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The Sound Pressure Level Meter

The Sound Pressure Level Meter

引用

2022 International Russian Automation Conference, RusAutoCon 2022

作者： Khusnutdinov, A.N. Nuriev, M.G. Kazan National Research Technical University Named after A. N. Tupolev - Kai Department of Computer Systems Kazan Russia Kazan National Research Technical University Named after A. N. Tupolev - Kai Department of Automated Information Processing and Control Systems Kazan Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

Changes in the human hearing organ cause a specific reaction of the body as a result of intense exposure to sound interference and noise. The increase in the threshold of auditory sensitivity is due to a process of adaptation of the auditory system, which in turn occurs due to prolonged exposure to noise. At first the changes in the auditory system slowly return to their initial level, this is called auditory fatigue;with longer exposure to noise, a permanent shift in the Auditory Sensitivity Threshold occurs, resulting in a rapid deterioration of hearing. In the article, the sound pressure level meter was developed to measure the psofometric noise level, which characterizes the standardized perception of human noise in the audio range from 20 Hz to 20 kHz. The device developed in this work satisfied the requirements of GOST R ISO 9612 - 2016. As a result of the work, recommendations for the use of this device were formed. © 2022 IEEE.

关键词： Audition

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Ant Colony Algorithm for Placing Electronic Components on a Printed Circuit Board, Taking into Account the Thermal Compatibility Criterion 16

Ant Colony Algorithm for Placing Electronic Components on a ...

引用

16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023

作者： Suzdaltsev, Ilya V. Suzdaltsev, Vladimir A. Bogula, Nelli Y. Dept. of Computer-aided Design System Kazan National Research Technical University named after A.N. Tupolev Kazan Russia Dept. of Automated Information Processing and Control Systems Kazan National Research Technical University named after A.N. Tupolev Kazan Russia

ISBN: (纸本)9798350330885

The problem of electronic components placing on a printed circuit board of an electronic device is formulated in this paper. The choice of constraints and criteria for the optimization problem is validated. We propose to take into account the thermal compatibility criterion to ensure normal thermal conditions of the printed circuit board. An indicator of the problem solution quality is the maximum temperature of a component among all components placed on a printed circuit board. The temperature of the components is determined based on the solution of the heat balance equation. The ant algorithm for placement problem solving is described. The sequence of vertices of the decision graph in the path formed by the ant colony determines the sequence of elements placement on the mounting plane of the printed circuit board and thus corresponds to the placement problem solution. The decrease in the maximum temperature of the printed circuit board temperature field was assessed depending on the number of the stage of constructing the best path by a ant colony, with different thermal conductivity between the components and the surrounding air environment. The results of computational experiments for effectiveness study of the ant algorithm are presented. The experiments carried out showed the effectiveness of the applied ant algorithm and that rational placement of the electronic components on the PCB makes it possible to reduce the maximum temperature of the components. © 2023 IEEE.

关键词： Printed circuit boards

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The Fiber-Optic Communication System in the Enterprise

The Fiber-Optic Communication System in the Enterprise

引用

2022 International Russian Automation Conference, RusAutoCon 2022

作者： Gerasimov, V.A. Nuriev, M.G. Gashigullin, D.A. Kazan National Research Technical University Named after A. N. Tupolev - Kai Department of Computer Systems Kazan Russia Kazan National Research Technical University Named after A. N. Tupolev - Kai Department of Automated Information Processing and Control Systems Kazan Russia Kazan National Research Technical University Named after A. N. Tupolev - Kai Department of Automated Design Systems Kazan Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

The article discusses the development of a system that provides more throughput up to 40 Gbit instead of 10 Gbit using the TWDM-PON (Time and Wavelength Division-Multiplexed Passive Optical Network) system, which was chosen by the international companies FSAN (Full-Service Access Network) and the ITU-T (Telecommunication Standardization Sector of the International Telecommunication Union) group. This system uses the time and frequency division of channels. In this article, this system is developed, and its operation is simulated at a distance of 40 km and with data transfer up to 40 Gbit/s. Also, the system was optimized and the optimal bandwidths for the optical and electrical filters were selected, as well as the optical power of the laser, etc. © 2022 IEEE.

关键词： Passive optical networks

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information System for Classifying the State of Arteriovenous Fistula Based on the Dynamics of Changes in Its Noise

Information System for Classifying the State of Arteriovenou...

引用

International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： D. A. Fedorov N. I. Yumagulov S. A. Sazonov The Faculty of Applied Informatics ITMO University Saint Petersburg Russia Dep. for Education in Physics and Mathematics Nizhnevartovsk state university Nizhnevartovsk Russia Department of Automated Information Processing and Control Systems Surgut State University Surgut Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

The study presents the results of research on an information system for classifying the condition of an arteriovenous fistula. The analysis of sound recordings of arteriovenous fistula murmurs has shown that they can be used for condition classification. A machine learning model for assessing the state of the arteriovenous fistula was developed and trained. The analysis of the spectral features of the audio signal allowed for the identification of a significant set of features for training the machine learning model. The study presents the results of feature importance evaluation using the built-in feature selection method of RandomForest, as well as filtering methods such as the F-test and chi-square test. The investigation into features affecting the model's training accuracy helped identify the most significant features and those that degrade accuracy. The machine learning model achieved an accuracy of 91 % in classifying the condition of the arteriovenous fistula.

关键词： Training Analytical models Accuracy Filtering Noise Machine learning Feature extraction Noise measurement Random forests information systems

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Detection of Sunflower Plants in UAV Photos 4

Detection of Sunflower Plants in UAV Photos

引用

4th International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2022

作者： Poleshchenko, Dmitriy Petrov, Vladislav Mikhailov, Ilia Ore-mining Chair Gubkin Branch of Nust 'MISIS' Gubkin Russia National University of Science and Technology 'MISIS' Automated and Information Control Systems Department Stary Oskol Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

The task of detecting sunflower plants in the field of UAV image to estimate an important agrotechnical parameter - the density of seedlings - is being solved in this work. The networks of YOLOv5 family were used as a detector model. The question of learning the quality in small samples is raised. The best result was obtained by the YOLOv5m network in an augmented sample of 363 photos. When the recognized images are being noised with weeds, the detection quality decreases. On the whole, the YOLOv5m model copes with the task, and further improvement of the detection quality is related to the expansion of the training data corresponding to different growth conditions of sunflower plants. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Computer Vision Method for Detecting Safety Violations Using the YOLOv8 Model

Computer Vision Method for Detecting Safety Violations Using...

引用

International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Pavel Medvedev Vladimir Mokshin Department of Automated Information Processing Systems and Management Kazan National Research Technical University named after A. N. Tupolev - KAI Kazan Russia

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

In today's world, where production processes are becoming increasingly automated, ensuring safety on production sites is becoming a priority. Conventional methods of monitoring and enforcing safety regulations require large resources and are not always effective. However, with the development of machine learning technologies, new prospects are emerging in the field of detecting safety violations in production. An information system has been developed for monitoring and analyzing video from cameras, which automatically detects violations and generates appropriate reports. To train the system, a neural network based on the YOLOv8 model was used. The automatic parameter tuning algorithm was implemented using the gradient descent method to optimize learning.

关键词： Head Computational modeling Welding Neural networks Production Cameras Safety

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Comparative Analysis of Renewable Energy Production Forecasting Algorithms Using Python

Comparative Analysis of Renewable Energy Production Forecast...

引用

information Computing and processing (ICP), Seminar on

作者： Pavel Yu. Buchatskiy Semen V. Teploukhov Dmitry N. Lisov Marina A. Rudenko Anatoliy N. Kazak Anna A. Dorofeeva Department of Automated Information Processing and Control Systems Adyghe State University Maykop Russia Department of Management and Touristic Business of V.I. Vernadsky Crimean Federal University Simferopol Russia

In this paper, forecasting algorithms were tested on renewable energy production data and their comparative analysis was carried outThe machine learning methods that were used in the work are DecisionTree, AdaBoost, RandomForest and LSTM *** the analysis of the results of the study, it can be seen that the accuracy of the forecast of the LSTM neural network is higher than all other methods considered, but at the cost of this was the speed of creating a forecast *** the considered methods, according to the combination of time and quality criteria, LSTM neural networks turned out to be the best for data analysis, showing the best accuracy. But for SMALL HYDRO and SOLAR data, RandomForest is the preferred method. It made it possible to build sufficiently accurate models in much less time than neural networks.

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Dynamic Pricing in Air Cargo: Machine Learning and Genetic Algorithm-Based Optimization

Dynamic Pricing in Air Cargo: Machine Learning and Genetic A...

引用

Wave Electronics and its Application in information and Telecommunication systems (WECONF)

作者： S. A. Kiselev A. A. Podberezkin A. M. Borzenkov A. V. Ostroukh C. B. Pronin Department of Information Technology University of Technology Sydney Sydney Australia Department of Automated Control Systems Moscow Automobile and Road Construction State Technical University (MADI) Moscow Russia

ISBN: (数字)9781665465687

ISBN: (纸本)9781665465694

Air cargo tariff calculation is a complex process influenced by various dynamic factors such as weight, distance, fuel price, airport fees, weather conditions, and market demand. This paper explores the application of machine learning models, particularly XGBoost, to predict the optimal tariff for cargo shipments. Additionally, a genetic algorithm (GA) is designed to further optimize the pricing strategy. A comparative analysis between traditional regression models, XGBoost, and GA-based optimization is conducted. Results indicate that XGBoost outperforms other machine learning models in terms of predictive accuracy, while the GA provides an effective complementary optimization approach.

关键词： Adaptation models Accuracy Atmospheric modeling Biological system modeling Tariffs Pricing Predictive models Real-time systems Optimization Genetic algorithms

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Automatic Car License Plate Detection Based on the Image Weight Model

Automatic Car License Plate Detection Based on the Image Wei...

引用

2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2022

作者： Gizatullin, Zinnur M. Lyasheva, Maya M. Shleymovich, Mikhail P. Lyasheva, Stella A. Kazan National Research Technical University Named after A.N. Tupolev-KAI Department for Automated Systems for Information Processing and Control Kazan Russia Kazan National Research Technical University Named after A.N. Tupolev-KAI Department for Applied Mathematics and Informatics Kazan Russia

ISBN: (纸本)9781665409933

Methods of detecting car license plates in images are considered. A method for detecting a car license plate based on a weight model that reflects the importance of pixels for determining image features is described. A scheme for constructing a model using a multiple-scale wavelet transform is presented. It is shown that the model can be represented as a grayscale image - a weight image. A method is proposed for selecting contours on a weighted image using the operation of calculating the morphological gradient and analyzing them for compliance with the parameters of the quadrangular area of the car license plate using the contour length, the area of the inner area and the difference between the area of the inner area and the area of the smallest bounding quadrilateral. It is shown that the described method provides fast and accurate detection of the car number in the image. © 2022 IEEE.

关键词： Computer vision

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On the Possibility of Developing a System for Predicting Students Academic Performance Using Machine Learning Methods

On the Possibility of Developing a System for Predicting Stu...

引用

International Conference on control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Y.A. Tsygankov A.Iu. Solovev M.S. Sergeychik Automated and Information Control Systems Department Stary Oskol Technological Institute (Branch) NUST "MISIS" Stary Oskol Russia

This article considers the possibility of developing data mining techniques and applying machine learning models to identify hidden dependencies that uniquely characterize different categories of students in order to predict their academic performance. The application of machine learning in this subject area is conditioned by their ability to analyze large amounts of data, obtaining, as a result, generalizing models capable of classifying the objects of study and predicting the required indicators. The development of such a system will allow, based on the analysis of a priori data and current indicators, to predict the expected performance of students in disciplines of different orientation, providing information on the categories of performance in a visual form.

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