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open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey

RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2024年第PartB期189卷

作者： Nie, Yuhao Li, Xiatong Paletta, Quentin Aragon, Max Scott, Andea Brandt, Adam Stanford Univ Dept Energy Sci & Engn Stanford CA 94305 USA Stanford Univ Dept Civil & Environm Engn Stanford CA 94305 USA Univ Cambridge Dept Engn Cambridge CB2 1PZ England Engie Lab CRIGEN F-93240 Stains France European Space Agcy European Space Res Inst I-00044 Frascati Italy European Space Agcy European Ctr Space Applicat & Telecommun Harwell OX11 0FD England Univ Salzburg Dept Geoinformat A-5020 Salzburg Austria Univ Bretagne Sud Dept Comp Sci F-56000 Vannes France

Sky image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty of solar power generation. However, a major challenge is the lack of large quantity of sky image data encompassing diverse sky conditions for model training. This study presents a comprehensive survey of open-source sky image datasets for solar forecasting and related research areas, including cloud segmentation, classification and motion prediction which could potentially enhance solar forecasting capabilities. In total, 72 open-source sky image datasets are identified globally that satisfy the needs of deep learning-based method development. A database containing information about various aspects of the identified datasets is constructed. A multi-criteria ranking system is further developed to evaluate each dataset based on eight dimensions which could have important impacts on the data usage. Finally, insights on the applications of these datasets are provided. This study streamlines the processes of identifying and selecting sky image datasets, and could potentially accelerate the method development and benchmark in solar forecasting and related fields including energy meteorology and atmospheric science.

关键词： open-source datasets Ground-based sky images Solar irradiance Photovoltaic power Solar forecasting Cloud segmentation Cloud classification Cloud motion prediction Deep learning

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A Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric Motors

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IEEE ACCESS 2025年 13卷 54534-54546页

作者： Ferrari, Simone Solimene, Luigi Torchio, Riccardo Anerdi, Costanza Freschi, Fabio Giaccone, Luca Lorenti, Gianmarco Lucchini, Francesco Alotto, Piergiorgio Pellegrino, Gianmario Repetto, Maurizio Politecn Torino Dept Energy Galileo Ferraris DENERG I-10129 Turin Italy Univ Padua Dept Ind Engn I-35129 Padua Italy

This paper presents the work done to address two main challenges in the simulation and design of electric machines for traction applications. On one hand, the modeling process is becoming increasingly complex as the demand for higher efficiency, high power density, and low cost pushes the speed and compactness of the motor to high levels. As a result, the interactions between multiple physical domains (e.g., electromagnetic, thermal, structural, etc.) can no longer be neglected, even in preliminary designs. Consequently, research into new modeling solutions in this area is currently active and widespread. On the other hand, new computational methodologies based on data-driven machine learning are becoming increasingly widespread as the computational power available for this task increases. However, to assess their performance and realize their potential in surrogate and meta-modeling electrical machines, a standardized benchmark for comparing these new approaches is needed. To address these challenges, the paper presents an open-source dataset that provides a reliable foundation for the multi-physical analysis of electric motors used in traction applications. One of the main novelties of this approach is that geometrical and physical data of the motor configuration are shared among different analysis codes. Attention is focused on tailoring the numerical discretization so that the same mesh can be used in different domains, avoiding data conversions and possible numerical inaccuracies. The paper thoroughly explains the workflow developed to create the database, detailing the methodological aspects. Ultimately, the resulting database is made available as an open resource for other researchers in the field. The resulting dataset represents a tool for benchmarking advanced computational methodologies and promoting reproducibility in research.

关键词： Motors Electromagnetics Geometry Electric motors Machine learning Databases Codes Benchmark testing Torque Stress Data-driven techniques machine learning multi-physics analysis open-source datasets traction electric motors

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Synergy in motion: Exploring the similarity and variability of muscle synergy patterns in healthy individuals

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HUMAN MOVEMENT SCIENCE 2024年 98卷 103300页

作者： Feng, Luying Yu, Linfan Lyu, Hui Yang, Canjun Liu, Xiaoguang Zhou, Congcong Yang, Wei Zhejiang Univ Ningbo Innovat Ctr Ningbo Zhejiang Peoples R China Zhejiang Univ Coll Mech Engn Hangzhou Peoples R China Ningbo Rehabil Hosp Dept Spinal Cord Injury Ningbo Peoples R China Zhejiang Univ Sir Run Run Shaw Hosp Sch Med Hangzhou Peoples R China

Background: Recent studies suggest that muscle synergy patterns can be a guide for diagnosis and rehabilitation. Research question: Does human's lower limb synergy pattern significantly change with changes in walking speed? Are there large differences in synergy patterns among different healthy individuals? Methods: 22 healthy subjects from an open-source datasets were included. Non-negative matrix factorization was applied to identify the module composition of surface electromyography(sEMG) data, and the similarity index was adopted to quantify the overall similarity between synergy patterns. Results: Results demonstrated that healthy individuals have their own intrinsic muscle recruitment and coordination characteristics for locomotion at various speeds, additionally, their synergy patterns exhibit predictability under speed variations. Significance: This study develop reference synergy patterns for the lower limbs across 28 different walking speeds. The developed synergy patterns and the above findings may guide the study of gait synergy in rehabilitation and assistance.

关键词： Muscle synergy Surface electromyography Synergy similarity index open-source datasets

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Creating a 3D Multi-Dataset Bubble in Support of OGC Testbed-19 and Metaverse Standards Prototypes

Creating a 3D Multi-Dataset Bubble in Support of OGC Testbed...

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18th International 3D GeoInfo Conference

作者： Clarke, James Smyth, Steve Smith, Rob Morley, Jeremy Ordnance Survey Southampton SO16 0AS Hants England OpenSitePlan Kirkland WA 98033 USA Away Team Software Crowthorne RG45 7AW Berks England

ISBN: (纸本)9783031437014;9783031436994;9783031436987

open Geospatial Consortium Testbeds are an annual research and development initiative that explore geospatial technology. For Testbed-19 we generate a multipurpose 3D dataset in support of the development of Augmented Reality/Metaverse architectures and infrastructure prototypes: Road Hazard Monitoring, Mixed Euclidean and Minkowski GeoPose Graphs, and GeoPose Graphs in a Minkowski Bubble. The dataset comprises: LiDAR capture, position and orientation, video/imagery from a survey vehicle, drone imagery, static camera and GoPro imagery, and derived data. This is extended by a simulated orbiting platform with high-resolution sensor. Our coverage is a bubble of radius 256 m, centered near the Ordnance Survey headquarters in Southampton, UK. The data is published in an open-source repository to support OGC projects, and a prototype of parts of the Ride Hailing use case of the Metaverse Standards Forum-Real/Virtual World Integration Domain Working Group. open availability of this standard dataset will enable independent testing and benchmarking of architectures and algorithms for a variety of mobile applications.

关键词： OGC GeoPose open-source datasets 3D data

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High-fidelity Intrusion Detection datasets for Smart Grid Cybersecurity Research

High-fidelity Intrusion Detection Datasets for Smart Grid Cy...

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2024 International Conference on Communications Control and Computing Technologies for Smart Grids

作者： Tan, Heng Chuan Hossain, Md Adeeb Mashima, Daisuke Kalbarczyk, Zhigniew Illinois Adv Res Ctr IARCS Singapore Singapore Univ Illinois Champaign IL USA

ISBN: (纸本)9798350318562;9798350318555

Intrusion Detection Systems (IDSes) are key defense mechanisms for securing smart grids against cyberattacks. They require realistic datasets to develop accurate models for detecting network anomalies. However, acquiring realistic datasets is challenging due to the need for expert knowledge to accurately label attack data, the sensitive nature of the information, and safety issues related to attacking the actual power systems. Consequently, there is a lack of high-fidelity datasets for testing and validating the efficacy of IDSes. While synthetic datasets provide a good workaround, they are often unrealistic and fail to capture the physical dynamics of power systems under cyberattacks. To address this gap, we leverage the Electric Power and Intelligent Control (EPIC) testbed, a hardware-based smart grid security testbed, to simulate false data injection attacks (FDIA) and time delay attacks (TDA) on two critical power grid operations, namely generator synchronization and reverse power prevention. Our goal is to generate representative datasets that accurately model those operations under normal and attack conditions. By making these datasets publicly available, we enable the research community to develop more effective IDS solutions to enhance the security of smart grids.

关键词： Smart grids Cybersecurity Testbeds False data injection attacks Time delay attacks open-source datasets

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openFashionCLIP: Vision-and-Language Contrastive Learning with open-source Fashion Data 22nd

OpenFashionCLIP: Vision-and-Language Contrastive Learning wi...

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22nd International Conference on Image Analysis and Processing (ICIAP)

作者： Cartella, Giuseppe Baldrati, Alberto Morelli, Davide Cornia, Marcella Bertini, Marco Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy Univ Florence Florence Italy Univ Pisa Pisa Italy

ISBN: (纸本)9783031431470;9783031431487

The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements. In the context of automatic tagging classification and multimodal retrieval, prior works either defined a low generalizable supervised learning approach or more reusable CLIP-based techniques while, however, training on closed source data. In this work, we propose openFashionCLIP, a vision-and-language contrastive learning method that only adopts open-source fashion data stemming from diverse domains, and characterized by varying degrees of specificity. Our approach is extensively validated across several tasks and benchmarks, and experimental results highlight a significant out-of-domain generalization capability and consistent improvements over state-of-the-art methods both in terms of accuracy and recall. source code and trained models are publicly available at: https://***/aimagelab/open-fashion-clip.

关键词： Fashion Domain Vision-and-Language Pre-Training open-source datasets

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A BENCHMARK OF STATE-OF-THE-ART SOUND EVENT DETECTION SYSTEMS EVALUATED ON SYNTHETIC SOUNDSCAPES 47

A BENCHMARK OF STATE-OF-THE-ART SOUND EVENT DETECTION SYSTEM...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Ronchini, Francesca Serizel, Romain Univ Lorraine INRIA CNRS Loria Nancy France

ISBN: (纸本)9781665405409

This paper proposes a benchmark of submissions to Detection and Classification Acoustic Scene and Events 2021 Challenge (DCASE) Task 4 representing a sampling of the state-of-the-art in Sound Event Detection task. The submissions are evaluated according to the two polyphonic sound detection score scenarios proposed for the DCASE 2021 Challenge Task 4, which allow to make an analysis on whether submissions are designed to perform fine-grained temporal segmentation, coarse-grained temporal segmentation, or have been designed to be polyvalent on the scenarios proposed. We study the solutions proposed by participants to analyze their robustness to varying level target to non-target signal-to-noise ratio and to temporal localization of target sound events. A last experiment is proposed in order to study the impact of non-target events on systems outputs. Results show that systems adapted to provide coarse segmentation outputs are more robust to different target to non-target signal-to-noise ratio and, with the help of specific data augmentation methods, they are more robust to time localization of the original event. Results of the last experiment display that systems tend to spuriously predict short events when non-target events are present. This is particularly true for systems that are tailored to have a fine segmentation.

关键词： Sound event detection synthetic soundscapes open-source datasets deep learning

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WHAT'S ALL THE FUSS ABOUT FREE UNIVERSAL SOUND SEPARATION DATA?

WHAT'S ALL THE FUSS ABOUT FREE UNIVERSAL SOUND SEPARATION DA...

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

作者： Wisdom, Scott Erdogan, Hakan Ellis, Daniel P. W. Serizel, Romain Turpault, Nicolas Fonseca, Eduardo Salamon, Justin Seetharaman, Prem Hershey, John R. Google Res Mountain View CA 94043 USA Univ Lorraine Loria INRIA CNRS Nancy France Univ Pompeu Fabra Mus Technol Grp Barcelona Spain Adobe Res San Francisco CA USA Descript Inc San Francisco CA USA

ISBN: (纸本)9781728176055

We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound types. The dataset consists of 23 hours of single-source audio data drawn from 357 classes, which are used to create mixtures of one to four sources. To simulate reverberation, an acoustic room simulator is used to generate impulse responses of box-shaped rooms with frequency-dependent reflective walls. Additional open-source data augmentation tools are also provided to produce new mixtures with different combinations of sources and room simulations. Finally, we introduce an open-source baseline separation model, based on an improved time-domain convolutional network (TDCN++), that can separate a variable number of sources in a mixture. This model achieves 9.8 dB of scale-invariant signal-to-noise ratio improvement (SI-SNRi) on mixtures with two to four sources, while reconstructing single-source inputs with 35.8 dB absolute SI-SNR. We hope this dataset will lower the barrier to new research and allow for fast iteration and application of novel techniques from other machine learning domains to the sound separation challenge.

关键词： Universal sound separation variable source separation open-source datasets deep learning

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