Ulyanovsk State Technical University (Russian: Ульяновский государственный технический университет, abbreviated as UlSTU) is a major technical higher education and research institution in Ulyanovsk. Founded in 1957 as Ulyanovsk Polytech University, the university is located in the Volga River region.Ulyanovsk State Technical University (UlSTU) is one of the leading science centers of the Volga region that trains specialists in various areas, including economy, humanities, technical sciences, etc.The university is well known in Russia for its research-and-development activities; graduates of UlSTU are employed by a large variety of companies. Today Ulyanovsk State Technical University has over 12,000 students in different educational programs at 12 faculties and 48 departments; more than 500 tutors, 42 Ph.D professors and 250 Ph.D associate professors; scientific and research works and papers awarded in Paris, Brussels etc.; and one of the biggest concert halls in the Middle Volga region.The University maintains firm contacts with more than 60 state, public and educational organizations of many countries: universities, research centers, foundations. UlSTU closely cooperates with such educational and scientific centers as Darmstadt University of Applied Sciences, Germany; Durham University, Great Britain; University of Göttingen, Germany.Sporting facilities, a calendar of social events and a reputation for teaching makes UlSTU a place to study. Students from Ukraine, Kazakhstan, Germany, China, Vietnam, Turkey, Ghana, Cameroon, and Nigeria successfully study at the University..
An innovative sixth algebraic order technique of the Adams-Bashforth-Moulton predictor–corrector–corrector type is introduced in this paper. The innovative method allows for the accurate integration of any set of functions [ 1, x, x^2, x^3, x^4, e^I v x] . The new method’s stability zones have been drawn. This new approach was used in quantum chemistry to solve the difficult Schrödinger-type coupled differential equations problem. Other related problems are also addressed with the novel method.
Background. The quality of the functioning of radar equipment largely determines the effectiveness of aerospace defense equipment and is often determined by the state of the power amplifier unit, which depends both on the characteristics of the klystron and on the supply voltage and the temperature of the external influence. The purpose of the work is to develop a method for classifying the states of the unit. Possible states of the unit from the point of view of its serviceability can be divided into three classes: good (ensures stable operation of the equipment), satisfactory (ensures operability within the framework of technical conditions) or unsatisfactory (operation of the unit is unacceptable). Materials and methods. The study was carried out on real statistical data using machine learning methods (neural network, gradient boosting, random forest, and support vector method) with hyperparameter estimation using modified grid enumeration, as well as using fuzzy logic meods. When forming the training sample, the opinion of experts – specialists in the operation of radar equipment was taken into account. The quality of the classification taking into account the imbalance of the sample was assessed by the F-mer. Results. The use of fuzzy logic provided slightly better classification results compared to machine learning at all three points in the frequency range. The best quality improvement turned out to be at the lowest point – about 6 %. Conclusions. To diagnose the condition of radar components, both machine learning methods and an approach based on fuzzy logic can be used: it is impossible to say in advance which of the methods will provide the best result.
Разработана методика построения траектории беспилотного летательного аппарата, основанная на анализе видеопотока с расположенной на нём камеры без применения информации глобальных навигационных систем. Траектория строится по найденным параметрам аффинной модели взаимного пространственного рассогласования смежных видеокадров. Использованы методы нахождения особых точек Ши - Томаси, оптического потока Лукаса - Канаде и фильтр Калмана. Приведены результаты реализации методики на одноплатных компьютерах и её апробации в условиях ограниченных вычислительных ресурсов на реальных видеопоследовательностях. A method of unmanned aerial vehicle trajectory planning has been developed. This method is based on the analysis of a video stream from a camera mounted on the vehicle, without using information from global navigation systems. The trajectory is planned using the determined parameters of an affine model of mutual spatial misalignment of adjacent video frames. The method utilizes the Shi-Tomassi singularity detection method, the Lucas-Kanade optical flow method, and the Kalman filter. The results of the method implementation on single-board computers and testing it under limited computing resources on real video sequences are presented.
The article addresses the problem of assessing and developing the organizational culture of higher professional education institutions in the context of digital transformation. The relevance of the study stems from the need to overcome resistance to change and institutional barriers on the part of the academic community when achieving the regulatory digital maturity indicators established by the ministries (Ministry of Digital Development and Ministry of Science and Higher Education of the Russian Federation). The author has developed an original economic-mathematical model that couples the qualitative parameters of organizational culture (assessed using the OCAI methodology by K. Cameron and R. Quinn) with the quantitative metrics of departmental regulations. For the technological decomposition of the model, a detailed registry of IT indicators for the "Digital University" project has been formed, adapted for the internal audit system of educational institutions. Based on the proposed framework, the Index of Socio-Cultural Compliance of Digitalization is introduced, which allows for a mathematical comparison of the technological and socio-cultural readiness of an organization. The results obtained and the developed mathematical framework are aimed at higher education leaders for the predictive design of digitalization roadmaps and overcoming cultural barriers to transformation.
Modern combined heat and power (CHP) plants are one of the main sources of energy and thermal resources for the private and industrial sectors. They are also a source of a large amount of data, which is sufficient for the application of machine learning methods. For accurate optimization of power equipment, it is necessary to predict the generated power to ensure sufficient electrical and thermal energy. This study develops an LSTM-based forecasting model. The model is trained on a time series collected from a combined heat and power (CHP) plant over 15 months of operation. Compared with a gradient-boosting model, the LSTM achieves higher accuracy across regression metrics. The developed model can serve as a component of decision-support systems and for subsequent optimization of equipment operation at power stations, thereby improving overall plant efficiency.
