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Charging Load Prediction Method for Electric Vehicles using CNN-GRU-Attention model with SSA optimization strategy 7

Charging Load Prediction Method for Electric Vehicles using ...

引用

7th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Fan, Xinyu Shvedov, Galaktion V. Cai, Chuqiao National Research University 'MPEI' Electrical Power Systems Department Moscow Russian Federation Bauman Moscow State Technical University Faculty of Computer Science and Control Systems Information Processing and Management Systems Department Moscow Russian Federation

ISBN: (纸本)9798331531836

This paper proposes a CNN-GRU-Attention model for short-term charging load prediction at electric vehicle (EV) charging stations. The model leverages the strengths of Convolutional Neural Networks (CNN) for feature extraction and dimensionality reduction, while Gated Recurrent Units (GRU) enhance temporal sequence learning. The attention mechanism assigns dynamic weights to the extracted features, improving the prediction accuracy. Hyperparameters are optimized using the Sparrow Search Algorithm (SSA), and an improved version, ISSA, further enhances model performance. Experimental results demonstrate that the CNN-GRU-Attention model outperforms baseline models, including CNN, GRU, LSTM (Long Short-Term Memory), and BiLSTM (Bidirectional Long Short-Term Memory), with higher prediction accuracy and fewer trainable parameters. The model's predictive capabilities enable optimization of power supply strategies in smart grids. However, it faces challenges in long-term load prediction and computational resource demands during hyperparameter optimization. © 2025 IEEE.

关键词： attention mechanism convolutional neural network electric vehicles gated recurrent unit short-term load forecasting

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Development of Hardware Module for Collecting Parameters of Microhydroelectric Power Plant Operation in Mountainous Conditions

Development of Hardware Module for Collecting Parameters of ...

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

作者： Stefan V. Onishchenko Aliy R. Mamiy Konstantin A. Yurkaev Laboratory of Renewable Energy Sources Adyghe State University Maykop Russian Federation Department of Automated Systems of Processing Information and Control Adyghe State University Maykop Russian Federation

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

Hydropower plants play a major role in the global energy sector, generating up to 17% of all energy generated. Despite this, the spread of small hydropower is not as extensive, although it has an extremely large potential and is a competitive renewable energy source, as small hydropower plants have high efficiencies of up to 80%. The implementation of such energy complexes requires a preliminary assessment of their efficiency, for which atlases and data from existing GIS systems are most often used, but they may not have information on the potential of small rivers and streams, and do not allow for seasonal changes in the water landscape, which is particularly pronounced in mountainous terrain. Therefore, this paper proposes to examine the process of developing a small generating station suitable for use on small streams in mountainous terrain, which can be used as a means to collect data on the energy production profile of small mountain streams, which will help to establish seasonal dependencies and balance the energy system, achieving sustainable energy production through the involvement of other generation sources.

关键词： Microhydro power Temperature Voltage Production Turning Generators Rivers

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Using Convolutional Neural Networks to Monitor Security at an Industrial Facility 8

Using Convolutional Neural Networks to Monitor Security at a...

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8th International Conference on information Technology and Nanotechnology, ITNT 2022

作者： Mokshin, Vladimir Sultanova, Aliya Sharnin, Leonid Kazan National Research Technical University Named after A. N. Tupolev - KAI Department of Automated Information Processing Systems and Management Kazan Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The article deals with the tasks of searching and classifying objects in images for the purpose of analyzing industrial risks. The objects of recognition are the presence of signal vests and protective helmets for an employee of production facilities. The YOLOv3 (You Only Look Once) convolutional neural network is used as a research tool. Unlike other convolutional neural networks, YOLO only needs to 'look' at the image once to detect objects. This significantly reduces the time required to detect an object in the frame, while maintaining recognition accuracy. The practical implementation was made in the Python programming language for the Windows platform. The article contains a description of the stages of training a neural network and assessing the quality of the resulting model. Training and test sets (dataset) were formed in the Pascal VOC format, containing images and their annotations in the form of an XL file. During training, 'transfer irradiation' was used. It involves using as a starting point the weight from a pre-trained model on the COCO dataset. As a result of training, 30 models were obtained. To assess the quality of work of each neural network model obtained during training, a test data set was used. A summary table of the results of testing the neural network model is also provided, which contains the average accuracy for each class (vest class - vest and helmet class - helmet), as well as the mAP metric. The result of the work of the trained YOLOv3 convolutional neural network model is an image marked with rectangles with labels to which class the recognized object belongs and with what accuracy it belongs to this class. Based on the results obtained, it is possible to build higher-level models for use in research purposes, as well as to create systems for analyzing and monitoring safety at production facilities. © 2022 IEEE.

关键词： Python

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Who Is to Err? A Limited Effect of Data Labeling in Prediction of Users' Subjective Impressions of Web Designs

Who Is to Err? A Limited Effect of Data Labeling in Predicti...

引用

Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Anna V. Bortnikova Maxim A. Bakaev Department of Automated Control Systems Novosibirsk State Technical University Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

User Experience (UX) researchers and designers who seek to predict users’ subjective impressions nowadays turn to Machine Learning (ML) models trained on marked-up data. Labeling of graphical user interfaces (UIs) is a rather time-consuming process that is also prone to errors and inaccuracies, particularly if motivation of the human labelers is low. Correspondingly, the size of datasets in Human-Computer Interaction (HCI) rarely exceeds the order of 10,000, and the field largely relies on feature-based models. In our paper, we compare two principal ways of obtaining the features, with respect to their effect on the quality of the models that predict users’ impressions of web pages’ Complexity, Aesthetics and Orderliness. First, we calculated several layout-related metrics - balance, equilibrium, symmetry - for 368 website screenshots labeled by 40 human volunteers. Second, we obtained the similar metrics from ML-based online services working with raw web UI images. Our results suggest that despite the limitations of the current automated web UI parametrization tools, their metrics can actually yield superior models (having R 2 s higher by 107% on average). Our key implication for UI/UX designers and ML researchers is that the expected ROI should be carefully weighted before committing to the tedious manual web UI data labeling.

关键词： Measurement Human computer interaction Web design Manuals Machine learning Predictive models Data models User experience Labeling Informatics

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Discount Eye-Tracking for Usability Testing with Webcam, CNN, and the Selected Training Data

Discount Eye-Tracking for Usability Testing with Webcam, CNN...

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Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Ivan A. Kolidov Maxim Bakaev Department of Automated Control Systems Novosibirsk State Technical University Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

Not many people realize that eye-tracking might be a fruitful addition to the widely established usability testing, at the same time not becoming prohibitively expensive for smaller companies. Unlike in research applications of the human gaze training technologies that require high precision, usability engineering professionals are mostly interested in concrete improvements to the user interfaces they develop. Correspondingly, affordable budgets and more frequent testing sessions yield better practical results. This paper presents a “discount” approach implementing “good enough” eye-tracking for usability testing with a standard webcam. A convolutional neural network was trained to predict screen coordinates from eye images, compensating for the precision provided by the specialized equipment. To improve the accuracy, three different data collection methods were tested, and the combined mesh-point based one allowed the best error (Root Mean Square Error, RMSE) of 78 pixels on a 1920xl080 screen. Although the error was about 2–3 inferior to the one obtained for the professional equipment, our results demonstrate that a webcam combined with deep learning techniques can serve as an affordable alternative for real-time eye-tracking. This approach has the potential to significantly reduce costs and make eye-tracking technology more accessible to a broader community of software developers and user interface designers.

关键词： Accuracy Costs Webcams Gaze tracking Data collection User interfaces Data models Convolutional neural networks Usability Testing

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An Approach to Digital Transformation of the Registration Expertise of Devices using Blockchain Technologies 7

An Approach to Digital Transformation of the Registration Ex...

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7th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Yakovleva, Maria V. Antonov, Artem I. Bauman Moscow State Technical University 'Management' Department Moscow Russian Federation Bauman Moscow State Technical University 'Information Processing and Control Systems' Department Moscow Russian Federation

ISBN: (纸本)9798331531836

In the article, the authors conducted an extensive study of the current situation with the use of digital transformation tools in the life cycle management system of medical devices based on the blockchain. The analysis showed that blockchain technology is being actively implemented in two directions: in the field of supply chain management of medicines, products, equipment and in the field of ensuring the security of medical data exchange in the digital space. It is concluded that at the moment an ideal blockchain-based system has not yet been formed to solve the problem of supervision of the supply chain of medical devices. In this study, attention is paid to the development of recommendations for organizing the possibility of submitting documents for the registration dossier of medical devices to the state body by using the possibilities of electronic document management and blockchain technology. © 2025 IEEE.

关键词： blockchain technologies certification data security digital transformation electronic document management medical devices registration

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An Approach to the Definition of System Intelligence in the Management of Complex systems

An Approach to the Definition of System Intelligence in the ...

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International Conference on Soft Computing and Measurements (SCM)

作者： Vladimir S. Simankov Stefan V. Onishchenko Pavel Yu. Buchatskiy Semen V. Teploukhov Department of Computer Technologies and Information Security Kuban State Technological University Krasnodar Russia Department of Automated Systems of Processing Information and Control Adyghe State University Maykop Russian Federation

Currently, the concept of artificial intelligence has become extremely widespread, but there is a difficulty in accurately defining this concept. In general, artificial intelligence is divided into two main categories: “weak AI” and “strong AI”.In parallel with these definitions, the concept of hybrid intelligent systems arises, which can be defined from two main positions: as systems based on a combination of two or more artificial intelligence methods, and as systems based on the synthesis of artificial intelligence methods and natural human *** this regard, this paper proposes to analyze approaches to the organization of artificial intelligence and formulate a definition of system intelligence, which allows combining various structures of the organization of computer systems using various intelligent agents.

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Development of a Model for Predicting Student Academic Performance Based on Academic Indicators

Development of a Model for Predicting Student Academic Perfo...

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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

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The primary experiments proved the principal separability of objects based on a set of features characterizing students. In the course of further experiments, the development of classification models was performed to classify students into classes based on the selected features. In the course of the experiments, both types of models and algorithms for preprocessing of data sets were varied. From the resulting set of models, the best one was selected based on analyzing the performance on the test sample. After that, to visualize the work of the analytical system, a dashboard presenting the main academic indicators in different sections was developed.

关键词： Analytical models Machine learning algorithms Data visualization Prototypes Predictive models Prediction algorithms Data models Mathematical models Classification algorithms Visual databases

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Development of Regression Model for Predicting the Ball Loading of the Grinding Unit of a Mining Plant

Development of Regression Model for Predicting the Ball Load...

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

作者： Y.A. Tsygankov I.S. Mikhailov A.A. Parshin Automated and information control systems department Stary Oskol technological institute (branch) NUST “MISIS” Stary Oskol Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The paper considers the possibility of using the apparatus of regression models for predicting the ball mill loading of the concentrator of the ore dressing plant of a mining and processing plant. This indicator has a significant impact on the operation of the unit and the quality of the obtained product. And its estimation is carried out only at stops of the unit, at the rest of the time the ball flow rate is assumed constant and additional loads are carried out discretely according to the schedule. Such an approach does not allow to take into account changes in the properties of incoming ore and drift of the unit parameters. The operator has to compensate for this disadvantage by changing the nature of the control process, which is not fully effective. The availability of the functionality that predicts the current level of ball loading will allow to continuously maintain the ball mill operation mode within the technological tolerances, which will have a positive impact on the quality characteristics of the unit operation.

关键词： Schedules Ores Loading Process control Estimation Predictive models Mathematical models Real-time systems Random forests Load modeling

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Development of a System for Detecting Problematic Areas of Agricultural Fields by UAV Photos 5

Development of a System for Detecting Problematic Areas of A...

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5th International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Poleshchenko, Dmitriy Petrov, Vladislav Mikhailov, Ilia Shulik, Maksim Gubkin Branch of NUST MISIS Ore-Mining Gubkin Russia National University of Science and Technology MISIS Stary Oskol Technological Institute N.a. Automated and Information Control Systems Department Stary Oskol Russia

ISBN: (纸本)9798350309126

In this paper, the developed system for detecting problematic areas of agricultural fields by using computer vision in unmanned aerial vehicle (UAV) photography was investigated. The structures of the neural networks of the YOLOv5 and YOLOv8 family were considered to find a solution to the task of detecting problem areas. The application of the developed software will reduce labour and time spent on image analysis, which in turn will reduce the response time in detecting problem areas in agricultural fields to achieve higher yields. © 2023 IEEE.

关键词： Antennas

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