Acta Botanica Hungarica publishes papers by scientists of Hungary and of surrounding countries working on the topics listed below. Studies by foreign researchers written in the framework of international projects and cooperations are also welcome. Main subjects: plant anatomy and histology, cryptogam and phanerogam taxonomy, molecular phylogeny, plant geography, plant sociology, vegetation science, tropical botany, ethnobotany, paleobotany and palynology. Publishes book reviews and advertisements.

The journal "AIMS Electronics and Electrical Engineering" welcomes submissions from scientists in Hungary and surrounding countries, particularly on the following topics: plant anatomy and histology, cryptogam and phanerogam taxonomy, molecular phylogeny, plant geography, plant sociology, vegetation science, tropical botany, ethnobotany, paleobotany, and palynology. Studies by foreign researchers conducted within the framework of international projects and collaborations are also encouraged. The journal also publishes book reviews and advertisements.

《AIMS Electronics and Electrical Engineering》期刊欢迎匈牙利及周边国家的科学家投稿，特别是在以下主题领域的研究：植物解剖学与组织学、隐花植物与显花植物分类学、分子系统发育、植物地理学、植物社会学、植被科学、热带植物学、民族植物学、古植物学及花粉学。同时也欢迎国际项目和合作框架下的外国研究者的研究成果。期刊还会发布书评和广告。

AIMS Electronics and Electrical Engineering

&lt;abstract&gt;&lt;p&gt;In the last decade, research has been started due to accelerated growth in power demand has mainly concentrated on the large power production and quality of power. After the digital revolution, non-conventional energy sources, many state-of-art equipment, power electronics loads, reactive power compensating devices, sophisticated measuring devices, etc., entered the power industry. The reactive power compensating devices, connected electrical equipment, renewable energy sources can be anticipated/unanticipated action can cause considerable reactions may be failure issues to power grids. To deal with these challenges, the power sector crucially needs to design and implement new security systems to protect its systems. The Internet-of-Things (IoT) is treated as revolution technology after the invention of the digital machine and the internet. New developments in sensor devices with wireless technologies through embedded processors provide effective monitoring and different types of faults can be detected during electric power transmission. The wavelet (WT) is one of the mathematical tools to asses transient signals of different frequencies and provides crucial information in the form of detailed coefficients. Machine learning (ML) methods are recommended in the power systems community to simplify digital reform. ML and AI techniques can make effective and rapid decisions to improve the stability and safety of the power grid. This recommended approach can contribute critical information about symmetrical or asymmetrical faults through machine learning assessment of IoT supervised microgrid protection in the presence of SVC using the wavelet approach covers diversified types of faults combined with fault-inception-angles (FIA).&lt;/p&gt;&lt;/abstract&gt;

K.V. Dhana Lakshmi

P.K. Panigrahi

Ravi kumar Goli

Machine learning assessment of IoT managed microgrid protection in existence of SVC using wavelet methodology

基于小波方法的机器学习评估在存在SVC的情况下对物联网管理微电网保护的影响

&lt;abstract&gt; &lt;p&gt;This manuscript has introduced an algorithm based on current signals and frequency rate change (ROCOF) to identify islanding events. Current is analyzed by the use of Stockwell transform (ST) at 3.84 kHz sampling frequency (SF) and a median of absolute values of every column of output matrix (CSIRI) is computed. Rate of change of CSIRI (ROCOCSIRI) is computed. Proposed current based islanding recognition index (IRIC) is computed by multiplying ROCOF with CSIRI &amp;amp; ROCOCSIRI and a weight factor (WC). Threshold values THI1 &amp;amp; THI2 are selected 100 and 3000 for IRIC for identifying the Islanding condition. These are also effective to differentiate islanding conditions from non-islanding events which include both the faulty and operational events. Magnitude of IRIC is greater than 3000 for the faulty events and lower than 100 for operational events. For islanding events magnitude of IRIC falls in between the 100 and 3000. Algorithm is effective to identify and classify the events in three categories which are islanding events, faulty events and operational events effectively. Study is realized in MATLAB/Simulink scenario.&lt;/p&gt; &lt;/abstract&gt;

Om Prakash Mahela

Pappu Ram Bheel

M.K. Bhaskar

Baseem Khan

Islanding detection in utility grid with renewable energy using rate of change of frequency and signal processing technique

利用频率变化率和信号处理技术在含可再生能源的配电网中进行孤岛检测

&lt;abstract&gt; &lt;p&gt;Internet of Things (IoT) is a terminology used for a mixed connection of heterogeneous objects to the internet and to each other with the employment of recent technological and communication infrastructures. Its incorporation into engineering systems have gradually become very popular in recent times as it promises to transform and ease the life of end users. The use of IoT in smart energy systems (SES) facilitates an ample offer of variety of applications that transverses through a wide range of areas in energy systems. With the numerous benefits that includes unmatched fast communication between subsystems, the maximization of energy use, the decrease in environmental impacts and a boost in the dividends of renewable energies, IoT has grown into an emerging innovative technology to be integrated into smart energy systems. In this work, we have provided an overview of the link between SES, IoT and Internet of Energy (IoE). The main applications of IoT in smart energy systems consisting of smart industries, smart homes and buildings, and smart cities are explored and analyzed. The paper also explores the challenges limiting the employment of IoT technologies in SES and the possible remedies to these challenges. In addition, the future trends of this technology, its research direction and reasons why industry should adopt it are also addressed. The aim of this work is to furnish researchers in this field, decision and energy policy makers, energy economist and energy administrators with a possible literature outline on the roles and impacts of IoT technology in smart energy systems.&lt;/p&gt; &lt;/abstract&gt;

Efe Francis Orumwense

Khaled Abo-Al-Ez

Internet of Things for smart energy systems: A review on its applications, challenges and future trends

物联网在智能能源系统中的应用、挑战及未来趋势综述

&lt;abstract&gt; &lt;p&gt;The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions.&lt;/p&gt; &lt;/abstract&gt;

Dipak R. Swain

Sunita S. Biswal

Pravat Kumar Rout

P. K. Ray

R. K. Jena

Optimal fractional sliding mode control for the frequency stability of a hybrid industrial microgrid

混合工业微电网频率稳定性的最优分数阶滑模控制

&lt;abstract&gt; &lt;p&gt;The rate of increase in skin cancer incidences has become worrying in recent decades. This is because of constraints like eventual draining of ozone levels, air's defensive channel capacity and progressive arrival of Sun-oriented UV radiation to the Earth's surface. The failure to diagnose skin cancer early is one of the leading causes of death from the disease. Manual detection processes consume more time well as not accurate, so the researchers focus on developing an automated disease classification method. In this paper, an automated skin cancer classification is achieved using an adaptive neuro-fuzzy inference system (ANFIS). A hybrid feature selection technique was developed to choose relevant feature subspace from the dermatology dataset. ANFIS analyses the dataset to give an effective outcome. ANFIS acts as both fuzzy and neural network operations. The input is converted into a fuzzy value using the Gaussian membership function. The optimal set of variables for the Membership Function (MF) is generated with the help of the firefly optimization algorithm (FA). FA is a new and strong meta-heuristic algorithm for solving nonlinear problems. The proposed method is designed and validated in the Python tool. The proposed method gives 99% accuracy and a 0.1% false-positive rate. In addition, the proposed method outcome is compared to other existing methods like improved fuzzy model (IFM), fuzzy model (FM), random forest (RF), and Naive Byes (NB).&lt;/p&gt; &lt;/abstract&gt;

J. Rajeshwari

M. Sughasiny

Dermatology disease prediction based on firefly optimization of ANFIS classifier

基于萤火虫优化的ANFIS分类器的皮肤病预测

&lt;p&gt;Access to information can destroy nations and change the course of history altogether. Communication is very important, and in today's internet age, nothing moves without real-time information support. For securing communication, a commonly know technique is to use cryptography and public channels. Engineers have been working to create a better and more secure cryptographic system. Quantum key distribution stands at the top of this security system. Although QKD, based on principles of physics, provides a near-perfect security solution. It has a few drawbacks of its own, like low key generation rates and vulnerability to cyberattacks. Owning to these limitations, authors propose an adaptive quantum key distribution system based on software-defined networks. The authors propose to introduce redundancy in the key generation, thereby increasing the key generation rate and improving the resilience to cyberattacks. A performance comparison of novel quantum key distribution was done with BB84 and B92 quantum key distribution protocols.&lt;/p&gt;

Hardeer Kaur

Jai Sukh Paul Singh

Software defined network implementation of multi-node adaptive novel quantum key distribution protocol

多节点自适应新型量子密钥分发协议的软件定义网络实现

&lt;p&gt;Several industries, particularly the automotive sector, are increasingly incorporating more electronics into their products. As a result, these products are becoming more complex and difficult to analyze. This complexity poses a significant challenge for manufacturers in proving the functional safety of their products. Not only do random faults present risks, but component tolerances can also lead to unexpected safety hazards. Current methods are struggling to keep pace with these challenges. We have identified key issues with existing methods and introduce a new approach that leverages computer automation and a model-based framework to enhance the process. We explain how this new method not only improves upon existing practices but also introduces additional capabilities.&lt;/p&gt;&lt;p&gt;In this paper, we examine methods for proving the functional safety of electronic systems. We begin by identifying the challenges associated with current established methods. Next, we introduce our new approach, which relies heavily on computer assistance and offers novel techniques for conducting broader and more in-depth analyses of these systems. We then explain a new workflow that utilizes this approach. To illustrate its application, we provide a demonstrative example. Our conclusion summarizes our findings and results, and we share our thoughts on potential future developments.&lt;/p&gt;

Levent Ergün

Roman Müller Hainbach

Stefan Butzmann

Methodology of a hierarchical and automated failure analysis and its advantages

分层自动化故障分析方法及其优势

&lt;abstract&gt; &lt;p&gt;This paper presents an integrated approach combining a sequential neural network (SNN) with model predictive control (MPC) to enhance the performance of a permanent magnet synchronous motor (PMSM). We address the challenges of traditional control methods that struggle with the dynamics and nonlinear nature of PMSMs, offering a solution that leverages the predictive capabilities of MPC and the adaptive learning potential of neural networks. Our SNN-MPC model is contrasted with state-of-the-art genetic algorithm (GA) and ant colony optimization (ACO) methods through a comprehensive simulation analysis. This analysis critically examines the dynamic responses, including current, torque, and speed profiles, of the PMSM under proposed hybrid control strategies. The heart of the work deals with the optimal switching states and subsequent voltage injection to the inverter fed PMSM drive by a predefined minimization principle of a current modulated objective function, where MPC constitutes an integral finite control set (IFCS) mechanism for voltage vector selection and thereby selects the optimized integral gains &lt;italic&gt;K&lt;/italic&gt;&lt;sub&gt;d&lt;/sub&gt; and &lt;italic&gt;K&lt;/italic&gt;&lt;sub&gt;q&lt;/sub&gt; for direct and quadrature axes, respectively, with the FCS gain &lt;italic&gt;K&lt;/italic&gt;&lt;sub&gt;fcs &lt;/sub&gt; obtained from implemented intelligent techniques. Based on the control criteria, the SNN-MPC scheme was established as the preferred benchmark with optimized tuning values of &lt;italic&gt;K&lt;/italic&gt;&lt;sub&gt;d&lt;/sub&gt; = 0.01, &lt;italic&gt;K&lt;/italic&gt;&lt;sub&gt;q&lt;/sub&gt; = 0.006, and &lt;italic&gt;K&lt;/italic&gt;&lt;sub&gt;fcs&lt;/sub&gt; = 0.13, as compared to the gain values tuned from GA and ACO. The experimental setup utilized MATLAB and a Python environment for robust and flexible simulation, ensuring an equitable basis for comparison across all models.&lt;/p&gt; &lt;/abstract&gt;

Shaswat Chirantan

Bibhuti Bhusan Pati

Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement

预测与计算智能技术的融合：一种用于增强PMSM动力学的混合优化机制

&lt;abstract&gt; &lt;p&gt;During the COVID-19 pandemic, it was crucial for the healthcare sector to detect and classify the virus using X-ray and CT scans. This has underlined the need for advanced Deep Learning and Machine Learning approaches to effectively spot and manage the virus's spread. Indeed, researchers worldwide have dynamically participated in the field by publishing an important number of papers across various databases. In this context, we present a bibliometric analysis focused on the detection and classification of COVID-19 using Deep Learning and Machine Learning techniques, based on X-Ray and CT images. We analyzed published documents of the six prominent databases (IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect) during the period between 2019 and November 2023. Our results showed that rising forces in economy and technology, especially India, China, Turkey, and Pakistan, began to compete with the great powers in the field of scientific research, which could be seen from their number of publications. Moreover, researchers contributed to Deep Learning techniques more than the use of Machine Learning techniques or the use of both together and preferred to submit their works to Springer Database. An important result was that more than 57% documents were published as Journal Articles, which was an important portion compared to other publication types (conference papers and book chapters). Moreover, the PubMed journal "Multimedia Tools and Applications" tops the list of journals with a total of 29 published articles.&lt;/p&gt; &lt;/abstract&gt;

Youness Chawki

Khalid Elasnaoui

Mohamed Ouhda

Classification and detection of Covid-19 based on X-Ray and CT images using deep learning and machine learning techniques: A bibliometric analysis

基于深度学习和机器学习技术的X射线和CT图像的COVID-19分类与检测：文献计量分析

&lt;abstract&gt; &lt;p&gt;As electric vehicles (EVs) continue to acquire prominence in the transportation industry, improving the outcomes and efficiency of their propulsion systems is becoming increasingly critical. Switched Reluctance Motors (SRMs) have become a compelling option for EV applications due to their simplicity, magnet-free design, robustness, and cost-effectiveness, making them an attractive choice for the growing EV market. Despite all these features and compared to other electrical machines, SRMs suffer from some restrictions, such as torque ripple and audible noise generation, stemming from their markedly nonlinear characteristics, which affect their productivity and efficiency. Therefore, to address these problems, especially the torque ripple, it is crucial and challenging to enhance the performance of the SRM drive system. This paper proposed a comprehensive review of torque ripple minimization strategies of SRMs in EV applications. It covered a detailed overview and categorized and compared many strategies, including two general categories of torque ripple mitigation encompassing optimization design topologies and control strategy developments. Then, focused on control strategy improvements and divided them into torque and current control strategies, including the sub-sections. In addition, the research also provided an overview of SRM fundamental operations, converter topologies, and excitation angle approaches. Last, a comparison between each method in torque control and current control strategies was listed, including the adopted method, features, and drawbacks.&lt;/p&gt; &lt;/abstract&gt;

ISSN	2578-1588
h-index	10

u9zg3I	生物植物生态学地质学地理古生物学遗传学医学化学动物园艺生物化学农学属
+5d/bs	e+/GRg: American Institute of Mathematical Sciences，uzYam8: ，dxwaWB: journal，8x2mMQ: Xn96QY
ak9yM6	A7IHrH: 2017，sQJ/D8: 0.00%，sE9og+:N.A.， wzlKC/: 0.00%
JrpmBe	8 weeks

AIMS Electronics and Electrical Engineering

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Machine learning assessment of IoT managed microgrid protection in existence of SVC using wavelet methodology

Islanding detection in utility grid with renewable energy using rate of change of frequency and signal processing technique

Internet of Things for smart energy systems: A review on its applications, challenges and future trends

Optimal fractional sliding mode control for the frequency stability of a hybrid industrial microgrid

Dermatology disease prediction based on firefly optimization of ANFIS classifier

Software defined network implementation of multi-node adaptive novel quantum key distribution protocol

Methodology of a hierarchical and automated failure analysis and its advantages

Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement

Classification and detection of Covid-19 based on X-Ray and CT images using deep learning and machine learning techniques: A bibliometric analysis

Control techniques of switched reluctance motors in electric vehicle applications: A review on torque ripple reduction strategies