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;Over the last decade, automated distribution networks have grown in importance since traditional distribution networks are insufficiently intelligent to meet the growing need for reliable electricity supplies. Because the distribution network is the least reliable and the sole link between the utility and its customers, it is critical to improve its reliability. The remote-controlled switch (RCS) is a viable choice for boosting system reliability. It shortens the interruption period, which also minimizes the expected interruption cost and the amount of energy not served. Using the greedy search algorithm, this research expands the current reliability evaluation technique to include RCSs in distribution networks. The optimal location and numbers of RCSs have been evaluated with compromised cost. This study simultaneously takes into account the effects of load growth on system reliability indices, the impact of age on equipment failure rates and the hidden failure rate of fuses. The Roy Billinton test system's distribution network connected at bus 2 and bus 5 has been used to test the effectiveness of the suggested approach. The outcomes demonstrate that effective RCS deployment improves the radial distribution network's reliability indices significantly.&lt;/p&gt; &lt;/abstract&gt;

Umesh Agarwal

Naveen Jain

Manoj Kumawat

Reliability enhancement of distribution networks with remote-controlled switches considering load growth under the effects of hidden failures and component aging

&lt;p&gt;Insulators play a crucial role in transmission lines. Insulators exposed to natural environments are prone to various malfunctions. These faults will seriously affect the safety and stability of the power grid system operation, so intelligent detection of insulator defects has become increasingly important. This paper presents an insulator defect detection model based on the improved MViTv2-T (Multiscale Vision Transformers Version 2 Tiny). The new model utilizes the sore penalty mechanism (SPM) cluster non-maximum suppression (NMS) algorithm instead of the batched non-maximum suppression (NMS) algorithm from the original model. Additionally, it introduces the stage query recollection method, which integrates high-level and low-level module queries within each stage, along with various experimentation on integration functions between the two. The experimental results indicate that the improved MViTv2-T model attains an mAP (mean average precision)@0.5:0.95 of 76.1$ \% $, mAP@0.5 of 96.1$ \% $, and mAR@0.5 of 97.2$ \% $ in insulator defect detection. Compared to the original model, there is a 1.8$ \% $ increase in mAP@0.5:0.95 and a 17$ \% $ decrease in the detection error rate at an Intersection over Union (IoU) threshold of 0.5. Furthermore, when compared to standard two-stage detection models and YOLO series models, the improved MViTv2-T model also exhibits distinct performance advantages.&lt;/p&gt;

Fuhong Meng

Guowu Yuan

Hao Zhou

Hao Wu

Yi Ma

Improved MViTv2-T model for insulator defect detection

改进的MViTv2-T模型用于绝缘子缺陷检测

&lt;p&gt;This paper introduces a novel hybrid approach that integrates Latin hypercube sampling (LHS) and Bayesian optimization for optimizing artificial neural networks (ANNs) in fault detection, classification, and location for transmission lines. The proposed method advances the accuracy and efficiency of fault diagnosis in power systems, representing a significant step forward compared to conventional approaches. The test system is a 400 kV, 50 Hz, 300 km transmission system, and the simulations were carried out in MATLAB/Simulink environment. Using the strategic insight of LHS, optimal initial points were determined, which subsequently formed the basis for the Bayesian optimization to further refine the learning rate and training epochs. Within the fault detection domain, the model showcased remarkable precision when deployed on an evaluation dataset of 168 cases, accurately detecting every instance of normal and faulty scenarios. This culminated in an astounding 100% accuracy in fault detection. In terms of fault classification, the ANN model, trained on a dataset of 495 instances, revealed perfect regression coefficients across the training, testing, and validation subsets. When tested against unseen data, it demonstrated its proficiency by correctly classifying 154 out of 154 cases, showcasing a 100% F1 score. Also, the accuracy figures in terms of fault location fluctuated between 99.826% and 99.999%, with a mean absolute percentage error (MAPE) of 0.053%. The model's mean square error (MSE) stood at 0.0083, while the mean absolute error (MAE) was calculated at 0.0717. A deep dive into diverse fault types reaffirmed the model's precision, underscoring its consistent performance across various fault scenarios. The trained models were deployed on three different transmission lines and the models exhibited remarkable precision in all the cases tested. In summary, the innovative hybrid optimized ANN model, weaving together the strengths of LHS and Bayesian optimization, signifies an advancement in the field of power system fault analysis, ushering in heightened efficiency and reliability.&lt;/p&gt;

Abdul Yussif Seidu

Elvis Twumasi

Emmanuel Assuming Frimpong

Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines

使用拉丁超立方抽样和贝叶斯优化的混合优化人工神经网络用于输电线路故障的检测、分类和定位

&lt;p&gt;This paper contributes to the design of a modified rectangular-shaped metamaterial with a tri-band coverage for reflection and transmission coefficients. Two symmetrical rectangular split ring resonators (SRR) were carefully engineered and then connected by their edges along the axis (Ox) with a substantial C-shaped structure, experiencing the peak surface current value near the magnetic resonances, causing the proposed unit cell to resonate at 5.73 GHz, 8.67 GHz, and 13.78 GHz, where it exhibited negative effective permittivity (ENG), permittivity and permeability (DNG), and permeability (MNG), respectively. A total of 6 × 6 mm&lt;sup&gt;2&lt;/sup&gt; modified SRR was printed on Rogers RO3006 to achieve a better effective medium ratio (EMR) in the C band (4.55-6.27 GHz), X band (7.81-9.45 GHz), and Ku band (13.15-14.37 GHz), respectively. A comprehensive parametric analysis was performed to illustrate the effect of crucial parameter h on the scattering parameters (S&lt;sub&gt;11&lt;/sub&gt;, S&lt;sub&gt;21&lt;/sub&gt;) of the metamaterial resonant structure (MRSRR) in the specified frequency ranges. Structure underwent additional testing with 1 × 2, 2 × 1, 2 × 2, and 4 × 4 arrays, yielding results that demonstrated sufficient concordance for consideration in the C band [4-8 GHz], X band [8-12 GHz], and Ku band [12-18 GHz] satellite applications. Computer Simulation Technology (CST) Microwave Studio was utilized to reach the scattering parameters and their effective medium characteristics, specifically permittivity and permeability, via the Nicolson–Ross–Weir (NRW) approach, executed through MATLAB code. The surface current was examined, and the corresponding circuit model was confirmed utilizing the Advanced Design System (ADS) software, with results compared against the CST simulation outcomes.&lt;/p&gt;

Abderraouf Fadhel

Souad Berhab

Rahma Aloulou

Hassene Mnif

Abdennour Belhedri

Compact modified rectangular split ring resonator for tri-band satellite applications

紧凑型改进矩形分裂环谐振器用于三频段卫星应用

&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;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;

ISSN	2578-1588
h-index	10

Publication Information	Publisher: American Institute of Mathematical Sciences，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2017，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%
Average review cycle	8 weeks

AIMS Electronics and Electrical Engineering

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Share Submission Experience

Reliability enhancement of distribution networks with remote-controlled switches considering load growth under the effects of hidden failures and component aging

Improved MViTv2-T model for insulator defect detection

Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines

Compact modified rectangular split ring resonator for tri-band satellite applications

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

Improved MViTv2-T model for insulator defect detection

Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines

Compact modified rectangular split ring resonator for tri-band satellite applications

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

Methodology of a hierarchical and automated failure analysis and its advantages