A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> ELECTRONICS (Total: 207 journals)
The end of the list has been reached or no journals were found for your choice.
Similar Journals
Journal Cover
Elektronika ir Elektortechnika
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1392-1215 - ISSN (Online) 2029-5731
Published by Kaunas University of Technology Homepage  [10 journals]
  • Title

    • Authors: Elektronika ir Elektrotechnika
      Pages: 1 - 1
      PubDate: 2023-10-31
      Issue No: Vol. 29, No. 5 (2023)
  • Identification of Wiener Systems with Recursive Gauss-Seidel Algorithm

    • Authors: Metin Hatun
      Pages: 4 - 10
      Abstract: The Recursive Gauss-Seidel (RGS) algorithm is presented that is implemented in a one-step Gauss-Seidel iteration for the identification of Wiener output error systems. The RGS algorithm has lower processing intensity than the popular Recursive Least Squares (RLS) algorithm due to its implementation using one-step Gauss-Seidel iteration in a sampling interval. The noise-free output samples in the data vector used for implementation of the RGS algorithm are estimated using an auxiliary model. Also, a stochastic convergence analysis is presented, and it is shown that the presented auxiliary model-based RGS algorithm gives unbiased parameter estimates even if the measurement noise is coloured. Finally, the effectiveness of the RGS algorithm is verified and compared with the equivalent RLS algorithm by computer simulations.
      PubDate: 2023-10-31
      DOI: 10.5755/j02.eie.35119
      Issue No: Vol. 29, No. 5 (2023)
  • Impact of DGs on the Reactive Power-Supported Optimal EDN for Profit

    • Authors: Srinivasan G, Lavanya M
      Pages: 11 - 20
      Abstract: Significant issues such as high-Power Loss (PLoss) and drop in node voltages in Electric Distribution Networks (EDNs) can be well mitigated using renowned techniques such as Alteration of Electric Distribution Network Switches (AEDNS), Optimal Capacitor Support (OCS), and Integration of Dispersed Generation (IDG), which are identified as the most economical and efficient approaches. This study presents the optimisation of AEDNS with and without OCS considering four different scenarios to maximise the profit through reduction in PLoss, which is regarded as the first-step process. To further increase profit, IDG was integrated into the EDNs after the combined optimisation of OCS and AEDNS. In this work, Levy Flight Mechanism (LFM) was incorporated into the Seagull Optimisation Algorithm (SOA) and applied to solve the objective function based on economics. The effectiveness of the presented methodology was evaluated and confirmed using a real 59-bus in Cairo, Egypt, EDN, as well as a conventional 33-bus test system. For each scenario, the PLoss reductions and net profit of the proposed methodology were contrasted with those obtained from previously reported approaches. The collected findings show that by optimising AEDNS, OCS, and IDG, the established methodology effectively yields more economic gain for all scenarios.
      PubDate: 2023-10-31
      DOI: 10.5755/j02.eie.34665
      Issue No: Vol. 29, No. 5 (2023)
  • Current Control of Battery Pack Modules in Parallel Connection According
           to SoC

    • Authors: Mario Vrazic, Antonio Persic, Peter Virtic, Tomislav Ivanis
      Pages: 21 - 27
      Abstract: Electric vehicles, especially cars, have been in the spotlight for some time now. In the focus of environmentalists, engineers, users, media, etc. With the growth and advancement of the market for such vehicles, other electric vehicles are also focussing on. One of such vehicles is boats, particularly smaller boats up to 8–10 meters in length. Of course, the biggest problem here is charging. The general idea is to use battery modules that can be easily carried and enable hot swapping. This paper investigates scenarios and simulations of the control system for hot swapping of the battery module. Simulations of connection of two and three battery modules to parallel operation and current control are presented in this paper, as well as applied control rules.
      PubDate: 2023-10-31
      DOI: 10.5755/j02.eie.35451
      Issue No: Vol. 29, No. 5 (2023)
  • Overview of Problems and Solutions in the Design of Intrinsically Safe

    • Authors: Sławomir Chmielarz, Tomasz Molenda, Wojciech Korski, Krzysztof Oset, Waldemar Sobierajski
      Pages: 28 - 35
      Abstract: The article presents selected basic problems occurring in the design of intrinsically safe apparatuses, solutions to some of them, and points out mistakes made during their design. The article addresses selected issues related to the design of intrinsically safe apparatuses and systems and includes a systematisation of the news and conclusions. The article also presents several project solutions for hazardous areas.
      PubDate: 2023-10-31
      DOI: 10.5755/j02.eie.34552
      Issue No: Vol. 29, No. 5 (2023)
  • Systematic Design of a Pseudodifferential VCO Using Monomial Fitting

    • Authors: Nicolai J. Dahl, Pere L. Muntal, Michael A. E. Andersen
      Pages: 36 - 43
      Abstract: Digital integrated electronics benefits from its higher abstraction level, allowing optimisation methods and automated workflows. However, analogue integrated circuit design is still predominantly done manually, leading to lengthy design cycles. This paper proposes a new systematic design approach for the sizing of analogue integrated circuits to address this issue. The method utilises a surrogate optimisation technique that approximates a simple monomial function based on few simulation results. These monomials are convex and can be optimised using a simple linear optimisation routine, resulting in a single global optimal solution. We show that monomial functions, in many cases, have an analytic relation to integrated circuits, making them well suited for the application. The method is demonstrated by designing a 14 MHz pseudodifferential voltage-controlled oscillator (VCO) with minimised current consumption and is manufactured in a 180 nm process. The measured total current matches the predicted and is lower than that for other similar state-of-the-art VCOs.
      PubDate: 2023-10-31
      DOI: 10.5755/j02.eie.35279
      Issue No: Vol. 29, No. 5 (2023)
  • Multiagent System-Based Adaptive Numerical Relay Design and Development:
           Part I - Firmware

    • Authors: Abdulfetah Abdela Shobole, Motuma Abafogi, Hammad Khalid, Yahia Amireh, Abdurrahman Zaim
      Pages: 44 - 50
      Abstract: Protection relays that incorporate advanced microprocessors are vital to electrical grids, providing fast and reliable responses to faulty conditions using efficient communication protocols. Instant detection and response to various faults are essential to minimise the risk of damage. Numerical relays can identify faulty conditions and trigger circuit breakers to open, thus preventing further damage to the system. Due to the lack of autonomous decision-making capabilities, existing numerical relays require manual reconfiguration in situations such as a change in network configuration and protection settings. These relays also do not have the ability to coordinate fault clearance when multiple sources supply power to the grid. A comprehensive overview of the research aimed at developing a multiagent system (MAS)-based adaptive protection relay will be provided by dividing it into separate articles such as firmware and hardware. This first part delves into the firmware aspects of this innovative relay, highlighting its adaptive capabilities and key considerations in its development. The second part will provide detailed design descriptions for the hardware features of the relay. An STM32MPU multicore advanced microprocessor is utilised to design and develop the adaptive numerical relay firmware. It incorporates several protection relay ANSI codes, communication protocols, and MAS-based adaptive protection schemes as part of the firmware.
      PubDate: 2023-10-31
      DOI: 10.5755/j02.eie.34783
      Issue No: Vol. 29, No. 5 (2023)
  • Machine Learning Approach for Diagnosis and Prognosis of Cardiac
           Arrhythmia Condition Using a Minimum Feature Set and
           Auto-Segmentation-Based Window Optimisation

    • Authors: Swetha Rameshbabu, Sabitha Ramakrishnan
      Pages: 51 - 61
      Abstract: Cardiovascular diseases have become extremely prevalent in the global population. Several accurate classification methods for arrhythmias have been proposed in the healthcare literature. However, extensive research is required to improve the prediction accuracy of various arrhythmia conditions. In this paper, discussion is focussed on two major objectives: optimisation of windows based on our proposed auto-segmentation method for the exact diagnosis of the heart condition within the segment and prediction of arrhythmia progression. For prediction, identification of features is vital. Identified efficient independent feature sets such as RR interval, peak-to-peak amplitude, and unique derived parameters such as coefficient of variation (CV) of RR interval and CV of peak-to-peak amplitude. The progression of arrhythmia includes the following steps such as data preprocessing, time and frequency domain feature extraction, and feature selection using principal component analysis. A hypertuned support vector machine is utilised for accurate diagnosis. Proposed two techniques to predict the progression of arrhythmias: the regression-based trend curve (RBTC) and the fuzzy enhanced Markov model (FEMM). We have effectively evaluated our prediction algorithms using offline Massachusetts Institute of Technology Physio Net database signals, using automatic segmentation with prediction accuracy of 98 %. In terms of accuracy, FEMM outperforms RBTC. Thus, an auto-segmentation algorithm was proposed to classify various arrhythmia signals using a minimal feature set and to predict future conditions using our proposed method, FEMM.
      PubDate: 2023-10-31
      DOI: 10.5755/j02.eie.34357
      Issue No: Vol. 29, No. 5 (2023)
  • Machine Vision Method for Quantitative Statistics Analysis of Industrial
           Product Images

    • Authors: Jie Zan, Yaosheng Hu, Shoufeng Jin, Ruichao Zhang, Rafal Stanislawski
      Pages: 62 - 69
      Abstract: To address the problems of unstable accuracy, low efficiency, and subjective influence of manual counting, a machine vision-based method to count the quantity of tobacco shreds is proposed for the first time. In this paper, the complex tobacco shred image is obtained by backlight imaging. The adaptive threshold segmentation method is used to segment tobacco shreds. The pixel area of the tobacco shred area is calculated by connected domain labelling. Second, independent tobacco shreds and adhesive tobacco shreds were identified based on the pixel area, and the quantity of segmented tobacco shreds was counted for the first time. Subsequently, in complex scenarios (such as tobacco shreds adhesive and overlapping), an image is usually obtained by manually drawing the contours of the adhesive and overlapping tobacco shreds on the basis of primary statistics. Finally, different individuals are distinguished, segmentation is completed, and tobacco shred quantity statistics are realised. The experimental results show that the average accuracy is 100.0 % for quantitative statistics of independent tobacco shred images. For tobacco shred images with adhesive and overlapping interference, the minimum accuracy is 90 %, and the accuracy increases with the increase in tobacco shred quantity. Furthermore, the efficiency of the tobacco shred quantity statistics conducted by the method in this paper was only affected by complex scenarios. Compared to artificial processing, the efficiency was increased by more than 100 %. The work in this paper can provide the technical basis for measuring the dimensions of tobacco shreds.
      PubDate: 2023-10-31
      DOI: 10.5755/j02.eie.35083
      Issue No: Vol. 29, No. 5 (2023)
  • Hybrid Technique for Detecting Extremism in Arabic Social Media Texts

    • Authors: Israa Akram Alzuabidi, Layla Safwat Jamil, Amjed Abbas Ahmed, Shahrul Azman Mohd Noah, Mohammad Kamrul Hasan
      Pages: 70 - 78
      Abstract: Today, social media sites like Twitter provide effective platforms to share opinions and thoughts in public with millions of other users. These opinions shared on such sites influence a large number of people who may easily retweet them and accelerate their spread. Unfortunately, some of these opinions were expressed by extremists who promoted hateful content. Since Arabic is one of the most spoken languages, it is crucial to automate the process of monitoring Arabic content published on social sites. Therefore, this study aims to propose a hybrid technique to detect extremism in Arabic social media texts and articles to monitor the situation of published extremist content. The proposed technique combines the lexicon-based approach with the rough set theory approach. The rough set theory is employed with two approximation strategies: lower approximation and accuracy approximation. The hybrid technique used the rough set theory as a classifier and the lexicon-based as a vector. Furthermore, this study built three types of corpuses (V1, V2, and V3) collected from Twitter. The experimental findings show that among the proposed hybrid methods, the accuracy approximation was superior to the lower approximation with seed vector. It was also revealed that hybrid methods outperformed machine learning techniques in terms of efficiency. Moreover, the study recommends using an accuracy approximation method with seed vector to identify the polarity of the text.
      PubDate: 2023-10-31
      DOI: 10.5755/j02.eie.34743
      Issue No: Vol. 29, No. 5 (2023)
  • Operation Parameters Optimisation of a Machine Swarm Using Artificial

    • Authors: Lin Zhong, Wei Rao, Xiaohang Zhang, Zhibin Zhang, Grzegorz Krolczyk
      Pages: 79 - 85
      Abstract: Due to improper setting of operating parameters, cigarette machines are subject to a high unqualified production rate. For this reason, in this study, a multiobjective optimisation (MOP) method based on the metaheuristic intelligence optimisation is proposed in this study. First, to eliminate interference parameters, the random forest (RF) is used to analyse the parameter importance of the cigarette machine and select the most important operation parameters for the multiobjective optimisation. Second, an artificial neural network (ANN) optimised by the grey wolf optimiser is designed to establish a mirror model of the cigarette machine to fast calculate the machine output quality factors, including the rod break rate, single cigarette weight, and circumference index. Lastly, an improved multiobjective grey wolf optimisation algorithm is used to optimise these three quality factors simultaneously to obtain the optimal operating parameters of the cigarette machine. A machine swarm (including four cigarette machines) in the real world is used to evaluate the developed optimisation method, and the testing results demonstrate that the proposed multiobjective optimisation method is able to improve the three quality factors by at least 50 %, which greatly reduces the unqualified rate of cigarettes.
      PubDate: 2023-10-31
      DOI: 10.5755/j02.eie.35085
      Issue No: Vol. 29, No. 5 (2023)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762

Your IP address:
Home (Search)
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-