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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Arnold, Kellen P. Slaby, Joel B. Dattilo, Hannah M. Kaylor, C. Alex Schrimpf, Ronald D. Fleetwood, Daniel M. Ralph, Stephen E. Reed, Robert Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States

We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra... 详细信息

关键词： Photonic devices

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Hybrid Deep Learning Model for Accurate Short-Term Electricity Price Forecasting

Hybrid Deep Learning Model for Accurate Short-Term Electrici...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Moradzadeh, Arash Mouhammadpourfard, Mostafa Weng, Yang Pol, Suhas Muyeen, S.M. Qatar University Department of Electrical Engineering Doha Qatar National Wind Institute Texas Tech University LubbockTX United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States Texas Tech University Renewable Energy Program LubbockTX United States

ISBN: (纸本)9798331541125

Accurate short-term electricity price forecasting (STEPF) is critical for efficient energy market operations, guiding investment strategies, resource allocation, and consumer behavior. This study introduces a hybrid deep learning approach specifically designed to improve STEPF accuracy by leveraging historical Hourly Ontario Energy Price (HOEP) data from 2017 to 2019. The model integrates advanced techniques, including data preprocessing and denoising through a Stacked Denoising Autoencoder (SDAE), along with enhanced temporal modeling via Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) networks. By capturing the complex dynamics inherent in electricity pricing data, the proposed hybrid model significantly enhances forecasting accuracy. Trained on data from 2017 and 2018, with 2019 used for testing, the model achieves a strong correlation coefficient (R = 99.86%) and substantially lowers forecasting errors. Comparative evaluations against established forecasting methods highlight the model's superior performance. This work demonstrates the practical value of deep learning techniques in the energy sector, particularly in responding to the volatility of demand and supply in real-time electricity markets. © 2025 IEEE.

关键词： Electricity market forecasting Energy management Hybrid deep learning models Short-term electricity price forecasting Temporal data analysis

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Exploring 3D Reconstruction with Drone Images: Advances and Challenges in Urban Environments 50

Exploring 3D Reconstruction with Drone Images: Advances and ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Miranda Baldivieso, Thiago João da Silva Batista, Taise Grazielle Chagas, Fabio Suim Rodrigues Velho, Luiz Carlos Pacheco Ferreira Rosa, Paulo Fernando Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea RJ Rio de Janeiro Brazil Department of Science and Industry systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (纸本)9781665464543

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software-both commercial and open-source-and concluding with a qualitative evaluation of the results. © 2024 IEEE.

关键词： Aerial photography

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program.type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting 38

Introducing Spectral Attention for Long-Range Dependency in ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kang, Bong Gyun Lee, Dongjun Kim, HyunGi Chung, DoHyun Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Future Automotive Mobility Seoul National University Korea Republic of

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture long-range dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results. © 2024 Neural information processing systems foundation. All rights reserved.

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Performance Evaluation of Deep Feature in Cross Spectral Face Recognition

Performance Evaluation of Deep Feature in Cross Spectral Fac...

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International Conference on Information Technology, computer and electrical engineering (ICITACEE)

作者： Ernita Dewi Meutia Fitri Arnia Nasaruddin Rusdha Muharar Maulisa Oktiana Khairun Saddami Department of Electrical and Computer Engineering Doctoral Program School of Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

Cross-spectral face recognition (CSFR), particularly in the Near Infra-Red (NIR) and visual (VIS) spectrum, presents challenges due to differences in illumination and sensor characteristics. NIR sensors are commonly used in practical applications, such as in nighttime outdoor surveillance systems, due to their ability to capture images in low light conditions and at various standoff distances. This study proposes a simple CSFR method that combines deep learning techniques with photometric normalization using Homomorphic Filtering (HF) to identify subjects in NIR images across different standoff distances. Both NIR and VIS images are filtered to mitigate illumination discrepancies. A deep convolutional neural network (CNN) model is employed to automatically extract discriminative deep features from the normalized images of both spectrums. Four deep CNN models of ResNet-50, DenseNet, EfficientNet, and MobileNetV2 are tested. The performance of the models in feature extraction for CSFR is evaluated on the LDHF (Long Distance Heterogeneous Face) database. The Hamming distance metric is employed to match NIR probe images with VIS gallery images. The experimental findings, expressed in recognition rate RR, demonstrate the effectiveness of the approach in enhancing CSFR accuracy, with MobileNetV2 achieving the highest accuracy of 90% for a short standoff. In addition, HF improves image quality, enhancing the deep learning model's ability to discern image characteristics.

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Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision

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Light(Science & Applications) 2022年第11期11卷 2817-2831页

作者： Yueming Li Ying Jiang Lu Lan Xiaowei Ge Ran Cheng Yuewei Zhan Guo Chen Linli Shi Runyu Wang Nan Zheng Chen Yang Ji-Xin Cheng Department of Mechanical Engineering Boston UniversityBostonMA 02215USA Graduate Program for Neuroscience Boston UniversityBostonMA 02215USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Chemistry Boston UniversitBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Division of Materials Science and Engineering Boston UniversityBostonMA 02215USA Department of Biological Engineering Massachusetts Institute of TechnologyCambridgeMA 02142USA

High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological *** non-invasive neuromodulation methods offer limited precision at the milimeter ***,we report opticallygenerated focused ultrasound(OFUS)for non-invasive brain stimulation with ultrahigh *** is generated by a soft optoacoustic pad(SOAP)fabricated through embedding candle soot nanoparticles in a curved polydimethylsiloxane *** generates a transcranial ultrasound focus at 15 MHz with an ultrahigh lateral resolution of 83μm,which is two orders of magnitude smaller than that of conventional transcranial-focused ultrasound(tFUS).Here,we show effective OFUS neurostimulation in vitro with a single ultrasound *** demonstrate submillimeter transcranial stimulation of the mouse motor cortex in *** acoustic energy of 0.6 mJ/cm?,four orders of magnitude less than that of tFUS,is suffcient for successful OFUS *** offers new capabilities for neuroscience studies and disease treatments by delivering a focus with ultrahigh precision noninvasively.

关键词： SOAP stimulation precision

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MODELING THE HODGKIN-HUXLEY NEURON TO DETERMINE NEURONAL ENERGY CONSUMPTION EFFICIENCY AND OXYGEN CONSUMPTION VALUES

Biomedical Sciences Instrumentation

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Biomedical Sciences Instrumentation 2022年第2期58卷 50-58页

作者： Enochson, Avery Ewert, Daniel Mitar, Dipankar Striker, Ryan Cleveland, Jerika Wolf, Henry Gieser, Anna Braaten, Ben Allen, Jeffery Allen, Monica Department of Integrated Engineering at Iron Range Engineering Program of Minnesota State University Mankato MN United States Department of Computer Science and Computer Engineering University of Wisconsin – La Crosse La CrosseWI United States Department of Electrical and Computer Engineering North Dakota State University FargoND United States Air Force Research Laboratory Munitions Directorate Eglin Air Force Base Eglin AFB FL United States

Understanding neuronal structure and function is essential to studying the human brain. The goal of this project was to create a model of human brain neurons that accurately reflects neuronal function, energy consumption, and oxygen consumption. Extensive work has been performed on the Hodgkin-Huxley model of neurons to accurately model neuronal firing. This study focuses on the creation of a model of the Hodgkin-Huxley neuron in MATLAB with the assistance of the DynaSim toolbox. This model was used to compute values and using another model the energy efficiency of the neuron was calculated and related to oxygen consumption, which then corresponds to Blood Oxygenation Level Dependent (BOLD) imaging in fMRI. The results of this study provide detailed visual and technical information about how the brain neurons function, which is crucial to the further development in brain imaging techniques and to further our understanding of why and how our complex brains function. © 2022 IAE All rights reserved.

关键词： Neurons

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SF(DA)2: SOURCE-FREE DOMAIN ADAPTATION THROUGH THE LENS OF DATA AUGMENTATION 12

SF(DA)2: SOURCE-FREE DOMAIN ADAPTATION THROUGH THE LENS OF D...

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12th International Conference on Learning Representations, ICLR 2024

作者： Hwang, Uiwon Lee, Jonghyun Shin, Juhyeon Yoon, Sungroh Division of Digital Healthcare Yonsei University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In the face of the deep learning model's vulnerability to domain shift, source-free domain adaptation (SFDA) methods have been proposed to adapt models to new, unseen target domains without requiring access to source domain data. Although the potential benefits of applying data augmentation to SFDA are attractive, several challenges arise such as the dependence on prior knowledge of class-preserving transformations and the increase in memory and computational requirements. In this paper, we propose Source-free Domain Adaptation Through the Lens of Data Augmentation (SF(DA)2), a novel approach that leverages the benefits of data augmentation without suffering from these challenges. We construct an augmentation graph in the feature space of the pretrained model using the neighbor relationships between target features and propose spectral neighborhood clustering to identify partitions in the prediction space. Furthermore, we propose implicit feature augmentation and feature disentanglement as regularization loss functions that effectively utilize class semantic information within the feature space. These regularizers simulate the inclusion of an unlimited number of augmented target features into the augmentation graph while minimizing computational and memory demands. Our method shows superior adaptation performance in SFDA scenarios, including 2D image and 3D point cloud datasets and a highly imbalanced dataset. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep learning

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Comparative Analysis of Clusterization Methods Applied to a Solid-State LiDAR

Comparative Analysis of Clusterization Methods Applied to a ...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Henrique N. Poleselo Andre G. S. Conceicao Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Object segmentation is one of the main activities for the robot to create a sense of its environment. This task is a precursor to other activities, such as autonomous navigation in a given environment. Through sensors such as LiDAR, it's possible to generate high-resolution three-dimensional maps of the environment in which the robot is located, thus enabling their interpretation so that tasks such as object segmentation can be performed. In this article, the DBSCAN and HDBSCAN unsupervised clustering methods are explored. Results in a simulated environment in Gazebo together with Robot Operating System ROS framework for capturing sensory data from LiDAR Livox Mid-70 coupled to a mobile robot show the performance of such techniques through comparisons.

关键词： Laser radar Operating systems Clustering methods Object segmentation Robot sensing systems Sensors Mobile robots Autonomous robots

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