Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer

Scale formation inside oil and gas pipelines is always one of the main threats to the efficiency of equipment and their depreciation. In this study, an artificial intelligence method method is presented to provide the flow regime and volume percentage of a two-phase flow while considering the presen...

Full description

Autores:
Mayet, Abdulilah
Salama, Ahmed S.
Alizadeh, Mehdi
Nesic, Slavko
Grimaldo Guerrero, John William
Eftekhari-Zadeh, Ehsan
nazemi, ehsan
Iliyasu, Abdullah
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:11323/9119
Acceso en línea:
https://hdl.handle.net/11323/9119
https://doi.org/10.3390/electronics11030459
https://repositorio.cuc.edu.co/
Palabra clave:
Pipeline’s scale
RBF neural network
Two-phase flow
Oil and gas
Artificial intelligence
Rights
openAccess
License
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
id RCUC2_bb5bc993644d8fbbf54c113b15a8d7fa
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9119
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer
title Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer
spellingShingle Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer
Pipeline’s scale
RBF neural network
Two-phase flow
Oil and gas
Artificial intelligence
title_short Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer
title_full Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer
title_fullStr Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer
title_full_unstemmed Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer
title_sort Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer
dc.creator.fl_str_mv Mayet, Abdulilah
Salama, Ahmed S.
Alizadeh, Mehdi
Nesic, Slavko
Grimaldo Guerrero, John William
Eftekhari-Zadeh, Ehsan
nazemi, ehsan
Iliyasu, Abdullah
dc.contributor.author.spa.fl_str_mv Mayet, Abdulilah
Salama, Ahmed S.
Alizadeh, Mehdi
Nesic, Slavko
Grimaldo Guerrero, John William
Eftekhari-Zadeh, Ehsan
nazemi, ehsan
Iliyasu, Abdullah
dc.subject.proposal.eng.fl_str_mv Pipeline’s scale
RBF neural network
Two-phase flow
Oil and gas
Artificial intelligence
topic Pipeline’s scale
RBF neural network
Two-phase flow
Oil and gas
Artificial intelligence
description Scale formation inside oil and gas pipelines is always one of the main threats to the efficiency of equipment and their depreciation. In this study, an artificial intelligence method method is presented to provide the flow regime and volume percentage of a two-phase flow while considering the presence of scale inside the test pipe. In this non-invasive method, a dual-energy source of barium-133 and cesium-137 isotopes is irradiated, and the photons are absorbed by a detector as they pass through the test pipe on the other side of the pipe. The Monte Carlo N Particle Code (MCNP) simulates the structure and frequency features, such as the amplitudes of the first, second, third, and fourth dominant frequencies, which are extracted from the data recorded by the detector. These features use radial basis function neural network (RBFNN) inputs, where two neural networks are also trained to accurately determine the volume percentage and correctly classify all flow patterns, independent of scale thickness in the pipe. The advantage of the proposed system in this study compared to the conventional systems is that it has a better measuring precision as well as a simpler structure (using one detector instead of two).
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-04-07T20:47:11Z
dc.date.available.none.fl_str_mv 2022-04-07T20:47:11Z
dc.date.issued.none.fl_str_mv 2022
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.issn.spa.fl_str_mv 2079-9292
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9119
dc.identifier.url.spa.fl_str_mv https://doi.org/10.3390/electronics11030459
dc.identifier.doi.spa.fl_str_mv 10.3390/electronics11030459
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 2079-9292
10.3390/electronics11030459
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9119
https://doi.org/10.3390/electronics11030459
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Electronics
dc.relation.references.spa.fl_str_mv 1. Abro, E.; Khoryakov, V.A.; Johansen, G.A. 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, 619–630.
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. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 2015, 784, 616–620. [CrossRef]
4. Roshani, M.; Phan, G.T.; Ali, P.J.M.; Roshani, G.H.; Hanus, R.; Duong, T.; Corniani, E.; Nazemi, E.; Kalmoun, E.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.A.; Balubaid, M.; Eftekhari-Zadeh, E.; Nazemi, E.; Taylan, O.; Kalmoun, E.M. 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.H.; Setayeshi, S. Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation. Measurement 2015, 62, 25–32. [CrossRef]
7. Nazemi, E.; Roshani, G.H.; Feghhi, S.A.H.; Gholipour Peyvandi, R.; 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. Nondestr. Eval. 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. 2020, 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.M.; Brandão, L.E.B.; Conti, C.C.; Salgado, W.L. Density prediction for petroleum and derivatives by gamma-ray attenuation and artificial neural networks. Appl. Radiat. Isot. 2016, 116, 143–149. [CrossRef] [PubMed]
14. Roshani, M.; Sattari, M.A.; 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 radioisotope source in radiation based two phase flowmeters. Appl. Radiat. Isot. 2020, 164, 109255. [CrossRef] [PubMed]
15. Biswal, J.; Pant, H.J.; Goswami, S.; Samantray, J.S.; Sharma, V.K.; Sarma, K.S.S. 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.P.; De Oliveira, D.F.; Dos Anjos, M.J.; Lopes, R.T. Scale analysis using X-ray microfluorescence and computed radiography. Radiat. Phys. Chem. 2014, 95, 408–411. [CrossRef]
17. 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. [CrossRef]
18. 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.
19. Nussbaumer, H.J. The Fast Fourier Transform. In Fast Fourier Transform and Convolution Algorithms; Springer: Berlin, Germany, 1981; pp. 80–111.
20. 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]
21. Tang, L.; Zhang, Y.; Li, C.; Zhou, Z.; Nie, X.; Chen, Y.; Cao, H.; Liu, B.; Zhang, N.; Said, Z.; et al. Biological Stability of Water-Based Cutting Fluids: Progress and Application. Chin. J. Mech. Eng. 2022, 35, 3. [CrossRef]
22. 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]
23. 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. Numer. Methods Heat Fluid Flow 2018, 28, 1845–1865. [CrossRef]
24. 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]
25. Yang, M.; Li, C.; Luo, L.; Li, R.; Long, Y. Predictive model of convective heat transfer coefficient in bone micro-grinding using nanofluid aerosol cooling. Int. Commun. Heat Mass Transf. 2021, 125, 105317. [CrossRef]
26. 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]
27. Nazemi, E.; Roshani, G.H.; Feghhi, S.A.H.; Setayeshi, S.; Eftekhari Zadeh, 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]
28. Roshani, G.; Nazemi, E.; Roshani, M. Usage of two transmitted detectors with optimized orientation in order to three phase flow metering. Measurement 2017, 100, 122–130. [CrossRef]
29. Khounani, Z.; Hosseinzadeh-Bandbafha, H.; Nazemi, F.; Shaeifi, M.; Karimi, K.; Tabatabaei, M.; Aghbashlo, M.; Lam, S.S. Exergy analysis of a whole-crop safflower biorefinery: A step towards reducing agricultural wastes in a sustainable manner. J. Environ. Manag. 2021, 279, 111822. [CrossRef]
30. Gao, T.; Li, C.; Yang, M.; Zhang, Y.; Jia, D.; Ding, W.; Debnath, S.; Yu, T.; Said, Z.; Wang, J. Mechanics analysis and predictive force models for the single-diamond grain grinding of carbon fiber reinforced polymers using CNT nano-lubricant. J. Mater. Process. Technol. 2021, 290, 116976. [CrossRef]
31. Zhang, Z.; Sui, M.; Li, C.; Zhou, Z.; Liu, B.; Chen, Y.; Said, Z.; Debnath, S.; Sharma, S. Residual stress of MoS2 nano-lubricant grinding cemented carbide. Int. J. Adv. Manuf. Technol. 2021, 1–15. [CrossRef]
32. Gao, T.; Li, C.; Wang, Y.; Liu, X.; An, Q.; Li, H.N.; Zhang, Y.; Cao, H.; Liu, B.; Wang, D.; et al. Carbon fiber reinforced polymer in drilling: From damage mechanisms to suppression. Compos. Struct. 2022, 286, 115232. [CrossRef]
33. Nazemi, F.; Karimi, K.; Denayer, J.F.; Shafiei, M. Techno-economic aspects of different process approaches based on brown macroalgae feedstock: A step toward commercialization of seaweed-based biorefineries. Algal Res. 2021, 58, 102366. [CrossRef]
34. Roshani, S.; Roshani, S. Two-Section Impedance Transformer Design and Modeling for Power Amplifier Applications. Appl. Comput. Electromagn. Soc. J. 2017, 32, 1042–1047.
35. 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]
36. Hayerikhiyavi, M.; Dimitrovski, A. A Practical Assessment of the Power Grid Inertia Constant Using PMUs. In Proceedings of the 2020 52nd North American Power Symposium (NAPS), virtual, 11–14 April 2021; pp. 1–5.
37. 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]
38. Lalbakhsh, A.; Afzal, M.U.; Esselle, K.P.; Smith, S.L. Low-cost nonuniform metallic lattice for rectifying aperture near-field of electromagnetic bandgap resonator antennas. IEEE Trans. Antennas Propag. 2020, 68, 3328–3335. [CrossRef]
39. Roshani, S.; Roshani, S. A compact coupler design using meandered line compact microstrip resonant cell (MLCMRC) and bended lines. Wirel. Netw. 2021, 27, 677–684. [CrossRef]
40. Lalbakhsh, A.; Ghaderi, A.; Mohyuddin, W.; Simorangkir, R.B.V.B.; Bayat-Makou, N.; Ahmad, M.S.; Lee, G.H.; Kim, K.W. A Compact C-Band Bandpass Filter with an Adjustable Dual-Band Suitable for Satellite Communication Systems. Electronics 2020, 9, 1088. [CrossRef]
41. 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]
42. 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 filter based on modal analysis for modern communication systems. Electronics 2020, 9, 1770. [CrossRef]
43. 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]
44. Lalbakhsh, A.; Afzal, M.U.; Hayat, T.; Esselle, K.P.; Manda, K. All-metal wideband metasurface for near-field transformation of medium-to-high gain electromagnetic sources. Sci. Rep. 2021, 11, 9421. [CrossRef]
45. 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]
46. Lalbakhsh, A.; Jamshidi, M.B.; Siahkamari, H.; Ghaderi, A.; Golestanifar, A.; Linhart, R.; Talla, J.; Simorangkir, R.B.; Mandal, K. A compact lowpass filter for satellite communication systems based on transfer function analysis. AEU Int. J. Electron. Commun. 2020, 124, 153318. [CrossRef]
47. 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]
48. 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]
49. Roshani, S.; Roshani, S. Design of a high efficiency class-F power amplifier with large signal and small signal measurements. Measurement 2020, 149, 106991. [CrossRef]
50. 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]
51. Roshani, S.; Roshani, S. Design of a compact LPF and a miniaturized Wilkinson power divider using aperiodic stubs with harmonic suppression for wireless applications. Wirel. Netw. 2019, 26, 1493–1501. [CrossRef]
52. Pirasteh, A.; Roshani, S.; Roshani, S. Design of a miniaturized class F power amplifier using capacitor loaded transmission lines. Frequenz 2020, 74, 145–152. [CrossRef]
53. Lotfi, S.; Roshani, S.; Roshani, S.; Gilan, M.S. Wilkinson power divider with band-pass filtering response and harmonics suppression using open and short stubs. Frequenz 2020, 74, 169–176. [CrossRef]
54. Hookari, M.; Roshani, S.; Roshani, S. Design of a low pass filter using rhombus-shaped resonators with an analyticallc equivalent circuit. Turk. J. Electr. Eng. Comput. Sci. 2020, 28, 865–874. [CrossRef]
55. Roshani, S.; Koziel, S.; Roshani, S.; Jamshidi, M.B.; Parandin, F.; Szczepanski, S. Design of a Patch Power Divider with Simple Structure and Ultra-Broadband Harmonics Suppression. IEEE Access 2021, 9, 165734–165744. [CrossRef]
56. Pourbemany, J.; Mirjalily, G.; Abouei, J.; Raouf, A.H.F. Load Balanced Ad-Hoc On-Demand Routing Basedon Weighted Mean Queue Length Metric. In Proceedings of the Iranian Conference on Electrical Engineering (ICEE), Mashhad, Iran, 8–10 May 2018; pp. 470–475.
57. Lotfi, S.; Roshani, S.; Roshani, S. Design of a miniaturized planar microstrip Wilkinson power divider with harmonic cancellation. Turk. J. Electr. Eng. Comput. Sci. 2020, 28, 3126–3136. [CrossRef]
58. Bavandpour, S.K.; Roshani, S.; Pirasteh, A.; Roshani, S.; Seyedi, H. A compact lowpass-dual bandpass diplexer with high output ports isolation. AEU-Int. J. Electron. Commun. 2021, 135, 153748. [CrossRef]
59. Roshani, S.; Dehghani, K.; Roshani, S. A Lowpass Filter Design Using Curved and Fountain Shaped Resonators. Frequenz 2019, 73, 267–272. [CrossRef]
60. Dudek, G. Short-Term Load Forecasting Using Neural Networks with Pattern Similarity-Based Error Weights. Energies 2021, 14, 3224. [CrossRef]
61. Mahum, R.; Rehman, S.U.; Okon, O.D.; Alabrah, A.; Meraj, T.; Rauf, H.T. A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging. Electronics 2022, 11, 26. [CrossRef]
62. Lalbakhsh, A.; Afzal, M.U.; Esselle, K. Simulation-driven particle swarm optimization of spatial phase shifters. In Proceedings of the 2016 International Conference on Electromagnetics in Advanced Applications (ICEAA), Cairns, Australia, 19–23 September 2016.
63. Yan, A.; Fan, Z.; Ding, L.; Cui, J.; Huang, Z.; Wang, Q.; Zheng, H.; Girard, P.; Wen, X. Cost-Effective and Highly Reliable Circuit Components Design for Safety-Critical Applications. IEEE Trans. Aerosp. Electron. Syst. 2021, 1. [CrossRef]
64. Sadeqi, S.; Xiros, N.; Rouhi, S.; Ioup, J.; VanZwieten, J.; Sultan, C. WAVELET TRANSFORMATION ANALYSIS APPLIED TO INCOMPRESSIBLE FLOW FIELD ABOUT A SOLID CYLINDER. In Proceedings of the ASTFE 5–6th Thermal and Fluids Engineering Conference (TFEC), virtual, 26–28 May 2021.
65. Yan, A.; Xu, Z.; Feng, X.; Cui, J.; Chen, Z.; Ni, T.; Huang, Z.; Girard, P.; Wen, X. Novel Quadruple-Node-Upset-Tolerant Latch Designs with Optimized Overhead for Reliable Computing in Harsh Radiation Environments. IEEE Trans. Emerg. Top. Comput. 2020, 1. [CrossRef]
66. Kim, S.; Akpudo, U.E.; Hur, J.-W. A Cost-Aware DNN-Based FDI Technology for Solenoid Pumps. Electronics 2021, 10, 2323. [CrossRef]
67. Jamshidi, M.; Lalbakhsh, A.; Talla, J.; Peroutka, Z.; Hadjilooei, F.; Lalbakhsh, P.; Jamshidi, M.; La Spada, L.; Mirmozafari, M.; Dehghani, M.; et al. Artificial intelligence and COVID-19: Deep learning approaches for diagnosis and treatment. IEEE Access 2020, 8, 109581–109595. [CrossRef]
68. Chapnevis, A.; Sadeghiyan, B. A Secure Two-Party Computation Protocol for Intersection Detection between Two Convex Hulls. arXiv 2020, arXiv:2011.00319.
69. Walid, W.; Awais, M.; Ahmed, A.; Masera, G.; Martina, M. Real-time implementation of fast discriminative scale space tracking algorithm. J. Real-Time Image Process. 2021, 18, 2347–2360. [CrossRef]
70. Vesel, C.; Rashidisabet, H.; Zulueta, J.; Stange, J.P.; Duffecy, J.; Hussain, F.; Piscitello, A.; Bark, J.; A Langenecker, S.; Young, S.; et al. Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study. J. Am. Med. Inform. Assoc. 2020, 27, 1007–1018. [CrossRef] [PubMed]
71. Ramtin, A.R.; Nain, P.; Towsley, D.; e Silva, E.D.S.; Menasche, D.S. Are covert ddos attacks facing multi-featuredetectors feasible. ACM SIGMETRICS Perform. Eval. Rev. 2021, 49, 33–35. [CrossRef]
72. Shiri, A. A Novel Implementation of CORDIC Algorithm Based on Dynamic Microrotation Generation. Mapta J. Electr. Comput. Eng. MJECE 2021, 3, 17–27.
73. Liu, L.; Xiang, H.; Li, X. A novel perturbation method to reduce the dynamical degradation of digital chaotic maps. Nonlinear Dyn. 2021, 103, 1099–1115. [CrossRef]
74. Pourjabar, S.; Choi, G.S. A High-Throughput Multi-Mode LDPC Decoder for 5G NR. arXiv 2021, arXiv:2102.13228.
75. Luo, G.; Yuan, Q.; Zhou, H.; Cheng, N.; Liu, Z.; Yang, F.; Shen, X.S. Cooperative vehicular content distribution in edge computing assisted 5G-VANET. China Commun. 2018, 15, 1–17. [CrossRef]
76. Hookari, M.; Roshani, S.; Roshani, S. High-efficiency balanced power amplifier using miniaturized harmonics suppressed coupler .Int. J. RF Microw. Comput.-Aided Eng. 2020, 30, e22252. [CrossRef]
77. Nazemi, B.; Rafiean, M. Forecasting house prices in Iran using GMDH. Int. J. Hous. Mark. Anal. 2021, 14, 555–568. [CrossRef]
78. Moradi, M.J.; Hariri-Ardebili, M.A. Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model. Appl. Sci. 2019, 9, 2562. [CrossRef]
79. Nazemi, B.; Rafiean, M. Modelling the affecting factors of housing price using GMDH-type artificial neural networks in Isfa-han city of Iran. Int. J. Hous. Mark. Anal. 2021, 15, 4–18. [CrossRef]
80. 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]
81. Khaibullina, K. Technology to Remove Asphaltene, Resin and Paraffin Deposits in Wells Using Organic Solvents. In Proceedings of the SPE Annual Technical Conference and Exhibition, Dubai, United Arab Emirates, 26–28 September 2016. [CrossRef]
82. 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]
83. Nurgalieva, K.S.; Saychenko, L.A.; Riazi, M. Improving the efficiency of oil and gas wells complicated by the formation of asphalt-resin-paraffin deposits. Energies 2021, 14, 6673. [CrossRef]
84. 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]
85. Syah, R.; Alizadeh, S.M.; Nurgalieva, K.S.; Guerrero, J.W.G.; Nasution, M.K.M.; Davarpanah, A.; Ramdan, D.; Metwally, A.S.M. A laboratory approach to measure enhanced gas recovery from a tight gas reservoir during supercritical carbon dioxide injection. Sustainability 2021, 13, 11606. [CrossRef]
86. 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]
87. Khaibullina, K.S.; Sagirova, L.R.; Sandyga, M.S. Substantiation and selection of an inhibitor for preventing the formation of asphalt-resin-paraffin deposits. Period. Tche Quim. 2020, 17, 541–551. (In Portuguese) [CrossRef]
88. 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]
89. 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]
90. Roshani, M.; Phan, G.; Faraj, R.H.; Phan, N.H.; Roshani, G.H.; Nazemi, B.; Corniani, E.; Nazemi, E. Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products. Neural Eng. Technol. 2021, 53, 1277–1283. [CrossRef]
91. Khaibullina, K.S.; Korobov, G.Y.; Lekomtsev, A.V. Development of an asphalt-resin-paraffin deposits inhibitor and substantiation of the technological parameters of its injection into the bottom-hole formation zone. Period. Tche Quim. 2020, 17, 769–781. (In Portuguese) [CrossRef]
92. Roshani, M.; Phan, G.; Roshani, G.H.; Hanus, R.; Nazemi, B.; Corniani, E.; Nazemi, E. Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil-water three phase flows. Measurement 2021, 168, 108427. [CrossRef]
93. Tikhomirova, E.A.; Sagirova, L.R.; Khaibullina, K.S. A Review on Methods of Oil Saturation Modelling using IRAP RMS. In Proceedings of the International Conference on Innovations and Prospects of Development of Mining Machinery and ElectricalEngineering 2019, Saint-Petersburg, Russia, 24–27 April 2019; Volume 378. [CrossRef]
94. 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]
95. Mou, J.; Duan, P.; Gao, L.; Liu, X.; Li, J. An effective hybrid collaborative algorithm for energy-efficient distributed permutation flow-shop inverse scheduling. Future Gener. Comput. Syst. 2022, 128, 521–537. [CrossRef]
96. Wang, Y.; Zou, R.; Liu, F.; Zhang, L.; Liu, Q. A review of wind speed and wind power forecasting with deep neural networks. Appl. Energy 2021, 304, 117766. [CrossRef]
97. Hosseinzadeh-Bandbafha, H.; Nazemi, F.; Khounani, Z.; Ghanavati, H.; Shafiei, M.; Karimi, K.; Lam, S.S.; Aghbashlo, M.; Tabatabaei, M. Safflower-based biorefinery producing a broad spectrum of biofuels and biochemicals: A life cycle assessment perspective. Sci. Total Environ. 2021, 802, 149842. [CrossRef]
98. Kang, Y.; Wu, Q. Pressure Calculation of Gas Hydrate in the Coastal Area of the Coastal Area Based on the Set Pair Analysis. J. Coast. Res. 2020, 103 (Suppl. 1), 1018–1021. [CrossRef]
99. Nouriani, A.; Moradi, H. Smooth switching in power control of wind turbines using a combination strategy of hysteresis and modified middle regions. Sustain. Energy Technol. Assess. 2020, 37, 100585. [CrossRef]
100. Yin, Q.; Li, C.; Dong, L.; Bai, X.; Zhang, Y.; Yang, M.; Jia, D.; Li, R.; Liu, Z. Effects of Physicochemical Properties of Different Base Oils on Friction Coefficient and Surface Roughness in MQL Milling AISI 1045. Int. J. Precis. Eng. Manuf.-Green Technol. 2021, 8, 1629–1647. [CrossRef]
101. Nouriani, A.; Moradi, H. Variable speed wind turbine power control: A comparison between multiple MPPT based methods. Int. J. Dyn. Control. 2021, 1–14. [CrossRef]
102. Ghanbari, B. Abundant exact solutions to a generalized nonlinear Schrödinger equation with local fractional derivative. Math. Methods Appl. Sci. 2021, 44, 8759–8774. [CrossRef]
103. Kharazmi, O.; Jahangard, S. A new family of lifetime distributions in terms of cumulative hazard rate function. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2020, 69, 1–22. [CrossRef]
104. Ghanbari, B. Chaotic behaviors of the prevalence of an infectious disease in a prey and predator system using fractional derivatives. Math. Methods Appl. Sci. 2021, 44, 9998–10013. [CrossRef]
105. Kharazmi, O.; Saadatinik, A.; Jahangard, S. Odd hyperbolic cosine exponential-exponential (OHC-EE) distribution. Ann. Data Sci. 2019, 6 , 765–785. [CrossRef]
106. Ghanbari, B. On novel nondifferentiable exact solutions to local fractional Gardner ’s equation using an effective technique. Math. Methods Appl. Sci. 2021, 44, 4673–4685. [CrossRef]
107. Nabti, A.; Ghanbari, B. Global stability analysis of a fractional SVEIR epidemic model. Math. Methods Appl. Sci. 2021, 44, 8577–8597. [CrossRef]
108. Ghanbari, B. A fractional system of delay differential equation with nonsingular kernels in modeling hand-foot-mouth disease. Adv. Differ. Equ. 2020, 2020, 536. [CrossRef]
109. Nouriani, A.; McGovern, R.A.; Rajamani, R. Step Length Estimation Using Inertial Measurements Units. In Proceedings of the 2021 American Control Conference (ACC), virtual, 25–28 May 2021; pp. 666–671. 110. Ghanbari, B. On approximate solutions for a fractional prey-predator model involving the Atangana-Baleanu derivative. Adv. Differ. Equ. 2020, 2020, 679. [CrossRef]
111. Rashidisabet, H.; Thomas, P.J.; Ajilore, O.; Zulueta, J.; Moore, R.C.; Leow, A. A systems biology approach to the digital behaviorome. Curr. Opin. Syst. Biol. 2020, 20, 8–16. [CrossRef]
112. Sanaat, A.; Zafarghandi, M.S.; Ay, M.R. Design and performance evaluation of high resolution small animal PET scanner based on Monolithic Crystal: A Simulation Study. J. Instrum. 2019, 14, P01005. [CrossRef]
113. Azmoodeh, M.R.; Keshavarz, A.; Batooei, A.; Saberinejad, H.; Payandeh Doost, M.; Keshtkar, H. Experimental study and thermal analysis of a Gamma type Stirling engine for multi-objective optimization. Automot. Sci. Eng. 2020, 10, 3281–3294.
114. Sarhadi, M.; Aryan, L.; Zarei, M. The Estrogen Receptor and Breast Cancer: A Complete Review. CRPASE Trans. Appl. Sci. 2020, 6, 309–314. 115. Mostoufi, A.; Chamkouri, N.; Kordrostami, S.; Alghasibabaahmadi, E.; Mojaddami, A. 3-Hydroxypyrimidine-2, 4-dione deriva-tives as HIV Reverse Transcriptase-Associated RNase H Inhibitors: QSAR analysis and molecular dockingstudies. Iran. J. Pharm. Res. IJPR 2020, 19, 84.
116. Pourbemany, J.; Essa, A.; Zhu, Y. Real Time Video based Heart and Respiration Rate Monitoring. arXiv 2021, arXiv:2106.02669.
117. Hartman, E.J.; Keeler, J.D.; Kowalski, J.M. Layered neural networks with Gaussian hidden units as universal approxima-tors. Neural Comput. 1990,2, 210–215. [CrossRef]
dc.relation.citationendpage.spa.fl_str_mv 14
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationissue.spa.fl_str_mv 3
dc.relation.citationvolume.spa.fl_str_mv 11
dc.rights.spa.fl_str_mv © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Atribución 4.0 Internacional (CC BY 4.0)
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Atribución 4.0 Internacional (CC BY 4.0)
https://creativecommons.org/licenses/by/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv 14 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv MDPI Multidisciplinary Digital Publishing Institute
dc.publisher.place.spa.fl_str_mv Switzerland
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv https://www.mdpi.com/2079-9292/11/3/459
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/15ec8ab8-f557-486d-b327-820b4db34b20/download
https://repositorio.cuc.edu.co/bitstreams/0f89b28c-8433-407a-b429-f0c03816e3dd/download
https://repositorio.cuc.edu.co/bitstreams/02672acc-652a-40a2-aaa2-89c48c364b66/download
https://repositorio.cuc.edu.co/bitstreams/92bb98dc-1997-4b98-940f-d3ba2bac4ec3/download
bitstream.checksum.fl_str_mv 15017b0027b01a0ea871b9a42b52caf1
e30e9215131d99561d40d6b0abbe9bad
8e58af71b908a134c26cd67520a5f6c5
445462a165e2a7d04f95b090ef7f30b5
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1811760820158201856
spelling Mayet, AbdulilahSalama, Ahmed S.Alizadeh, MehdiNesic, SlavkoGrimaldo Guerrero, John WilliamEftekhari-Zadeh, Ehsannazemi, ehsanIliyasu, Abdullah2022-04-07T20:47:11Z2022-04-07T20:47:11Z20222079-9292https://hdl.handle.net/11323/9119https://doi.org/10.3390/electronics1103045910.3390/electronics11030459Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Scale formation inside oil and gas pipelines is always one of the main threats to the efficiency of equipment and their depreciation. In this study, an artificial intelligence method method is presented to provide the flow regime and volume percentage of a two-phase flow while considering the presence of scale inside the test pipe. In this non-invasive method, a dual-energy source of barium-133 and cesium-137 isotopes is irradiated, and the photons are absorbed by a detector as they pass through the test pipe on the other side of the pipe. The Monte Carlo N Particle Code (MCNP) simulates the structure and frequency features, such as the amplitudes of the first, second, third, and fourth dominant frequencies, which are extracted from the data recorded by the detector. These features use radial basis function neural network (RBFNN) inputs, where two neural networks are also trained to accurately determine the volume percentage and correctly classify all flow patterns, independent of scale thickness in the pipe. The advantage of the proposed system in this study compared to the conventional systems is that it has a better measuring precision as well as a simpler structure (using one detector instead of two).14 páginasapplication/pdfengMDPI Multidisciplinary Digital Publishing InstituteSwitzerland© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layerArtículo 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/2079-9292/11/3/459Electronics1. Abro, E.; Khoryakov, V.A.; Johansen, G.A. 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, 619–630.2. 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. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 2015, 784, 616–620. [CrossRef]4. Roshani, M.; Phan, G.T.; Ali, P.J.M.; Roshani, G.H.; Hanus, R.; Duong, T.; Corniani, E.; Nazemi, E.; Kalmoun, E.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.A.; Balubaid, M.; Eftekhari-Zadeh, E.; Nazemi, E.; Taylan, O.; Kalmoun, E.M. 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.H.; Setayeshi, S. Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation. Measurement 2015, 62, 25–32. [CrossRef]7. Nazemi, E.; Roshani, G.H.; Feghhi, S.A.H.; Gholipour Peyvandi, R.; 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. Nondestr. Eval. 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. 2020, 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.M.; Brandão, L.E.B.; Conti, C.C.; Salgado, W.L. Density prediction for petroleum and derivatives by gamma-ray attenuation and artificial neural networks. Appl. Radiat. Isot. 2016, 116, 143–149. [CrossRef] [PubMed]14. Roshani, M.; Sattari, M.A.; 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 radioisotope source in radiation based two phase flowmeters. Appl. Radiat. Isot. 2020, 164, 109255. [CrossRef] [PubMed]15. Biswal, J.; Pant, H.J.; Goswami, S.; Samantray, J.S.; Sharma, V.K.; Sarma, K.S.S. 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.P.; De Oliveira, D.F.; Dos Anjos, M.J.; Lopes, R.T. Scale analysis using X-ray microfluorescence and computed radiography. Radiat. Phys. Chem. 2014, 95, 408–411. [CrossRef]17. 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. [CrossRef]18. Pelowitz, D.B. MCNP-X TM User’s Manual, version 2.5.0; LA-CP-05e0369; Los Alamos National Laboratory: Los Alamos, NM, USA, 2005.19. Nussbaumer, H.J. The Fast Fourier Transform. In Fast Fourier Transform and Convolution Algorithms; Springer: Berlin, Germany, 1981; pp. 80–111.20. 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]21. Tang, L.; Zhang, Y.; Li, C.; Zhou, Z.; Nie, X.; Chen, Y.; Cao, H.; Liu, B.; Zhang, N.; Said, Z.; et al. Biological Stability of Water-Based Cutting Fluids: Progress and Application. Chin. J. Mech. Eng. 2022, 35, 3. [CrossRef]22. 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]23. 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. Numer. Methods Heat Fluid Flow 2018, 28, 1845–1865. [CrossRef]24. 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]25. Yang, M.; Li, C.; Luo, L.; Li, R.; Long, Y. Predictive model of convective heat transfer coefficient in bone micro-grinding using nanofluid aerosol cooling. Int. Commun. Heat Mass Transf. 2021, 125, 105317. [CrossRef]26. 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]27. Nazemi, E.; Roshani, G.H.; Feghhi, S.A.H.; Setayeshi, S.; Eftekhari Zadeh, 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]28. Roshani, G.; Nazemi, E.; Roshani, M. Usage of two transmitted detectors with optimized orientation in order to three phase flow metering. Measurement 2017, 100, 122–130. [CrossRef]29. Khounani, Z.; Hosseinzadeh-Bandbafha, H.; Nazemi, F.; Shaeifi, M.; Karimi, K.; Tabatabaei, M.; Aghbashlo, M.; Lam, S.S. Exergy analysis of a whole-crop safflower biorefinery: A step towards reducing agricultural wastes in a sustainable manner. J. Environ. Manag. 2021, 279, 111822. [CrossRef]30. Gao, T.; Li, C.; Yang, M.; Zhang, Y.; Jia, D.; Ding, W.; Debnath, S.; Yu, T.; Said, Z.; Wang, J. Mechanics analysis and predictive force models for the single-diamond grain grinding of carbon fiber reinforced polymers using CNT nano-lubricant. J. Mater. Process. Technol. 2021, 290, 116976. [CrossRef]31. Zhang, Z.; Sui, M.; Li, C.; Zhou, Z.; Liu, B.; Chen, Y.; Said, Z.; Debnath, S.; Sharma, S. Residual stress of MoS2 nano-lubricant grinding cemented carbide. Int. J. Adv. Manuf. Technol. 2021, 1–15. [CrossRef]32. Gao, T.; Li, C.; Wang, Y.; Liu, X.; An, Q.; Li, H.N.; Zhang, Y.; Cao, H.; Liu, B.; Wang, D.; et al. Carbon fiber reinforced polymer in drilling: From damage mechanisms to suppression. Compos. Struct. 2022, 286, 115232. [CrossRef]33. Nazemi, F.; Karimi, K.; Denayer, J.F.; Shafiei, M. Techno-economic aspects of different process approaches based on brown macroalgae feedstock: A step toward commercialization of seaweed-based biorefineries. Algal Res. 2021, 58, 102366. [CrossRef]34. Roshani, S.; Roshani, S. Two-Section Impedance Transformer Design and Modeling for Power Amplifier Applications. Appl. Comput. Electromagn. Soc. J. 2017, 32, 1042–1047.35. 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]36. Hayerikhiyavi, M.; Dimitrovski, A. A Practical Assessment of the Power Grid Inertia Constant Using PMUs. In Proceedings of the 2020 52nd North American Power Symposium (NAPS), virtual, 11–14 April 2021; pp. 1–5.37. 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]38. Lalbakhsh, A.; Afzal, M.U.; Esselle, K.P.; Smith, S.L. Low-cost nonuniform metallic lattice for rectifying aperture near-field of electromagnetic bandgap resonator antennas. IEEE Trans. Antennas Propag. 2020, 68, 3328–3335. [CrossRef]39. Roshani, S.; Roshani, S. A compact coupler design using meandered line compact microstrip resonant cell (MLCMRC) and bended lines. Wirel. Netw. 2021, 27, 677–684. [CrossRef]40. Lalbakhsh, A.; Ghaderi, A.; Mohyuddin, W.; Simorangkir, R.B.V.B.; Bayat-Makou, N.; Ahmad, M.S.; Lee, G.H.; Kim, K.W. A Compact C-Band Bandpass Filter with an Adjustable Dual-Band Suitable for Satellite Communication Systems. Electronics 2020, 9, 1088. [CrossRef]41. 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]42. 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 filter based on modal analysis for modern communication systems. Electronics 2020, 9, 1770. [CrossRef]43. 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]44. Lalbakhsh, A.; Afzal, M.U.; Hayat, T.; Esselle, K.P.; Manda, K. All-metal wideband metasurface for near-field transformation of medium-to-high gain electromagnetic sources. Sci. Rep. 2021, 11, 9421. [CrossRef]45. 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]46. Lalbakhsh, A.; Jamshidi, M.B.; Siahkamari, H.; Ghaderi, A.; Golestanifar, A.; Linhart, R.; Talla, J.; Simorangkir, R.B.; Mandal, K. A compact lowpass filter for satellite communication systems based on transfer function analysis. AEU Int. J. Electron. Commun. 2020, 124, 153318. [CrossRef]47. 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]48. 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]49. Roshani, S.; Roshani, S. Design of a high efficiency class-F power amplifier with large signal and small signal measurements. Measurement 2020, 149, 106991. [CrossRef]50. 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]51. Roshani, S.; Roshani, S. Design of a compact LPF and a miniaturized Wilkinson power divider using aperiodic stubs with harmonic suppression for wireless applications. Wirel. Netw. 2019, 26, 1493–1501. [CrossRef]52. Pirasteh, A.; Roshani, S.; Roshani, S. Design of a miniaturized class F power amplifier using capacitor loaded transmission lines. Frequenz 2020, 74, 145–152. [CrossRef]53. Lotfi, S.; Roshani, S.; Roshani, S.; Gilan, M.S. Wilkinson power divider with band-pass filtering response and harmonics suppression using open and short stubs. Frequenz 2020, 74, 169–176. [CrossRef]54. Hookari, M.; Roshani, S.; Roshani, S. Design of a low pass filter using rhombus-shaped resonators with an analyticallc equivalent circuit. Turk. J. Electr. Eng. Comput. Sci. 2020, 28, 865–874. [CrossRef]55. Roshani, S.; Koziel, S.; Roshani, S.; Jamshidi, M.B.; Parandin, F.; Szczepanski, S. Design of a Patch Power Divider with Simple Structure and Ultra-Broadband Harmonics Suppression. IEEE Access 2021, 9, 165734–165744. [CrossRef]56. Pourbemany, J.; Mirjalily, G.; Abouei, J.; Raouf, A.H.F. Load Balanced Ad-Hoc On-Demand Routing Basedon Weighted Mean Queue Length Metric. In Proceedings of the Iranian Conference on Electrical Engineering (ICEE), Mashhad, Iran, 8–10 May 2018; pp. 470–475.57. Lotfi, S.; Roshani, S.; Roshani, S. Design of a miniaturized planar microstrip Wilkinson power divider with harmonic cancellation. Turk. J. Electr. Eng. Comput. Sci. 2020, 28, 3126–3136. [CrossRef]58. Bavandpour, S.K.; Roshani, S.; Pirasteh, A.; Roshani, S.; Seyedi, H. A compact lowpass-dual bandpass diplexer with high output ports isolation. AEU-Int. J. Electron. Commun. 2021, 135, 153748. [CrossRef]59. Roshani, S.; Dehghani, K.; Roshani, S. A Lowpass Filter Design Using Curved and Fountain Shaped Resonators. Frequenz 2019, 73, 267–272. [CrossRef]60. Dudek, G. Short-Term Load Forecasting Using Neural Networks with Pattern Similarity-Based Error Weights. Energies 2021, 14, 3224. [CrossRef]61. Mahum, R.; Rehman, S.U.; Okon, O.D.; Alabrah, A.; Meraj, T.; Rauf, H.T. A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging. Electronics 2022, 11, 26. [CrossRef]62. Lalbakhsh, A.; Afzal, M.U.; Esselle, K. Simulation-driven particle swarm optimization of spatial phase shifters. In Proceedings of the 2016 International Conference on Electromagnetics in Advanced Applications (ICEAA), Cairns, Australia, 19–23 September 2016.63. Yan, A.; Fan, Z.; Ding, L.; Cui, J.; Huang, Z.; Wang, Q.; Zheng, H.; Girard, P.; Wen, X. Cost-Effective and Highly Reliable Circuit Components Design for Safety-Critical Applications. IEEE Trans. Aerosp. Electron. Syst. 2021, 1. [CrossRef]64. Sadeqi, S.; Xiros, N.; Rouhi, S.; Ioup, J.; VanZwieten, J.; Sultan, C. WAVELET TRANSFORMATION ANALYSIS APPLIED TO INCOMPRESSIBLE FLOW FIELD ABOUT A SOLID CYLINDER. In Proceedings of the ASTFE 5–6th Thermal and Fluids Engineering Conference (TFEC), virtual, 26–28 May 2021.65. Yan, A.; Xu, Z.; Feng, X.; Cui, J.; Chen, Z.; Ni, T.; Huang, Z.; Girard, P.; Wen, X. Novel Quadruple-Node-Upset-Tolerant Latch Designs with Optimized Overhead for Reliable Computing in Harsh Radiation Environments. IEEE Trans. Emerg. Top. Comput. 2020, 1. [CrossRef]66. Kim, S.; Akpudo, U.E.; Hur, J.-W. A Cost-Aware DNN-Based FDI Technology for Solenoid Pumps. Electronics 2021, 10, 2323. [CrossRef]67. Jamshidi, M.; Lalbakhsh, A.; Talla, J.; Peroutka, Z.; Hadjilooei, F.; Lalbakhsh, P.; Jamshidi, M.; La Spada, L.; Mirmozafari, M.; Dehghani, M.; et al. Artificial intelligence and COVID-19: Deep learning approaches for diagnosis and treatment. IEEE Access 2020, 8, 109581–109595. [CrossRef]68. Chapnevis, A.; Sadeghiyan, B. A Secure Two-Party Computation Protocol for Intersection Detection between Two Convex Hulls. arXiv 2020, arXiv:2011.00319.69. Walid, W.; Awais, M.; Ahmed, A.; Masera, G.; Martina, M. Real-time implementation of fast discriminative scale space tracking algorithm. J. Real-Time Image Process. 2021, 18, 2347–2360. [CrossRef]70. Vesel, C.; Rashidisabet, H.; Zulueta, J.; Stange, J.P.; Duffecy, J.; Hussain, F.; Piscitello, A.; Bark, J.; A Langenecker, S.; Young, S.; et al. Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study. J. Am. Med. Inform. Assoc. 2020, 27, 1007–1018. [CrossRef] [PubMed]71. Ramtin, A.R.; Nain, P.; Towsley, D.; e Silva, E.D.S.; Menasche, D.S. Are covert ddos attacks facing multi-featuredetectors feasible. ACM SIGMETRICS Perform. Eval. Rev. 2021, 49, 33–35. [CrossRef]72. Shiri, A. A Novel Implementation of CORDIC Algorithm Based on Dynamic Microrotation Generation. Mapta J. Electr. Comput. Eng. MJECE 2021, 3, 17–27.73. Liu, L.; Xiang, H.; Li, X. A novel perturbation method to reduce the dynamical degradation of digital chaotic maps. Nonlinear Dyn. 2021, 103, 1099–1115. [CrossRef]74. Pourjabar, S.; Choi, G.S. A High-Throughput Multi-Mode LDPC Decoder for 5G NR. arXiv 2021, arXiv:2102.13228.75. Luo, G.; Yuan, Q.; Zhou, H.; Cheng, N.; Liu, Z.; Yang, F.; Shen, X.S. Cooperative vehicular content distribution in edge computing assisted 5G-VANET. China Commun. 2018, 15, 1–17. [CrossRef]76. Hookari, M.; Roshani, S.; Roshani, S. High-efficiency balanced power amplifier using miniaturized harmonics suppressed coupler .Int. J. RF Microw. Comput.-Aided Eng. 2020, 30, e22252. [CrossRef]77. Nazemi, B.; Rafiean, M. Forecasting house prices in Iran using GMDH. Int. J. Hous. Mark. Anal. 2021, 14, 555–568. [CrossRef]78. Moradi, M.J.; Hariri-Ardebili, M.A. Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model. Appl. Sci. 2019, 9, 2562. [CrossRef]79. Nazemi, B.; Rafiean, M. Modelling the affecting factors of housing price using GMDH-type artificial neural networks in Isfa-han city of Iran. Int. J. Hous. Mark. Anal. 2021, 15, 4–18. [CrossRef]80. 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]81. Khaibullina, K. Technology to Remove Asphaltene, Resin and Paraffin Deposits in Wells Using Organic Solvents. In Proceedings of the SPE Annual Technical Conference and Exhibition, Dubai, United Arab Emirates, 26–28 September 2016. [CrossRef]82. 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]83. Nurgalieva, K.S.; Saychenko, L.A.; Riazi, M. Improving the efficiency of oil and gas wells complicated by the formation of asphalt-resin-paraffin deposits. Energies 2021, 14, 6673. [CrossRef]84. 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]85. Syah, R.; Alizadeh, S.M.; Nurgalieva, K.S.; Guerrero, J.W.G.; Nasution, M.K.M.; Davarpanah, A.; Ramdan, D.; Metwally, A.S.M. A laboratory approach to measure enhanced gas recovery from a tight gas reservoir during supercritical carbon dioxide injection. Sustainability 2021, 13, 11606. [CrossRef]86. 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]87. Khaibullina, K.S.; Sagirova, L.R.; Sandyga, M.S. Substantiation and selection of an inhibitor for preventing the formation of asphalt-resin-paraffin deposits. Period. Tche Quim. 2020, 17, 541–551. (In Portuguese) [CrossRef]88. 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]89. 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]90. Roshani, M.; Phan, G.; Faraj, R.H.; Phan, N.H.; Roshani, G.H.; Nazemi, B.; Corniani, E.; Nazemi, E. Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products. Neural Eng. Technol. 2021, 53, 1277–1283. [CrossRef]91. Khaibullina, K.S.; Korobov, G.Y.; Lekomtsev, A.V. Development of an asphalt-resin-paraffin deposits inhibitor and substantiation of the technological parameters of its injection into the bottom-hole formation zone. Period. Tche Quim. 2020, 17, 769–781. (In Portuguese) [CrossRef]92. Roshani, M.; Phan, G.; Roshani, G.H.; Hanus, R.; Nazemi, B.; Corniani, E.; Nazemi, E. Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil-water three phase flows. Measurement 2021, 168, 108427. [CrossRef]93. Tikhomirova, E.A.; Sagirova, L.R.; Khaibullina, K.S. A Review on Methods of Oil Saturation Modelling using IRAP RMS. In Proceedings of the International Conference on Innovations and Prospects of Development of Mining Machinery and ElectricalEngineering 2019, Saint-Petersburg, Russia, 24–27 April 2019; Volume 378. [CrossRef]94. 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]95. Mou, J.; Duan, P.; Gao, L.; Liu, X.; Li, J. An effective hybrid collaborative algorithm for energy-efficient distributed permutation flow-shop inverse scheduling. Future Gener. Comput. Syst. 2022, 128, 521–537. [CrossRef]96. Wang, Y.; Zou, R.; Liu, F.; Zhang, L.; Liu, Q. A review of wind speed and wind power forecasting with deep neural networks. Appl. Energy 2021, 304, 117766. [CrossRef]97. Hosseinzadeh-Bandbafha, H.; Nazemi, F.; Khounani, Z.; Ghanavati, H.; Shafiei, M.; Karimi, K.; Lam, S.S.; Aghbashlo, M.; Tabatabaei, M. Safflower-based biorefinery producing a broad spectrum of biofuels and biochemicals: A life cycle assessment perspective. Sci. Total Environ. 2021, 802, 149842. [CrossRef]98. Kang, Y.; Wu, Q. Pressure Calculation of Gas Hydrate in the Coastal Area of the Coastal Area Based on the Set Pair Analysis. J. Coast. Res. 2020, 103 (Suppl. 1), 1018–1021. [CrossRef]99. Nouriani, A.; Moradi, H. Smooth switching in power control of wind turbines using a combination strategy of hysteresis and modified middle regions. Sustain. Energy Technol. Assess. 2020, 37, 100585. [CrossRef]100. Yin, Q.; Li, C.; Dong, L.; Bai, X.; Zhang, Y.; Yang, M.; Jia, D.; Li, R.; Liu, Z. Effects of Physicochemical Properties of Different Base Oils on Friction Coefficient and Surface Roughness in MQL Milling AISI 1045. Int. J. Precis. Eng. Manuf.-Green Technol. 2021, 8, 1629–1647. [CrossRef]101. Nouriani, A.; Moradi, H. Variable speed wind turbine power control: A comparison between multiple MPPT based methods. Int. J. Dyn. Control. 2021, 1–14. [CrossRef]102. Ghanbari, B. Abundant exact solutions to a generalized nonlinear Schrödinger equation with local fractional derivative. Math. Methods Appl. Sci. 2021, 44, 8759–8774. [CrossRef]103. Kharazmi, O.; Jahangard, S. A new family of lifetime distributions in terms of cumulative hazard rate function. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2020, 69, 1–22. [CrossRef]104. Ghanbari, B. Chaotic behaviors of the prevalence of an infectious disease in a prey and predator system using fractional derivatives. Math. Methods Appl. Sci. 2021, 44, 9998–10013. [CrossRef]105. Kharazmi, O.; Saadatinik, A.; Jahangard, S. Odd hyperbolic cosine exponential-exponential (OHC-EE) distribution. Ann. Data Sci. 2019, 6 , 765–785. [CrossRef]106. Ghanbari, B. On novel nondifferentiable exact solutions to local fractional Gardner ’s equation using an effective technique. Math. Methods Appl. Sci. 2021, 44, 4673–4685. [CrossRef]107. Nabti, A.; Ghanbari, B. Global stability analysis of a fractional SVEIR epidemic model. Math. Methods Appl. Sci. 2021, 44, 8577–8597. [CrossRef]108. Ghanbari, B. A fractional system of delay differential equation with nonsingular kernels in modeling hand-foot-mouth disease. Adv. Differ. Equ. 2020, 2020, 536. [CrossRef]109. Nouriani, A.; McGovern, R.A.; Rajamani, R. Step Length Estimation Using Inertial Measurements Units. In Proceedings of the 2021 American Control Conference (ACC), virtual, 25–28 May 2021; pp. 666–671. 110. Ghanbari, B. On approximate solutions for a fractional prey-predator model involving the Atangana-Baleanu derivative. Adv. Differ. Equ. 2020, 2020, 679. [CrossRef]111. Rashidisabet, H.; Thomas, P.J.; Ajilore, O.; Zulueta, J.; Moore, R.C.; Leow, A. A systems biology approach to the digital behaviorome. Curr. Opin. Syst. Biol. 2020, 20, 8–16. [CrossRef]112. Sanaat, A.; Zafarghandi, M.S.; Ay, M.R. Design and performance evaluation of high resolution small animal PET scanner based on Monolithic Crystal: A Simulation Study. J. Instrum. 2019, 14, P01005. [CrossRef]113. Azmoodeh, M.R.; Keshavarz, A.; Batooei, A.; Saberinejad, H.; Payandeh Doost, M.; Keshtkar, H. Experimental study and thermal analysis of a Gamma type Stirling engine for multi-objective optimization. Automot. Sci. Eng. 2020, 10, 3281–3294.114. Sarhadi, M.; Aryan, L.; Zarei, M. The Estrogen Receptor and Breast Cancer: A Complete Review. CRPASE Trans. Appl. Sci. 2020, 6, 309–314. 115. Mostoufi, A.; Chamkouri, N.; Kordrostami, S.; Alghasibabaahmadi, E.; Mojaddami, A. 3-Hydroxypyrimidine-2, 4-dione deriva-tives as HIV Reverse Transcriptase-Associated RNase H Inhibitors: QSAR analysis and molecular dockingstudies. Iran. J. Pharm. Res. IJPR 2020, 19, 84.116. Pourbemany, J.; Essa, A.; Zhu, Y. Real Time Video based Heart and Respiration Rate Monitoring. arXiv 2021, arXiv:2106.02669.117. Hartman, E.J.; Keeler, J.D.; Kowalski, J.M. Layered neural networks with Gaussian hidden units as universal approxima-tors. Neural Comput. 1990,2, 210–215. [CrossRef]141311Pipeline’s scaleRBF neural networkTwo-phase flowOil and gasArtificial intelligencePublicationORIGINALApplying Data Mining and Artificial Intelligence Techniques.pdfApplying Data Mining and Artificial Intelligence Techniques.pdfapplication/pdf4047003https://repositorio.cuc.edu.co/bitstreams/15ec8ab8-f557-486d-b327-820b4db34b20/download15017b0027b01a0ea871b9a42b52caf1MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/0f89b28c-8433-407a-b429-f0c03816e3dd/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTApplying Data Mining and Artificial Intelligence Techniques.pdf.txtApplying Data Mining and Artificial Intelligence Techniques.pdf.txttext/plain62124https://repositorio.cuc.edu.co/bitstreams/02672acc-652a-40a2-aaa2-89c48c364b66/download8e58af71b908a134c26cd67520a5f6c5MD53THUMBNAILApplying Data Mining and Artificial Intelligence Techniques.pdf.jpgApplying Data Mining and Artificial Intelligence Techniques.pdf.jpgimage/jpeg16597https://repositorio.cuc.edu.co/bitstreams/92bb98dc-1997-4b98-940f-d3ba2bac4ec3/download445462a165e2a7d04f95b090ef7f30b5MD5411323/9119oai:repositorio.cuc.edu.co:11323/91192024-09-17 12:50:21.775https://creativecommons.org/licenses/by/4.0/© 2022 by the authors. Licensee MDPI, Basel, Switzerland.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares