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arXiv 2024年

作者： McMillin, Connor Rodriguez, Leo Rodriguez, Shanshan Ram-Mohan, L.R. Xing, Zhenzhong Department of Physics Grinnell College GrinnellIA50112 United States Department of Physics Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

We provide a unique and novel extension of da Costa’s calculation of a quantum mechanically constrained particle. This is achieved by analyzing the perturbative back reaction of the quantum confined particle’s eigenstates and spectra upon the geometry of the curved surface itself, thereby addressing the problem of shape optimization in this scenario. We do this by first formulating a two dimensional action principle of the quantum constrained particle, which upon wave function variation reproduces Schrödinger’s equation including da Costa’s surface curvature induced potentials. We further demonstrate that our derived action principle is dual to a two dimensional dilation gravity theory and we vary its functional with respect to the embedded two dimensional inverse-metric to obtain the respective geometrodynamical Einstein equation. We solve this resulting Einstein equation perturbatively by first solving the da Costa’s Schrödinger equation to obtain an initial eigensystem, which is used as initial-input data for a perturbed metric inserted into the derived Einstein equation. As a proof of concept, we perform this calculation on a two-sphere and show its first iterative perturbed shape evolution. We also turn on external electromagnetic fields, which manifest themselves via a surface induced, pulled-back U(1) coupling in our two dimensional dual gravity theory, thereby revealing interesting and rich new surface physics in this specific paradigm. © 2024, CC BY.

关键词： Wave functions

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Blackout Mitigation via Physics-guided RL

arXiv

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arXiv 2024年

作者： Dwivedi, Anmol Paternain, Santiago Tajer, Ali Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute TroyNY12180 United States

This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system’s stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black-box counterparts. One important observation is that strategically removing transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts. © 2024, CC BY.

关键词： Electric load flow

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Realistic Data Generation for 6D Pose Estimation of Surgical Instruments

arXiv

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arXiv 2024年

作者： Barragan, Juan Antonio Zhang, Jintan Zhou, Haoying Munawar, Adnan Kazanzides, Peter Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01608 United States

Automation in surgical robotics has the potential to improve patient safety and surgical efficiency, but it is difficult to achieve due to the need for robust perception algorithms. In particular, 6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers based on visual feedback. In recent years, supervised deep learning algorithms have shown increasingly better performance at 6D pose estimation tasks;yet, their success depends on the availability of large amounts of annotated data. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs of 6D pose datasets. However, this strategy does not translate well to surgical domains as commercial graphics software have limited tools to generate images depicting realistic instrument-tissue interactions. To address these limitations, we propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets for 6D pose estimation of surgical instruments. Among the improvements, we developed an automated data generation pipeline and an improved surgical scene. To show the applicability of our system, we generated a dataset of 7.5k images with pose annotations of a surgical needle that was used to evaluate a state-of-the-art pose estimation network. The trained model obtained a mean translational error of 2.59 mm on a challenging dataset that presented varying levels of occlusion. These results highlight our pipeline’s success in training and evaluating novel vision algorithms for surgical robotics applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Investigation of a New Stator Permanent Magnet with the effects of Flux-Reversal and Flux-Switching

Investigation of a New Stator Permanent Magnet with the effe...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Mohammad Reza Sarshar Mohammad Amin Jalali Kondelaji Mojtaba Mirsalim Seyed Hamid Montazeri Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge USA

ISBN: (数字)9798331533946

ISBN: (纸本)9798331533953

This article studies a new stator-permanent magnet (PM) motor with flux-switching (FS) and flux-reversal (FR) effects synergies. The proposed structure benefits from the splitting stator pole PMs and consequent-pole FRPM, which reduces the PM utilization compared to the conventional ones. Also, the proposed structure is optimized by a random search multiobjective algorithm to attain the structure with the highest output torque and minimum torque ripple. Additionally, the proposed motor is compared to a conventional FSPM to investigate the advantages of the main motor. Using finite element analysis (FEA) studies, the flux density distribution results show that the FR effect lowers the saturation risk compared to FSPM. Also, the back-EMF generation of the proposed motor has improved by 135% compared to its benchmark. Correspondingly, fullload results show that the proposed topology has enhanced the average torque by 75% and deteriorated the ripple by about 55% compared to FSPM. Therefore, the proposed structure offers a profitable for high torque density applications.

关键词： Magnetic flux Torque Switches Stators Motors Permanent magnets Power electronics Topology Torque measurement Saturation magnetization

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Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Jewani, Yordanos Petry, Michael Sanchez-Arias, Rei Habib, Md Selim Department of Data Science and Business Analytics Florida Polytechnic University LakelandFL33805 United States Department of Aerospace and Geodesy Technical University of Munich Munich80333 Germany Department of Electrical Engineering and Computer Science Florida Institute of Technology FL32901 United States

A supervised machine learning framework is implemented to predict the propagation loss of randomly structured nested hollow-core anti-resonant fiber for the first time. The random forest classifier outperforms other m... 详细信息

ISBN: (纸本)9781957171395

关键词： Prediction models

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On the Role of Uncertainty in Poisson Target Models Used for Placement of Spatial Sensors 15

On the Role of Uncertainty in Poisson Target Models Used for...

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Ocean Sensing and Monitoring XV 2023

作者： Kim, Mingyu Yetkin, Harun Stilwell, Daniel J. Jimenez, Jorge Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University BlacksburgVA United States Department of Mechatronics Engineering Bartin University Bartin Turkey Naval Surface Warfare Center Panama CityFL United States

ISBN: (纸本)9781510662025

This paper addresses the role of uncertainty in spatial point-process models, such as those that might arise in modelling ship traffic. We consider a doubly stochastic Poisson point process where the intensity function is uncertain. To assess the role of uncertainty, we conduct a large set of numerical trials where we estimate a doubly stochastic Poisson point-process model from historical target data, and the evaluate the model by assessing the target detection performance of a set of sensors whose locations are selected using the model. Our work is motivated by seabed sensors that detect ship traffic, and we conduct numerical trials using historical ship traffic data near the mouth of the Chesapeake Bay, Virginia, USA, that was recorded by the Automated Identification System. © 2023 SPIE.

关键词： Stochastic systems

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Human Context Recognition: A Controllable GAN Approach

Human Context Recognition: A Controllable GAN Approach

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2021 IEEE MIT Undergraduate Research Technology Conference, URTC 2021

作者： Deoliveira, Joshua Kim, Harrison Martin, Maryclare Gerych, Walter Rundensteiner, Elke Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States Northeastern University Department of Electrical and Computer Engineering BostonMA United States College of the Holy Cross Department of Mathematics WorcesterMA United States

ISBN: (纸本)9781665405959

Human activity recognition (HAR), the use of sensor data to identify activities performed by an individual, has garnered substantial research attention. However, obtaining data for HAR is costly. As such, recent studies have used Generative Adversarial Networks (GANs) to create synthetic HAR data. Problematically, these solutions cannot generate personalized data that match the behavior of a user, limiting potential HAR applications as prior work has shown that personalized HAR models outperform generalized ones. To address this problem, we propose a novel controllable GAN where user and activity attributes can be explicitly requested for generation under one model. The GAN can learn these attributes independently and infer context pairs never seen during training: An impossibility for existing methodologies. We show that while our model is comparable to standard HAR GANs, its ability to consider user differences and generate novel context pairs outperforms these state-of-The-Art GANs. © 2021 IEEE.

关键词： Generative adversarial networks

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Optimal Location of Renewable Energy Generators in Transmission and Distribution System of Deregulated Power Sector:A Review

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Energy engineering 2025年第3期122卷 823-859页

作者： Digambar Singh Najat Elgeberi Mohammad Aljaidi Ramesh Kumar Rabia Emhamed Al Mamlook Manish Kumar Singla Department Electrical Engineering Government Polytechnic CollegeBTE LucknowEtah207120India University of Nevada RenoNV 89557USA Department of Computer Science Zarqa UniversityZarqa13110Jordan Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering&TechnologyChitkara UniversityChandigarh160002India Jadara University Research Center Jadara UniversityIrbid00962Jordan Department of Industrial Engineering Al Zawiya UniversityAl-ZawiyaP.O.Box 16418Libya Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Biosciences Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India

The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in *** study focuses on the most relevant publications directly addressing the research problem at ***,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission ***,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively *** demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution *** presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and ***,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy *** paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.

关键词： Optimization of RESs distributed generation modeling and selection of RESs hybrid systems standalone systems optimal location

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Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Adversarial machine learning

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Analyzing the limitations of parallelism in hardware and software through threaded programming

Analyzing the limitations of parallelism in hardware and sof...

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作者： Chenyi Wang Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University

Parallel computing is applied across various technology industries for solving complex scientific problems that desire high performance and efficiency while can help utilize the resource to save time, money, and energy. This technique has been developed over decades as the physical limits of chips have been reached. The development of parallel computing is closely bonded with the evolution of hardware and software implementation, and there are many forms of parallelism applied to different systems to improve their performance. This paper focused on threaded programming by applying parallelism to a decision tree program and testing the program performance with variables of thread number and device configuration while aiming to demonstrate the limitation of parallelism from both software and hardware aspects. The experiment's outcome in this paper shows that the overall trend of the performance variation for different devices is similar. Still, an improvement in the performance will be affected by the hardware implementation. In contrast, a device with a better configuration performs better. To summarize this research, parallelism can only be fully utilized when applied to a properly designed program running on a robust machine. Therefore, the innovation of hardware and software is essential to the future development of parallel computing.

关键词： technology industries high performance decision tree Thread programming

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