Elektronika ir Elektrotechnika https://eejournal.ktu.lt/index.php/elt <div><em>ELEKTRONIKA IR ELEKTROTECHNIKA</em> (ISSN 1392-1215) is a peer-reviewed open access bimonthly research journal of Kaunas University of Technology.</div> <p>The research journal <em>ELEKTRONIKA IR ELEKTROTECHNIKA</em> publishes original research papers on featuring practical developments that might have a significant impact in the field of <em>electronics and electrical engineering</em>, and focuses on automation, robotics &amp; control; automotive electronics; electric vehicles; electrical engineering; electronic measurements; electronics; high frequency technologies, microwaves; micro &amp; nano-electronics; power electronics; renewable energy; signal technologies; telecommunications engineering. It is aimed not only to researchers of certain field , but also to the wider public.</p> <p><strong><em>WoS</em></strong><strong><em>: </em></strong><em>Impact</em> <em>Factor</em><em> 0.707 (</em><em>2019); </em><em>5-Year </em><em>Impact</em> <em>Factor</em> <em>0.656 (</em><em>2019) </em><strong><em>Scopus</em></strong><strong><em>:</em></strong> <em>SCImago</em> <em>Journal</em> <em>Rank</em><em> 0.18 (2019)</em></p> en-US <p>The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">Creative Commons Attribution 4.0 (CC BY 4.0)</a> agreement under which the paper in the Journal is licensed.</p> <p>By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.</p> eejournal@ktu.lt (Elektronika ir Elektrotechnika) eejournal@ktu.lt (Executive Editor Darius Andriukaitis) Wed, 26 Oct 2022 13:45:24 +0300 OJS http://blogs.law.harvard.edu/tech/rss 60 Forecasting Energy Demand Using Conditional Random Field and Convolution Neural Network https://eejournal.ktu.lt/index.php/elt/article/view/30740 <p>Electric load forecasting has been identified as an effective strategy to increase output and revenues in electrical manufacturing and distribution organizations. Several strategies for forecasting power consumption have been suggested; however, they all fail to account for small variations in power demand throughout the prediction. Therefore, the aim of this study was to develop a CRF-based power consumption prediction technique (CRF-PCP) to meet the difficulty of estimating energy consumption (EC). The EC of regions in the area is forecasted using convolution neural networks (CNNs) and conditional random fields (CRFs). Then, using the cloud, the predicted results are delivered to the electricity distribution system. To our knowledge, this is the first attempt to forecast electricity demand using CNN and CRF algorithms. In comparison to state-of-the-art algorithms, this proposed technique achieves 98.9 % accuracy. This recommended work also obtained minimum values of root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE) by using 10-fold cross-validation (CV) and a hold-out (CV) methodology.</p> Aravind Thangavel, Vijayakumar Govindaraj Copyright (c) 2022 Aravind Thangavel, Vijayakumar Govindaraj https://eejournal.ktu.lt/index.php/elt/article/view/30740 Wed, 26 Oct 2022 00:00:00 +0300 Real-Time Investigation of Temperature Effect on Induction Motor Equivalent Circuit Parameter Change https://eejournal.ktu.lt/index.php/elt/article/view/31198 <p>Today’s developing technology and increasing demands in production areas continue the importance of studies on induction motors (IMs). To meet the demands, the mathematical modeling of IMs must be performed fully and accurately. Variable conditions cause changes in many electrical, magnetic, and thermal parameters. A change in parameters affects the intensity, efficiency of the operating currents of the machine, thereby changing the motor losses.</p> <p>In this article, the objective is to determine the changes in the equivalent circuit parameters of three-phase squirrel-cage induction motors (SCIMs) with different powers under the variable conditions (stator winding temperature, load, and motor shaft speed). Supervisory control and data acquisition (SCADA) program read real-time information (temperature, current, voltage, power, shaft speed, and torque) from the experiments and necessary calculations were made. When the test results were examined, a maximum of 9 % change was observed in the motor ECPs (R<sub>S</sub>, R<sub>2</sub>, R<sub>M</sub>, X<sub>S</sub>, X<sub>2</sub>, and X<sub>M</sub>) at different shaft speeds as a result of the change in the stator winding temperature between 100 % and 110 %. At different loads, this rate of change increases to 16 %. This has shown that motor shaft speed and load, together with temperature, have a significant effect on the ECPs.</p> Hakan Terzioglu, Abdullah Cem Agacayak Copyright (c) 2022 Hakan Terzioglu, Abdullah Cem Agacayak https://eejournal.ktu.lt/index.php/elt/article/view/31198 Wed, 26 Oct 2022 00:00:00 +0300 Title https://eejournal.ktu.lt/index.php/elt/article/view/32593 Elektronika ir Elektrotechnika Copyright (c) 2022 Elektronika ir Elektrotechnika https://eejournal.ktu.lt/index.php/elt/article/view/32593 Wed, 26 Oct 2022 00:00:00 +0300 Blind Source Separation with Multi-Objective Optimization for Denoising https://eejournal.ktu.lt/index.php/elt/article/view/31232 <p>Blind Source Separation is an optimization method frequently used in statistical signal processing applications. There are many application areas such as ambient listening, denoising, signal detection, and so on. In this study, a new Strength Pareto Evolutionary Algorithm 2-based signal separation method is proposed, which combines Multi-Objective Optimization and Blind Source Separation algorithms. The proposed method has been tested for denoising, which is widely used in biomedical signal processing. That is, the Electrocardiogram (ECG) and White Gaussian Noise are mixed together with normally distributed random numbers and the original signals of the mixed signals are obtained again. To evaluate the performance of the proposed method and others (Multi-Objective Blind Source Separation and Independent Component Analysis), the Signal-to-Noise Ratio (SNR) of the ECG signal obtained from mixed signals has been measured. As a result of the simulation studies, it is seen that the performance of the proposed method is satisfactory.</p> Husamettin Celik, Nurhan Karaboga Copyright (c) 2022 Husamettin Celik, Nurhan Karaboga https://eejournal.ktu.lt/index.php/elt/article/view/31232 Wed, 26 Oct 2022 00:00:00 +0300 A Hybrid Phishing Detection System Using Deep Learning-based URL and Content Analysis https://eejournal.ktu.lt/index.php/elt/article/view/31197 <p>Phishing attacks are one of the most preferred types of attacks for cybercriminals, who can easily contact a large number of victims through the use of social networks, particularly through email messages. To protect end users, most of the security mechanisms control Uniform Resource Locator (URL) addresses because of their simplicity of implementation and execution speed. However, due to sophisticated attackers, this mechanism can miss some phishing attacks and has a relatively high false positive rate. In this research, a hybrid technique is proposed that uses not only URL features, but also content-based features as the second level of detection mechanism, thus improving the accuracy of the detection system while also minimizing the number of false positives. Additionally, most phishing detection algorithms use datasets that contain easily differentiated data pieces, either <em>phishing</em> or <em>legitimate</em>. However, in order to implement a more secure protection mechanism, we aimed to collect a larger and high-risk dataset. The proposed approaches were tested on this <em>High-Risk URL and Content-Based Phishing Detection Dataset</em> that only contains suspicious websites from PhishTank. According to experimental studies, an accuracy rate of 98.37 percent was achieved on a more realistic dataset for phishing detection.</p> Mehmet Korkmaz, Emre Kocyigit, Ozgur Koray Sahingoz, Banu Diri Copyright (c) 2022 Mehmet Korkmaz, Emre Kocyigit, Ozgur Koray Sahingoz, Banu Diri https://eejournal.ktu.lt/index.php/elt/article/view/31197 Wed, 26 Oct 2022 00:00:00 +0300 Improvement of Automotive Sensors by Migrating AUTOSAR End-to-End Communication Protection Library into Hardware https://eejournal.ktu.lt/index.php/elt/article/view/31154 <p>This paper explores new methods to increase the level of safety of data transfer between sensors and electronic control units (ECUs) in automotive communication. A new model of basic sensors to be used in automotive electronics is proposed. This model contains hardware modules that implement the end-to-end communication protection (E2E) mechanism, as defined by the Automotive Open System Architecture (AUTOSAR) standard. By adding this feature inside the sensors, it is possible that, in addition to increasing the safety level, these sensors can be directly connected to the network ECUs via standard communication buses (e.g., Local Interconnect Network (LIN), Controller Area Network (CAN), Flexray, etc.). This paper describes the model, design, and mapping (in a Field Programmable Gate Array device (FPGA)) of the hardware E2E module capable of generating the Cyclic Redundancy Code (CRC) and counter signal for a customized message. This message represents the output of the new sensor E2E module used in a safety communication as requested by the automotive E2E standard. The model is validated also by comparing the data output of the E2E hardware with the data output of the AUTOSAR software E2E library. Finally, future needs and directions are suggested in this area.</p> Horia V. Caprita, Dan Selisteanu Copyright (c) 2022 Horia V. Caprita, Dan Selisteanu https://eejournal.ktu.lt/index.php/elt/article/view/31154 Wed, 26 Oct 2022 00:00:00 +0300 Power and Energy Consumption Models for Embedded Applications https://eejournal.ktu.lt/index.php/elt/article/view/31345 <p>This paper describes a study on the power and energy consumption estimation models that have been defined to facilitate the development of ultra-low power embedded applications. During the study, various measurements have been carried out on the instruction and application level to challenge the models against empirical data. The study has been performed on the multicore heterogeneous hardware platform developed for ultra-low power Digital Signal Processors (DSP) applications. The final goal was to develop a tool that can provide insight into power dissipation during the execution of embedded applications, so that one can refactor the source code in an energy-efficient manner, or ideally to develop an energy-aware C compiler. The side effect of the research presents interesting insight into how the custom hardware architecture influences power dissipation. The selected platform has been chosen simply because it represents R&amp;D state of the art ultra-low power hardware used in hearing aids. The presented solution has been developed and tested in an Eclipse environment using Java programming language.</p> Momcilo V. Krunic Copyright (c) 2022 Momcilo V. Krunic https://eejournal.ktu.lt/index.php/elt/article/view/31345 Wed, 26 Oct 2022 00:00:00 +0300 Device for Inactivation of SARS-CoV-2 Using UVC LEDs https://eejournal.ktu.lt/index.php/elt/article/view/31140 <p>In connection with the COVID-19 pandemic, there is an urgent need for disinfecting devices that can be used both indoors and in transport. Currently, the most common of these devices are ultraviolet (UV) germicidal lamps. However, they have significant disadvantages, such as short service life, presence of mercury, lack of flexible control, large dimensions, etc. The paper analyzes the sources of UV radiation to find an alternative to UV lamps. Although these elements currently have low efficiency and high cost, etc., it is proposed to use UVC LEDs as a UV source. Due to the COVID-19 pandemic and the general interest in the fight against viruses, as well as the ban on the use of mercury, investments have been attracted in the development of UVC LEDs, which will make them competitive in the future compared to germicidal lamps both in cost and efficiency. The paper presents a disinfection device developed on the basis of UVC LEDs. The principle of operation is described; the control system, the drawing, and the design of the UVC LED-based disinfection device are presented. Due to the described limitations of UVC LEDs, this design can be used for disinfection of small surface areas where frequent on/off switching is required and high power is not required.</p> Oleksandr Dziubenko, Shchasiana Arhun, Andrii Hnatov, Dmytro Bogdan, Antons Patlins Copyright (c) 2022 Oleksandr Dziubenko, Shchasiana Arhun, Andrii Hnatov, Dmytro Bogdan, Antons Patlins https://eejournal.ktu.lt/index.php/elt/article/view/31140 Wed, 26 Oct 2022 00:00:00 +0300 Creating a Data Generator and Implementing Algorithms in Process Analysis https://eejournal.ktu.lt/index.php/elt/article/view/31126 <p>Process mining is a new field of work that aims to meet the need of the business world to improve efficiency and productivity. This field focuses on analysing, discovering, managing, and improving business processes. Process mining uses event logs as a resource and works on this resource. Hence, the system is developed by analysing the event logs, including each step in the process model. Our study is made up of two significant stages: a data generator for processes and algorithms applied for discovering the created processes. In the first stage, the aim was to develop a simulator with the ability to generate data that could help process modelling and development. Within the framework of this study, a system was created that could work with various process models and extract meaningful information from these models. More productive and efficient processes can be developed as a result of his system. The simulator consists of three modules. The first module is the part where users create a process model. In this module, the user can create his own business process model in the system’s interface or select from other registered models. In the second module, team-based data are simulated through these process models. These generated data are used in the third module, called “analysis”, and meaningful information is extracted. In conclusion, the process can be improved considering the information about time, resource, and cost in the generated data. At the second stage, processes were discovered using alpha, heuristic, and genetic algorithms, which are process mining discovery algorithms and synthetic and real event logs. The discovered processes were demonstrated with Petri nets, and the algorithms’ performances were compared using the fitness function, accuracy rates, and running times. In our study, the heuristic algorithm is more successful because it improves the noise in the data and incomplete processes, which are the disadvantages of the alpha algorithm. However, the genetic algorithm yielded more successful results than the alpha and heuristic algorithms due to its genetic operators.</p> Cigdem Bakir, Mecit Yuzkat, Fatih Karabiber Copyright (c) 2022 Cigdem Bakir, Mecit Yuzkat, Fatih Karabiber https://eejournal.ktu.lt/index.php/elt/article/view/31126 Wed, 26 Oct 2022 00:00:00 +0300 Editorial Board https://eejournal.ktu.lt/index.php/elt/article/view/32594 Elektronika ir Elektrotechnika Copyright (c) 2022 Elektronika ir Elektrotechnika https://eejournal.ktu.lt/index.php/elt/article/view/32594 Wed, 26 Oct 2022 00:00:00 +0300 Combine Harvester Low Crushing Rate Operation Strategy Research by Using Bayesian Network https://eejournal.ktu.lt/index.php/elt/article/view/31179 <p>As the main harvesting machinery, the combine harvester is often due to improper adjustment of its operating parameters resulting in increased crushing rate and grain waste during the harvesting process. To quickly obtain the working range of key operating parameters under low crushing rate, this study conducted field tests on the relevant parameters affecting the crushing rate and finally selected the travel speed, feed rate, threshing drum speed, concave clearance, and crushing rate as node variables for the construction of the Bayesian network model. Based on the “search-and-score” algorithm, the best network structure can be obtained using the combination of the Akaike Information Criterion (AIC) scoring function and the hill-climbing method. In the obtained network, adjusting the proportion of the lowest level of the crushing rate nodes to 100 %, the operation strategy under the condition of low broken rate obtained by the network reasoning was: feed rate &lt; 6 kg/s, travel speed &lt; 5 km/h, concave clearance = 10 mm, threshing drum speed &lt; 900 rpm. Three field trials were carried out using this optimized operation strategy, and the measured crushing rates were 0.93 %, 0.95 %, and 1.07 %, respectively, and the average crushing rate was 0.98 %. At the same time, when the optimized strategy was not used, the crushing rates were, respectively, 1.12 %, 1.41 %, and 1.93 %, and the average crushing rate was 1.48 %. The test results prove that the operation strategy based on Bayesian network inference can effectively reduce the crushing rate in the harvesting process.</p> <p><a href="#_ftnref1" name="_ftn1"></a></p> Yehong Liu, Dong Sun, Baoyan Xu, Shumao Wang, Xin Wang Copyright (c) 2022 Yehong Liu, Dong Sun, Baoyan Xu, Shumao Wang, Xin Wang https://eejournal.ktu.lt/index.php/elt/article/view/31179 Wed, 26 Oct 2022 00:00:00 +0300