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COMPUTER OPTICS 2025年第3期49卷 443-450页

作者： Medvedeva, E. V. Perevoshchikova, A. A. Vyatka State Univ Dept Radio Elect Syst Moskovskaya Str 36 Kirov 610000 Russia Vyatka State Univ Moskovskaya Str 36 Kirov 610000 Russia

The article describes the development of two neural network algorithms for recognizing objects of the railway infrastructure in video images. Both algorithms are aimed at improving railway traffic safety. One algorithm detects foreign objects on railway tracks and objects relating to the railway infrastructure. The other algorithm implements the semantic segmentation of main and auxiliary railway tracks, as well as trains within the visible range of the locomotive. The algorithms are implemented based on convolutional neural networks (CNN) YOLO and U-Net. The CNN is trained and tested using the image database of the Research Institute of Information, Automation and Communications in Railway Transport. The experimental studies conducted are aimed at increasing the efficiency of algorithms for object detection and segmentation through the use of data augmentation methods and additional preprocessing, as well as selecting an architecture and optimal network hyperparameters. The detection algorithm works in real time, achieving an average accuracy of 64 % for 11 object classes according to the mAP metric. The operating speed of the semantic segmentation algorithm is 5 frames/s, the average accuracy for three classes of to the IoU metric is 92 %.

关键词： objects according object detection semantic segmentation railway infrastructure objects railway traf-fic safety machine vision systems neural network algorithms

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Nitrogen-Stabilized DLC Coatings: Optimization of Properties and Deposition Parameters Using Randomized Tree and neural network algorithms

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PHYSICAL MESOMECHANICS 2024年第4期27卷 355-369页

作者： Voropaev, A. I. Kolesnikov, V. I. Kudryakov, O. V. Varavka, V. N. Kolesnikov, I. V. Lifar, M. S. Guda, S. A. Guda, A. A. Sidashov, A. V. Rostov State Transport Univ Rostov Na Donu 344038 Russia Don State Tech Univ Rostov Na Donu 344000 Russia Southern Fed Univ Rostov Na Donu 344090 Russia

This work discusses the predictable control of plasma-assisted physical vapor deposition (PVD) of coatings. The multiple process parameters and the instability of the nonequilibrium ion plasma system create substantial obstacles to the wide industrial application of promising multicomponent functional coatings. Here we propose a solution to this problem, which includes: creation of a database of diamond-like carbon (DLC) coatings to identify a limited set of adjustable process control parameters, determination of how these parameters affect the coating properties, analysis of the revealed effects using statistical methods and neural network algorithms, and use of the results for the predictable tuning of specified coating properties. The object of research is original DLC coatings whose structure is stabilized with nitrogen instead of conventionally used hydrogen. The experimental database of DLC coatings is created based on our previous studies and includes structural, morphological and architectural characteristics of coatings, various types of substrates, sublayers, physical, mechanical and tribological properties, and various combinations of coating deposition parameters. A specific problem is solved to determine the influence of deposition parameters such as chamber pressure P, stabilizer content (% nitrogen), ion flux rate (coil current lambda) and deposition time t on hardness H and elastic modulus E of coatings. Based on the results obtained, the deposition parameters are optimized so as to obtain predictable strength values of the formed carbon coating. The optimization procedure is developed using both classical statistical methods and modern algorithms of ridge regression, randomized trees (ExtraTrees), and a fully connected neural network (multilayer perceptron MLP).

关键词： plasma-assisted physical vapor deposition coating deposition parameters DLC coatings microstructure scanning electron microscopy X-ray photoelectron spectroscopy indentation physical and mechanical properties of coatings database neural network algorithms

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Application effect of rehabilitation robots in rehabilitation of limb movement disorders based on neural network algorithms

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SLAS TECHNOLOGY 2025年 32卷 100277页

作者： Han, Dongchen Qinghai Normal Univ Coll Phys Educ Xining 810000 Qinghai Peoples R China

With the continuous advancement of computer technology and sensor technology, rehabilitation robots have shown great potential in the rehabilitation treatment of limb movement disorders. This paper designs a rehabilitation robot based on a neural network algorithm to improve the rehabilitation effect of patients with limb movement disorders. The robot consists of a four-axis robotic arm, a three-finger gripper, a wheeled chassis and a variety of sensors. The control program written in C language and the host computer program written in Python language realize the control of motion and force, and the neural network algorithm is used to accurately adjust the position and force. In the control system design, the back propagation algorithm is used to train the neural network, and it is optimized in combination with multiple data to ensure that the robot can accurately track and assist the patient's limb movement. In order to verify the effect of the rehabilitation robot, this paper conducted an experimental evaluation. The experimental group was patients who received rehabilitation training based on the neural network algorithm, and the control group was patients who received traditional physical therapy. Through the evaluation of multiple indicators such as the 10-meter walking test, Berg balance scale, JebsenTaylor hand function test and MOS SF-36 health questionnaire, the experimental results showed that the improvement of the experimental group in each test was significantly better than that of the control group. Especially in the Berg balance scale and Jebsen-Taylor hand function test, the scores of the experimental group were significantly improved, indicating that the rehabilitation robot plays an important supporting role in the rehabilitation training of patients with limb movement disorders and has broad application prospects.

关键词： Limb movement disorders neural network algorithms Rehabilitation robots Walking ability Quality of life Back propagation

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The Design of an Electric Power Field Tool Management System Based on RFID and neural network algorithms 2

The Design of an Electric Power Field Tool Management System...

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2nd IEEE International Conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Ji, Guang Wu, Yuhan Fan, Yichen Feng, Han Zhang, Dian Zhao, Hengxing State Grid Shenyang Electric Power Supply Company Liaoning Shenyang China

ISBN: (纸本)9798350373646

In the traditional manual tool management methods used in the electric power field, efficiency is relatively low. Additionally, in small-scale and complex electric power operation environments, tool loss occurs frequently. This article addresses the above issues by proposing an electric power field tool management system based on RFID and neural network positioning algorithms. First, by combining the path loss model of echo signal strength with RFID technology and neural network algorithms, an efficient method for tool identification and positioning is established. Subsequently, based on the actual conditions and requirements of the field, a tool management system is designed. To ensure the accuracy and practicality of the system, hardware optimization and module selection are conducted, along with the integration of the positioning algorithm. Finally, through experimental verification, the results demonstrate that this system can effectively manage and locate tools in the field, preventing tool loss and reducing costs while improving work efficiency. Moreover, it has the potential for widespread application in various fields. © 2024 IEEE.

关键词： artificial intelligence digitalization neural network algorithms positioning technology RFID technology tool management

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Management of the Torch Structure with the New Methodological Approaches to Regulation Based on neural network algorithms

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ENERGIES 2021年第7期14卷 1909-1909页

作者： Osintsev, Konstantin Aliukov, Sergei Prikhodko, Yuri South Ural State Univ Inst Engn & Technol 76 Prospekt Lenina Chelyabinsk 454080 Russia

A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average sizes, which are usually grouped and expressed as a fraction of the total mass of the fuel. The authors suggest taking into account the sequential nature of the entry into the chemical reactions of combustion of particles of different masses. In addition, for the application of the developed methodology, it is necessary to divide the furnace volume into zones and sections. In particular, the initial section of the torch, the zone of intense burning and the zone of afterburning. In this case, taking into account all the thermophysical characteristics of the torch, it is possible to make a thermal balance of the zone of intense burning. Then determines the rate of expiration of the fuel-air mixture, the time of combustion of particles of different masses and the temperature at the end of the zone of intensive combustion. The temperature of the torch, the speed of flame propagation, and the degree of particle burnout must be controlled. The authors propose an algorithm for controlling the thermophysical properties of the torch based on neural network algorithms. The system collects data for a certain time, transmits the information to the server. The data is processed and a forecast is made using neural network algorithms regarding the combustion modes. This allows to increase the reliability and efficiency of the combustion process. The authors present experimental data and compare them with the data of the analytical calculation. In addition, data for certain modes are given, taking into account the system's operation based on neural network algorithms.

关键词： boiler units combustion process neural network algorithms torch structure

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Investigation of the ill-posed inverse problem in measuring near-threshold electron and positron-induced atomic inner-shell ionization cross-sections based on neural network approaches

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RADIATION PHYSICS AND CHEMISTRY 2025年 235卷

作者： Wu, Jiaolong Wu, Ying Sheng, Xiaogang Li, Yudan Pan, Miao North China Elect Power Univ Sch Nucl Sci & Engn Beijing Key Lab Pass Safety Technol Nucl Energy Beijing Peoples R China

Accurate measurement of near-threshold positron or electron impact atomic inner-shell ionization cross-sections is of significant importance both theoretically and in practical applications. Due to the complexity of preparing thin targets, difficulty in precisely measuring their thickness, and the low characteristic X-ray collection efficiency of thin targets in positron collision experiments, the thick-target method is typically employed. Solving the corresponding inner-shell ionization cross-section from the experimental yield of thick targets is an ill-posed inverse problem. Existing methods such as the direct comparison method, yield differential method, and regularization method, do not achieve ideal accuracy. Although our research group has recently developed the MC-neural network method, which significantly improves solution accuracy, it is highly time-consuming to use Monte Carlo simulations to generate neural network datasets. To address this issue, we have developed a numerical-neural network method, which uses numerical calculations to quickly generate large-scale, high-quality datasets for training convolutional neural network models to solve the inverse problem of thick target experimental cross-sections. In this study, numerical-neural network and MC-neural network methods are used to process the experimental yield data of K alpha beta characteristic X-rays from Ti and L alpha beta gamma characteristic X-rays from Ag in positron collisions with pure thick targets at energies below 10 keV. The positron-induced K-shell ionization cross-section of Ti and the L alpha beta gamma characteristic X-ray production cross-section of Ag are obtained, which are compared with the experimental cross-sections obtained by the direct comparison method by other researchers and the DWBA theoretical values. The results show that the outcomes obtained by both neural network methods are in good agreement with the results from others and the DWBA theoretical values, and the n

关键词： Lepton collision Inner-shell ionization cross-section measurement Thick-target method neural network algorithms Numerical calculations

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Early gestational diabetes mellitus risk predictor using neural network with NearMiss

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GYNECOLOGICAL ENDOCRINOLOGY 2025年第1期41卷 2470317页

作者： Zhao, Min Su, Xiaojie Huang, Lihong Xiamen Univ Sch Med Affiliated Hosp 1 Dept Informat Ctr Xiamen Peoples R China Xiamen Univ Affiliated Hosp 1 Sch Med Dept Gynecol & Obstet Xiamen Peoples R China

Background: Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective prevention and timely intervention during the initial stages. Objective: The primary objective of this study is to pinpoint potential risk factors for GDM and develop an early GDM risk prediction model using neural networks to facilitate GDM screening in early pregnancy. Methods: Initially, we employed statistical tests and models, including univariate and multivariate logistic regression, to identify 14 potential risk factors. Subsequently, we applied various resampling techniques alongside a multi-layer perceptron (MLP). Finally, we evaluated and compared the classification performances of the constructed models using various metric indicators. Results: As a result, we identified several factors in early pregnancy significantly associated with GDM (p < 0.05), including BMI, age of menarche, age, higher education, folic acid supplementation, family history of diabetes mellitus, HGB, WBC, PLT, Scr, HBsAg, ALT, ALB, and TBIL. Employing the multivariate logistic model as the baseline achieved an accuracy and AUC of 0.777. In comparison, the MLP-based model using NearMiss exhibited strong predictive performance, achieving scores of 0.943 in AUC and 0.884 in accuracy. Conclusions: In this study, we proposed an innovative interpretable early GDM risk prediction model based on MLP. This model is designed to offer assistance in estimating the risk of GDM in early pregnancy, enabling proactive prevention and timely intervention.

关键词： Gestational diabetes mellitus logistic regression neural network algorithms NearMiss

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Evaluation Model of English Teaching Effect Based on neural network Algorithm and Support Vector Machine 1

Evaluation Model of English Teaching Effect Based on Neural ...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Li, Cui Wuhan Donghu University Hubei Wuhan China

ISBN: (纸本)9798350316469

The optimize the analysis of support vector machines in response to the problem that traditional English teaching models cannot solve the accuracy and efficiency of student English teaching effectiveness evaluation, nor can they meet the requirements of English teaching effectiveness evaluation. Firstly, a multi-indicator English teaching effectiveness evaluation system with teachers and students as the main body was constructed using relevant theories, and the indicators were divided according to the data requirements of English teaching effectiveness evaluation indicators to reduce support vector machines in interference factors. Then, the neural network algorithm is used to solve the optimal solution of the kernel function parameters and regularization parameters of the support vector machine, forming a support vector machine scheme, and the support vector machine results are comprehensively analyzed. MATLAB simulation shows that neural network algorithms and support vector machines have the best evaluation accuracy and short evaluation time for English teaching effectiveness under certain evaluation criteria. © 2023 IEEE.

关键词： English language teaching evaluation model neural network algorithms Support vector machines

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Computational fluid dynamics analysis and experimental study of artificially ventilated temperature sensors for meteorological observations

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TM-TECHNISCHES MESSEN 2025年第3-4期92卷 71-82页

作者： Jin, Wei Liu, Qingquan Yuan, Keya Yang, Jie Tang, Jie Amdadul, Haque M. Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China Beijing Union Univ Coll Robot Beijing 100101 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Key Lab Meteorol Observat & Informat Proc Nanjing 210044 Peoples R China

To address the critical role of atmospheric temperature in climate change and disaster monitoring, enhancing measurement accuracy to 0.1 degrees C is essential. Current instruments are susceptible to radiation interference, resulting in errors of approximately 1 degrees C. This study introduces a novel temperature sensor that improves accuracy by combining natural ventilation with forced ventilation. Silver-coated aluminum plates (95 % reflectivity) and white-coated deflectors (87 % reflectivity) minimize solar radiation errors. A neural network algorithm, along with CFD simulations, further corrects radiation errors under varying weather conditions. Field tests based on the 076B ventilation device demonstrate that this new sensor reduces the average radiation error to 0.02 degrees C, achieving a RMSE of 0.034 degrees C and a MAE of 0.028 degrees C. The correlation coefficient (r) with the reference temperature reached 0.999, demonstrating the sensor's high precision and providing an effective solution for reducing temperature measurement errors to below 0.1 degrees C. Um die entscheidende Rolle der atmosph & auml;rischen Temperatur im Klimawandel und bei der Katastrophen & uuml;berwachung zu adressieren, ist eine Verbesserung der Messgen & auml;uigkeit auf 0,1 degrees C unerl & auml;sslich. Aktuelle Instrumente sind anf & auml;llig f & uuml;r Strahlungsst & ouml;rungen, was zu Fehlern von etwa 1 degrees C f & uuml;hrt. Diese Studie stellt einen neuartigen Temperatursensor vor, der die Genauigkeit durch die Kombination von nat & uuml;rlicher Bel & uuml;ftung und erzwungener Bel & uuml;ftung verbessert. Silberbeschichtete Aluminiumplatten (95 % Reflektivit & auml;t) und wei ss beschichtete Abweiser (87 % Reflektivit & auml;t) minimieren Strahlungsfehler. Ein neuronales Netzwerk-Algorithmus sowie CFD-Simulationen korrigieren zus & auml;tzlich Strahlungsfehler unter variierenden Wetterbedingungen. Feldtests basierend auf dem 076B Bel & uuml;ftungsger & auml;t zeigen, d

关键词： atmospheric temperature temperature sensor computational fluid dynamics radiation error neural network algorithms atmosph & auml rische Temperatur Temperatursensor computergest & uuml tzte Str & ouml mungsdynamik Strahlungsfehler neuronale Netzwerk-Algorithmen

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Formation and Prediction of the Properties of Ion-Plasma Diamond-Like Coatings under Nitrogen Stabilization

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JOURNAL OF FRICTION AND WEAR 2024年第1期45卷 9-17页

作者： Kolesnikov, V. I. Kudryakov, O. V. Voropaev, A. I. Kolesnikov, I. V. Varavka, V. N. Lifar, M. S. Guda, A. A. Manturov, D. S. Novikov, E. S. Rostov State Transport Univ Rostov Na Donu 344038 Russia Don State Tech Univ Rostov Na Donu 344000 Russia Southern Fed Univ SFU Int Res Inst Smart Mat Rostov Na Donu 344090 Russia

One of the options for solving the scientific and applied problem of the predicted formation of ion-plasma coating tribological characteristics is presented. The problem is solved by creating and analyzing a carbon coating database. The object of research in this work is ion-plasma diamond-like coatings (DLCs) deposited on a steel substrate. It is shown that the use of nitrogen instead of hydrogen to stabilize carbon coatings not only ensures stable thicknesses of DLCs at the level of 1.0-1.5 mu m, but also serves as an important and convenient technological parameter for regulating the tribological coating characteristics during deposition. Based on the predicted and experimental values of friction coefficient mu and data on sample path length L, the intervals of optimal values of technological parameters %N and lambda are determined. The studied ion-plasma DLCs, obtained according to the established optimal application modes, can be recommended for application under friction conditions equivalent to the tribological tests carried out at friction load F approximate to 10 N.

关键词： vacuum ion-plasma technology diamond-like coatings (DLCs) coating microstructure mechanical properties sliding friction tests tribological properties machine learning neural network algorithms

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