{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:47:45Z","timestamp":1780512465775,"version":"3.54.1"},"reference-count":72,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T00:00:00Z","timestamp":1719619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Internal Affairs of Ukraine \u201cTheoretical and applied aspects of the development of the aviation sphere\u201d","award":["0123U104884"],"award-info":[{"award-number":["0123U104884"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The article\u2019s main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies.<\/jats:p>","DOI":"10.3390\/s24134246","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T10:14:46Z","timestamp":1719828886000},"page":"4246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8009-5254","authenticated-orcid":false,"given":"Serhii","family":"Vladov","sequence":"first","affiliation":[{"name":"Department of Scientific Work Organization and Gender Issues, Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17\/6 Peremohy Street, 39605 Kremenchuk, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7365-8888","authenticated-orcid":false,"given":"Lukasz","family":"Scislo","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Crak\u00f3w, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valerii","family":"Sokurenko","sequence":"additional","affiliation":[{"name":"Kharkiv National University of Internal Affairs, Ministry of Internal Affairs of Ukraine, 61080 Kharkiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleksandr","family":"Muzychuk","sequence":"additional","affiliation":[{"name":"Kharkiv National University of Internal Affairs, Ministry of Internal Affairs of Ukraine, 61080 Kharkiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6417-3689","authenticated-orcid":false,"given":"Victoria","family":"Vysotska","sequence":"additional","affiliation":[{"name":"Information Systems and Networks Department, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine"},{"name":"Institute of Computer Science, Osnabr\u00fcck University, 1 Friedrich-Janssen-Street, 49076 Osnabr\u00fcck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serhii","family":"Osadchy","sequence":"additional","affiliation":[{"name":"Flight Operation and Flight Safety Department, Flight Academy of the National Aviation University, 1 Chobanu Stepana Street, 25005 Kropyvnytskyi, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0907-3682","authenticated-orcid":false,"given":"Anatoliy","family":"Sachenko","sequence":"additional","affiliation":[{"name":"Research Institute for Intelligent Computer Systems, West Ukrainian National University, 11 Lvivska Street, 46009 Ternopil, Ukraine"},{"name":"Department of Teleinformatics, Kazimierz Pulaski University of Radom, 29, Malczewskiego Street, 26-600 Radom, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, S., Ma, A., Zhang, T., Ge, N., and Huang, X. (2024). A Performance Simulation Methodology for a Whole Turboshaft Engine Based on Throughflow Modelling. Energies, 17.","DOI":"10.20944\/preprints202401.0009.v1"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gu, Z., Pang, S., Zhou, W., Li, Y., and Li, Q. (2022). An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines. Energies, 15.","DOI":"10.3390\/en15041255"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108102","DOI":"10.1016\/j.ast.2022.108102","article-title":"Diagnostics using a physics-based engine model in aero gas turbine engine verification tests","volume":"133","author":"Kim","year":"2023","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"122954","DOI":"10.1016\/j.applthermaleng.2024.122954","article-title":"A digital twin approach for gas turbine performance based on deep multi-model fusion","volume":"246","author":"Zhang","year":"2024","journal-title":"Appl. Therm. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Catana, R.M., and Badea, G.P. (2023). Experimental Analysis on the Operating Line of Two Gas Turbine Engines by Testing with Different Exhaust Nozzle Geometries. Energies, 16.","DOI":"10.3390\/en16155627"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"121644","DOI":"10.1016\/j.energy.2021.121644","article-title":"Application of genetic algorithm in exergy and sustainability: A case of aero-gas turbine engine at cruise phase","volume":"238","author":"Aygun","year":"2022","journal-title":"Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.cja.2023.09.024","article-title":"Intelligent fault diagnosis methods toward gas turbine: A review","volume":"37","author":"Liu","year":"2024","journal-title":"Chin. J. Aeronaut."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103968","DOI":"10.1016\/j.engappai.2020.103968","article-title":"Group reduced kernel extreme learning machine for fault diagnosis of aircraft engine","volume":"96","author":"Li","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"127593","DOI":"10.1016\/j.energy.2023.127593","article-title":"Exergetic, sustainability and environmental assessments of a turboshaft engine used on helicopter","volume":"276","author":"Balli","year":"2023","journal-title":"Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"126487","DOI":"10.1016\/j.energy.2022.126487","article-title":"Thermodynamic, sustainability, environmental and damage cost analyses of jet fuel starter gas turbine engine","volume":"267","author":"Abdalla","year":"2023","journal-title":"Energy"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Castiglione, T., Perrone, D., Strafella, L., Ficarella, A., and Bova, S. (2023). Linear Model of a Turboshaft Aero-Engine Including Components Degradation for Control-Oriented Applications. Energies, 16.","DOI":"10.3390\/en16062634"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105149","DOI":"10.1016\/j.engappai.2022.105149","article-title":"Gas path fault diagnosis of aircraft engine using HELM and transfer learning","volume":"114","author":"Liu","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109263","DOI":"10.1016\/j.ast.2024.109263","article-title":"Low-emission optimization control method for coaxial compound helicopter\/engine based on variable geometry adjustment","volume":"151","author":"Song","year":"2024","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"129934","DOI":"10.1016\/j.energy.2023.129934","article-title":"Design and implementation for the state time-delay and input saturation compensator of gas turbine aero-engine control system","volume":"288","author":"Liu","year":"2024","journal-title":"Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"121523","DOI":"10.1016\/j.applthermaleng.2023.121523","article-title":"Gas turbine engine transient performance and heat transfer effect modelling: A comprehensive review, research challenges, and exploring the future","volume":"236","author":"Yang","year":"2024","journal-title":"Appl. Therm. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"127951","DOI":"10.1016\/j.energy.2023.127951","article-title":"A novel combined model for energy consumption performance prediction in the secondary air system of gas turbine engines based on flow resistance network","volume":"280","author":"Gong","year":"2023","journal-title":"Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119863","DOI":"10.1016\/j.energy.2021.119863","article-title":"A new performance adaptation method for aero gas turbine engines based on large amounts of measured data","volume":"221","author":"Kim","year":"2021","journal-title":"Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cja.2017.11.017","article-title":"Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system","volume":"31","author":"Hanachi","year":"2018","journal-title":"Chin. J. Aeronaut."},{"key":"ref_19","first-page":"554","article-title":"Modeling, Simulation and Validation of Mini SR-30 Gas Turbine Engine","volume":"51","author":"Singh","year":"2018","journal-title":"IFAC-Pap."},{"key":"ref_20","first-page":"64","article-title":"Diagnostic methods for an aircraft engine performance","volume":"8","author":"Ntantis","year":"2015","journal-title":"J. Eng. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106983","DOI":"10.1016\/j.ast.2021.106983","article-title":"Improved nonlinear MPC for aircraft gas turbine engine based on semi-alternative optimization strategy","volume":"118","author":"Pang","year":"2021","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_22","first-page":"48","article-title":"An Ensemble Learning-Based Remaining Useful Life Prediction Method for Aircraft Turbine Engine","volume":"53","author":"Zeng","year":"2020","journal-title":"IFAC-Pap."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zaletin, V.V., Savitsky, O.A., Silnikov, M.V., Sorokovikov, V.N., and Yakushenko, E.I. (2024). Acoustic emission diagnostics of a hull structures by a system of integrating fiber-optic sensors for the aircraft and spacecraft safe operation. Acta Astronaut., in press.","DOI":"10.1016\/j.actaastro.2024.02.001"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1016\/j.procir.2022.05.150","article-title":"Dynamic Partial Reconfiguration for Adaptive Sensor Integration in Highly Flexible Manufacturing Systems","volume":"107","author":"Schade","year":"2022","journal-title":"Procedia CIRP"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"062901","DOI":"10.1115\/1.4055941","article-title":"Numerical Investigation of the Intercooler Performance of Aircraft Piston Engines Under the Influence of High Altitude and Cruise Mode","volume":"145","author":"Sun","year":"2023","journal-title":"ASME J. Heat Mass Transf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112307","DOI":"10.1115\/1.4054287","article-title":"Machine Learning Assisted Analysis of an Ammonia Engine Performance","volume":"144","author":"Liu","year":"2022","journal-title":"J. Energy Resour. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Avrunin, O.G., Nosova, Y.V., Abdelhamid, I.Y., Pavlov, S.V., Shushliapina, N.O., W\u00f3jcik, W., Kisa\u0142a, P., and Kalizhanova, A. (2021). Possibilities of Automated Diagnostics of Odontogenic Sinusitis According to the Computer Tomography Data. Possibilities of Automated Diagnostics of Odontogenic Sinusitis According to the Computer Tomography Data. Sensors, 21.","DOI":"10.3390\/s21041198"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Baranovskyi, D., Bulakh, M., Michaj\u0142yszyn, A., Myamlin, S., and Muradian, L. (2023). Determination of the Risk of Failures of Locomotive Diesel Engines in Maintenance. Energies, 16.","DOI":"10.3390\/en16134995"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107031","DOI":"10.1016\/j.ast.2021.107031","article-title":"Unilateral alignment transfer neural network for fault diagnosis of aircraft engine","volume":"118","author":"Li","year":"2021","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106333","DOI":"10.1016\/j.ast.2020.106333","article-title":"An improved hybrid modeling method based on extreme learning machine for gas turbine engine","volume":"107","author":"Xu","year":"2020","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"131264","DOI":"10.1016\/j.energy.2024.131264","article-title":"A backpropagation neural network-based hybrid energy recognition and management system","volume":"297","author":"Zhu","year":"2024","journal-title":"Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/978-3-031-04812-8_29","article-title":"GEOCLUS: A Fuzzy-Based Learning Algorithm for Clustering Expression Datasets","volume":"134","author":"Hu","year":"2022","journal-title":"Lect. Notes Data Eng. Commun. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104900","DOI":"10.1016\/j.engappai.2022.104900","article-title":"Micro Gas Turbine fault detection and isolation with a combination of Artificial Neural Network and off-design performance analysis","volume":"113","author":"Talebi","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lytvynenko, V., Nikytenko, D., Voronenko, M., Savina, N., and Naumov, O. (2020, January 23\u201326). Assessing the Possibility of a Country\u2019s Economic Growth Using Dynamic Bayesian Network Models. Proceedings of the 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), Zbarazh, Ukraine.","DOI":"10.1109\/CSIT49958.2020.9321995"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"19655","DOI":"10.1038\/s41598-023-46785-7","article-title":"Features extraction from multi-spectral remote sensing images based on multi-threshold binarization","volume":"13","author":"Rusyn","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5736","DOI":"10.1038\/s41598-023-33015-3","article-title":"The criterion of development of processes of the self organization of subsystems of the second level in tribosystems of diesel engine","volume":"13","author":"Baranovskyi","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_37","unstructured":"Sachenko, A., Kochan, V., Turchenko, V., Tymchyshyn, V., and Vasylkiv, N. (1999, January 24\u201326). Intelligent nodes for distributed sensor network. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (IMTC\/99), Venice, Italy."},{"key":"ref_38","first-page":"19","article-title":"Development of a technique for the reconstruction and validation of gene network models based on gene expression","volume":"1","author":"Babichev","year":"2018","journal-title":"East. -Eur. J. Enterp. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.neunet.2020.07.001","article-title":"Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines","volume":"130","author":"Shen","year":"2020","journal-title":"Neural Netw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.55248\/gengpi.2022.3.8.55","article-title":"A Review-Differentiating TV2 and TV3 Series Turbo Shaft Engines","volume":"3","author":"Gebrehiwet","year":"2022","journal-title":"Int. J. Res. Publ. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Catana, R.M., and Dediu, G. (2023). Analytical Calculation Model of the TV3-117 Turboshaft Working Regimes Based on Experimental Data. Appl. Sci., 13.","DOI":"10.3390\/app131910720"},{"key":"ref_42","first-page":"97","article-title":"Control and Diagnostics of TV3-117 Aircraft Engine Technical State in Flight Modes Using the Matrix Method for Calculating Dynamic Recurrent Neural Networks","volume":"2864","author":"Vladov","year":"2021","journal-title":"CEUR Workshop Proc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1016\/S0925-2312(02)00412-5","article-title":"IQR: A distributed system for real-time real-world neuronal simulation","volume":"44\u201346","author":"Bernardet","year":"2002","journal-title":"Neurocomputing"},{"key":"ref_44","first-page":"71","article-title":"Adaptive control of a gas turbine plant with a reference model and signal tuning","volume":"2","author":"Bahirev","year":"2015","journal-title":"Control Syst. Inf. Technol."},{"key":"ref_45","first-page":"28","article-title":"Helicopters Aircraft Engines Self-Organizing Neural Network Automatic Control System","volume":"3137","author":"Vladov","year":"2022","journal-title":"CEUR Workshop Proc."},{"key":"ref_46","first-page":"32","article-title":"Design of intelligent control systems based on the principle of minimum complexity","volume":"9","author":"Vasiliev","year":"2007","journal-title":"Bull. USATU"},{"key":"ref_47","first-page":"118","article-title":"Adaptive control of a gas turbine plant with a reference model and a sigmoid function","volume":"3","author":"Bahirev","year":"2015","journal-title":"Control Syst. Inf. Technol."},{"key":"ref_48","first-page":"40","article-title":"Application of radial basis function networks for interpolating the equation factors of a gas turbine unit model","volume":"1","author":"Bahirev","year":"2014","journal-title":"Innov. Process. Res. Educ. Act."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"110157","DOI":"10.1016\/j.ymssp.2023.110157","article-title":"Sensor dynamic compensation method based on GAN and its application in shockwave measurement","volume":"190","author":"Wang","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.cja.2018.10.002","article-title":"Efficiency optimized fuel supply strategy of aircraft engine based on air-fuel ratio control","volume":"19","author":"Wang","year":"2019","journal-title":"Chin. J. Aeronaut."},{"key":"ref_51","first-page":"57","article-title":"Development of a method for structural optimization of a neural network based on the criterion of resource utilization efficiency","volume":"2","author":"Lutsenko","year":"2019","journal-title":"East. -Eur. J. Enterp. Technol."},{"key":"ref_52","first-page":"127470","article-title":"Improved Adaptive Fuzzy Control for Non-Strict Feedback Nonlinear Systems: A Dynamic Compensation System Approach","volume":"435","author":"Wu","year":"2022","journal-title":"Appl. Math. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.engappai.2008.07.004","article-title":"A direct adaptive neural command controller design for an unstable helicopter","volume":"22","author":"Suresh","year":"2009","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.neucom.2021.03.060","article-title":"Discrete-time super-twisting controller using neural networks","volume":"447","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_55","first-page":"179","article-title":"Neural Network Method for Parametric Adaptation Helicopters Turboshaft Engines On-Board Automatic Control","volume":"3403","author":"Vladov","year":"2023","journal-title":"CEUR Workshop Proc."},{"key":"ref_56","unstructured":"Widrow, B., and Stearns, D.S. (1985). Adaptive Signal Processing, Prentice-Hall Inc."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111551","DOI":"10.1016\/j.ymssp.2024.111551","article-title":"Aircraft engine remaining useful life prediction: A comparison study of Kernel Adaptive Filtering architectures","volume":"218","author":"Karatzinis","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"103160","DOI":"10.1016\/j.advengsoft.2022.103160","article-title":"Application of He\u2019s homotopy and perturbation method to solve heat transfer equations: A python approach","volume":"170","author":"Dumka","year":"2022","journal-title":"Adv. Eng. Softw."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Vladov, S., Yakovliev, R., Bulakh, M., and Vysotska, V. (2024). Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency. Energies, 17.","DOI":"10.3390\/en17092233"},{"key":"ref_60","first-page":"116","article-title":"Neural Network Modeling of Helicopters Turboshaft Engines at Flight Modes Using an Approach Based on \u201cBlack Box\u201d Models","volume":"3624","author":"Vladov","year":"2024","journal-title":"CEUR Workshop Proc."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Vladov, S., Shmelov, Y., and Yakovliev, R. (2022, January 20\u201322). Modified Method of Identification Potential Defects in Helicopters Turboshaft Engines Units Based on Prediction its Operational Status. Proceedings of the 2022 IEEE 4th International Conference on Modern Electrical and Energy System (MEES), Kremenchuk, Ukraine.","DOI":"10.1109\/MEES58014.2022.10005605"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Corotto, F.S. (2023). Appendix C\u2014The method attributed to Neyman and Pearson. Wise Use Null Hypothesis Tests, Academic Press.","DOI":"10.1016\/B978-0-323-95284-2.00012-4"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"14","DOI":"10.31767\/su.4(83)2018.04.02","article-title":"Analysis of Nonparametric and Parametric Criteria for Statistical Hypotheses Testing. Chapter 1. Agreement Criteria of Pearson and Kolmogorov","volume":"4","author":"Motsnyi","year":"2018","journal-title":"Stat. Ukr."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Babichev, S., Krejci, J., Bicanek, J., and Lytvynenko, V. (2017, January 5\u20138). Gene expression sequences clustering based on the internal and external clustering quality criteria. Proceedings of the 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine.","DOI":"10.1109\/STC-CSIT.2017.8098744"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"70","DOI":"10.31891\/1727-6209\/2020\/19\/1-70-77","article-title":"Deep multilayer neural network for predicting the winner of football matches","volume":"19","author":"Anfilets","year":"2020","journal-title":"Int. J. Comput."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"27","DOI":"10.47839\/ijc.16.1.868","article-title":"Simulation modeling of fuzzy logic controller for aircraft engines","volume":"16","author":"Pasieka","year":"2017","journal-title":"Int. J. Comput."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.patrec.2023.07.001","article-title":"Efficient 2D Tikhonov smoothness regularization with recursive filtering","volume":"175","author":"Ferreira","year":"2023","journal-title":"Pattern Recognit. Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"108302","DOI":"10.1016\/j.sigpro.2021.108302","article-title":"Sensor network data denoising via recursive graph median filters","volume":"189","author":"Tay","year":"2021","journal-title":"Signal Process."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"108952","DOI":"10.1016\/j.sigpro.2023.108952","article-title":"Robust kernel recursive adaptive filtering algorithms based on M-estimate","volume":"207","author":"Yang","year":"2023","journal-title":"Signal Process."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"120516","DOI":"10.1016\/j.neuroimage.2024.120516","article-title":"A unified filtering method for estimating asymmetric orientation distribution functions","volume":"287","author":"Poirier","year":"2024","journal-title":"NeuroImage"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"105111","DOI":"10.1016\/j.jappgeo.2023.105111","article-title":"An inverse Q filtering method with adjustable amplitude compensation operator","volume":"215","author":"Zhao","year":"2023","journal-title":"J. Appl. Geophys."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.camwa.2023.10.017","article-title":"A filtered Chebyshev spectral method for conservation laws on network","volume":"151","author":"Pellegrino","year":"2023","journal-title":"Comput. Math. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4246\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:07:59Z","timestamp":1760108879000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4246"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,29]]},"references-count":72,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24134246"],"URL":"https:\/\/doi.org\/10.3390\/s24134246","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,29]]}}}