Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness

One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial inte...

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Autores:
Alanazi, Abdullah
Alizadeh, Seyed Mehdi
Nurgalieva, Karina
Nesic, Slavko
Grimaldo Guerrero, John William
Abo-Dief, Hala M.
Eftekhari-Zadeh, Ehsan
nazemi, ehsan
Igor, Narozhnyy
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9121
Acceso en línea:
https://hdl.handle.net/11323/9121
https://doi.org/10.3390/app12031336
https://repositorio.cuc.edu.co/
Palabra clave:
Artificial intelligence
Feature extraction
Scale thickness
Two-phase flow
MLP neural network
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_b447fa17ef905dfe499ab96550d3e264
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9121
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness
title Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness
spellingShingle Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness
Artificial intelligence
Feature extraction
Scale thickness
Two-phase flow
MLP neural network
title_short Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness
title_full Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness
title_fullStr Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness
title_full_unstemmed Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness
title_sort Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness
dc.creator.fl_str_mv Alanazi, Abdullah
Alizadeh, Seyed Mehdi
Nurgalieva, Karina
Nesic, Slavko
Grimaldo Guerrero, John William
Abo-Dief, Hala M.
Eftekhari-Zadeh, Ehsan
nazemi, ehsan
Igor, Narozhnyy
dc.contributor.author.spa.fl_str_mv Alanazi, Abdullah
Alizadeh, Seyed Mehdi
Nurgalieva, Karina
Nesic, Slavko
Grimaldo Guerrero, John William
Abo-Dief, Hala M.
Eftekhari-Zadeh, Ehsan
nazemi, ehsan
Igor, Narozhnyy
dc.subject.proposal.eng.fl_str_mv Artificial intelligence
Feature extraction
Scale thickness
Two-phase flow
MLP neural network
topic Artificial intelligence
Feature extraction
Scale thickness
Two-phase flow
MLP neural network
description One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-04-07T20:50:29Z
dc.date.available.none.fl_str_mv 2022-04-07T20:50:29Z
dc.date.issued.none.fl_str_mv 2022-01-27
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.citation.spa.fl_str_mv Alanazi, A.K.; Alizadeh, S.M.; Nurgalieva, K.S.; Nesic, S.; Grimaldo Guerrero, J.W.; Abo-Dief, H.M.; Eftekhari-Zadeh, E.; Nazemi, E.; Narozhnyy, I.M. Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness. Appl. Sci. 2022, 12, 1336. https://doi.org/10.3390/ app12031336
dc.identifier.issn.spa.fl_str_mv 2076-3417
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9121
dc.identifier.url.spa.fl_str_mv https://doi.org/10.3390/app12031336
dc.identifier.doi.spa.fl_str_mv 10.3390/app12031336
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Alanazi, A.K.; Alizadeh, S.M.; Nurgalieva, K.S.; Nesic, S.; Grimaldo Guerrero, J.W.; Abo-Dief, H.M.; Eftekhari-Zadeh, E.; Nazemi, E.; Narozhnyy, I.M. Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness. Appl. Sci. 2022, 12, 1336. https://doi.org/10.3390/ app12031336
2076-3417
10.3390/app12031336
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9121
https://doi.org/10.3390/app12031336
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Applied Sciences
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spelling Alanazi, AbdullahAlizadeh, Seyed MehdiNurgalieva, KarinaNesic, SlavkoGrimaldo Guerrero, John WilliamAbo-Dief, Hala M.Eftekhari-Zadeh, Ehsannazemi, ehsanIgor, Narozhnyy2022-04-07T20:50:29Z2022-04-07T20:50:29Z2022-01-27Alanazi, A.K.; Alizadeh, S.M.; Nurgalieva, K.S.; Nesic, S.; Grimaldo Guerrero, J.W.; Abo-Dief, H.M.; Eftekhari-Zadeh, E.; Nazemi, E.; Narozhnyy, I.M. Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness. Appl. Sci. 2022, 12, 1336. https://doi.org/10.3390/ app120313362076-3417https://hdl.handle.net/11323/9121https://doi.org/10.3390/app1203133610.3390/app12031336Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.13 páginasapplication/pdfengMDPI Multidisciplinary Digital Publishing InstituteSwitzerlandAtribución 4.0 Internacional (CC BY 4.0)© 2022 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thicknessArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttps://www.mdpi.com/2076-3417/12/3/1336Applied Sciences1. Åbro, E.; Khoryakov, V.A.; Johansen, G.A.; Kocbach, L. Determination of Void Fraction and Flow Regime Using a Neural Network Trained on Simulated Data Based on Gamma-Ray Densitometry. Meas. Sci. Technol. 1999, 10, 619.2. Sattari, M.A.; Roshani, G.H.; Hanus, R. Improving the Structure of Two-Phase Flow Meter Using Feature Extraction and GMDH Neural Network. Radiat. Phys. Chem. 2020, 171, 108725. [CrossRef]3. Oliveira, D.F.; Nascimento, J.R.; Marinho, C.A.; Lopes, R.T. Gamma Transmission System for Detection of Scale in Oil Exploration Pipelines. Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 2015, 784, 616–620. [CrossRef]4. Roshani, M.; Phan, G.T.; AliP, J.M.; Roshani, G.H.; Hanus, R.; Duong, T.; Corniani, E.; Nazemi, E.; KalmounE, M. Evaluation of Flow Pattern Recognition and Void Fraction Measurement in Two Phase Flow Independent of Oil Pipeline’s Scale Layer Thickness. Alex. Eng. J. 2021, 60, 1955–1966. [CrossRef]5. Alamoudi, M.; Sattari, M.; Balubaid, M.; Eftekhari-Zadeh, E.; Nazemi, E.; Taylan, O.; Kalmoun, E. Application of Gamma Attenuation Technique and Artificial Intelligence to Detect Scale Thickness in Pipelines in Which Two-Phase Flows with Different Flow Regimes and Void Fractions Exist. Symmetry 2021, 13, 1198. [CrossRef]6. Roshani, G.H.; Nazemi, E.; Feghhi, S.A.; Setayeshi, S. Flow Regime Identification and Void Fraction Prediction in Two-Phase Flows Based on Gamma Ray Attenuation. Measurment 2015, 62, 25–32. [CrossRef]7. Nazemi, E.; Feghhi, S.A.H.; Roshani, G.H.; Peyvandi, R.G.; Setayeshi, S. Precise Void Fraction Measurement in Two-Phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation. Nucl. Eng. Technol. 2016, 48, 64–71. [CrossRef]8. Karami, A.; Roshani, G.H.; Salehizadeh, A.; Nazemi, E. The Fuzzy Logic Application in Volume Fractions Prediction of the Annular Three-Phase Flows. J. Nondestruct. Evaluation 2017, 36, 35. [CrossRef]9. Roshani, M.; Sattari, M.A.; Ali, P.J.M.; Roshani, G.H.; Nazemi, B.; Corniani, E.; Nazemi, E. Application of GMDH Neural Network Technique to Improve Measuring Precision of a Simplified Photon Attenuation Based Two-Phase Flowmeter. Flow Meas. Instrum. 2020, 75, 101804. [CrossRef]10. Sætre, C.; Tjugum, S.-A.; Johansen, G.A. Tomographic Segmentation in Multiphase Flow Measurement. Radiat. Phys. Chem. 2014, 95, 420–423. [CrossRef]11. Karami, A.; Roshani, G.H.; Khazaei, A.; Nazemi, E.; Fallahi, M. Investigation of Different Sources in Order to Optimize the Nuclear Metering System of Gas–Oil–Water Annular Flows. Neural Comput. Appl. 2018, 32, 3619–3631. [CrossRef]12. Mosorov, V.; Zych, M.; Hanus, R.; Sankowski, D.; Saoud, A. Improvement of Flow Velocity Measurement Algorithms Based on Correlation Function and Twin Plane Electrical Capacitance Tomography. Sensors 2020, 20, 306. [CrossRef] [PubMed]13. Salgado, C.; Brandão, L.; Conti, C.; Salgado, W. Density Prediction for Petroleum and Derivatives by Gamma-Ray Attenuation and Artificial Neural Networks. Appl. Radiat. Isot. 2016, 116, 143–149. [CrossRef]14. 14. Roshani, M.; Ali, P.J.M.; Roshani, G.H.; Nazemi, B.; Corniani, E.; Phan, N.H.; Tran, H.-N.; Nazemi, E. X-Ray Tube with Artificial Neural Network Model as a Promising Alternative for Radioi-Sotope Source in Radiation Based Two Phase Flowmeters. Appl. Radiat. Isot. 2020, 164, 109255.15. Biswal, J.; Pant, H.; Goswami, S.; Samantray, J.; Sharma, V.; Sarma, K. Measurement of Flow Rates of Water in Large Diameter Pipelines Using Radiotracer Dilution Method. Flow Meas. Instrum. 2018, 59, 194–200. [CrossRef]16. Candeias, J.; de Oliveira, D.; dos Anjos, M.; Lopes, R. Scale Analysis Using X-Ray Microfluorescence and Computed Radiography. Radiat. Phys. Chem. 2014, 95, 408–411. [CrossRef]17. Pelowitz, D.B. MCNP-X TM User’s Manual; Version 2.5.0. LA-CP-05e0369; Los Alamos National, Laboratory: Los Alamos, NM, USA, 2005.18. Nazemi, E.; Roshani, G.H.; Feghhi, S.A.H.; Setayeshi, S.; Zadeh, E.E.; Fatehi, A. Optimization of a Method for Identifying the Flow Regime and Measuring Void Fraction in a Broad Beam Gamma-Ray Attenuation Technique. Int. J. Hydrog. Energy 2016, 41, 7438–7444. [CrossRef]19. Meric, I.; Johansen, G.A.; Mattingly, J.; Gardner, R. On the Ill-Conditioning of the Multiphase Flow Measurement by Prompt Gamma-Ray Neutron Activation Analysis. Radiat. Phys. Chem. 2014, 95, 401–404. [CrossRef]20. Holstad, M.B.; Johansen, G.A. Produced Water Characterization by Dual Modality Gamma-Ray Measurements. Meas. Sci. Technol. 2005, 16, 1007–1013. [CrossRef]21. Sattari, M.A.; Roshani, G.H.; Hanus, R.; Nazemi, E. Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique. Measurement 2021, 168, 108474. [CrossRef]22. Hosseini, S.; Roshani, G.; Setayeshi, S. Precise Gamma Based Two-Phase Flow Meter Using Frequency Feature Extraction and only One Detector. Flow Meas. Instrum. 2020, 72, 101693. [CrossRef]23. Siavash, H.; Taylan, O.; Abusurrah, M.; Akilan, T.; Nazemi, E.; Eftekhari-Zadeh, E.; Bano, F.; Roshani, G.H. Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries. Polymers 2021, 13, 3647.24. Balubaid, M.; Sattari, M.A.; Taylan, O.; Bakhsh, A.A.; Nazemi, E. Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products. Math. 2021, 9, 3215. [CrossRef]25. Abdulrahman, B.; Sattari, M.A.; Taylan, O.; Nazemi, E. Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-Ray Radiation Based Two Phase Flow Meter. Mathematics 2021, 9, 1227.26. Roshani, G.H.; Feghhi, S.A.H.; Mahmoudi-Aznaveh, A.; Nazemi, E.; Adineh-Vand, A. Precise volume fraction prediction in oilwater-gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector. Measurement 2014, 51, 34–41. [CrossRef]27. Saberinejad, H.; Keshavarz, A.; Payandehdoost, M.; Azmoodeh, M.R.; Batooei, A. Numerical study of heat transfer performance in a pipe partially filled with non-uniform porous media under Ltne Condition. Int. J. Num. Methods Heat Fluid Flow 2018, 28, 1845–1865. [CrossRef]28. Nazemi, E.; Feghhi, S.; Roshani, G.; Setayeshi, S.A.; Peyvandi, R.G. A radiation-based hydrocarbon two-phase flow meter for estimating of phase fraction independent of liquid phase density in stratified regime. Flow Meas. Instrum. 2015, 46, 25–32. [CrossRef]29. Roshani, G.H.; Nazemi, E.; Feghhi, S.A.H. Investigation of using 60Co source and one detector for determining the flow regime and void fraction in gas-liquid two-phase flows. Flow Meas. Instrum. 2016, 50, 73–79. [CrossRef]30. Rouhi, S.; Xiros, N.; Sadeqi, S.; Ioup, J.; Sultan, C.; VanZwieten, J. CFD validation of the thermodynamic model of a compressed gaseous hydrogen storage tank. Proceeding of 5-6th Thermal and Fluids Engineering Conference (TFEC), Virtual, 26–28 May 2021.31. Roshani, G.H.; Nazemi, E.; Roshani, M.M. Flow regime independent volume fraction estimation in three-phase flows using dual-energy broad beam technique and artificial neural network. Neural Comput. Appl. 2016, 28, 1265–1274. [CrossRef]32. Roshani, G.; Nazemi, E.; Roshani, M. Usage of two transmitted detectors with optimized orientation in order to three phase flow me-tering. Measurement 2017, 100, 122–130. [CrossRef]33. Roshani, G.; Nazemi, E. Intelligent densitometry of petroleum products in stratified regime of two phase flows using gamma ray and neural network. Flow Meas. Instrum. 2017, 58, 6–11. [CrossRef]34. Roshani, G.; Nazemi, E.; Roshani, M. Intelligent recognition of gas-oil-water three-phase flow regime and determination of volume fraction using radial basis function. Flow Meas. Instrum. 2017, 54, 39–45. [CrossRef]35. Du, X.; Tian, W.; Pan, J.; Hui, B.; Sun, J.; Zhang, K.; Xia, Y. Piezo-phototronic effect promoted carrier separation in coaxial P-N junctions for self-powered photodetector. Nano Energy 2022, 92, 106694. [CrossRef]36. Karami, A.; Roshani, G.H.; Nazemi, E.; Roshani, S. Enhancing the performance of a dual-energy gamma ray based three-phase flow meter with the help of grey wolf optimization algorithm. Flow Meas. Instrum. 2018, 64, 164–172. [CrossRef]37. Roshani, G.H.; Roshani, S.; Nazemi, E.; Roshani, S. Online measuring density of oil products in annular regime of gas-liquid two phase flows. Measurement 2018, 129, 296–301. [CrossRef]38. Cai, T.; Dong, M.; Liu, H.; Nojavan, S. Integration of hydrogen storage system and wind generation in power systems under demand response program: A novel P-robust stochastic programming. Int. J. Hydrogen Energy 2022, 47, 443–458. [CrossRef]39. Roshani, G.; Hanus, R.; Khazaei, A.; Zych, M.; Nazemi, E.; Mosorov, V. Density and velocity determination for single-phase flow based on radiotracer technique and neural networks. Flow Meas. Instrum. 2018, 61, 9–14. [CrossRef]40. Charchi, N.; Li, Y.; Huber, M.; Kwizera, E.A.; Huang, X.; Argyropoulos, C.; Hoang, T. Small mode volume plasmonic film-coupled nanostar resonators. Nanoscale Adv. 2020, 2, 2397–2403. [CrossRef] [PubMed]41. Roshani, G.; Nazemi, E.; Roshani, M. Identification of flow regime and estimation of volume fraction independent of liquid phase density in gas-liquid two-phase flow. Prog. Nucl. Energy 2017, 98, 29–37. [CrossRef]42. Roshani, S.; Roshani, S. Two-Section Impedance Transformer Design and Modeling for Power Amplifier Applications. Appl. Comput. Electromagn. Soc. J. 2017, 32, 1042–1047.43. Lalbakhsh, A.; Alizadeh, S.M.; Ghaderi, A.; Golestanifar, A.; Mohamadzade, B.; Jamshidi, M.B.; Mandal, K.; Mohyuddin, W. A design of a dual-band bandpass ?lter based on modal analysis for modern communication systems. Electronics 2020, 9, 1770. [CrossRef]44. Liu, X.; Zheng, W.; Mou, Y.; Li, Y.; Yin, L. Microscopic 3D reconstruction based on point cloud data generated using defocused images. Measurement and Control 2021, 54, 1309–1318. [CrossRef]45. Pirasteh, A.; Roshani, S.; Roshani, S. Compact microstrip lowpass filter with ultrasharp response using a square-loaded mod-ified T-shaped resonator. Turk. J. Electr. Eng. Comput. Sci. 2018, 26, 1736–1746. [CrossRef]46. Ramtin, A.R.; Nain, P.; Menasche, D.S.; Towsley, D.; de Souza e Silva, E. Fundamental scaling laws of covert ddos attacks. Performance Evaluation 2021, 151, 102236. [CrossRef]47. Roshani, S.; Roshani, S. A compact coupler design using meandered line compact microstrip resonant cell (MLCMRC) and bended lines. Wireless Networks 2021, 27, 677–684. [CrossRef]48. Ma, Z.; Zheng, W.; Chen, X.; Yin, L. Joint embedding VQA model based on Dynamic Word Vector. PeerJ Comput. Sci. 2021, 7, e353. [CrossRef] [PubMed]49. Lalbakhsh, A.; Lotfi Neyestanak, A.A.; Naser-Moghaddasi, M. Microstrip hairpin bandpass filter using modified Minkowski fractal-shape for suppression of second harmonic. IEICE Trans. Electron. 2012, E95-C, 378–381. [CrossRef]50. Pourghebleh, B.; Aghaei Anvigh, A.; Ramtin, A.R.; Mohammadi, B. The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in Cloud Environments. Cluster Comput. 2021, 24, 2673–2696. [CrossRef]51. Seyedi, M.; Taher, S.A.; Ganji, B.; Guerrero, J. A hybrid islanding detection method based on the rates of changes in voltage and active power for the Multi-Inverter Systems. IEEE Trans. Smart Grid 2021, 12, 2800–2811. [CrossRef]52. Roshani, S.; Roshani, S. Design of a very compact and sharp bandpass diplexer with bended lines for GSM and LTE applica-tions. AEU. Int. J. Electron. Commun. 2019, 99, 354–360. [CrossRef]53. Wang, K.; Wang, H.; Li, S. Renewable quantile regression for streaming datasets. Knowledge-Based Systems 2022, 235, 107675. [CrossRef]54. Lalbakhsh, A.; Mohamadpour, G.; Roshani, S.; Ami, M.; Roshani, S.; Sayem, A.S.M.; Alibakhshikenari, M.; Koziel, S. Design of a compact planar transmission line for miniaturized rat-race coupler with harmonics suppression. IEEE Access 2021, 9, 129207–129217. [CrossRef]55. Roshani, S.; Roshani, S.; Zarinitabar, A. A modified Wilkinson power divider with ultra harmonic suppression using open stubs and lowpass filters. Analog. Integr. Circuits Signal Process. 2019, 98, 395–399. [CrossRef]56. Wang, K.; Li, S. Robust distributed modal regression for massive data. Comput. Stat. Data Anal. 2021, 160, 107225. [CrossRef]57. Jamshidi, M.B.; Siahkamari, H.; Roshani, S.; Roshani, S. A compact Gysel power divider design using U-shaped and T-shaped reso-nators with harmonics suppression. Electromagnetics 2019, 39, 491–504. [CrossRef]58. Jamshidi, M.B.; Roshani, S.; Talla, J.; Roshani, S.; Peroutka, Z. Size reduction and performance improvement of a microstrip Wilkinson power divider using a hybrid design technique. Sci. Rep. 2021, 11, 7773. [CrossRef] [PubMed]59. Liu, K.; Ke, F.; Huang, X.; Yu, R.; Lin, F.; Wu, Y.; Ng, D.W. Deepban: A temporal convolution-based communication framework for dynamic WBANS. IEEE Trans. Commun. 2021, 69, 6675–6690. [CrossRef]60. Pirasteh, A.; Roshani, S.; Roshani, S. A modified class-F power amplifier with miniaturized harmonic control circuit. AEU Int. J. Electron. Commun. 2018, 97, 202–209. [CrossRef]61. Roshani, S.; Roshani, S. Design of a high efficiency class-F power amplifier with large signal and small signal measure-ments. Measurement 2020, 149, 106991. [CrossRef]62. 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[CrossRef]131312Artificial intelligenceFeature extractionScale thicknessTwo-phase flowMLP neural networkPublicationORIGINALApplication of neural network and time-domain feature extraction techniques for determining volumetric percentages.pdfApplication of neural network and time-domain feature extraction techniques for determining volumetric percentages.pdfapplication/pdf3450981https://repositorio.cuc.edu.co/bitstreams/781a9ad3-332a-4702-920f-c07f954e4f33/downloadc743e3157c76867d4bc5d68b27cef03cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/895611a8-7a01-443f-9aa4-a5699a8c39be/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTApplication of neural network and time-domain feature extraction techniques for determining volumetric percentages.pdf.txtApplication of neural network and time-domain feature extraction techniques for determining volumetric 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