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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Barhoumi, El Manaa Zghaibeh, Manaf Okonkwo, Paul C. Hasan, Najam Ul Farhani, Slah Bacha, Faouzi College of Engineering Dhofar University Electrical and Computer Engineering Department Salalah Oman College of Engineering Dhofar University Mechanical and Mechatronic Engineering Department Salalah Oman Insat University of Carthage Laboratory of Computer and Industrial Systems Tunis Tunisia Ensit University of Tunis Department of Electrical Engineering Tunis Tunisia

ISBN: (纸本)9781665458832

This paper presents the design and implementation of an energy management system (EMS) for a renewable energy system using Matlab Simulink and Arduino Mega. The proposed renewable energy system is composed of an electrolyzer, a battery, photovoltaic modules, and fuel cells. The developed energy management is implemented on the Arduino board to control the connection of the different components to the load based on the load power consumption, the battery's charge state, and the energy produced by the photovoltaic modules. The energy management system inputs include the information on the amount of energy produced by the photovoltaic modules, the power used by the load, and the battery's charge state. When the produced energy exceeds the energy used by the load and the batteries are fully charged, the system allows the electrolyzer to connect to the photovoltaic modules to produce the hydrogen. Our proposed system is based on low cost technology that intends to extend the life of the batteries and enhance the efficiency of the renewable energy system by the production and using the hydrogen. The obtained experimental results show the effectiveness of the EMS in controlling the connection of the different components. © 2022 IEEE.

关键词： Energy management systems

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Robust Optimization for the Day-Ahead Scheduling of Cascaded Hydroelectric systems

Robust Optimization for the Day-Ahead Scheduling of Cascaded...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Zhong, Zhiming Fan, Neng Wu, Lei University of Arizona Department of Systems and Industrial Engineering TucsonAZ United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

Uncertain electricity prices resulting from the re-structuring of electricity market have brought new opportu-nities and challenges for hydroelectric producers. This paper presents a data-driven robust optimization approach for the day-ahead scheduling of cascaded hydroelectric systems (CHS) with electricity price uncertainty. In this paper, a minimum volume enclosing ellipsoid (MVEE) is adopted to construct an ellipsoidal uncertainty set that fully identifies and exploits the historical characteristics data. A second-order conic optimization formulation of the robust counterpart is derived for efficient computation. A real-world case study is conducted to demonstrate the capability of the proposed optimization approach compared with the traditional robust optimization approach. Response to Reviewers We express our sincere thanks to the committee numbers and all the anonymous review-ers for their constructive comments on our manuscript. We have revised the manuscript based on the comments and suggestions. The detailed one-to-one responses are summarized below. The texts highlighted in blue are our response to the reviewers comments. Reviewer 1 Comment 1.1 - The innovation of this paper is not clear, as the modeling and conversion techniques have been well established. The authors are suggested to consider the case where CHS are distributed at locations with different electricity prices. Reply: Thanks for your comment. A point-by-point description on the major innovations of this paper is provided at the end of introduction section in page 2. Our contribution is to apply ellipsoidal uncertainty set and second-order cone optimization method to first capture the time-correlation characteristic of uncertain electricity prices for hydroelectric producers. Testing the performance of the proposed optimization technique in various cases is indeed important. Unfortunately, due to the page limitation, we are unable to supplement more numerical examples in this paper, so that

关键词： Scheduling

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Cross-Modality Image Transformation Using Generative Adversarial Network 24th

Cross-Modality Image Transformation Using Generative Adversa...

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24th Conference on Man-Machine-Environment System engineering, MMESE 2024

作者： Liu, Haoting Yu, Shiqi Hou, Qingwen Chen, Shuai Guo, Kuiyuan Wang, Xu Li, Qing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CATARC Technology Co. Ltd. Shenzhen518118 China

ISBN: (纸本)9789819771387

A cross-modality image transformation method using the Generative Adversarial Network (GAN) is proposed in this paper. First, the preprocessing methods are performed to the original image data. The computational steps include adjusting image size and suppressing image noises. Second, a kind of GAN is constructed. Both the generator network and discriminator network are created. Third, the GAN is trained and the back propagation optimization is performed continuously. Finally, the trained GAN can be used for data enhancement. To test the effects of computational steps above, the image transformation from the visible light image to other noise-contaminated image is performed. Lots of experiments have illustrated the correctness of our method. © Beijing KeCui Man-Machine-Environment System engineering Technology Research Academy 2024.

关键词： Generative adversarial networks

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Analysis of Magnetic Field Simulations of Magnetoelastic Sensors Quasi-Statically using COMSOL Multiphysics and MATLAB Software 5

Analysis of Magnetic Field Simulations of Magnetoelastic Sen...

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5th IEEE International Conference on Modern electrical and Energy System, MEES 2023

作者： Gans, Simon Molnar, Jan Naida, Vitalii Maliakova, Mariia Technical University of Košice Department of Theoretical and Industrial Electrical Engineering Košice Slovakia Technical University of Košice Department of Theoretical and Electrical Engineering Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Department of Automation and Computer Systems Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Electrical Engineering Kremenchuk Ukraine

ISBN: (纸本)9798350359787

This work describes a procedure in which a transient simulation of electromagnetic fields can be done when the electrical conductivity of the ferromagnetic medium can be effectively neglected transforming it into a quasi-static problem. A pressductor-type magnetoelastic sensor simulation model was created. The sensor's output consists of a change of the induced voltage in the secondary coil due to applied compressive stress. Induced voltage is proportional to the time-derivative of the magnetic flux flowing through the coil and therefore is transient in nature. However, transient analyses tend to be computationally expensive and can yield noisy outputs when the model is not sufficiently densely discretized and appropriate tolerances are chosen. To overcome this, a quasi-stationary analysis of the same system is introduced and computed using the COMSOL Multiphysics FEM simulation software with further processing in the MATLAB environment, and the results are compared. © 2023 IEEE.

关键词： Multiphysics

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Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis Using Machine Learning Approaches 5

Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis ...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Sultan Bin Habib, Al Zadid Bin Syed, Md Asif Islam, Md. Ekramul Tasnim, Tanpia West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States Stamford University Bangladesh Department of Computer Science & Engineering Dhaka1217 Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Narayanganj Dhaka1461 Bangladesh

ISBN: (纸本)9798350394290

Polycystic Ovarian Syndrome (PCOS) is a widespread hormone problem for women of childbearing age. Women with PCOS may not ovulate;they might have high levels of androgens and have many small cysts on the ovaries. It can cause missed or irregular menstrual periods, excess hair growth, acne, infertility, and weight gain. Machine Learning (ML) can effectively diagnose this disease at an earlier stage as tons of medical data are available now. Traditional approaches to detect PCOS encompass a combination of clinical evaluation, medical history assessment, physical examination, and laboratory tests. These approaches aim to identify the characteristic symptoms and hormonal imbalances associated with PCOS. Physical examination requires good resources and costs time and money. In recent times, data-driven techniques have substantially advanced disease prediction within the medical field. We aim to utilize ML approaches, incorporating unique feature selection algorithms, to predict PCOS. This paper introduces a data-driven approach to PCOS diagnosis, combining Feature engineering and ML. Several feature selection approaches have been considered to select sets of features for training the ML model, including CatBoost, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), AdaBoost, Random Forest (RF). Results demonstrate that AdaBoost, with ten features selected by RF Feature Importance and Highest Correlation (HC), provides the highest test accuracy. © 2023 IEEE.

关键词： Adaptive boosting

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A General Framework for Human-Drone Interaction under Limited On-board Sensing

A General Framework for Human-Drone Interaction under Limite...

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IEEE Symposium on systems and Information engineering Design, SIEDS

作者： S. Nayhouse S. Chadha P. Hourican C. Moore N. Bezzo Departments of Systems and Information Engineering and Electrical and Computer Engineering University of Virginia Charlottesville VA USA Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

Recent advancements in unmanned aerial vehicles (UAVs), has allowed their deployment for numerous applications like aerial photography, infrastructure inspection, search and rescue, and surveillance. Despite the potential for full autonomy, many applications still necessitate human operators for navigating complex environments and decision-making. Existing solutions often employ high-precision and simple sensors like 2-D or 3-D LiDAR, which may provide more data than necessary and contribute to increased system complexity and cost. To address these challenges and bridge the gap between full autonomy and human-controlled UAVs, this work develops a shared-autonomy framework for UAVs, leveraging lightweight, low-cost 1-D LiDAR sensors combined with mobility behaviors to obtain performance comparable to more advanced 2-D/3-D LiDAR sensors while minimizing energy, computation overhead, and weight. Our framework includes a novel state machine method that exploits the UAV mobility to compensate for the limitations of 1-D LiDAR sensors, ensuring safety and obstacle avoidance through a physics-based algorithm that transitions between teleoperation and autonomous mode as needed based on environmental conditions and safety-critical issues. Experimental validations on real UAVs demonstrates the effectiveness of this shared autonomy scheme in complex environments, and the system is further generalized to larger UAVs and prototyped with a custom sensor configuration and onboard obstacle avoidance.

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Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regression

arXiv

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arXiv 2023年

作者： Anjarlekar, Ameya Etesami, Rasoul Srikant, R. Department of Electrical and Computer Engineering Coordinated Science Lab University of Illinois Urbana-Champaign United States Departments of Industrial and Systems Engineering and Electrical and Computer Engineering The Coordinated Science Lab University of Illinois Urbana-Champaign United States

We address the challenge of solving machine learning tasks using data from privacy-sensitive sellers. Since the data is private, we design a data market that incentivizes sellers to provide their data in exchange for payments. Therefore our objective is to design a mechanism that optimizes a weighted combination of test loss, seller privacy, and payment, striking a balance between building a good privacy-preserving ML model and minimizing payments to the sellers. To achieve this, we first propose an approach to solve logistic regression with known heterogeneous differential privacy guarantees. Building on these results and leveraging standard mechanism design theory, we develop a two-step optimization framework. We further extend this approach to an online algorithm that handles the sequential arrival of sellers. © 2023, CC BY-NC-ND.

关键词： Differential privacy

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DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework

arXiv

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arXiv 2024年

作者： Lin, Yu-Zheng Shi, Qinxuan Yang, Zhanglong Latibari, Banafsheh Saber Shao, Sicong Salehi, Soheil Satam, Pratik Department of Electrical and Computer Engineering University of Arizona United States Department of Systems and Industrial Engineering University of Arizona United States School of Electrical Engineering and Computer Science University of North Dakota United States

Digital twin (DT) technology has emerged as a transformative approach to simulate, predict, and optimize the behavior of physical systems, with applications that span manufacturing, healthcare, climate science, and more. However, the development of DT models often faces challenges such as high data requirements, integration complexity, and limited adaptability to dynamic changes in physical systems. This paper presents a new method inspired by dynamic data-driven applications systems (DDDAS), called the dynamic data-driven generative of digital twins framework (DDD-GenDT), which combines the physical system with LLM, allowing LLM to act as DT to interact with the physical system operating status and generate the corresponding physical behaviors. We apply DDD-GenDT to the computer numerical control (CNC) machining process, and we use the spindle current measurement data in the NASA milling wear data set as an example to enable LLMs to forecast the physical behavior from historical data and interact with current observations. Experimental results show that in the zero-shot prediction setting, the LLM-based DT can adapt to the change in the system, and the average RMSE of the GPT-4 prediction is 0.479A, which is 4.79% of the maximum spindle motor current measurement of 10A, with little training data and instructions required. Furthermore, we analyze the performance of DDD-GenDT in this specific application and their potential to construct digital twins. We also discuss the limitations and challenges that may arise in practical implementations. © 2024, CC BY-NC-ND.

关键词： Machining centers

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Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence

arXiv

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arXiv 2024年

作者： Pananjady, Ashwin Muthukumar, Vidya Thangaraj, Andrew Schools of Industrial and Systems Engineering Georgia Institute of Technology United States Electrical and Computer Engineering Georgia Institute of Technology United States Department of Electrical Engineering IIT Madras India

We study the problem of estimating the stationary mass—also called the unigram mass—that is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem has several applications—for example, estimating the stationary missing mass is critical for accurately smoothing probability estimates in sequence models. While the classical Good–Turing estimator from the 1950s has appealing properties for i.i.d. data, it is known to be biased in the Markovian setting, and other heuristic estimators do not come equipped with guarantees. Operating in the general setting in which the size of the state space may be much larger than the length n of the trajectory, we develop a linear-runtime estimator called Windowed Good–Turing (WingIt) and show that its risk decays as Oe(Tmix/n), where Tmix denotes the mixing time of the chain in total variation distance. Notably, this rate is independent of the size of the state space and minimax-optimal up to a logarithmic factor in n/Tmix. We also present an upper bound on the variance of the missing mass random variable, which may be of independent interest. We extend our estimator to approximate the stationary mass placed on elements occurring with small frequency in the trajectory. Finally, we demonstrate the efficacy of our estimators both in simulations on canonical chains and on sequences constructed from natural language text. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov chains

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Codes for Correcting t Limited-Magnitude Sticky Deletions

Codes for Correcting t Limited-Magnitude Sticky Deletions

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Wang, Shuche Vu, Van Khu Tan, Vincent Y. F. National University of Singapore Institute of Operations Research and Analytics Singapore National University of Singapore Department of Mathematics Singapore National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore Department of Industrial Systems Engineering and Management Singapore

ISBN: (纸本)9781665475549

Codes for correcting sticky insertions/deletions and limited-magnitude errors have attracted significant attention due to their applications of flash memories, racetrack memories, and DNA data storage systems. In this paper, we first consider the error type of t sticky deletions with l-limited-magnitude and propose a non-systematic code for correcting this type of error with redundancy 2t(1 - 1/p) • log(n + 1) + O(1), where p is the smallest prime larger than l + 1. Next, we present a systematic code construction with an efficient encoding and decoding algorithm with redundancy ? 2t(1 - 1/p)}? · ? log p?/ log p log (n + 1) + O(log log n), where p is the smallest prime larger than l + 1. © 2023 IEEE.

关键词： Redundancy

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