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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Lauren Bramblett Nicola Bezzo Departments of Systems and Information Engineering and Electrical and Computer Engineering University of Virginia Charlottesville VA USA

In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles.

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Sample complexity of neural policy mirror descent for policy optimization on low-dimensional manifolds

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 11019-11085页

作者： Zhenghao Xu Xiang Ji Minshuo Chen Mengdi Wang Tuo Zhao H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA Department of Electrical and Computer Engineering Princeton University Princeton NJ

Policy gradient methods equipped with deep neural networks have achieved great success in solving high-dimensional reinforcement learning (RL) problems. However, current analyses cannot explain why they are resistant to the curse of dimensionality. In this work, we study the sample complexity of the neural policy mirror descent (NPMD) algorithm with deep convolutional neural networks (CNN). Motivated by the empirical observation that many high-dimensional environments have state spaces possessing low-dimensional structures, such as those taking images as states, we consider the state space to be a d-dimensional manifold embedded in the D-dimensional Euclidean space with intrinsic dimension d ≪ D. We show that in each iteration of NPMD, both the value function and the policy can be well approximated by CNNs. The approximation errors are controlled by the size of the networks, and the smoothness of the previous networks can be inherited. As a result, by properly choosing the network size and hyperparameters, NPMD can find an ε-optimal policy with Õ(ε-d/α-2) samples in expectation, where α ∈ (0, 1] indicates the smoothness of environment. Compared to previous work, our result exhibits that NPMD can leverage the low-dimensional structure of state space to escape from the curse of dimensionality, explaining the efficacy of deep policy gradient algorithms.

关键词： deep reinforcement learning policy optimization function approximation convolutional neural network Riemannian manifold

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The group robustness is in the details: revisiting finetuning under spurious correlations 24

The group robustness is in the details: revisiting finetunin...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Tyler LaBonte John C. Hill Xinchen Zhang Vidya Muthukumar Abhishek Kumar H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology and H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Google DeepMind

ISBN: (纸本)9798331314385

Modern machine learning models are prone to over-reliance on spurious correlations, which can often lead to poor performance on minority groups. In this paper, we identify surprising and nuanced behavior of finetuned models on worst-group accuracy via comprehensive experiments on four well-established benchmarks across vision and language tasks. We first show that the commonly used class-balancing techniques of mini-batch upsampling and loss upweighting can induce a decrease in worst-group accuracy (WGA) with training epochs, leading to performance no better than without class-balancing. While in some scenarios, removing data to create a class-balanced subset is more effective, we show this depends on group structure and propose a mixture method which can outperform both techniques. Next, we show that scaling pretrained models is generally beneficial for worst-group accuracy, but only in conjunction with appropriate class-balancing. Finally, we identify spectral imbalance in finetuning features as a potential source of group disparities — minority group covariance matrices incur a larger spectral norm than majority groups once conditioned on the classes. Our results show more nuanced interactions of modern finetuned models with group robustness than was previously known. Our code is available at https://***/tmlabonte/revisiting-finetuning.

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Correction To: Photovoltaic system fault detection techniques: a review

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Neural Computing and Applications 2024年第29期36卷 18547-18548页

作者： El-Banby, Ghada M. Moawad, Nada M. Abouzalm, Belal A. Abouzaid, Wessam F. Ramadan, E. A. Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Electrical Engineering of Computer and Control Systems Faculty of Engineering Kafrelsheikh University Kafrelsheikh Egypt

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Characterization Procedure for Unsymmetrical Single-Phase Capacitor-Start Induction Machines

IEEE Open Access Journal of Power and Energy

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IEEE Open Access Journal of Power and Energy 2021年 8卷 2-10页

作者： Cale, J. Lute, C. Ross, G. Othee, A. Systems Engineering and Electrical and Computer Engineering Departments Colorado State University Fort CollinsCO United States Systems Engineering Department Colorado State University Fort CollinsCO United States Electrical and Computer Engineering Department Colorado State University Fort CollinsCO United States

This paper describes an experimental and numerical optimization procedure for off-line extraction of parameters for unsymmetrical, single-phase induction machines with capacitor-start operation. In addition to permitting asymmetry between phase winding parameters, the approach requires only average and rms measurements as inputs;electrical phase angle measurements are not required. The method is validated on an experimental motor with transient and steady-state simulations, using fitted parameters obtained using the proposed approach. © 2020 IEEE.

关键词： Optimization

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Open source solution for CNC milling machine monitoring and comparison with Matlab simulation in real time 5

Open source solution for CNC milling machine monitoring and ...

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

作者： Fecko, Branislav Aliona, Nikitina Burdilna, Evgeniia Technical University of Košice Department of Theoretical and Industrial Electrical Engineering Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Department of Automation and Computer Systems Kremenchuk Ukraine

ISBN: (纸本)9798350359787

This research paper focuses on the use of a thermal model of a CNC machine monitoring system that has been developed using open-source tools. By integrating these tools, predictive maintenance capabilities can be developed, leading to increased machine reliability. The methodology involves the implementation of a thermal model designed with Simulink software. The thermal model has been implemented into a developed monitoring system based on open-source tools, which allows access to real-time data of the CNC machine. The thermal model's correctness has been verified with the machine operation using the data obtained from the CNC machine monitoring system. The use of open-source tools provides flexibility and adaptability in tailoring the thermal model to specific requirements and gaining key insights into the thermal behavior of the machine. The final section of the publication presents a comparison of simulated and measured outputs. © 2023 IEEE.

关键词： MATLAB

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A Comparative Analysis of Transient Analysis Results for DC-DC Regulator in PSpice, Tina, and LTspice 5

A Comparative Analysis of Transient Analysis Results for DC-...

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

作者： Vince, Tibor Cizmarik, Dominik Dziak, Jozef Mamchur, Dmytro Anna, Iudina Vitalii, Naida Technical University in Kosice Department of Theoretical and Industrial Electrical Engineering Fei Kosice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Department of Computer Engineering and Electronics Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Automation and Computer Systems Kremenchuk Ukraine

ISBN: (纸本)9798350359787

The paper presents a comparative analysis of the simulation results of a DC-DC buck regulator based on LM2595 on three different simulation platforms: PSpice, LTspice, and Tina. The comparative analysis includes visual comparison of selected waveforms, comparison of statistical parameters of the observed signals, and comparison based on time step signal analysis. The simulation was performed using three different maximal time step conditions. The research focused on examining the differences caused by different simulation platforms under the same maximal time step condition, as well as the differences caused by different maximal time step conditions within a single simulation platform. This analysis is crucial, particularly when comparing different simulation Spice models that require simulations to be conducted on various simulation platforms. By comparing the results obtained from different platforms under the same simulation conditions, the impact of the simulation platform on the accuracy and behavior of the models can be assessed. © 2023 IEEE.

关键词： Simulation platform

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Computationally efficient reductions between some statistical models

arXiv

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arXiv 2024年

作者： Lou, Mengqi Bresler, Guy Pananjady, Ashwin Schools of Industrial and Systems Engineering Georgia Tech United States Schools of Electrical and Computer Engineering Georgia Tech United States Department of Electrical Engineering and Computer Science MIT United States

We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source model, and construct several computationally efficient such reductions between canonical statistical experiments. In particular, we provide computationally efficient procedures that approximately reduce uniform, Erlang, and Laplace location models to general target families. We illustrate our methodology by establishing nonasymptotic reductions between some canonical high-dimensional problems, spanning mixtures of experts, phase retrieval, and signal denoising. Notably, the reductions are structure-preserving and can accommodate missing data. We also point to a possible application in transforming one differentially private mechanism to another. Copyright © 2024, The Authors. All rights reserved.

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Evaluating Dry Electrodes and Bioinstrumentation for Wearable Arm ECG Acquisition

Evaluating Dry Electrodes and Bioinstrumentation for Wearabl...

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2024 International Conference on the Challenges, Opportunities, Innovations and Applications in Electronic Textiles, E-Textiles 2024

作者： Veeturi, Suparna Ravichandran, Vignesh Cerullo, Alex Chomal, Shubham Shahriari, Yalda Carreiro, Stephanie Venkatasubramanian, Krishna Mankodiya, Kunal Solanki, Dhaval University of Rhode Island Department of Electrical Computer and Biomedical Engineering United States University of Massachusetts Chan Medical School Department of Emergency Medicine United States University of Rhode Island Department of Mechanical Industrial and Systems Engineering KingstonRI United States University of Rhode Island Department of Computer Science and Statistics United States

ISBN: (纸本)9783000804298

Arm-based electrocardiogram (ECG) offers a more comfortable and user-friendly alternative to traditional chest ECG for wearable cardiac monitoring in daily health applications. However, arm ECG faces challenges such as capturing extremely low-amplitude signals in the microvolt range and significant electromyogram (EMG) noise contamination. The aim of this study was to evaluate the performance of dry electrodes for arm and chest ECG monitoring using gold standard BIOPAC and proposed mDAQ systems. This study consisted of two phases. In Phase 1, we evaluated different dry electrode materials, including silver knit fabric and gold-plated electrodes, with six healthy adult participants (N = males and N =3 females) having upper arm circumference ranging from 27 to 42 cm. We used a BIOPAC system as a gold standard to collect arm ECG, upper arm EMG, and reference chest ECG during standardized movement tasks. Results showed that silver knit electrodes demonstrated superior QRS energy (0.705 ± 0.151), surpassing chest gel floating ECG QRS energy (0.328 ± 0.026), with lower EMG interference across movement conditions. In Phase 2, we developed a bioinstrumentation system (mDAQ) optimized for dry electrodes, demonstrating reliable ECG acquisition in both chest Lead I and arm configurations. -We validated our bioinstrumentation with sixteen healthy adult participants (N = 8 male and N = female. The mDAQ system maintained high signal consistency with a skewness effect size of 0.963 and showed a mean heart rate bias of only -1.842 bpm compared to the BIOPAC system, confirming its viability for wearable ECG monitoring applications. © 2024 Fraunhofer IZM.

关键词： Gold plating

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Structure and Control of Biology-inspired Networks

arXiv

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arXiv 2024年

作者： Sun, Zexin Baillieul, John Division of Systems Engineering Boston University MA02215 United States Departments of Mechanical Engineering Electrical and Computer Engineering Division of Systems Engineering Boston University MA02215 United States

There is increasing interest in developing the theoretical foundations of networked control systems that illuminate how brain networks function so as to enable sensory perception, control of movement, memory and all the operations that are needed for animals to survive. The present paper proposes a biologically inspired network model featuring dynamic connections regulated by Hebbian learning. Drawing on the machinery of graph theory and classical control we show that our novel nonlinear model exhibits such biologically plausible features as bounded evolution, stability, resilience, and a kind of structural stability-meaning that perturbations of the model parameters leave the essential properties of the model in tact. The proposed network model involves generalized cactus graphs with multiple control input nodes, and it is shown that the properties of the network are resilient to various changes in network topology provided these changes preserve the generalized cactus structure. A particular example described in what follows is an idealized network model of the visual system of a macaque monkey. The model displays resilience to network disruptions such as might occur in a living organism due to disease or injury. A different model of the same type provides an example of a system that can perform data classification. © 2024, CC BY-NC-SA.

关键词： Sensory perception

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