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13th IEEE Integrated STEM Education Conference, ISEC 2023

作者： Satam, Pratik Philipp, Carter Shao, Sicong Salehi, Soheil University of Arizona Department of Systems and Industrial Engineering TucsonAZ85721 United States University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

ISBN: (纸本)9798350300017

The widespread deployment of 'Smart' Infrastructure, which uses networked computing systems to manage physical processes, has brought the onset of the fourth industrial Revolution (4IR). The 4IR relies on network interconnectivity, sensing, automation, robotics, and artificial intelligence to improve the productivity of modern manufacturing environments while reducing their workforce needs. Although beneficial, this growing adoption of 4IR has increased the complexity of industrial Control systems (ICS), creating a huge workforce skills gap and causing 2.1 million manufacturing jobs to go unfulfilled by 2030. In the context of these challenges, this paper presents Cyber-Physical systems Training Range (CPS-TR), a cloud-based scalable student training platform to address 4IR needs, especially cybersecurity. The CPS-TR, built on Amazon Web Services (AWS), allows instructors to create experimental scenarios using virtual machines in AWS' Elastic Cloud (EC2), connected via AWS Virtual Private Cloud (VPC) based subnet providing an isolated communication environment. The CPSTR front-end provides a web application-based interface for students and instructors to design new material and perform experiments. This paper evaluates the performance and efficiency of the CPS-TR environment while showcasing an experiment teaching trainees about the safe use of the MQTT protocol from 4IR infrastructure software updates. The experimental evaluation shows that a trainee's network traffic volume while using the CPS-TR peaks at 2000 packets in a second;the network bandwidth requirements of a basic network connection suited for usage with mobile and tablet devices. The experimental evaluation also shows the VPC subnet communication peaks at eight packets in a second, with a cumulative packet count of fewer than 70 packets. This low VPC subnet utilization is in combination with EC2 *** VMs allows the CPS-TR operation costs to stay low. © 2023 IEEE.

关键词： Cyber Physical System

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Koopman-based Deep Learning for Nonlinear System Estimation

Koopman-based Deep Learning for Nonlinear System Estimation

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IEEE Conference on Decision and Control

作者： Zexin Sun Mingyu Chen John Baillieul Division of Systems Engineering Boston University Department of Electrical and Computer Engineering Boston University Departments of Mechanical Engineering Electrical and Computer Engineering Division of Systems Engineering Boston University Boston MA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world. Common features of these systems are that their behaviors are difficult to describe exactly and invariably unmodeled dynamics present challenges in making precise predictions. In this paper, we present a novel data-driven linear estimator based on Koopman operator theory to extract meaningful finite-dimensional representations of complex non-linear systems. The Koopman model is used together with deep reinforcement networks that learn the optimal stepwise actions to predict future states of nonlinear systems. Our estimator is also adaptive to a diffeomorphic transformation of the estimated nonlinear system, which enables it to compute optimal state estimates without re-learning.

关键词： Neurons Estimation Stochastic processes Reinforcement learning Fluid flow Predictive models Feature extraction Mathematical models Nonlinear dynamical systems Oscillators

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Current Vector Control of AC Servo Motor Considering Output Delay of PWM Inverter

Current Vector Control of AC Servo Motor Considering Output ...

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18th IEEE International Conference on Advanced Motion Control, AMC 2024

作者： Shimamoto, Haruta Yoshioka, Takashi Urushihara, Shiro National Institute of Technology Kagawa College Advanced Course in Industrial and Systems Engineering Takamatsu Japan National Institute of Technology Kagawa College Dept. of Electrical and Computer Engineering Takamatsu Japan

ISBN: (纸本)9798350342819

In this paper, to expand a control bandwidth of AC servo motor, a new current vector control considering an output delay of PWM Inverter is proposed. The proposed current control is a I-P control with an additional feedback gain, which is based on an extended state feedback consisted from a d-q motor model, the output delay of PWM inverter and an integrator of ACR. In addition, the proposed method is easy to implement to the existing AC servo system. The effectiveness of proposed method is validated in a computer simulation and an experiments using a 3.7 kW class motor test bench. In the condition that a cut-off frequency of ACR is close to a carrier frequency, the simulation results show that the proposed method dramatically improves the current response at the higher cut-off frequency. Moreover, the experimental results show that the proposed method dramatically improves the current response at each operating point. From the results, the proposed method is effective to the expand the bandwidth of AC servo motor. © 2024 IEEE.

关键词： Electric current control

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The good, the bad and the ugly sides of data augmentation: an implicit spectral regularization perspective

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 4470-4554页

作者： Chi-Heng Lin Chiraag Kaushik Eva L. Dyer Vidya Muthukumar School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA Coulter Department of Biomedical Engineering School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA School of Electrical & Computer Engineering School of Industrial & Systems Engineering Georgia Institute of Technology Atlanta GA

Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are traditionally believed to improve generalization by generating new (artificial) data from the same distribution. However, this traditional viewpoint does not explain the success of prevalent augmentations in modern machine learning (e.g. randomized masking, cutout, mixup), that greatly alter the training data distribution. In this work, we develop a new theoretical framework to characterize the impact of a general class of DA on underparameterized and overparameterized linear model generalization. Our framework reveals that DA induces implicit spectral regularization through a combination of two distinct effects: a) manipulating the relative proportion of eigenvalues of the data covariance matrix in a training-data-dependent manner, and b) uniformly boosting the entire spectrum of the data covariance matrix through ridge regression. These effects, when applied to popular augmentations, give rise to a wide variety of phenomena, including discrepancies in generalization between over-parameterized and under-parameterized regimes and differences between regression and classification tasks. Our framework highlights the nuanced and sometimes surprising impacts of DA on generalization, and serves as a testbed for novel augmentation design.

关键词： data augmentation generalization analysis overparameterized models spectral analysis regression classification

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Accelerated gradient methods for nonconvex optimization: Escape trajectories from strict saddle points and convergence to local minima

arXiv

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arXiv 2023年

作者： Dixit, Rishabh Gürbüzbalaban, Mert Bajwa, Waheed U. Department of Electrical and Computer Engineering Departments of Electrical & Computer Engineering Management Science and Information Systems and Statistics United States Departments of Electrical & Computer Engineering and Statistics

This paper considers the problem of understanding the behavior of a general class of accelerated gradient methods on smooth nonconvex functions. Motivated by some recent works that have proposed effective algorithms, based on Polyak’s heavy ball method and the Nesterov accelerated gradient method, to achieve convergence to a local minimum of nonconvex functions, this work proposes a broad class of Nesterov-type accelerated methods and puts forth a rigorous study of these methods encompassing the escape from saddle-points and convergence to local minima through a both asymptotic and a nonasymptotic analysis. In the asymptotic regime, this paper answers an open question of whether Nesterov’s accelerated gradient method (NAG) with variable momentum parameter avoids strict saddle points almost surely. This work also develops two metrics of asymptotic rate of convergence and divergence, and evaluates these two metrics for several popular standard accelerated methods such as the NAG, and Nesterov’s accelerated gradient with constant momentum (NCM) near strict saddle points. In the local regime, this work provides an analysis that leads to the "linear" exit time estimates from strict saddle neighborhoods for trajectories of these accelerated methods as well the necessary conditions for the existence of such trajectories. Finally, this work studies a sub-class of accelerated methods that can converge in convex neighborhoods of nonconvex functions with a near optimal rate to a local minima and at the same time this sub-class offers superior saddle-escape behavior compared to that of NAG. Copyright © 2023, The Authors. All rights reserved.

关键词： Gradient methods

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A Two-stage Stochastic Programming Approach for Aircraft to Ground Resource Assignment

A Two-stage Stochastic Programming Approach for Aircraft to ...

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2024 IEEE International Conference on industrial engineering and engineering Management, IEEM 2024

作者： Zewde, Lidia Nguyen, Hieu T. Jiang, Steven Yadav, Om P. Department of Electrical and Computer Engineering North Carolina Agricultural and Technical State University GreensboroNC United States Department of Industrial and Systems Engineering North Carolina Agricultural and Technical State University GreensboroNC United States

ISBN: (纸本)9798350386097

This paper presents a two-stage stochastic optimization model for the assignment problem of aircraft to gate and charging stations in urban air mobility (UAM). We develop a rigorous mathematical model for the ground resource assignment with electric vertical takeoff and landing (eVTOL) aircraft in which the uncertain charging times and constraints of required infrastructure are captured. We employ scenario-based approaches to capture the uncertain charging times of aircraft in terms of a set of scenarios, thus reformulating the problem as a large-scale mixed integer linear program using the Python-based Pyomo modeling framework. The obtained problem, which is called deterministic equivalent form, can then be solved using the branch and cut algorithm embedded in available MILP solvers. Performance measuring criteria are also implemented to see the effectiveness of incorporating uncertainties in the ground resource assignment and indicate future improvements. © 2024 IEEE.

关键词： Stochastic systems

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain systems

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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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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2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

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

ISBN: (纸本)9781665453813

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. © 2023 IEEE.

关键词： Supply chains

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A Comparative Analysis of SiC MOSFET Performance in a Traction Inverter 20

A Comparative Analysis of SiC MOSFET Performance in a Tracti...

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20th IEEE Asia Pacific Conference on Circuits and systems and IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics Electronics, APCCAS and PrimeAsia 2024

作者： Wu, Chih-Chiang Rout, Uma Sankar Wang, Yu-Long Lin, Yi-Ling Hu, Jwu-Sheng National Yang Ming Chiao Tung University Department of Electrical Engineering & Computer Science Taiwan Industrial Technology Research Institute Mechanical and Mechatronics Systems Research Laboratories Hsinchu Taiwan

ISBN: (纸本)9798350378771

This study rigorously tests and verifies silicon carbide (SiC) power modules within a developed 800 V electric vehicle traction inverter. SiC traction inverters offer high efficiency, power density, and compact size. The developed SiCbased traction inverter is thoroughly evaluated on the established platform with various switching frequency, achieving a peak drive efficiency exceeding 98% at 6 kHz and a power density of 50 kW/L. © 2024 IEEE.

关键词： Power MOSFET

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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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2024 Advances in Science and engineering Technology International Conferences, ASET 2024

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

ISBN: (纸本)9798350344134

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. © 2024 IEEE.

关键词： Blood pressure

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