https://eejournal.ktu.lt/index.php/elt/issue/feed Elektronika ir Elektrotechnika 2021-08-23T14:09:54+03:00 Elektronika ir Elektrotechnika eejournal@ktu.lt Open Journal Systems <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> https://eejournal.ktu.lt/index.php/elt/article/view/28723 Effect of Annealing Atmosphere on the Diode Behaviour of ZnO/Si Heterojunction 2021-03-26T09:12:12+02:00 Sadia Muniza Faraz smuniza@neduet.edu.pk Syed Riaz un Nabi Jafri riazun1036@neduet.edu.pk Zarreen Tajvar ztajvar@ssuet.edu.pk Naveed ul Hassan Alvi naveed.ul.hassan.alvi@ri.se Qamar-ul Wahab wahab.qu@gmail.com Omer Nur omer.nour@liu.se <p>The effect of thermal annealing atmosphere on the electrical characteristics of Zinc oxide (ZnO) nanorods/p-Silicon (Si) diodes is investigated. ZnO nanorods are grown by low-temperature aqueous solution growth method and annealed in Nitrogen and Oxygen atmosphere. As-grown and annealed nanorods are studied by scanning electron microscopy (SEM) and photoluminescence (PL) spectroscopy. Electrical characteristics of ZnO/Si heterojunction diodes are studied by current-voltage (I-V) and capacitance-voltage (C-V) measurements at room temperature. Improvements in rectifying behaviour, ideality factor, carrier concentration, and series resistance are observed after annealing. The ideality factor of 4.4 for as-grown improved to 3.8 and for Nitrogen and Oxygen annealed improved to 3.5 nanorods diodes. The series resistances decreased from 1.6 to 1.8 times after annealing. An overall improved behaviour is observed for oxygen annealed heterojunction diodes. The study suggests that by controlling the ZnO nanorods annealing temperatures and atmospheres the electronic and optoelectronic properties of ZnO devices can be improved.</p> 2021-08-17T00:00:00+03:00 Copyright (c) 2021 Sadia Muniza Faraz, Syed Riaz un Nabi Jafri, Zarreen Tajvar, Naveed ul Hassan Alvi, Qamar-ul Wahab, Omer Nur https://eejournal.ktu.lt/index.php/elt/article/view/29631 Title 2021-08-19T11:07:10+03:00 Elektronika ir Elektrotechnika eejournal@ktu.lt 2021-08-17T00:00:00+03:00 Copyright (c) 2021 Elektronika ir Elektrotechnika https://eejournal.ktu.lt/index.php/elt/article/view/28916 Robust Discrete-Time Nonlinear Attitude Stabilization of a Quadrotor UAV Subject to Time-Varying Disturbances 2021-04-16T13:22:11+03:00 Fatih Adiguzel fatihadiguzel1@istanbul.edu.tr Tarik Veli Mumcu tarik.mumcu@istanbul.edu.tr <p class="Abstract">A discrete-time improved input/output linearization controller based on a nonlinear disturbance observer is considered to secure the stability of a four-rotor unmanned aerial vehicle under constant and time-varying disturbances, as well as uncertain system parameters for its attitude behaviour. Due to the nature of the quadrotor system, it contains the most extreme high level of nonlinearities, system parameter uncertainties (perturbations), and it has to cope with external disturbances that change over time. In this context, an offset-less tracking for the quadrotor system is provided with the input/output linearization controller together with a discrete-time pre-controller. In addition, the robustness of the system is increased with a discrete-time nonlinear disturbance observer for time-varying disturbances affecting the system. The main contribution of this study is to provide highly nonlinearities cancellation to guarantee the aircraft attitude stability and to propose a robust control structure in discrete-time, considering all uncertainties. Various simulation studies have been carried out to illustrate the robustness and effectiveness of the proposed controller structure.</p> 2021-08-17T00:00:00+03:00 Copyright (c) 2021 Fatih Adiguzel, Tarik Veli Mumcu https://eejournal.ktu.lt/index.php/elt/article/view/29005 Combine Harvester Cooling Water Temperature Prediction Based on CDAE-LSTM Hybrid Model 2021-04-26T13:02:58+03:00 Yining Fu S20193071139@cau.edu.cn Baoyan Xu cmmxby@163.com Xindong Ni nxd@cau.edu.cn Yehong Liu liuyehong_zb@163.com Xin Wang wangxin117@cau.edu.cn <p>Cooling water temperature of the combine harvester during operations can reflect the changes of its power consumption and even overloads caused by extreme workload. There is an existing problem when extracting water temperature information from harvesters: data redundancy and the loss of time series feature. To solve such problem, a Convolutional denoising autoencoder and Long-Short Term Memory Artificial Neural Network (CDAE-LSTM) hybrid model based on parameter migration is proposed to predict temperature trends. Firstly, the historical data of the combine harvester are taken into account to perform correlation analysis to verify the input rationality of the proposed model. Secondly, pre-training has been performed to determine the model’s initial migration parameters, along with the adoption of CDAE to denoise and reconstruct the input data. Finally, after the migration, the CNN-LSTM hybrid model was trained with a real dataset and was able to predict the cooling water temperature. The accuracy of the model has been verified by field test data gathered in June 2019. Results show that the root mean squared error (RMSE) of the model is 0.0817, and the mean absolute error (MAE) is 0.0989. Compared with the performance of LSTM on the prediction data, the RMSE improvement rate is 2.272 %, and the MAE improvement rate is 20.113 %. It is proven that the adoption of CDAE stabilizes the model, and the CDAE-LSTM hybrid model shows higher accuracy and lower uncertainty for time series prediction.</p> 2021-08-17T00:00:00+03:00 Copyright (c) 2021 Yining Fu, Baoyan Xu, Xindong Ni, Yehong Liu, Xin Wang https://eejournal.ktu.lt/index.php/elt/article/view/28881 Binary Quantization Analysis of Neural Networks Weights on MNIST Dataset 2021-04-13T15:03:14+03:00 Zoran H. Peric zoran.peric@elfak.ni.ac.rs Bojan D. Denic bojan.denic@elfak.ni.ac.rs Milan S. Savic milan.savic1@pr.ac.rs Nikola J. Vucic nikola.vucic@elfak.ni.ac.rs Nikola B. Simic nikolasimic@uns.ac.rs <p>This paper considers the design of a binary scalar quantizer of Laplacian source and its application in compressed neural networks. The quantizer performance is investigated in a wide dynamic range of data variances, and for that purpose, we derive novel closed-form expressions. Moreover, we propose two selection criteria for the variance range of interest. Binary quantizers are further implemented for compressing neural network weights and its performance is analysed for a simple classification task. Good matching between theory and experiment is observed and a great possibility for implementation is indicated.</p> 2021-08-23T00:00:00+03:00 Copyright (c) 2021 Zoran H. Peric, Bojan D. Denic, Milan S. Savic, Nikola J. Vucic, Nikola B. Simic https://eejournal.ktu.lt/index.php/elt/article/view/28877 Efficient Feature Set Developed for Acoustic Gunshot Detection in Open Space 2021-04-12T20:55:57+03:00 Milan Sigmund sigmund@feec.vutbr.cz Martin Hrabina hrabina@phd.feec.vutbr.cz <p>This paper presents an efficient approach to automatic gunshot detection based on a combination of two feature sets: adapted standard sound features and hand-crafted novel features. The standard features are mel-frequency cepstral coefficients adapted for gunshot recognition in terms of uniform gamma-tone filters linearly spaced over the whole frequency range from 0 kHz to 16 kHz. The first 18 coefficients calculated from the 41 filters represent the best set of the optimized cepstral coefficients. The novel features were derived in the time domain from individual significant points of the raw waveform after amplitude normalization. Experiments were performed using single and ensemble neural networks to verify the effectiveness of the novel features for supplementing the standard features. The novelty of the work is the proposed feature combination, which allows to achieve very effective detection of gunshots from hunting weapons using 23 features and a simple neural network. In binary classification, the developed approach achieved an accuracy of 95.02 % in gunshot detection and 98.16 % in disregarding other sounds (i.e., non-gunshot).</p> 2021-08-23T00:00:00+03:00 Copyright (c) 2021 Milan Sigmund, Martin Hrabina https://eejournal.ktu.lt/index.php/elt/article/view/28864 Estimating the Distributed Generation Unit Sizing and Its Effects on the Distribution System by Using Machine Learning Methods 2021-04-11T02:22:49+03:00 Mikail Purlu purlu@itu.edu.tr Belgin Emre Turkay turkayb@itu.edu.tr <p>Many approaches about the planning and operation of power systems, such as network reconfiguration and distributed generation (DG), have been proposed to overcome the challenges caused by the increase in electricity consumption. Besides the positive effects on the grid, contributions on environmental pollution and other advantages, the rapid developments in renewable energy technologies have made the DG resources an important issue, however, improper DG allocation may result in network damages. A lot of studies have been practised with analytical and heuristic methods based on load flow for optimal DG integration to the network. This novel method based on estimation is proposed to determine the size of DG and its effects on the network to get rid of the coercive and time-consuming load flow techniques. Machine learning algorithms, such as Linear Regression, Artificial Neural Network, Support Vector Regression, K-Nearest Neighbor, and Decision Tree, have been used for the estimations and have been applied to well-known test systems, such as IEEE 12-bus, 33-bus, and 69-bus distribution systems. The accuracy of the proposed estimation methods has been verified with R-squared and mean absolute percentage error. Results show that the proposed DG allocation method is effective, applicable, and flexible.</p> 2021-08-17T00:00:00+03:00 Copyright (c) 2021 Mikail Purlu, Belgin Emre Turkay https://eejournal.ktu.lt/index.php/elt/article/view/28917 Reactive Power Support in Radial Distribution Network Using Mine Blast Algorithm 2021-04-16T13:43:37+03:00 Mohsin Shahzad mohsinshahzad@cuiatd.edu.pk Qazi Shafiullah qazishafiullah@gmail.com Waseem Akram engrwaseemakram1@gmail.com Muhammad Arif marif@cuiatd.edu.pk Barkat Ullah barkat@ciitwah.edu.pk <p>The passive power distribution networks are prone to imperfect voltage profile and higher power losses, especially at the far end of long feeders. The capacitor placement is studied in this article using a novel Mine Blast Algorithm (MBA). The voltage profile improvement and reduction in the net annual cost are also considered along with minimizing the power loss. The optimization problem is formulated and solved in two steps. Firstly, the Voltage Stability Index (VSI) is used to rank the nodes for placement of the capacitors. Secondly, from the priority list of nodes in the previous step, the MBA is utilized to provide the optimal location and sizes of the capacitors ensuring loss minimization, voltage profile improvement, and reduced net annual cost. Finally, the results are tested on 33 and 69 radial node systems in MATLAB. The results for the considered variables are presented which show a significant improvement in active and reactive power loss reduction and voltage profile with lesser reactive power injection.</p> 2021-08-17T00:00:00+03:00 Copyright (c) 2021 Mohsin Shahzad, Qazi Shafiullah, Waseem Akram, Muhammad Arif, Barkat Ullah https://eejournal.ktu.lt/index.php/elt/article/view/29632 Editorial Board 2021-08-19T11:08:40+03:00 Elektronika ir Elektrotechnika eejournal@ktu.lt 2021-08-17T00:00:00+03:00 Copyright (c) 2021 Elektronika ir Elektrotechnika https://eejournal.ktu.lt/index.php/elt/article/view/29090 A Case Study of Direct Current Resistivity Method for Disaster Water Source Detection in Coal Mining 2021-05-13T10:29:23+03:00 Yangzhou Wang wyzou021@163.com Jingcun Yu yujcun@163.com Xihui Feng huixinxin@126.com Li Ma mali@crecu.cn Qianhui Gao TS19010016A31@cumt.edu.cn Benyu Su subenyu@cumt.edu.cn Xiuju Xing huixinxin@126.com <p>Disaster water sources in underground coal seam always danger mining safety. The current electromagnetic methods are not effective to detect the disaster water sources in the underground due to noise interference generated by large metal equipment. To address this problem, the direct current resistivity method is proposed to detect the disaster water sources in the underground coal seams. The relationship between the resistivity distribution and water faults was investigated, and we found that a low resistivity distribution indicates a disaster water source. To verify the proposed method, both numerical simulations and field test have been carried out and the analysis results show that the low resistivity distribution can be used to correctly detect the disaster water sources. Most importantly, the proposed method has detected three threats of mine water disaster in the field test in a real coal seam in Lan County coal mine, China. As a result, the present work provides an important and solid support to coal mining safety.</p> 2021-08-17T00:00:00+03:00 Copyright (c) 2021 Yangzhou Wang, Jingcun Yu, Xihui Feng, Li Ma, Qianhui Gao, Benyu Su, Xiuju Xing https://eejournal.ktu.lt/index.php/elt/article/view/28903 Scalable Balanced Pipelined IPv6 Lookup Algorithm 2021-04-14T00:02:47+03:00 Zoran Cica zoran.cica@etf.bg.ac.rs <p>One of the most critical router’s functions is the IP lookup. For each incoming IP packet, IP lookup determines the output port to which the packet should be forwarded. IPv6 addresses are envisioned to replace IPv4 addresses because the IPv4 address space is exhausted. Therefore, modern IP routers need to support IPv6 lookup. Most of the existing IP lookup algorithms are adjusted for the IPv4 lookup, but not for the IPv6 lookup. Scalability represents the main problem in the existing IP lookup algorithms because the IPv6 address space is much larger than the IPv4 address space due to longer IPv6 addresses. In this paper, we propose a novel IPv6 lookup algorithm that supports very large IPv6 lookup tables and achieves high IP lookup throughput.</p> 2021-08-23T00:00:00+03:00 Copyright (c) 2021 Zoran Cica