduce a locally differentially private (LDP) algorithm for online federated learning that employs temporally correlated noise to improve utility while preserving privacy. To address challenges posed by the correlated noise and local updates with streaming non-IID data, we develop a perturbed iterate analysis that controls the impact of the noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed for several classes of nonconvex loss functions. Subject to an (ϵ,δ)-LDP budget, we establish a dynamic regret bound that quantifies the impact of key parameters and the intensity of changes in the dynamic environment on the learning performance. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024, CC BY.

关键词： Federated learning

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A Novel Machine Learning Method for the Design Optimization of Dielectric Waveguides Fabricated by Femtosecond Laser Writing

SSRN

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SSRN 2024年

作者： Ince, Faik Derya Morova, Yagiz Özel, Tuğrul Sennaroglu, Alphan Rutgers University - New Brunswick Department of Industrial & Systems Engineering Manufacturing and Automation Research Laboratory New Jersey United States Laser Research Laboratory Departments of Physics and Electrical-Electronics Engineering Koç University Istanbul34450 Turkey Istanbul34450 Turkey

We report on a novel machine learning method for the design optimization of femtosecond (fs) laser written dielectric waveguides. Experimental results previously obtained from the optical characterization of fs laser written depressed cladding diamond waveguides have been used to form statistically generated regression models. Design variables such as core diameter and number of written tracks were varied to both minimize the propagation loss as well as to establish a full-factorial experimental design. The regression models were used to conduct a multi-objective optimization study to optimize the competing objectives such as maximizing the refractive index contrast while minimizing the propagation loss and V-number by using a genetic algorithm. Optimization was subject to a nonlinear Rayleigh range constraint to ensure that the structure was in the waveguiding regime. Results from the optimization revealed the optimum variables to achieve low-loss and nearly single-mode guiding for a fs laser written diamond waveguide. Using the solution sets of design parameters resulting from the optimization study and their corresponding objective function values, important correlations between the design parameters and the objective functions have been revealed. With this regard, it has been shown that the number of written tracks is a much more dominant parameter, when compared to core diameter, during the design of a fs laser written circular depressed cladding diamond waveguide. © 2024, The Authors. All rights reserved.

关键词： Diamonds

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Fuzzy-based Telework Capability Evaluation Using AppSheet-based Mobile Application 14

Fuzzy-based Telework Capability Evaluation Using AppSheet-ba...

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14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Vicerra, Ryan Rhay P. Gamara, Rex Paolo C. San Juan, Jayne Lois G. Bandala, Argel A. Concepcion, Ronnie Dadios, Elmer P. Loresco, Pocholo James M. Espanola, Jason E. Culaba, Alvin Mayol, Andres Philip De la Salle University Department of Manufacturing Engineering and Management Manila Philippines De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Department of Industrial and Systems Engineering Manila Philippines FEU Institute of Technology Electrical and Electronics Engineering Department Manila Philippines De la Salle University Department of Mechanical Engineering Manila Philippines

ISBN: (纸本)9781665464932

By the beginning of 2020, the illness had been named as COVID-19, which had spread due to its extreme severity affecting multiple industries and sectors throughout the world. To protect the public's health and safety, the Philippine government has established a number of quarantine regulations and travel restrictions in reaction to the current COVID-19 outbreak. Nonetheless, the ILO predicted that the pandemic would initially disrupt the economy and labor markets, affecting 11 million employees, or around 25% of the workforce in the Philippines. Therefore, the government continues to urge employers of local companies and enterprises to use alternative work plans, such as a WFH - work-from-home operation in accordance with the established policies. In line with the concept of telework, several studies have already been carried out, though some were declared inconclusive and require additional study. Hence, in this research, a mobile application was created to evaluate the employee's telework capability assessment using a Fuzzy-based model which utilizes Google AppSheet, Apps Script, and Sheets. The developed mobile application is able to provide capacity evaluation utilizing the four key input variables, which are also reasonably characterized for potential telecommuting cost evaluation. © 2022 IEEE.

关键词： COVID-19

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Wildfire Spread Prediction Model Calibration Using Metaheuristic Algorithms 48

Wildfire Spread Prediction Model Calibration Using Metaheuri...

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48th Annual Conference of the IEEE industrial Electronics Society, IECON 2022

作者： Pereira, Jorge Mendes, Jerome Junior, Jorge S. S. Viegas, Carlos Paulo, Joao Ruivo University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Pólo Ii CoimbraPT-3030-290 Portugal University of Coimbra Association for the Development of Industrial Aerodynamics CoimbraPT-3030-289 Portugal

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Every year, wildfires cause significant losses and destruction around the globe. In order to attempt to reduce their damages, resources have been put into developing fire propagation prediction systems. In a real wildfire event, these systems provide the authorities with information about the fire propagation in the near future, thus allowing them to make better decisions. Wildfire spread prediction systems are based on fire propagation models, from which the most used and accepted model is the Rothermel model. However, given the complexity of the wildfire phenomena and the uncertainty of some of its input parameter values, the Rothermel model can produce misleading results of fire propagation. This paper uses 3 metaheuristic algorithms, genetic algorithm (GA), differential evolution (DE) and simulated annealing (SA), for calibration of input parameters from the Rothermel model. These algorithms were validated using 37 datasets containing data from controlled experimental fires. Results have shown that these algorithms provide a precise wildfire spread prediction accounting for the uncertainties in the model's selected parameters. © 2022 IEEE.

关键词： Forecasting

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GPS-IDS: An Anomaly-based GPS Spoofing Attack Detection Framework for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Abrar, Murad Mehrab Youssef, Amal Islam, Raian Satam, Shalaka Latibari, Banafsheh Saber Hariri, Salim Shao, Sicong Salehi, Soheil Satam, Pratik The Department of Electrical and Computer Engineering The University of Arizona TucsonAZ United States Electronics and Telecommunications Engineering The University of Mumbai India United States Ohio State University United States The Department of Systems and Industrial Engineering The University of Arizona TucsonAZ United States

Autonomous Vehicles (AVs) heavily rely on sensors and communication networks like Global Positioning System (GPS) to navigate autonomously. Prior research has indicated that networks like GPS are vulnerable to cyber-attacks such as spoofing and jamming, thus posing serious risks like navigation errors and system failures. These threats are expected to intensify with the widespread deployment of AVs, making it crucial to detect and mitigate such attacks. This paper proposes GPS Intrusion Detection System, or GPS-IDS, an Anomaly-based intrusion detection framework to detect GPS spoofing attacks on AVs. The framework uses a novel physics-based vehicle behavior model where a GPS navigation model is integrated into the conventional dynamic bicycle model for accurate AV behavior representation. Temporal features derived from this behavior model are analyzed using machine learning to detect normal and abnormal navigation behaviors. The performance of the GPS-IDS framework is evaluated on the AV-GPS-Dataset— a GPS security dataset for AVs comprising real-world data collected using an AV testbed, and simulated data representing urban traffic environments. To the best of our knowledge, this dataset is the first of its kind and has been publicly released for the global research community to address such security challenges. Copyright © 2024, The Authors. All rights reserved.

关键词： Global positioning system

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Predictive Logistics Management of Car Sales Based on Machine Learning Algorithm for Supply Chain 2

Predictive Logistics Management of Car Sales Based on Machin...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Gaikwad, Pranav Pandurang Alhomaidi, Esam Gupta, Shishir Perada, Anvesh Dande, Mangesh Purushottam Muthukumar, E. University of Cumberlands Department of Information Technology WilliamsburgKY40769 United States King Fahd University of Petroleum and Minerals Department of Industrial & Systems Engineering Dhahran31261 Saudi Arabia Allenhouse Business School Department of Business Administration Uttar Pradesh Kanpur208008 India Drexel University Department of Electrical and Computer Engineering PhiladelphiaPA19104 United States Indira School of Business Studies Pgdm Department of Operations Management Pune411033 India Nehru Institute of Engineering and Technology Department of Mba Coimbatore641105 India

ISBN: (纸本)9798350364699

Machine learning algorithms are implemented in predictive logistics management to optimize the supply chain for vehicle sales. These algorithms are capable of predicting future demand, managing inventory levels, and optimizing distribution procedures by analyzing historical data, prevailing market patterns, and additional external factors. Organizations may enhance customer satisfaction, minimize lead times, and maintain appropriate inventory levels by implementing predictive models. This approach not only improves the overall efficacy of the supply chain, but it also decreases the costs associated with inventory shortages and excess inventory. The integration of machine learning (ML) into logistics management (LM) offers a data-driven solution to the complex and ever-changing supply chain scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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