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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Gangopadhyay, Soumyajit Chakraborty, Soham Das, Sarasij Electrical Engineering Indian Institute of Science Bangalore India University of Minnesota Electrical and Computer Engineering Minneapolis United States

ISBN: (纸本)9781665490719

The proliferation of measuring devices and sensors has resulted in a surge in smart grid (SG) data volume. However, the potential users of SG data usually face difficulty in accessing the SG data. The lack of an effective SG data exchange platform can be one of the underlying causes behind this issue. Limited accessibility of SG data can be one of the major bottlenecks towards the anticipated success of data-driven algorithms in SGs. One of the possible solutions is to establish smart grid data markets (SGDMs) for trading SG data. In this regard, recently an online survey was conducted in order to understand the stakeholders' views on various aspects of data trading in SGs. The survey helped in identifying several obstacles including data privacy, security threat to the grid, data authenticity and money availability. These obstacles must be addressed before data trading becomes a reality in SGs. Hence, this paper proposes several directions for future research in the area of SGDMs. At first, several unique characteristics of SG data are discussed. These characteristics should be considered while addressing the challenges associated with SGDMs. Then, this paper sheds light on several areas of technical research related to data trading in SGs such as data pricing, data quality assessment, privacy pricing, privacy-preserving data trading, data value assessment, and security of data trading in SGs. In addition, this paper also identifies several areas of policy-oriented research related to SGDMs. There is a need for extensive research along the proposed research directions in order to make data trading in SGs viable in the future. © 2023 IEEE.

关键词： Smart power grids

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Nonlinear Sub-Optimal Control of SIR Infectious Disease Model 3

Nonlinear Sub-Optimal Control of SIR Infectious Disease Mode...

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3rd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2024

作者： Islam, Samiul Paul, Sudipta Pramanik, Md. Abu Hanif Bhattacharjee, Srijita Naidu, D. Subbaram Rajshahi University Of Eng. & Tech. Electrical & Computer Engineering Rajshahi Bangladesh Rensselaer Polytechnic Institute Electrical Engineering New York United States University Of Minnesota Duluth Electrical Engineering Duluth United States

ISBN: (纸本)9798350318609

The Susceptible-Infectious-Recovered (SIR) disease model is a basic epidemiological framework for analyzing the spread of infectious diseases using various control theories. It plays a vital role in assessing disease control strategies and minimizing the impact of an infectious disease. This paper employs a nonlinear closed-loop infinite-horizon optimal tracking control via state-dependent Riccati equation (IH-SDRE) to the nonlinear SIR model with vaccination, aiming to reduce the number of infected individuals within a desired timeframe. This method, by transforming the nonlinear SIR model into a state-dependent coefficient form, solves the Riccati equation and the vector equation at every time step to determine the control law. The results obtained via MATLAB/Simulink demonstrate that the IH-SDRE minimizes the number of infected populations, reducing it to nearly zero within 30 days of the desired timeframe with an effective vaccination strategy. The findings show the effectiveness of the IH-SDRE as a robust method in the control and mitigation of infectious diseases. © 2024 IEEE.

关键词： Riccati equations

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Nonlinear Optimal Regulation of SIR Epidemic Model via Finite-Horizon SDRE 26

Nonlinear Optimal Regulation of SIR Epidemic Model via Finit...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Paul, Sudipta Islam, Samiul Naidu, Desineni Subbaram Abu Hanif Pramanik, Md. Rensselaer Polytechnic Institute Department of Electrical Computer & Systems Engineering New York United States Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Bangladesh University of Minnesota Duluth Department of Electrical Engineering United States

ISBN: (纸本)9798350359015

A Susceptible-Infectious-Recovered (SIR) model is a popular and fundamental epidemiological model often used to assess the efficacy of disease prevention and control measures. SIR disease model, with the implementation of various optimal control methods, is investigated to determine the optimal ways to lessen the effects of an infectious disease. In this research work, a non-linear, closed-loop, finite-horizon, optimal regulation with state-dependent Riccati equation (FH-SDRE) is implemented in an epidemic model called SIR model with vaccination to minimize the infected and susceptible population. Given the nonlinear dynamics of the SIR epidemic model, initially, this method converts the nonlinear equations into a state-dependent coefficient (SDC) matrix, which holds the nonlinearities of the dynamical system. After that, SDRE is solved at every small time instant over a specified finite time period. The core idea here is to utilize an approximate analytical approach that transforms the differential Riccati equation (DRE) into a differential Lyapunov equation (DLE) during every time period. Then, the real-time solution of the DLE at each of these time steps will result in the control law required to achieve the desired system performance. The effectiveness of the FH-SDRE regulation for the SIR model is demonstrated by minimizing the infected and susceptible population within a desired 30-day time period. © 2023 IEEE.

关键词： finite-horizon regulation SIR epidemic model state-dependent Riccati equation

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Method for Synthetic Generation of LFP Data for Testing of Feature Extraction Algorithms 46

Method for Synthetic Generation of LFP Data for Testing of F...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Breidenbach, Heather J. Woods, Virginia Shin, Uisub Rijn, Evan Dastin-Van Shoaran, Mahsa Widge, Alik S. University of Minnesota MinneapolisMN55455 United States Cornell University School of Electrical and Computer Engineering IthacaNY14850 United States Neuro-X Institute Epfl Institute of Electrical and Micro Engineering Geneva1202 Switzerland

ISBN: (纸本)9798350371499

Recent interest in closed-loop neuromodulation devices has driven development of algorithms capable of real-time biomarker extraction. Synthetic data for tuning algorithmic parameters in various oscillatory cases is a useful tool but must be generated to model realistic neural behavior. We extracted key oscillatory behaviors from rodent LFPs and used this information to create a realistic generation method for synthetic signal production. We then used the generated signals to optimize the feature extraction performance of a real-time feature extraction algorithm. The results of the algorithm testing closely mirrored results from testing on recorded neural LFPs and resembled this real data more closely than a simplistic model of synthetic neural data. © 2024 IEEE.

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Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints 41

Fundamental Limits of Distributed Covariance Matrix Estimati...

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41st International Conference on Machine Learning, ICML 2024

作者： Rahmani, Mohammad Reza Yassaee, Mohammad Hossein Maddah-Ali, Mohammad Ali Aref, Mohammad Reza Department of Electrical Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings. Copyright 2024 by the author(s)

关键词： Budget control

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Feasibility of Data Markets in Smart Grids: an Online Survey

Feasibility of Data Markets in Smart Grids: an Online Survey

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2022 IEEE Power and Energy Conference at Illinois, PECI 2022

作者： Chakraborty, Soham Gangopadhyay, Soumyajit Das, Sarasij University of Minnesota Department of Electrical and Computer Engineering United States Indian Institute of Science Department of Electrical Engineering Bangalore India

ISBN: (纸本)9781665402293

Novel data-driven algorithms are being developed using smart grid (SG) data. However, the success of data-driven algorithms such as machine learning algorithms heavily depends on the availability of SG data. Currently, SG data mostly stay with the data generators such as utilities, market operators and system operators. The flow of SG data among various stakeholders is currently limited due to the lack of a proper data exchange platform. Recently, the concept of data markets for SGs has been introduced to address this issue. However, the success of data markets in SGs heavily depends on the large-scale participation of the various stakeholders. So, there is a need to understand the opinion of the stakeholders on various aspects of SG data markets. This paper presents the results of an online survey that was conducted in order to investigate the feasibility of data trading in SGs. A comprehensive analysis of the survey responses is also presented. It is found that the majority of the survey participants support the formation of data markets in SGs to increase the accessibility of practical SG data. However, a section of the participants raised concerns regarding privacy, grid security and money availability. The results of this survey will be helpful in deciding future research directions in this area. © 2022 IEEE.

关键词： Data mining

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Atomistic Mechanism of Phase Transformations in the Pt-Sn System Studied by In-situ TEM

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1659-1660页

作者： Yun, Hwanhui Zhang, Delin Birol, Turan Wang, Jian-Ping Mkhoyan, K Andre Chemical Engineering and Materials Science University of Minnesota Twin Cities United States Korea Research Institute of Chemical Technology Daejeon South Korea Electrical engineering and computer science University of Minnesota Twin Cities United States

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Practical Phase Retrieval Using Double Deep Image Priors 21

Practical Phase Retrieval Using Double Deep Image Priors

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IS and T International Symposium on Electronic Imaging: 21st Computational Imaging, COIMG 2023

作者： Zhuang, Zhong Yang, David Hofmann, Felix Barmherzig, David A. Sun, Ju Electrical & Computer Engineering University of Minnesota Minneapolis United States Department of Engineering Science University of Oxford Oxford United Kingdom Center for Computational Mathematics Flatiron Institute New York City United States Computer Science & Engineering University of Minnesota Minneapolis United States

Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes. We identify the connection between the difficulty level and the number and variety of symmetries in PR problems. We focus on the most difficult far-field PR (FFPR), and propose a novel method using double deep image priors. In realistic evaluation, our method outperforms all competing methods by large margins. As a single-instance method, our method requires no training data and minimal hyperparameter tuning, and hence enjoys good practicality. Our paper is also available at: https: // arxiv. org/ abs/ 2211. 00799 . © 2023, Society for Imaging Science and Technology.

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On Supervised Class-Imbalanced Learning: An Updated Perspective and Some Key Challenges

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第6期3卷 973-993页

作者： Das, Swagatam Mullick, Sankha Subhra Zelinka, Ivan Indian Statistical Institute Electronics and Communication Sciences Unit Kolkata700108 India VŠB-TUO Department of Computer Science Faculty of Electrical Engineering and Computer Science Czech Republic IT4Innovations National Supercomputing Centre Ostrava70800 Czech Republic

The problem of class imbalance has always been considered as a significant challenge to traditional machine learning and the emerging deep learning research communities. A classification problem can be considered as class imbalanced if the training set does not contain an equal number of labeled examples from all the classes. A classifier trained on such an imbalanced training set is likely to favor those classes containing a larger number of training examples than the others. Unfortunately, the classes that contain a small number of labelled instances usually correspond to rare and significant events. Thus, poor classification accuracy on these classes may lead to severe consequences. In this article, we aim to provide a comprehensive summary of the rich pool of research works attempting to combat the adversarial effects of class imbalance efficiently. Specifically, following a formal definition of the problem of class imbalance, we explore the plethora of traditional machine learning approaches aiming to mitigate its adversarial effects. We further discuss the state-of-the-art deep-learning-based approaches for improving a classifier's resilience against class imbalance and highlight the need for techniques tailored for such a paradigm. Moreover, we look at the emerging applications where class imbalance can be a major concern. Finally, we outline a few open problems along with the various challenges emerging with the advent of modern applications, deep learning paradigm, and new sources of data. © 2020 IEEE.

关键词： Object detection

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PWM Inverter Based Active Filter for High Frequency AC Systems in Space Applications

PWM Inverter Based Active Filter for High Frequency AC Syste...

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International Conference on Telecommunications Energy (INTELEC)

作者： Mohammed Tuhin Rana Surjakanta Mazumder Soham Chakraborty Murti V. Salapaka Kaushik Basu Department of Electrical and Computer Engineering University of Minnesota USA Department of Electrical Engineering Indian Institute of Science India

ISBN: (数字)9798350370577

ISBN: (纸本)9798350370584

This article describes a PWM inverter based shunt active filtering scheme for high frequency AC systems in space applications. A relatively simple control architecture using only proportional, integral, and resonant controllers along with a sec-ond order generalised integrator is proposed for the shunt filter. The controllers are designed such that they are implementable using Op-Amp based analog circuits which is preferable for space application. Example design parameters are given for designing the analog controllers. Simulation results are shown to compare the performance of the proposed system with classical passive filter based compensation. Dynamic response of the proposed system due to load change is also shown.

关键词： Pulse width modulation inverters Satellites Passive filters Simulation Aerospace electronics Analog circuits Active filters

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