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On the Selection of Intermediate Length Representative Periods for Capacity Expansion

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arXiv 2024年

作者： Anderson, Osten Yu, Nanpeng Oikonomou, Konstantinos Wu, Di Department of Electrical and Computer Engineering University of California Riverside RiversideCA United States System Evaluation Group Pacific Northwest National Laboratory RichlandWA United States

As the decarbonization of power systems accelerates, there has been increasing interest in capacity expansion models for their role in guiding this transition. Representative period selection is an important component of capacity expansion modeling, enabling computational tractability of optimization while ensuring fidelity between the representative periods and the full year. However, little attention has been devoted to selecting representative periods longer than a single day. This prevents the capacity expansion model from directly simulating interday energy sharing, which is of key importance as energy generation becomes more variable and storage more important. To this end, we propose a novel method for selecting representative periods of any length. The method is validated using a capacity expansion model and production cost model based on California’s decarbonization goals. We demonstrate that the representative period length has a substantial impact in the results of the capacity expansion investment plan. © 2024, CC BY-NC-ND.

关键词： Decarbonization

Adaptive Task Allocation in Multi-Human Multi-Robot Teams under Team Heterogeneity and Dynamic Information Uncertainty

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arXiv 2024年

作者： Yuan, Ziqin Wang, Ruiqi Kim, Taehyeon Zhao, Dezhong Obi, Ike Min, Byung-Cheol SMART Laboratory Department of Computer and Information Technology Purdue University West LafayetteIN United States College of Mechanical and Electrical Engineering Beijing University of Chemical Technology Beijing China

Task allocation in multi-human multi-robot (MH-MR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches often fail to address these challenges simultaneously, resulting in suboptimal performance. To tackle this, we propose ATA-HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic operational states. Additionally, we introduce an auxiliary state representation learning task to manage information uncertainty and enhance task execution. Through an extensive case study in large-scale environmental monitoring tasks, we demonstrate the benefits of our approach. More details are available on our website: https://***/view/ata-hrl. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

Pose Estimation of Buried Deep-Sea Objects using 3D vision Deep Learning Models

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arXiv 2024年

作者： Yan, Jerry Talegaonkar, Chinmay Antipa, Nicholas Terrill, Eric Merrifield, Sophia Marine Physical Laboratory Scripps Institution of Oceanography UCSD La Jolla CA United States Department of Electrical and Computer Engineering UCSD La Jolla CA United States

We present an approach for pose and burial fraction estimation of debris field barrels found on the seabed in the Southern California San Pedro Basin. Our computational workflow leverages recent advances in foundation models for segmentation and a vision transformer-based approach to estimate the point cloud which defines the geometry of the barrel. We propose BarrelNet for estimating the 6-DOF pose and radius of buried barrels from the barrel point clouds as input. We train BarrelNet using synthetically generated barrel point clouds, and qualitatively demonstrate the potential of our approach using remotely operated vehicle (ROV) video footage of barrels found at a historic dump site. We compare our method to a traditional least squares fitting approach and show significant improvement according to our defined benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Remotely operated vehicles

Data-Driven Model for Photovoltaic Generation: Comparison with Physical Models Using a Microgrid in Puerto Rico

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Data-Driven Model for Photovoltaic Generation: Comparison wi...

IEEE General Meeting Power& Energy Society

作者： Marcos R. Pesante Colón Alberto I. Cruz Salamán Dylan Cruz Figueroa Aditya Sundararajan Maximiliano Ferrari Electrical and Computer Engineering Department University of Puerto Rico Mayagüez USA Grid Systems Architecture Group Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Photovoltaic (PV) generation is a critical component of microgrids, but its accurate modeling is challenging due to the complex and dynamic interactions between solar irradiance, temperature, and PV system installation. This paper develops a multilayer perceptron (MLP) model that inputs solar irradiance and temperature to estimate the PV generation, and it compares the proposed data-driven model’s performance to two well-known physical models: the single-diode model and the inverter model. The results demonstrate that all the models can reach high levels of accuracy. However, the MLP model outperforms the physical models on average by 4.5 to 6.6 percent in R squared scores and 220 to 290 Watts in RMSE scores, and it does not require physical system parameters. Moreover, the data-driven model can overcome the limitations of the lack of real-time PV generation data.

关键词： Temperature measurement Solar irradiance Photovoltaic systems Adaptation models Accuracy Voltage measurement Predictive models Multilayer perceptrons Data models Inverters

Design of a Graphene-Based Hexagonal Circular Patch Yagi-Uda MIMO Antenna

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Design of a Graphene-Based Hexagonal Circular Patch Yagi-Uda...

International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Asif Gulzar Mohaira Ahmad Lixia Yang School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

The purpose of this study is to build a Terahertz (THz)-based 2×2 multi-input, multi-output (MIMO) hexagonal angular patch (HCP) antenna. Graphene's unique electrical properties have made it a compelling material for THz-frequency antennas, owing to its capacity to support surface plasmon polariton (SPPs) and high conductivity in this range. We've constructed the antenna on a silicon nitride (Si3N4) substrate, 17 microns thick, with 0.1-micron-thick graphene layers to enhance MIMO antenna efficiency and directivity. The hexagonal patch structure of graphene was chosen for its exceptional characteristics at Terahertz frequencies. Our study highlights the radiation properties of THz-based HCP antennas across the 0.1 THz to 1 THz frequency range. Notably, we achieved return loss (S11) better than -10 dB at 0.52 THz for the unit cell and for the MIMO antenna at 0.69 THz, more bandwidth was achieved. Both the radiation patterns and S-parameters exhibit significantly improved gain and directivity compared to traditional antennas.

关键词： Surface plasmon polaritons Antenna theory Yagi-Uda antennas Design methodology Graphene Terahertz radiation Bandwidth Antennas Terahertz communications Antenna radiation patterns

Electron Energy Loss Spectroscopy of 2D Materials in a Scanning Electron Microscope

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arXiv 2024年

作者： Simonaitis, John W. Alongi, Joseph A. Slayton, Benjamin Putnam, William P. Berggren, Karl K. Keathley, Phillip D. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering University of California DavisCA95616 United States

This work demonstrates electron energy loss spectroscopy of 2D materials in a 1-30 keV electron microscope, observing 50-times stronger electron-matter coupling relative to 125 keV microscopes. We observe that the universal curve relating beam energy to scattering holds for the transition from bulk graphite to graphene, albeit with a scale factor. We calculate that optimal coupling for most 2D materials and optical nanostructures falls in this range, concluding that spectroscopy of such systems will greatly benefit from use of this previously unexplored energy regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Electron energy loss spectroscopy

ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, Word Segmentation and Entity Dictionary 2nd

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ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, ...

2nd EAI International Conference on Applied Cryptography in computer and Communications, AC3 2022

作者： Lv, Haifeng Ding, Yong School of Data Science and Software Engineering Wuzhou University Wuzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

ISBN: (纸本)9783031170805

Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, one hand, since the lattice structure is dynamic and complex, although some existing lattice-based models are effectively utilize the parallel computation of GPUs, they do not fully utilize word segmentation boundary tags that as features are helpful for NER task. On the other hand, the character-word vector needs to be trained, and the user-defined entity dictionary cannot be effectively used. In this paper, we propose ALFLAT: based on a flat-lattice Transformer to incorporate ALBERT pre-trained model, word segmentation information and user-defined entity dictionary for Chinese NER. ALFLAT converts the lattice structure into a flat structure consisting of spans, integrate word segmentation embedding with the output of flat-lattice Transformer model, then modifies the emission scores according to the user-defined entity dictionary, finally utilize Viterbi decoding of the CRF layer to obtain the correct entity results. Each span corresponds to a character or latent word and its position in the original lattice. With the power of ALBERT pre-trained model, Transformer and position encoding, ALFLAT can fully leverage the lattice, word segmentation and user-defined entity dictionary information. Experiments on MSRA dataset show ALFLAT outperforms other lexicon-based models in performance and efficiency. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Computational linguistics

Class-specific image encryption using a diffractive optical processor

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Class-specific image encryption using a diffractive optical ...

CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Bai, Bijie Wei, Heming Yang, Xilin Gan, Tianyi Mengu, Deniz Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los Angeles90095 United States Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China University of California Los AngelesCA United States

We present a framework for all-optical image encryption that is class-specific, performing a distinct image transformation function for each data class. Experimental validation of this method was conducted using both ... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical depth

Invertible Voice Conversion with Parallel Data

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Invertible Voice Conversion with Parallel Data

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zexin Cai Ming Li Department of Electrical and Computer Engineering Duke University Durham NC USA Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan PR China

This paper introduces an innovative deep learning framework for parallel voice conversion to mitigate inherent risks associated with such systems. Our approach focuses on developing an invertible model capable of countering potential spoofing threats. Specifically, we present a conversion model that allows for the retrieval of source voices, thereby facilitating the identification of the source speaker. This framework is constructed using a series of invertible modules composed of affine coupling layers to ensure the reversibility of the conversion process. We conduct comprehensive training and evaluation of the proposed framework using parallel training data. Our experimental results reveal that this approach achieves comparable performance to non-invertible systems in voice conversion tasks. Notably, the converted outputs can be seamlessly reverted to the original source inputs using the same parameters employed during the forwarding process. This advancement holds considerable promise for elevating the security and reliability of voice conversion.

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