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ACS/IEEE International Conference on computer systems and Applications

作者： Ahmed Alhamadah Muntasir Mamun Henry Harms Mathew Redondo Yu-Zheng Lin Jesus Pacheco Soheil Salehi Pratik Satam Department of Systems and Industrial Engineering University of Arizona Department of Electrical and Computer Engineering University of Arizona School of Industrial Engineering University of Sonora Mexico

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

The onset of Industry 4.0 is rapidly transforming the manufacturing world through the integration of cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity, resulting in performance optimization and increased productivity. Digital Twins (DT) are one such transformational technology that leverages software systems to replicate physical process behavior, and representing it in a digital environment. This paper aims to explore the use of photogrammetry (which is the process of reconstructing physical objects into virtual 3D models using photographs) and 3D Scanning techniques to create accurate visual representation of the ‘Physical Process', to interact with the ML/AI based behavior models. To achieve this, we have used a readily available consumer device, the iPhone 15 Pro, which features stereo vision capabilities, to capture the depth of an Industry 4.0 system. By processing these images using 3D scanning tools, we created a raw 3D model for 3D modeling and rendering software for the creation of a DT model. The paper highlights the reliability of this method by measuring the error rate in between the ground truth (measurements done manually using a tape measure) and the final 3D model created using this method. The overall mean error is 4.97 % and the overall standard deviation error is 5.54% between the ground truth measurements and their photogrammetry counterparts. The results from this work indicate that photogrammetry using consumer-grade devices can be an efficient and cost-efficient approach to creating DTs for smart manufacturing, while the approaches flexibility allows for iterative improvements of the models over time.

关键词： Solid modeling Visualization Three-dimensional displays Accuracy Measurement uncertainty Fourth industrial Revolution Digital twins Photogrammetry Standards Smart manufacturing

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Machine Learning Approaches for EV Charging Management: A Systematic Literature Review

Machine Learning Approaches for EV Charging Management: A Sy...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Thwany, Hanan Alolaiwy, Muhammad Zohdy, Mohamed Edwards, William Kobus, Chris J. Oakland University Industrial and Systems Engineering Department RochesterMI United States Oakland University Electrical and Computer Engineering Department RochesterMI United States Oakland University Mechanical Engineering Department RochesterMI United States

ISBN: (纸本)9798350322347

The article discusses the importance of EV charging management in meeting the growing demand for EV charging infrastructure. The review covers various topics related to EV charging management, including charging protocols, energy management, demand response, smart charging, vehicle-to-grid (V2G), load balancing, dynamic pricing, renewable energy integration, grid integration, interoperability, cybersecurity, battery management systems, data analytics, and artificial intelligence. The article also highlights the importance of machine learning-based approaches for the efficient performance of EV charging management. The systematic literature review is divided into three phases, i.e., identification, screening, and eligibility, and includes relevant research papers that address approaches for EV charging management. The article aims to provide insights into the development of effective policies and strategies for sustainable and reliable EV charging infrastructure. The overall findings show the growing significance and progress within EV charging management, spurred by the imperative to establish sustainable and effective charging infrastructure for electric vehicles. © 2023 IEEE.

关键词： Data Analytics

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Quantized Transformer Language Model Implementations on Edge Devices 22

Quantized Transformer Language Model Implementations on Edge...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Ur Rahman, Mohammad Wali Abrar, Murad Mehrab Copening, Hunter Gibbons Hariri, Salim Shao, Sicong Satam, Pratik Salehi, Soheil University of Arizona Electrical and Computer Engineering TucsonAZ85721 United States University of Arizona Computer Science TucsonAZ85721 United States University of Arizona Systems and Industrial Engineering TucsonAZ85721 United States University of North Dakota Electrical Engineering and Computer Science Grand ForksND58202 United States

ISBN: (纸本)9798350345346

Large-scale transformer-based models like the Bidi-rectional Encoder Representations from Transformers (BERT) are widely used for Natural Language Processing (NLP) applications, wherein these models are initially pre-trained with a large corpus with millions of parameters and then fine-tuned for a downstream NLP task. One of the major limitations of these large-scale models is that they cannot be deployed on resource- constrained devices due to their large model size and increased inference latency. In order to overcome these limitations, such large-scale models can be converted to an optimized FlatBuffer format, tailored for deployment on resource-constrained edge devices. Herein, we evaluate the performance of such FlatBuffer transformed MobileBERT models on three different edge devices, fine-tuned for Reputation analysis of English language tweets in the Rep Lab 2013 dataset. In addition, this study encompassed an evaluation of the deployed models, wherein their latency, performance, and resource efficiency were meticulously assessed. Our experiment results show that, compared to the original BERT large model, the converted and quantized MobileBERT models have 160x smaller footprints for a 4.1 % drop in accuracy while analyzing at least one tweet per second on edge devices. Furthermore, our study highlights the privacy-preserving aspect of TinyML systems as all data is processed locally within a serverless environment. © 2023 IEEE.

关键词： BERT Embedded systems loT Machine Learning Natural Language Processing Privacy Reputation Polarity Social Media TinyML

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Throughput Maximizing Takeoff Scheduling for eVTOL Vehicles in On-Demand Urban Air Mobility systems

arXiv

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arXiv 2025年

作者： Pooladsanj, Milad Savla, Ketan Ioannou, Petros A. The Department of Electrical Engineering University of Southern California Los AngelesCA90007 United States The Departments of Civil and Environmental Engineering Electrical and Computer Engineering and the Industrial and Systems Engineering University of Southern California Los AngelesCA90089 United States

Urban Air Mobility (UAM) offers a solution to current traffic congestion by using electric Vertical Takeoff and Landing (eVTOL) vehicles to provide on-demand air mobility in urban areas. Effective traffic management is crucial for efficient operation of UAM systems, especially for high-demand scenarios. In this paper, we present a centralized framework for conflict-free takeoff scheduling of eVTOLs in on-demand UAM systems. Specifically, we provide a scheduling policy, called VertiSync, which jointly schedules UAM vehicles for servicing trip requests and rebalancing, subject to safety margins and energy requirements. We characterize the system-level throughput of VertiSync, which determines the demand threshold at which the average waiting time transitions from being stable to being increasing over time. We show that the proposed policy maximizes throughput for sufficiently large fleet size and if the UAM network has a certain symmetry property. We demonstrate the performance of VertiSync through a case study for the city of Los Angeles, and show that it significantly reduces average passenger waiting time compared to a first-come first-serve scheduling policy. Copyright © 2025, The Authors. All rights reserved.

关键词： Traffic congestion

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Risk-Aware Security-Constrained Unit Commitment: Taming the Curse of Real-Time Volatility and Consumer Exposure

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第4期2卷 536-551页

作者： Bienstock, Daniel Dvorkin, Yury Guo, Cheng Mieth, Robert Wang, Jiayi Columbia University Department of Industrial Engineering and Operations Research New YorkNY10027 United States Johns Hopkins University Department of Electrical and Computer Engineering Department of Civil and System Engineering Ralph O'Connor Sustainable Energy Institute BaltimoreMD21218 United States Clemson University School of Mathematical and Statistical Sciences ClemsonSC29634 United States Rutgers University Department of Industrial and Systems Engineering New BrunswickNJ08901 United States Stanford University Department of Management Science and Engineering StanfordCA94305 United States

We propose an enhancement to wholesale electricity markets to contain the exposure of consumers to increasingly large and volatile consumer payments arising as a byproduct of volatile real-time net loads - i.e., loads minus renewable outputs - and prices, both compared to day-ahead cleared values. We incorporate a trade-off, motivated by portfolio optimization methods, between standard day-ahead payments and a robust estimate of such excess payments into the day-ahead computation and specifically seek to account for volatility in real-time net loads and renewable generation. Our model features a data-driven uncertainty set based on principal component analysis, which accommodates both load and wind production volatility and captures locational correlation of uncertain data. To solve the model more efficiently, we develop a decomposition algorithm that can handle nonconvex subproblems. Our extensive experiments on a realistic NYISO data set show that the risk-aware model protects the consumers from potential high costs caused by adverse circumstances. © 2024 IEEE.

关键词： Principal component analysis

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Extrinsic and Temporal Calibration of Automotive Radar and 3-D LiDAR in Factory and On-Road Calibration Settings

IEEE Open Journal of Intelligent Transportation Systems

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IEEE Open Journal of Intelligent Transportation systems 2023年 4卷 708-719页

作者： Lee, Chia-Le Hou, Chun-Yu Wang, Chieh-Chih Lin, Wen-Chieh Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan Industrial Technology Research Institute Mechanical and Mechatronics Systems Research Laboratories Hsinchu31040 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan

While automotive radars are widely used in ADAS and autonomous driving, extrinsic and temporal calibration of automotive radars with other sensors is still daunting due to the sparsity, uncertainty, and missing elevation angles of automotive radar measurements. We propose a target-based calibration approach of 3D automotive radar and 3D LiDAR that performs extrinsic and temporal calibration in both factory and on-road settings. In factory calibration settings, a map is built with precise target poses;target trajectories are estimated based on map-based target localization in which the accuracy of both nearby and faraway target pose estimates can be ensured. The spatial and temporal relationships between radar and LiDAR measurements are established with target trajectories to accomplish extrinsic and temporal calibration. The proposed data collection procedure provides sufficient motion for analyzing time delay between sensors and can significantly reduce the data collection effort and time. There is 52.3% distance error reduction after time delay compensation in the experiment, which shows the improvements of temporal calibration. In on-road calibration settings, the metal objects with semantic labels, such as traffic signs, are selected as calibration targets. Although there could be insufficient correspondences to infer the missing dimension of planar radar for six DoF extrinsic calibration as demonstrated in factory calibration settings, the three extrinsic parameters and the time delay are shown still to be accurate. We validated the proposed method using the nuScenes datasets, which provide sensor measurements, poses, and HD map. With twenty-two data logs, each has over 1000 correspondences, the result of extrinsic parameters reaches centimeter-level accuracy compared with the offered benchmark. The time delay compensation improves 1 meter error for radar tracking in a 20 m/s vehicle case and improves mapping quality in real world data. © 2020 IEEE.

关键词： Calibration

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Voting by mail: a Markov chain model for managing the security risks of absentee voting

arXiv

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arXiv 2024年

作者： Haseltine, Carmen A. Albert, Laura A. Electrical & Computer Engineering and Industrial & Systems Engineering University of Wisconsin-Madison MadisonWI53706 United States

The scrutiny surrounding vote-by-mail (VBM) in the United States has increased in recent years, raising concerns about the integrity and security of absentee voting. This paper addresses these issues by introducing a dynamic mathematical modeling framework for performing a risk assessment of VBM processes. We introduce a discrete-time Markov chain (DTMC) to model the VBM process and assess election performance and risk with a novel layered network approach that considers the interplay between VBM processes, malicious and non-malicious threats, and security mitigations. The time-inhomogeneous DTMC framework captures dynamic risks and evaluates performance over time. The DTMC model accounts for a spectrum of outcomes, from unintended voter errors to sophisticated, targeted attacks, representing a significant advancement in the risk assessment of VBM planning and protection. A case study based on real-world data from Milwaukee County, Wisconsin, is used to evaluate the DTMC model. The analysis includes the development of attack scenarios to assess the system’s resilience and the evaluation of security measures. The analysis suggests that ballot drop boxes and automatic ballot notification systems are crucial for ensuring secure and reliable operations. © 2024, CC BY.

关键词： Markov chains

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Designing a Peer-to-Peer Energy Trading Market to Improve Prosumer Participation in a Community: A Numerical Case Study

Designing a Peer-to-Peer Energy Trading Market to Improve Pr...

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Annual North-American Power Symposium

作者： La'Darius M. Thomas Valerie M. Thomas Daniel K. Molzahn School of Electrical and Computer Engineering Georgia Institute of Technology School of Industrial and Systems Engineering Georgia Institute of Technology

ISBN: (数字)9798331521035

ISBN: (纸本)9798331521042

Peer-to-peer (P2P) energy trading has emerged as a transformative approach to electrical power distribution, empowering individuals and communities to directly exchange energy resources within local networks. Challenges arise in modeling the behavior of prosumers (market participants who can both buy and sell electricity). Understanding how prosumers interact in the market is crucial for determining prices and the feasibility of energy transactions. Their decisions to buy or sell energy depend on various factors, which influence market dynamics and the overall success of P2P energy trading. This paper proposes a trilevel framework for buyers who determine their purchase quantities and the fraction of power to purchase from each seller. Sellers determine the prices that maximize profit based on the buyer's demand. A numerical case study involving five prosumers - two sellers and three buyers - is presented and results in an equilibrium state for prosumer transactions in this P2P energy trading market. The trilevel approach successfully balances supply and demand in the P2P energy market where the sellers maximize revenue by adjusting prices and buyers meeting their energy demands.

关键词： Energy consumption Supply and demand Energy resources Electricity Peer-to-peer computing North America

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Optimization for Robustness Evaluation Beyond p Metrics 48

Optimization for Robustness Evaluation Beyond p Metrics

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

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

ISBN: (纸本)9781728163277

Empirical evaluation of the adversarial robustness of deep learning models involves solving non-trivial constrained optimization problems. Popular numerical algorithms to solve these constrained problems rely predominantly on projected gradient descent (PGD) and mostly handle adversarial perturbations modeled by the 1, 2, and ∞ metrics. In this paper, we introduce a novel algorithmic framework that blends a general-purpose constrained-optimization solver PyGRANSO, With Constraint-Folding (PWCF), to add reliability and generality to robustness evaluation. PWCF 1) finds good-quality solutions without the need of delicate hyperparameter tuning and 2) can handle more general perturbation types, e.g., modeled by general p (where p > 0) and perceptual (nonp) distances, which are inaccessible to existing PGD-based algorithms. © 2023 IEEE.

关键词： adversarial attack adversarial robustness constrained optimization deep neural networks perceptual distance robustness evaluation

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MINIMUM-RANK POSITIVE SEMIDEFINITE MATRIX COMPLETION WITH CHORDAL PATTERNS AND APPLICATIONS TO SEMIDEFINITE RELAXATIONS

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and Optimization 2023年第2期5卷 265-283页

作者： Jiang, Xin Sun, Yifan Andersen, Martin S. Vandenberghe, Lieven Department of Industrial and Systems Engineering Lehigh University BethlehemPA18015-1582 United States Department of Computer Science Stony Brook University Stony BrookNY11794-2424 United States Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States

We present an algorithm for computing the minimum-rank positive semidefinite completion of a sparse matrix with a chordal sparsity pattern. This problem is tractable, in contrast to the minimumrank positive semidefinite completion problem for general sparsity patterns. We also present a similar algorithm for the Euclidean distance matrix completion with minimum embedding dimension. The two algorithms use efficient recursions over a clique tree associated with the chordal sparsity pattern. As an application, we use the minimum-rank completion method as a rounding technique to convert the solution of a sparse semidefinite optimization problem with non-unique solutions to an optimal solution of lower rank. In experiments with semidefinite relaxations of optimal power flow problems, the minimum-rank completion often results in solutions of lower rank than the solutions computed by interior-point solvers. ©2023 Applied Set-Valued Analysis and Optimization.

关键词： Electric load flow

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