Elektronika ir Elektrotechnika

Title

2024-05-16T00:46:32+03:00

Weather-Based Nonlinear Regressions for Digital TV Received Signal Strength Prediction

2024-04-04T16:21:39+03:00

In this research, the impact of various weather conditions on digital television signals is investigated. Machine learning and nonlinear regression models were used to estimate the strength of the received signal. The received signal strength might vary significantly depending on the weather condition, especially in higher frequency ranges or millimetre wavelengths. Predictive analysis was performed for the radio-relay link Aval Tower-Vršac Hill, which is used for the distribution of television and radio programmes by the public company Broadcasting Technology and Connections in Serbia. The prediction was made using temperature, temperature index, relative humidity, and received signal strength data for the months of June, July, and August in 2022. The best results were obtained using the RandomForest model. Extreme variations in the strength of the received signal can be predicted by using the model mentioned above. More effective management of the broadcasting infrastructure can be done with the ability to predict sudden falls and fluctuations in received signal strength.

Recent Progress on Digital Twins in Intelligent Connected Vehicles: A Review

2024-03-27T14:56:25+02:00

As an important enabling technology in the era of Industry 4.0, the intelligent connected vehicle (ICV) facilitates robust data interaction with the outside through sensors and communication technologies, ultimately making scientific decisions based on environmental perception information. However, due to constraints such as limited communication bandwidth and computing resources, the influx of data simultaneously impedes the sustainable optimisation of the vehicle decision making process at the same time. As a novel technology that effectively connects physical and virtual space, the special ability of the digital twin (DT) is to identify characteristics within a certain lifecycle, thereby garnering widespread attention across various industries. The purpose of this paper is to review the contribution of digital twins in the application field of intelligent vehicles and explore its potential for development. First, the key technologies of ICV provide a basis for the embedding of digital twins. Then, by analysing the development process and technical composition of digital twins, readers can better understand the concept of digital twins. Finally, the application of DTs in ICV is reviewed from the perspective of vehicles, traffic facilities, and occupants. Future challenges and opportunities in this direction are described at the same time.

Gesture Scoring Based on Gaussian Distance-Improved DTW

2024-01-05T10:27:03+02:00

The power industry has been dedicated to applying virtual reality (VR) technology to build training systems in virtual environments, enabling personnel to complete skill training in real simulated environments while ensuring their safety. Conventional action scoring systems struggle to provide accurate scores for fine movements. Accurate scoring of fine movements can help workers identify their shortcomings during power operations, thus improving learning efficiency. This is of great significance for training on virtual environment-based power operation. This paper proposes a power operation-orientated VR action evaluation method based on the Gaussian distance-improved dynamic time warping (DTW) algorithm and the temporal convolutional network (TCN) model. First, the adaptive adapter is used to extract one-dimensional features from the three-dimensional data of the data gloves. Then, based on the TCN model, action data with significant discrepancies are filtered out. Finally, the obtained data are input into the Gaussian distance-improved DTW algorithm, where the path size is calculated. Corresponding scoring criteria are established on the basis of the path size to evaluate the actions. The results demonstrate that the VR action evaluation method based on the Gaussian distance-improved DTW algorithm and the TCN model significantly improves the accuracy of evaluating fine movements compared to traditional evaluation algorithms.

Application of 2D Variational Mode Decomposition Method in Seismic Signal Denoising

2024-03-27T15:21:53+02:00

Seismic data are typical nonlinear and nonstationary data. In the acquisition and processing of seismic data, many factors interfere with it. Seismic data contain both effective waves and random noises, seriously affecting the quality of seismic data and not conducive to the goal of fine interpretation of subsequent seismic data. Therefore, studying new seismic data denoising methods is beneficial for improving the quality of seismic data and plays a very important role in subsequent seismic data interpretation. In this paper, the principle of variational mode decomposition (VMD) and 2D-VMD is introduced in detail, and the seismic profile with a simple signal and fault model is denoised. Compared to traditional empirical mode decomposition (EMD), the 2D-VMD method has the best seismic data denoising effect. The test results of the synthesised signal show that the 2D-VMD method has a signal-to-noise ratio of 47.14 dB after denoising, which is higher than the signal-to-noise ratio after EMD and VMD denoising, indicating that it has a better denoising effect. The VMD and 2D-VMD methods are applied to the denoising of actual seismic data. The application results show that the 2D-VMD method can effectively improve the quality of the seismic data, enhance the continuity and reliability of the seismic data, and is conducive to the fine interpretation of subsequent seismic data.

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

2023-04-04T23:19:21+03:00

Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, F-score parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems.

Editorial Board

2024-05-16T00:49:07+03:00

A Series-Fed Conformal Antenna at 60 GHz for 6G and Beyond Applications

2024-03-18T16:40:38+02:00

This work deals with the design and development of a conformal series-fed antenna structure operating at the 60 GHz frequency. The proposed antenna operates from 57 GHz to 62 GHz with good return loss and radiation characteristics for 6G and beyond applications. The antenna is shown to give a good gain of more than 14.7 dBi with directional radiation beam in the hemispherical boresight direction. The fabricated prototype is verified with the simulated result, and it is found to be a good matching. The step-by-step antenna design process, parametric variation, and a detailed study are also reported. For a case study, this series-fed antenna conformal configuration is also embedded on a cylindrical structure. From this study, similar resonant and radiation performance characteristics are observed. Since the structure is compact, conformal, and gives better performance, it can be suitable for applications such as 6G, radar, guided missiles, body-centric medical imaging, etc.

Applying eXplainable AI Techniques to Interpret Machine Learning Predictive Models for the Analysis of Problematic Internet Use among Adolescents

2024-02-10T18:52:54+02:00

This research focusses on the potential application of artificial intelligence (AI) techniques in the analysis of behavioural addictions, specifically addressing problematic Internet use among adolescents. Using tabular data from a representative sample from Serbian high schools, the authors investigated the feasibility of employing eXplainable AI (XAI) techniques, placing special emphasis on feature selection and feature importance methods. The results indicate a successful application to tabular data, with global interpretations that effectively describe predictive models. These findings align with previous research, which confirms both relevance and accuracy. Interpretations of individual predictions reveal the impact of features, especially in cases of misclassified instances, underscoring the significance of XAI techniques in error analysis and resolution. Although AI’s influence on the medical domain is substantial, the current state of XAI techniques, although useful, is not yet advanced enough for the reliable interpretation of predictions. Nevertheless, XAI techniques play a crucial role in problem identification and the validation of AI models.

Enhanced Content-Based Recommendation Using Topic Modelling and Knowledge Graph

2023-11-17T05:52:04+02:00

Content-based (CB) recommendation algorithms recommend items to a user based on items the user liked in the past. CB methodologies have gained attention due to their higher accuracy and transparency and the emergence of new technologies, such as knowledge graphs (KGs), advances in natural language processing (NLP), and sentiment analysis. While most previous studies have mainly focussed on the use of term frequency-inverse document frequency (TF-IDF) and other related enhancements, little work can be found on using KGs in CB recommendations. This paper presents an enhancement of the conventional CB recommendation by incorporating KGs for a movie domain. The graph is constructed using the MovieLens data set, which is augmented with additional features such as actors, directors, and genres. Furthermore, the graph is expanded by incorporating topics derived from latent dirichlet allocation (LDA) extraction. Using the KGs, the proposed approach enhances user profiles by leveraging the interconnected user-movie relationship within a graph structure. The results of the experiments showed that the proposed approach exceeded the tested baselines in terms of precision, recall, and F-score metrics.

Determination of Optimal Locations and Parameters of Passive Harmonic Filters in Unbalanced Systems Using the Multiobjective Genetic Algorithm

2024-01-19T13:03:14+02:00

This paper discusses the problem of optimal placement and sizing of passive harmonic filters to mitigate harmonics in unbalanced distribution systems. The problem is formulated as a nonlinear multiobjective optimisation problem and solved using the multiobjective genetic algorithm. The performance of the proposed algorithm is tested on unbalanced IEEE 13- and 37-bus three-phase systems. The optimal solutions are obtained based on the following objective functions: 1) minimisation of total harmonic distortion in voltage, 2) minimisation of costs of filters, 3) minimisation of voltage unbalances, and 4) a simultaneous minimisation of total harmonic distortion in voltage, costs of filters, and voltage unbalances. Finally, an analysis of the influence of uncertainties of load powers and changes in system frequency and filter parameters on filter efficiency was performed.