{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T17:56:35Z","timestamp":1783619795893,"version":"3.55.0"},"reference-count":75,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>This article discusses the development of an enhanced monitoring and control system for helicopter turboshaft engines during flight operations, leveraging advanced neural network techniques. The research involves a comprehensive mathematical model that effectively simulates various failure scenarios, including single and cascading failure, such as disconnections of gas-generator rotor sensors. The model employs differential equations to incorporate time-varying coefficients and account for external disturbances, ensuring accurate representation of engine behavior under different operational conditions. This study validates the NARX neural network architecture with a backpropagation training algorithm, achieving 99.3% accuracy in fault detection. A comparative analysis of the genetic algorithms indicates that the proposed algorithm outperforms others by 4.19% in accuracy and exhibits superior performance metrics, including a lower loss. Hardware-in-the-loop simulations in Matlab Simulink confirm the effectiveness of the model, showing average errors of 1.04% and 2.58% at 15 \u00b0C and 24 \u00b0C, respectively, with high precision (0.987), recall (1.0), F1-score (0.993), and an AUC of 0.874. However, the model\u2019s accuracy is sensitive to environmental conditions, and further optimization is needed to improve computational efficiency and generalizability. Future research should focus on enhancing model adaptability and validating performance in real-world scenarios.<\/jats:p>","DOI":"10.3390\/jsan13050066","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T11:34:36Z","timestamp":1728560076000},"page":"66","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Helicopters Turboshaft Engines Neural Network Modeling under Sensor Failure"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8009-5254","authenticated-orcid":false,"given":"Serhii","family":"Vladov","sequence":"first","affiliation":[{"name":"Kharkiv National University of Internal Affairs, 27, L. 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Pract."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111721","DOI":"10.1016\/j.automatica.2024.111721","article-title":"Practical Prescribed-Time Active Fault-Tolerant Control for Mixed-Order Heterogeneous Multiagent Systems: A Fully Actuated System Approach","volume":"166","author":"Ma","year":"2024","journal-title":"Automatica"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vladov, S., Yakovliev, R., Vysotska, V., Nazarkevych, M., and Lytvyn, V. (2024). The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks. Appl. Syst. Innov., 7.","DOI":"10.3390\/asi7030053"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107624","DOI":"10.1016\/j.ymssp.2021.107624","article-title":"Robust Fault Estimation for a 3-DOF Helicopter Considering Actuator Saturation","volume":"155","author":"Zhu","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104836","DOI":"10.1016\/j.pnucene.2023.104836","article-title":"Recurrent Neural Network Based Sensor Fault Detection and Isolation for Nonlinear Systems: Application in PWR","volume":"163","author":"Kumar","year":"2023","journal-title":"Prog. Nucl. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"114872","DOI":"10.1016\/j.sna.2023.114872","article-title":"An Intelligent Online Fault Diagnosis System for Gas Turbine Sensors Based on Unsupervised Learning Method LOF and KELM","volume":"365","author":"Cheng","year":"2024","journal-title":"Sens. Actuators A Phys."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"012012","DOI":"10.1088\/1742-6596\/2472\/1\/012012","article-title":"Parameter Modelling of Fleet Gas Turbine Engines Using Gated Recurrent Neural Networks","volume":"2472","author":"Shuai","year":"2023","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e25120","DOI":"10.1016\/j.heliyon.2024.e25120","article-title":"Multi-Modal LSTM Network for Anomaly Prediction in Piston Engine Aircraft","volume":"2024 10","author":"Khattak","year":"2024","journal-title":"Heliyon"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.ifacol.2022.07.353","article-title":"Attention-Based LSTM for Remaining Useful Life Estimation of Aircraft Engines","volume":"55","author":"Boujamza","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.prostr.2021.12.050","article-title":"Highly Sensitive Methods for Vibration Diagnostics of Fatigue Damage in Structural Elements of Aircraft Gas Turbine Engines","volume":"35","author":"Bovsunovsky","year":"2022","journal-title":"Procedia Struct. Integr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/S1000-9361(08)60064-3","article-title":"Aircraft Engine Sensor Fault Diagnostics Based on Estimation of Engine\u2019s Health Degradation","volume":"22","author":"Wei","year":"2009","journal-title":"Chin. J. Aeronaut."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103645","DOI":"10.1016\/j.advengsoft.2024.103645","article-title":"Aircraft Engine Remaining Useful Life Prediction Using Neural Networks and Real-Life Engine Operational Data","volume":"192","author":"Szrama","year":"2024","journal-title":"Adv. Eng. Softw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"120520","DOI":"10.1016\/j.apenergy.2022.120520","article-title":"A Hierarchical Structure Built on Physical and Data-Based Information for Intelligent Aero-Engine Gas Path Diagnostics","volume":"332","author":"Zhao","year":"2023","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.ifacol.2022.07.213","article-title":"Fault-Tolerant Control for a High Altitude Long Endurance Aircraft","volume":"55","author":"Weiser","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111502","DOI":"10.1016\/j.measurement.2022.111502","article-title":"Measurement of Health Evolution Tendency for Aircraft Engine Using a Data-Driven Method Based on Multi-Scale Series Reconstruction and Adaptive Hybrid Model","volume":"199","author":"Jiang","year":"2022","journal-title":"Measurement"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Skarka, W., Nalepa, R., and Musik, R. (2023). Integrated Aircraft Design System Based on Generative Modelling. Aerospace, 10.","DOI":"10.3390\/aerospace10080677"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.trpro.2023.12.008","article-title":"The Application of Internet of Things in Air Transport","volume":"75","year":"2023","journal-title":"Transp. Res. Procedia"},{"key":"ref_21","first-page":"514","article-title":"An Efficient Cloud Prognostic Approach for Aircraft Engines Fleet Trending","volume":"42","author":"Bouzidi","year":"2018","journal-title":"Int. J. Comput. Appl."},{"key":"ref_22","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_23","first-page":"124","article-title":"Hybrid Neural Network Identifying Complex Dynamic Objects: Comprehensive Modelling and Training Method Modification","volume":"3702","author":"Vysotska","year":"2024","journal-title":"CEUR Workshop Proc."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Vladov, S., Shmelov, Y., and Yakovliev, R. (2022, January 3\u20137). Modified Searchless Method for Identification of Helicopters Turboshaft Engines at Flight Modes Using Neural Networks. Proceedings of the 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine.","DOI":"10.1109\/KhPIWeek57572.2022.9916422"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100693","DOI":"10.1016\/j.paerosci.2020.100693","article-title":"Gas Turbine Aero-Engines Real Time on-Board Modelling: A Review, Research Challenges, and Exploring the Future","volume":"121","author":"Wei","year":"2020","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isatra.2023.06.020","article-title":"Dual-Frequency Enhanced Attention Network for Aircraft Engine Remaining Useful Life Prediction","volume":"141","author":"Yang","year":"2023","journal-title":"ISA Trans."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.eswa.2014.08.007","article-title":"Bayesian Hierarchical Models for Aerospace Gas Turbine Engine Prognostics","volume":"42","author":"Zaidan","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"115700","DOI":"10.1016\/j.applthermaleng.2020.115700","article-title":"Thermo-Mechanical Analysis and Estimation of Turbine Blade Tip Clearance of a Small Gas Turbine Engine under Transient Operating Conditions","volume":"179","author":"Kumar","year":"2020","journal-title":"Appl. Therm. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.isatra.2022.01.032","article-title":"Periodic Analysis on Gas Path Fault Diagnosis of Gas Turbines","volume":"129","author":"Zhou","year":"2022","journal-title":"ISA Trans."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7722","DOI":"10.1016\/j.jfranklin.2023.06.011","article-title":"Prescribed Performance Control for Nonlinear Parameter-Varying Systems with an Application to Turbofan Engine","volume":"360","author":"Zhu","year":"2023","journal-title":"J. Frankl. Inst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vladov, S., Kovtun, V., Sokurenko, V., Muzychuk, O., and Vysotska, V. (2024). The Helicopter Turboshaft Engine\u2019s Reconfigured Dynamic Model for Functional Safety Estimation. Electronics, 13.","DOI":"10.3390\/electronics13173477"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"120700","DOI":"10.1016\/j.energy.2021.120700","article-title":"Nonlinear Generalized Predictive Controller Based on Ensemble of NARX Models for Industrial Gas Turbine Engine","volume":"230","author":"Ibrahem","year":"2021","journal-title":"Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.isatra.2021.03.019","article-title":"Fault Detection and Isolation of Gas Turbine Using Series\u2013Parallel NARX Model","volume":"120","author":"Amirkhani","year":"2022","journal-title":"ISA Trans."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"130512","DOI":"10.1016\/j.energy.2024.130512","article-title":"Model-Based Deduction Learning Control: A Novel Method for Optimizing Gas Turbine Engine Afterburner Transient","volume":"292","author":"Feng","year":"2024","journal-title":"Energy"},{"key":"ref_35","first-page":"97","article-title":"Development of the method of prediction of content of explosive gases in mines","volume":"1","author":"Fedorov","year":"2015","journal-title":"Sci. Pap. Donetsk Natl. Tech. Univ. Ser. Inform. Cybern. Comput. Sci."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Vladov, S., Scislo, L., Sokurenko, V., Muzychuk, O., Vysotska, V., Osadchy, S., and Sachenko, A. (2024). Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions. Sensors, 24.","DOI":"10.3390\/s24134246"},{"key":"ref_38","first-page":"179","article-title":"Neural Network Method for Parametric Adaptation Helicopters Turboshaft Engines On-Board Automatic Control System Parameters","volume":"3403","author":"Vladov","year":"2023","journal-title":"CEUR Workshop Proc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"152","DOI":"10.5395\/rde.2017.42.2.152","article-title":"Statistical Notes for Clinical Researchers: Chi-Squared Test and Fisher\u2019s Exact Test","volume":"42","author":"Kim","year":"2017","journal-title":"Restor. Dent. Endod."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2407","DOI":"10.1109\/OJCOMS.2022.3224835","article-title":"Second Order Statistics of -Fisher-Snedecor Distribution and Their Application to Burst Error Rate Analysis of Multi-Hop Communications","volume":"3","author":"Stefanovic","year":"2022","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_41","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_42","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_43","first-page":"65","article-title":"Micro Gas Turbine Engine Imitation Model","volume":"16","author":"Kuznetsov","year":"2017","journal-title":"Aerosp. Mech. Eng."},{"key":"ref_44","first-page":"122","article-title":"Development of semi-naturalistic modeling stands for studying the gas turbine engines automatic control systems","volume":"7","author":"Denisova","year":"2019","journal-title":"Mod. High Technol."},{"key":"ref_45","first-page":"1639","article-title":"Optimization of Helicopters Aircraft Engine Working Process Using Neural Networks Technologies","volume":"3171","author":"Vladov","year":"2022","journal-title":"CEUR Workshop Proc."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"129118","DOI":"10.1016\/j.energy.2023.129118","article-title":"Digital Twin for Electronic Centralized Aircraft Monitoring by Machine Learning Algorithms","volume":"283","author":"Kilic","year":"2023","journal-title":"Energy"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Barthorpe, R.J., and Worden, K. (2020). Emerging Trends in Optimal Structural Health Monitoring System Design: From Sensor Placement to System Evaluation. J. Sens. Actuator Netw., 9.","DOI":"10.3390\/jsan9030031"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1007\/978-3-031-61221-3_36","article-title":"Federated Learning: A Solution for Improving Anomaly Detection Accuracy of Autonomous Guided Vehicles in Smart Manufacturing","volume":"1198","author":"Shubyn","year":"2024","journal-title":"Lect. Notes Electr. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/978-3-030-92435-5_28","article-title":"Application Peculiarities of Deep Learning Methods in the Problem of Big Datasets Classification","volume":"831","author":"Rusyn","year":"2021","journal-title":"Lect. Notes Electr. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"109180","DOI":"10.1016\/j.ast.2024.109180","article-title":"Research on Adaptive Feedforward Control Method for Tiltrotor Aircraft\/Turboshaft Engine System Based on Radial Basis Function Neural Network","volume":"150","author":"Li","year":"2024","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106486","DOI":"10.1016\/j.engappai.2023.106486","article-title":"A Novel Inter-Domain Attention-Based Adversarial Network for Aero-Engine Partial Unsupervised Cross-Domain Fault Diagnosis","volume":"123","author":"Wang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"106496","DOI":"10.1016\/j.engappai.2023.106496","article-title":"An Adaptive Matching Control Method of Multiple Turboshaft Engines","volume":"123","author":"Wang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_53","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_54","first-page":"60","article-title":"Processes of managing information infrastructure of a digital enterprise in the framework of the \u00abIndustry 4.0\u00bb concept","volume":"1","author":"Andriushchenko","year":"2019","journal-title":"East. Eur. J. Enterp. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"11","DOI":"10.47839\/ijc.19.1.1688","article-title":"Score fusion of finger vein and face for human recognition based on convolutional neural network model","volume":"19","author":"Cherrat","year":"2020","journal-title":"Int. J. Comput."},{"key":"ref_56","first-page":"16","article-title":"Augmenting Sentiment Analysis Prediction in Binary Text Classification through Advanced Natural Language Processing Models and Classifiers","volume":"16","author":"Hu","year":"2024","journal-title":"Int. J. Inf. Technol. Comput. Sci."},{"key":"ref_57","first-page":"628","article-title":"Neuro-Fuzzy System for Detection Fuel Consumption of Helicopters Turboshaft Engines","volume":"3628","author":"Vladov","year":"2023","journal-title":"CEUR Workshop Proc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e24708","DOI":"10.1016\/j.heliyon.2024.e24708","article-title":"Entropy-Metric Estimation of the Small Data Models with Stochastic Parameters","volume":"10","author":"Kovtun","year":"2024","journal-title":"Heliyon"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kovtun, V., Grochla, K., and Po\u0142ys, K. (2023). Investigation of the Information Interaction of the Sensor Network End IoT Device and the Hub at the Transport Protocol Level. Electronics, 12.","DOI":"10.3390\/electronics12224662"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"100396","DOI":"10.1016\/j.egyai.2024.100396","article-title":"Gas Exchange Optimization in Aircraft Engines Using Sustainable Aviation Fuel: A Design of Experiment and Genetic Algorithm Approach","volume":"17","author":"Xu","year":"2024","journal-title":"Energy AI"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"101226","DOI":"10.1016\/j.measen.2024.101226","article-title":"Coupling Mechanical Model and Failure of Aeroengine Ceramic Matrix Composite Based on Genetic Algorithm","volume":"33","author":"Feng","year":"2024","journal-title":"Meas. Sens."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.paerosci.2018.11.003","article-title":"Meta-Heuristic Global Optimization Algorithms for Aircraft Engines Modelling and Controller Design; A Review, Research Challenges, and Exploring the Future","volume":"104","author":"Jafari","year":"2019","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Khan, N., Abdi, S.A.A., Khan, T.A., and Rizvi, S.S.A. (2023). Minimization of High Maintenance Cost and Hazard Emissions Related to Aviation Engines: An Implementation of Functions Optimizations by Using Genetic Algorithm for Better Performance. Eng. Proc., 46.","DOI":"10.3390\/engproc2023046011"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/S0965-9978(03)00100-5","article-title":"Optimal Design of an Aircraft Engine Mount via Bit-Masking Oriented Genetic Algorithms","volume":"34","author":"Iuspa","year":"2003","journal-title":"Adv. Eng. Softw."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"137427","DOI":"10.1109\/ACCESS.2024.3414651","article-title":"Rethinking Deep CNN Training: A Novel Approach for Quality-Aware Dataset Optimization","volume":"12","author":"Rusyn","year":"2024","journal-title":"IEEE Access"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Vladov, S., Shmelov, Y., Yakovliev, R., Petchenko, M., and Drozdova, S. (2022, January 20\u201322). Neural Network Method for Helicopters Turboshaft Engines Working Process Parameters Identification at Flight Modes. Proceedings of the 2022 IEEE 4th International Conference on Modern Electrical and Energy System (MEES), Kremenchuk, Ukraine.","DOI":"10.1109\/MEES58014.2022.10005670"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"48","DOI":"10.32620\/reks.2023.3.05","article-title":"Helicopter Radio System for Low Altitudes and Flight Speed Measuring with Pulsed Ultra-Wideband Stochastic Sounding Signals and Artificial Intelligence Elements","volume":"3","author":"Vlasenko","year":"2023","journal-title":"Radioelectron. Comput. Syst."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"de Voogt, A., and Nero, K. (2023). Technical Failures in Helicopters: Non-Powerplant-Related Accidents. Safety, 9.","DOI":"10.3390\/safety9010010"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"105169","DOI":"10.1016\/j.ssci.2021.105169","article-title":"Safety of Twin-Engine Helicopters: Risks and Operational Specificity","volume":"136","year":"2021","journal-title":"Saf. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wei, Y., Chen, R., Yuan, Y., and Wang, L. (2023). Influence of Engine Dynamic Characteristics on Helicopter Handling Quality in Hover and Low-Speed Forward Flight. Aerospace, 11.","DOI":"10.3390\/aerospace11010034"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1007\/s12555-021-0368-6","article-title":"Modeling of Operation of Information System for Critical Use in the Conditions of Influence of a Complex Certain Negative Factor","volume":"20","author":"Bisikalo","year":"2022","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"8","DOI":"10.47839\/ijc.18.1.1270","article-title":"A deep convolutional auto-encoder with pooling\u2013Unpooling layers in caffe","volume":"1","author":"Turchenko","year":"2019","journal-title":"Int. J. Comput."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.eij.2022.11.002","article-title":"A Computationally Efficient Method for Assessing the Impact of an Active Viral Cyber Threat on a High-Availability Cluster","volume":"24","author":"Altameem","year":"2023","journal-title":"Egypt. Inform. J."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"200146","DOI":"10.1016\/j.sasc.2024.200146","article-title":"Modeling and Control Strategy of Small Unmanned Helicopter Rotation Based on Deep Learning","volume":"6","author":"Xia","year":"2024","journal-title":"Syst. Soft Comput."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/13\/5\/66\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:10:44Z","timestamp":1760112644000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/13\/5\/66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"references-count":75,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["jsan13050066"],"URL":"https:\/\/doi.org\/10.3390\/jsan13050066","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,10]]}}}