Authors: Alekseyeva M.E., Alekseyev R.I.

Abstract. This article provides an overview of the evolution of bypass turbojet engines, examines the structure of modern commercial aircraft engines, the engineering solutions, materials and technologies used in their design. The study highlights the problems that arise when creating ultra-high bypass ratio engines, and also presents the prospects for their further energy efficiency increase. The authors conclude that the development of ultra-high bypass ratio aircraft engines, including propfan ones, is a key direction for achieving high efficiency, fuel economy and environmental friendliness of future aircraft propulsion systems.

Keywords: bypass turbojet engine, bypass ratio, power efficiency, turbofan aircraft engines, composites, propfan propulsion systems.

Authors: Isgandarov İ.A.,Karimov Y.A

Abstract: This paper explores the application of artificial intelligence (AI) systems to augment the information content and efficacy of remote-control operations for on-board systems in aircraft. With the aviation industry rapidly advancing towards automation and digitalization, there is a growing need for intelligent solutions that optimize remote control processes. This study investigates various AI techniques such as machine learning, deep learning, and natural language processing to analyze vast amounts of data generated by on-board systems. By leveraging AI, this research aims to enhance the efficiency, reliability, and safety of remote-control operations in aircraft. Additionally, the paper discusses challenges, potential benefits, and ethical considerations associated with implementing AI in aviation. Through a comprehensive examination of AI applications, this research contributes to advancing the integration of intelligent technologies in aircraft operations, paving the way for more autonomous and adaptive air transportation systems.

Keywords: Artificial Intelligence (AI), Aircraft Systems, Remote Control, Automation, Machine Learning, Deep Learning, Information Content Aviation, Safety, Efficiency.

Authors: Karipbayev S.J., Bimagambetov M.A. , Moldabekov A.K., Zhumadilov J.S., Azelkhanova J.A.

Abstract. The study of the motion of a solid body in electric and magnetic fields is closely related to many applied problems that arise in the development of new machines and devices in various fields of modern technology. The lack of clean smoothness of the rotor surface can lead to fluctuations in the accuracy of the gyroscope due to the non-spherical electrodes, displacement of the center of mass of the Rotor in the suspension, which occurs during overload and vibrations of the base, as well as in the absence of a zero-electrode filling the interelectrode space of the suspension. The expression of the nutation angle is taken as a function of time and the time constant of the decay process of the nutation oscillations of the Rotor is determined. Taking into account the aspherization of the Rotor, deviations for a specific gyroscope were revealed.

Keywords: electrostatic gyroscope, electrode, rotor aspherization, inertial forces.

Authors: Makarov V.V.

Abstract. The article deals with one of the important areas of application of uavs – thermal control of violations of hydro-insulation coating of heating mains. Mapping of the territory with heating mains in settlements can be performed by uav in automatic (overflight of the territory according to the set programme) or automated (UAV operator manually changes uav flight modes) modes. Such mapping or thermography in the specified modes is much more effective than the methods of heat pipelines bypassing by the control teams used nowadays. An approach for obtaining optimal trajectories of unmanned aerial vehicles (UAVs) during the formation of thermographic maps is proposed.
at the end of the paper it is noted that the estimation of the length of the uav motion trajectory and, consequently, of the flight time is given for the case of precise motion along a given trajectory without taking into account the influence of external factors: side wind, headwind, upward and downward air currents. It is possible to estimate the length of the perturbed trajectory for different permissible wind load using simulation modelling.

Keywords: unmanned aerial vehicle (UAV), ultralight unmanned aerial vehicle, thermographic maps, thermal imager, hazardous industrial facilities, heat network pipelines.

Authors: Akylbekova G.A., Donenko I.L.

Abstract. This paper presents an innovative fractal approach to the cultivation of agricultural land using unmanned aerial vehicles (UAVs). An approach to analyzing the earth’s surface using fractal analysis is considered, including the Fraunhofer diffraction fractal and Sierpinski carpet scaling. Code for Arduino is provided that allows you to collect data from sensors and send it in real time to a smartphone via Telegram. For more complex data analysis and integration with machine learning systems, the Raspberry Pi is proposed, with code for collecting data from a camera and LiDAR, as well as for recognizing crops using TensorFlow. The focus is on the ability to transmit data in real time, which allows you to quickly respond to changes and analyze large volumes of data on more powerful equipment or a server.

Key words: fractal, unmanned aerial vehicle, artificial intelligence, mathematical modeling, differential equation, agriculture.

Authors: Saidumarov I.M., Boatswain I.Zh.

Abstract. The paper creates a method for increasing the capacity of high-speed taxiways in order to increase the efficiency of the runway, taking into account the conditions of seasonal growth in the flow of aircraft, and develops a tool in the form of an algorithm and a software package.

Keywords: Airfield, runway, efficiency, aviation, airport, aircraft, throughput.

Authors: Anayatova R., Koshekov К.

Abstract. This study presents an innovative method for classifying emotional states through speech signals, leveraging advanced signal processing and machine learning techniques. The proposed method incorporates a multi-step approach, including feature extraction, selection, and classification. Initially, key acoustic features such as pitch, intensity, formants, and Mel-frequency cepstral coefficients (MFCCs) are extracted from the speech signals. Subsequently, feature selection techniques are applied to identify the most relevant features for distinguishing different emotional states. The classification is performed using a combination of supervised learning algorithms, including support vector machines (SVM), random forests, and neural networks. To evaluate the effectiveness of the developed method, a comprehensive dataset comprising various emotional speech recordings was utilized. The dataset included diverse emotional states such as happiness, sadness, anger, fear, and neutrality. The performance of the classification models was assessed using standard metrics such as accuracy, precision, recall.
Experimental results demonstrated that the proposed method achieved a high accuracy rate, outperforming existing state-of-the-art techniques. The neural network model, in particular, showed superior performance in capturing the nuances of emotional expressions in speech. Additionally, the feature selection process significantly enhanced the model’s efficiency by reducing computational complexity while maintaining high classification accuracy. In conclusion, the developed method provides a robust and effective solution for classifying emotional states from speech signals, with potential applications in fields such as human-computer interaction, mental health monitoring, and affective computing. Future work will focus on further refining the model by incorporating more diverse datasets and exploring real-time implementation possibilities.

Keywords: emotional state classification, speech signal, feature extraction, machine learning, neural networks, human-computer interaction, affective computing, AI.

Authors: Akimbay Sh.K.

Abstract. Annually, Kazakhstan generates about 5-7 million tons of pollutants, about one third of which are related to transport. In addition, air pollution in urban catchments of Kazakhstan is caused by pollutant emissions from metallurgical, oil refining and chemical enterprises, motor vehicles and railway transport. Toxic substances emitted by motor vehicles have a negative impact on the atmosphere, water bodies, soil and the Earth’s biosphere. Currently, a car engine consumes about 3 kg of atmospheric oxygen when burning one kiloliter of gasoline. Each car emits 60 m3 of gas per hour, and each truck – 120 m3. These substances are very dangerous for living organisms and can be a solution to the problem.
The purpose of this work is mathematical and numerical modeling of transfer processes in automobile neutralizers using modern programming languages and the latest computational technologies. The solution of such problems makes a special contribution to the problem of automobile exhaust gases. The task of exhaust gas treatment is to study the catalytic oxidation of exhaust gases in automobile neutralizers. The mathematical model is a system of differential equations. This system of equations is solved using a computer in an automotive neutralizer.

Key words: protection of the environment, exhaust gases, neutralizer, heat and mass transfer, numerical method.