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Research models and methodologies on the smart city: a systematic literature review.

1. Introduction

2. theoretical background and previous studies, 2.1. definition of smart city, 2.2. previous studies, 3. research method, 3.1. research subject, 3.2. systematic review, 4. results and discussion, 4.1. results of research method analysis, 4.2. results of research content analysis, 4.2.1. infrastructure/monitoring, 4.2.2. citizen/sustainability, 4.2.3. big data/algorithm, 4.2.4. smart grid, 4.2.5. the internet of things/cloud, 4.2.6. governance, 4.2.7. transportation, 5. conclusions and discussion, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Myeong, S.; Park, J.; Lee, M. Research Models and Methodologies on the Smart City: A Systematic Literature Review. Sustainability 2022 , 14 , 1687. https://doi.org/10.3390/su14031687

Myeong S, Park J, Lee M. Research Models and Methodologies on the Smart City: A Systematic Literature Review. Sustainability . 2022; 14(3):1687. https://doi.org/10.3390/su14031687

Myeong, Seunghwan, Jaehyun Park, and Minhyung Lee. 2022. "Research Models and Methodologies on the Smart City: A Systematic Literature Review" Sustainability 14, no. 3: 1687. https://doi.org/10.3390/su14031687

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2012 guidelines to Defra/DECC’s GHG conversion factors for company reporting: Methodology paper for emission factors

This paper outlines the methodology used for the 2012 GHG Conversion Factors. These have been superseded by the 2013 factors, integrated into a web based tool.

2012 Guidelines to Defra / DECC’s GHG Conversion Factors for Company Reporting: Methodology Paper for Emission Factors

Ref: PB13792

PDF , 1.35 MB , 85 pages

This file may not be suitable for users of assistive technology.

The 2012 Guidelines to Defra and DECC’s Greenhouse Gas (GHG) Conversion Factors for Company Reporting have been superseded by the 2013 factors which are integrated into a new web based tool .

A new methodology paper for the 2013 factors will be available in July 2013.

Updates to this page

Amended to explain that these factors have been superseded by the 2013 factors.

First published.

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Deep learning applied to seismic facies classification: A methodology for training

In this work, we discuss how to train convolutional neural networks to classify seismic images. We present a methodology to process and organize post-stack data into appropriate data sets for training and testing the model. We generated a few data sets by varying several parameters and analyzed the effects of those modifications on the performance of the model. In our experiments, we simulated the workflow using real data where the expert feeds the system with some interpreted lines from a cube, and a CNN classifies the remaining lines. We used two public seismic data sets: the Netherlands Offshore in F3 block and Penobscot. Finally, we obtained up to 99\% of accuracy using less than 5\% of the available data for training. It is important to highlight that the model had a good performance in identifying the main portions of the seismic images and distinguishing the layer related to salt deposit in Netherlands.

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• Daniel Civitarese
• Daniela Szwarcman
• R.M. Gamae Silva
• Emilio Vital Brazil

Ore content estimation based on spatial geological data through 3D convolutional neural networks

A benchmark dataset for semi-automatic seismic interpretation based on a new zealand's seismic survey, quantum-inspired evolutionary algorithm applied to neural architecture search.

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Deep Learning Applied to Seismic Facies Classification: a Methodology for Training

• Authors D.S. Chevitarese 1 , D. Szwarcman 1 , R.M. Gama e Silva 1  and E. Vital Brazil 1
• View Affiliations Hide Affiliations Affiliations: 1 IBM
• Publisher: European Association of Geoscientists & Engineers
• Source: Conference Proceedings , Saint Petersburg 2018 , Apr 2018, Volume 2018, p.1 - 5
• DOI: https://doi.org/10.3997/2214-4609.201800237
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In this work, we discuss how to train convolutional neural networks to classify seismic images. We present a methodology to process and organize post-stack data into appropriate data sets for training and testing the model. We generated a few data sets by varying several parameters and analyzed the effects of those modifications on the performance of the model. In our experiments, we simulated the workflow using real data where the expert feeds the system with some interpreted lines from a cube, and a CNN classifies the remaining lines. We used two public seismic data sets: the Netherlands Offshore in F3 block and Penobscot.

Finally, we obtained up to 99\% of accuracy using less than 5\% of the available data for training. It is important to highlight that the model had a good performance in identifying the main portions of the seismic images and distinguishing the layer related to salt deposit in Netherlands.

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• I.Goodfellow, Y.Bengio, and A.Courville . Deep Learning . MIT Press, 2016 . [Google Scholar]
• L.Huang, X.Dong, and T. E.Clee . A scalable deep learning platform for identifying geologic features from seismic attributes. The Leading Edge , 36(3):249–256, 2017 . [Google Scholar]
• A.Krizhevsky, I.Sutskever, and G. E.Hinton . Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 , pages 1097–1105. Curran Associates, Inc., 2012 . [Google Scholar]
• Y.Liu . Application of deep learning for seismic image interpretation . In GeoConvention, Calgary, 2017 . Extended abstrac. [Google Scholar]

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Most cited this month most cited rss feed, the natural combination of full and image‐based waveform inversion, poststack diffraction imaging using reverse‐time migration, characterizing the effect of elastic interactions on the effective elastic properties of porous, cracked rocks, fracture detection by gaussian beam imaging of seismic data and image spectrum analysis, laboratory measurements of guided‐wave propagation within a fluid‐saturated fracture.

Publication Date: 09 Apr 2018

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