Optimizing seismic-based reservoir property prediction: a synthetic data-driven approach using convolutional neural networks and transfer learning with real data integration
Sign Up to like & getrecommendations! Published in 2024 at "Artificial Intelligence Review"
DOI: 10.1007/s10462-024-11030-8
Abstract: Reservoir characterization through seismic data analysis is essential for exploration and production in the petroleum industry. However, seismic-to-well tie discrepancies, limited availability of high-quality well data, and resolution constraints pose a reliability challenge. While previous… read more here.
Keywords: convolutional neural; transfer learning; real data; synthetic data ... See more keywords
Modelling electrochemical energy storage devices in insular power network applications supported on real data
Sign Up to like & getrecommendations! Published in 2017 at "Applied Energy"
DOI: 10.1016/j.apenergy.2016.12.007
Abstract: This paper addresses different techniques for modelling electrochemical energy storage (ES) devices in insular power network applications supported on real data. The first contribution is a comprehensive performance study between a set of competing electrochemical… read more here.
Keywords: electrochemical energy; storage; energy; energy storage ... See more keywords
An order of magnitude: How a detailed, real-data-based return flow analysis identified large discrepancies in modeled water consumption volumes for Finland
Sign Up to like & getrecommendations! Published in 2020 at "Ecological Indicators"
DOI: 10.1016/j.ecolind.2019.105835
Abstract: Abstract Sustainable exploitation of water resources is an essential global challenge and several approaches have been applied for studying water flows through economies. However, the applicability, accuracy, and reliability of all approaches is generally constrained… read more here.
Keywords: water; water consumption; real data; return ... See more keywords
Data augmentation in microscopic images for material data mining
Sign Up to like & getrecommendations! Published in 2020 at "npj Computational Materials"
DOI: 10.1038/s41524-020-00392-6
Abstract: Recent progress in material data mining has been driven by high-capacity models trained on large datasets. However, collecting experimental data (real data) has been extremely costly owing to the amount of human effort and expertise… read more here.
Keywords: strategy; data mining; mining; material data ... See more keywords
Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks
Sign Up to like & getrecommendations! Published in 2022 at "Scientific Reports"
DOI: 10.1038/s41598-022-06057-2
Abstract: Due to the growing rise of cyber attacks in the Internet, the demand of accurate intrusion detection systems (IDS) to prevent these vulnerabilities is increasing. To this aim, Machine Learning (ML) components have been proposed… read more here.
Keywords: flow based; data used; adversarial networks; generative adversarial ... See more keywords
A general class of trimodal distributions: properties and inference
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Applied Statistics"
DOI: 10.1080/02664763.2023.2207785
Abstract: The modality is important topic for modelling. Using parametric models is an efficient way when real data set shows trimodality. In this paper we propose a new class of trimodal probability distributions, that is, probability… read more here.
Keywords: normal distribution; distribution; real data; class trimodal ... See more keywords
Fault detection and diagnosis for heat source system using convolutional neural network with imaged faulty behavior data
Sign Up to like & getrecommendations! Published in 2019 at "Science and Technology for the Built Environment"
DOI: 10.1080/23744731.2019.1651619
Abstract: Faults that impair performance can occur in a heat source system because it comprises various devices and has complex controls. This article presents a novel method for fault detection and diagnosis (FDD). This study focused… read more here.
Keywords: fault; system; source system; fault detection ... See more keywords
A real data-driven simulation strategy to select an imputation method for mixed-type trait data
Sign Up to like & getrecommendations! Published in 2023 at "PLOS Computational Biology"
DOI: 10.1101/2022.05.03.490388
Abstract: Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a… read more here.
Keywords: imputation method; trait; method; dataset ... See more keywords
PCGOD: Enhancing Object Detection With Synthetic Data for Scarce and Sensitive Computer Vision Tasks
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3572719
Abstract: Object detection models rely on large-scale, high-quality annotated datasets, which are often expensive, scarce, or restricted due to privacy concerns. Synthetic data generation has emerged as an alternative, yet existing approaches have limitations: generative models… read more here.
Keywords: real data; object detection; synthetic data; pcgod enhancing ... See more keywords
Staggered SAR: Performance Analysis and Experiments With Real Data
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2017.2731047
Abstract: Synthetic aperture radar (SAR) remote sensing allows high-resolution imaging independent of weather conditions and sunlight illumination and is therefore very attractive for the systematic observation of dynamic processes on the earth’s surface. Conventional SAR systems… read more here.
Keywords: performance analysis; resolution; sar performance; performance ... See more keywords
A Range-Doppler Method for Focusing Radar Sounder Data Generated by Coherent Electromagnetic Simulators
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3201047
Abstract: Radar sounders (RSs) are gaining importance in planetary missions thanks to their unique capability of providing direct measurements of subsurface (SS) structures. To support their design and data interpretation, several electromagnetic (e.m.) simulation techniques have… read more here.
Keywords: range doppler; data generated; radar; method ... See more keywords