Journal Description
Electronics
Electronics
is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2(Electrical and Electronic Engineering) CiteScore - Q2 (Electrical and Electronic Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Electronics include: Magnetism, Signals, Network and Software.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Magnetic Characterization of MR Fluid by Means of Neural Networks
Electronics 2024, 13(9), 1723; https://doi.org/10.3390/electronics13091723 (registering DOI) - 29 Apr 2024
Abstract
Magnetorheological and electrorheological fluids manifest a change in rheological behavior when subjected to a magnetic or electric field, respectively, such that they require electrical and magnetic characterization. In this paper, a simple and accurate mathematical model based on a small number of parameters
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Magnetorheological and electrorheological fluids manifest a change in rheological behavior when subjected to a magnetic or electric field, respectively, such that they require electrical and magnetic characterization. In this paper, a simple and accurate mathematical model based on a small number of parameters provides the relative magnetic permeability of magnetorheological fluids as a function of the applied magnetic field. Furthermore, for the testing and magnetic characterization of magnetorheological fluids, a new metering equipment setup is implemented. Starting with the achieved experimental data, the mathematical relation is represented by means of a radial basis function neural network, with neurons having a Gaussian activation function; by means of post-training pruning procedures, the trained neural network is applied using the proposed data. Therefore, the obtained mathematical relation is in good agreement with the experimental data, with an approximate error of 8%.
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(This article belongs to the Section Computer Science & Engineering)
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Detection and Classification of Rolling Bearing Defects Using Direct Signal Processing with Deep Convolutional Neural Network
by
Maciej Skowron, Oliwia Frankiewicz, Jeremi Jan Jarosz, Marcin Wolkiewicz, Mateusz Dybkowski, Sebastien Weisse, Jerome Valire, Agnieszka Wyłomańska, Radosław Zimroz and Krzysztof Szabat
Electronics 2024, 13(9), 1722; https://doi.org/10.3390/electronics13091722 - 29 Apr 2024
Abstract
Currently, great emphasis is being placed on the electrification of means of transportation, including aviation. The use of electric motors reduces operating and maintenance costs. Electric motors are subjected to various types of damage during operation, of which rolling bearing defects are statistically
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Currently, great emphasis is being placed on the electrification of means of transportation, including aviation. The use of electric motors reduces operating and maintenance costs. Electric motors are subjected to various types of damage during operation, of which rolling bearing defects are statistically the most common. This article focuses on presenting a diagnostic tool for bearing conditions based on mechanic vibration signals using convolutional neural networks (CNN). This article presents an alternative to the well-known classical diagnostic tools based on advanced signal processing methods such as the short-time Fourier transform, the Hilbert–Huang transform, etc. The approach described in the article provides fault detection and classification in less than 0.03 s. The proposed structures achieved a classification accuracy of 99.8% on the test set. Special attention was paid to the process of optimizing the CNN structure to achieve the highest possible accuracy with the fewest number of network parameters.
Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence in Modern Power and Energy Systems)
Open AccessArticle
Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace
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Sajad Moosavi, Roozbeh Razavi-Far, Vasile Palade and Mehrdad Saif
Electronics 2024, 13(9), 1721; https://doi.org/10.3390/electronics13091721 - 29 Apr 2024
Abstract
For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled
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For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled shutdowns. The problem of diagnosing faults in induction furnaces has not yet been studied, and this work is the first to propose a data-driven framework for diagnosing faults in this application. This paper presents a deep neural network framework for diagnosing electrical faults by measuring real-time electrical parameters at the supply side. Experimental and sensory measurements are collected from multiple energy analyzer devices installed in the foundry. Next, a semi-supervised learning approach, known as the local outlier factor, has been used to discriminate normal and faulty samples from each other and label the data samples. Then, a deep neural network is trained with the collected labeled samples. The performance of the developed model is compared with several state-of-the-art techniques in terms of various performance metrics. The results demonstrate the superior performance of the selected deep neural network model over other classifiers, with an average F-measure of 0.9187. Due to the black box nature of the constructed neural network, the model predictions are interpreted by Shapley additive explanations and local interpretable model-agnostic explanations. The interpretability analysis reveals that classified faults are closely linked to variations in odd voltage/current harmonics of order 3, 11, 13, and 17, highlighting the critical impact of these parameters on the model’s prediction.
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(This article belongs to the Special Issue Explainability in AI and Machine Learning)
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Category Level Object Pose Estimation via Global High-Order Pooling
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Changhong Jiang, Xiaoqiao Mu, Bingbing Zhang, Mujun Xie and Chao Liang
Electronics 2024, 13(9), 1720; https://doi.org/10.3390/electronics13091720 - 29 Apr 2024
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Category level 6D object pose estimation aims to predict the rotation, translation and size of object instances in any scene. In current research methods, global average pooling (first-order) is usually used to explore geometric features, which can only capture the first-order statistical information
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Category level 6D object pose estimation aims to predict the rotation, translation and size of object instances in any scene. In current research methods, global average pooling (first-order) is usually used to explore geometric features, which can only capture the first-order statistical information of the features and do not fully utilize the potential of the network. In this work, we propose a new high-order pose estimation network (HoPENet), which enhances feature representation by collecting high-order statistics to model high-order geometric features at each stage of the network. HoPENet introduces a global high-order enhancement module and utilizes global high-order pooling operations to capture the correlation between features and fuse global information. In addition, this module can capture long-term statistical correlations and make full use of contextual information. The entire network finally obtains a more discriminative feature representation. Experiments on two benchmarks, the virtual dataset CAMERA25 and the real dataset REAL275, demonstrate the effectiveness of HoPENet, achieving state-of-the-art (SOTA) pose estimation performance.
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Ontology-Driven Architecture for Managing Environmental, Social, and Governance Metrics
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Mingqin Yu, Fethi A. Rabhi and Madhushi Bandara
Electronics 2024, 13(9), 1719; https://doi.org/10.3390/electronics13091719 - 29 Apr 2024
Abstract
The burgeoning significance of environmental, social, and governance (ESG) metrics in realms such as investment decision making, corporate reporting, and risk management underscores the imperative for a robust, comprehensive solution capable of effectively capturing, representing, and analysing the multifaceted and intricate ESG data
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The burgeoning significance of environmental, social, and governance (ESG) metrics in realms such as investment decision making, corporate reporting, and risk management underscores the imperative for a robust, comprehensive solution capable of effectively capturing, representing, and analysing the multifaceted and intricate ESG data landscape. Facing the challenge of aligning with diverse standards and utilising complex datasets, organisations require robust systems for the integration of ESG metrics with traditional financial reporting. Amidst this, the evolving regulatory landscape and the demand for transparency and stakeholder engagement present significant challenges, given the lack of standardized ESG metrics in certain areas. Recently, the use of ontology-driven architectures has gained attention for their ability to encapsulate domain knowledge and facilitate integration with decision-support systems. This paper proposes a knowledge graph in the ESG metric domain to assist corporations in cataloguing and navigating ESG reporting requirements, standards, and associated data. Employing a design science methodology, we developed an ontology that serves as both a conceptual foundation and a semantic layer, fostering the creation of an interoperable ESG Metrics Knowledge Graph (ESGMKG) and its integration within operational layers. This ontology-driven approach promises seamless integration with diverse ESG data sources and reporting frameworks, while addressing the critical challenges of metric selection, alignment, and data verification, supporting the dynamic nature of ESG metrics. The utility and effectiveness of the proposed ontology were demonstrated through a case study centred on the International Financial Reporting Standards (IFRS) framework that is widely used within the banking industry.
Full article
(This article belongs to the Special Issue Ontology-Driven Architectures and Applications of the Semantic Web)
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Open AccessCommunication
An In-Band Low-Radar Cross Section Microstrip Patch Antenna Based on a Phase Control Metasurface
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Fang Li, Miao Lv, Min Wang and Yongtao Jia
Electronics 2024, 13(9), 1718; https://doi.org/10.3390/electronics13091718 - 29 Apr 2024
Abstract
An in-band low radar cross section (RCS) microstrip patch antenna based on a phase control metasurface is proposed. As the size of the phase control metasurface changes, it will have different phase adjustments to the incident electromagnetic wave. Two kinds of phase control
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An in-band low radar cross section (RCS) microstrip patch antenna based on a phase control metasurface is proposed. As the size of the phase control metasurface changes, it will have different phase adjustments to the incident electromagnetic wave. Two kinds of phase control metasurfaces with a 90° reflection phase difference are arranged in a checkerboard configuration and loaded above a microstrip array antenna. The metal of the microstrip array antenna can fully reflect the electromagnetic wave, so the incident wave passes through the metasurface again and forms a reflected wave with a phase difference of 180° ± 37° when passing through the phase control metasurfaces of different sizes. Thus, the microstrip array antenna can achieve in-band RCS reduction. The metamaterial forms a transmission window in the microstrip patch array antenna band to maintain the radiation performance. Finally, a reasonable agreement is obtained between the measured and simulated results.
Full article
(This article belongs to the Special Issue Advanced Technologies in Antennas and Their Applications)
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Open AccessArticle
Monotonic Asynchronous Two-Bit Full Adder
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Padmanabhan Balasubramanian and Douglas L. Maskell
Electronics 2024, 13(9), 1717; https://doi.org/10.3390/electronics13091717 - 29 Apr 2024
Abstract
Monotonic circuits are a class of input–output mode (IOM) asynchronous circuits that are relaxed compared to quasi-delay-insensitive (QDI) IOM asynchronous circuits in terms of signaling the completion of internal processing. Some recent works have demonstrated the superiority of monotonic logic over QDI logic
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Monotonic circuits are a class of input–output mode (IOM) asynchronous circuits that are relaxed compared to quasi-delay-insensitive (QDI) IOM asynchronous circuits in terms of signaling the completion of internal processing. Some recent works have demonstrated the superiority of monotonic logic over QDI logic for arithmetic circuits such as adders and multipliers. This paper presents a new monotonic asynchronous two-bit full adder (TFA) that can be duplicated and cascaded to form a ripple-carry adder (RCA). While an RCA is a slow adder with respect to synchronous design, with respect to IOM asynchronous design an RCA is a noteworthy adder since it has perhaps the least reverse latency that is not attainable through other IOM asynchronous adders. Conventionally, an RCA is constructed via a cascade of one-bit full adders (OFAs). An OFA adds two input bits along with any carry input and produces a sum bit and any carry output. On the other hand, a TFA simultaneously adds two pairs of input bits along with any carry input and produces two sum bits and any carry output. Using our proposed monotonic TFA, we realized an RCA to compare its performance with RCAs constructed using different asynchronous OFAs, and RCAs constructed using existing TFAs. We considered the popular delay-insensitive dual-rail scheme for encoding the adder inputs and outputs, and two 4-phase handshake protocols, namely return-to-zero handshaking (R0H) and return-to-one handshaking (R1H) for communication separately. We used a 28 nm CMOS process for implementation and considered a 32-bit addition as an example. Based on the design metrics estimated, the following inferences were derived: (i) compared to the RCA using the state-of-the-art monotonic OFA, the RCA incorporating the proposed TFA achieved a 26% reduction in cycle time for R0H and a 28.5% reduction in cycle time for R1H while dissipating almost the same power; the cycle time governs the data application rate in an IOM asynchronous circuit, and (ii) compared to the RCA comprising an early output QDI TFA, the RCA incorporating the proposed TFA achieved a 22.3% reduction in cycle time for R0H and a 25.4% reduction in cycle time for R1H while dissipating moderately less power. Also, compared to the existing early output QDI TFA, the proposed TFA occupies 40.9% less area for R0H and 42% less area for R1H.
Full article
(This article belongs to the Special Issue Design of Mixed Analog/Digital Circuits, Volume 2)
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A Method for Identifying External Short-Circuit Faults in Power Transformers Based on Support Vector Machines
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Hao Du, Linglong Cai, Zhiqin Ma, Zhangquan Rao, Xiang Shu, Shuo Jiang, Zhongxiang Li and Xianqiang Li
Electronics 2024, 13(9), 1716; https://doi.org/10.3390/electronics13091716 - 29 Apr 2024
Abstract
Being a vital component of electrical power systems, transformers significantly influence the system stability and reliability of power supplies. Damage to transformers may lead to significant economic losses. The efficient identification of transformer faults holds paramount importance for the stability and security of
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Being a vital component of electrical power systems, transformers significantly influence the system stability and reliability of power supplies. Damage to transformers may lead to significant economic losses. The efficient identification of transformer faults holds paramount importance for the stability and security of power grids. The existing methods for identifying transformer faults include oil chromatography analysis, temperature assessment, frequency response analysis, vibration characteristic examination, and leakage magnetic field analysis. These methods suffer from limitations such as limited sensitivity, complexity in operation, and a high demand for specialized skills. In this paper, we propose a method to identify external short-circuit faults of power transformers based on fault recording data on short-circuit currents. It involves analyzing the current signals of various windings during faults, extracting appropriate features, and utilizing a classification algorithm based on a support vector machine (SVM) to determine fault types and locations. The influence of different kernel functions on the classification accuracy of SVM is discussed. The results indicate that this method can proficiently identify the type and location of external short-circuit faults in transformers, achieving an accuracy rate of 98.3%.
Full article
(This article belongs to the Special Issue Advanced Control Methods and Artificial Intelligence Applications in Grid-Connected Inverters)
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Open AccessArticle
A Survey of Binary Code Similarity Detection Techniques
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Liting Ruan, Qizhen Xu, Shunzhi Zhu, Xujing Huang and Xinyang Lin
Electronics 2024, 13(9), 1715; https://doi.org/10.3390/electronics13091715 - 29 Apr 2024
Abstract
Binary Code Similarity Detection is a method that involves comparing two or more binary code segments to identify their similarities and differences. This technique plays a crucial role in areas such as software security, vulnerability detection, and software composition analysis. With the extensive
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Binary Code Similarity Detection is a method that involves comparing two or more binary code segments to identify their similarities and differences. This technique plays a crucial role in areas such as software security, vulnerability detection, and software composition analysis. With the extensive use of binary code in software development and system optimization, binary code similarity detection has become an important area of research. Traditional methods of source code similarity detection face challenges when dealing with the unreadable and complex nature of binary code, necessitating specialized techniques and algorithms. This review compares and summarizes various techniques and methods of binary code similarity detection, highlighting their strengths and limitations in handling different characteristics of binary code. Additionally, the article suggests potential future research directions. As research and innovation in this technology continue to advance, binary code similarity detection is expected to play an increasingly significant role in fields like software security.
Full article
(This article belongs to the Special Issue Advanced Machine Learning Applications for Security, Privacy, and Reliability)
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Open AccessReview
A Review of Carbon Emissions from Electrical Machine Materials
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Xuebei Zhang, David Gerada, Zeyuan Xu, Fengyu Zhang and Chris Gerada
Electronics 2024, 13(9), 1714; https://doi.org/10.3390/electronics13091714 - 29 Apr 2024
Abstract
As the world embarks on a global mission to tackle climate change, reducing carbon represents a key challenge given the escalating global warming. The U.K. is among many other nations that are determined to decarbonise all sectors and strive to achieve a net
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As the world embarks on a global mission to tackle climate change, reducing carbon represents a key challenge given the escalating global warming. The U.K. is among many other nations that are determined to decarbonise all sectors and strive to achieve a net zero carbon target by 2050. While much attention has been paid to improving performance and reducing carbon emissions in electrical machines, the current research landscape focuses mainly on the thermal and electromagnetic facets. Surprisingly, carbon emissions from the production stage, especially those related to raw material consumption, remain a largely unexplored area. This paper wishes to shed light on a neglected dimension by providing a comprehensive review of carbon emissions in the manufacture of electrical machines, thus contributing significantly to the wider discourse on carbon emission reduction by comparing the carbon emission values associated with various materials commonly used for the main components of these machines. A further case study is included to assess and explore the impact of material alterations on a synchronous machine, from a carbon emission perspective. A reliable material guide will provide engineers at the design stage with the critical insight needed to make informed material selection decisions, highlighting the critical role of carbon emission values beyond conventional thermal and electromagnetic considerations, achieving sustainable and environmentally conscious electrical machine design.
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(This article belongs to the Special Issue Recent Advances and Applications in Electric Machines Design, Modelling, Control, and Operation)
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A Low-Voltage Self-Starting Boost Converter Using MPPT with Pulse Multiplication for Energy Harvesting
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Ning Wang, Xiaofei Zhang, Shuxi Xu, Yuan Liu, Lei Zhang, Zhonghui Zhao, Zhiyang Hu and Hengsheng Shan
Electronics 2024, 13(9), 1713; https://doi.org/10.3390/electronics13091713 - 29 Apr 2024
Abstract
A single-inductor, low-voltage, three-step self-starting boost converter is proposed for photovoltaic (PV) energy harvesting. In order to enhance energy transfer efficiency, a variable-step Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) scheme has been devised based on a novel pulse multiplication technique.
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A single-inductor, low-voltage, three-step self-starting boost converter is proposed for photovoltaic (PV) energy harvesting. In order to enhance energy transfer efficiency, a variable-step Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) scheme has been devised based on a novel pulse multiplication technique. Upon overcoming the speed and accuracy limitations, the maximum power point (MPP) of the PV model is accurately tracked. In the boost converter, the average inductor current is utilized to implement closed-loop control of the MPPT loop, enhancing the stability of the tracking process and enabling efficient energy transmission. Finally, the boost converter is implemented using a 0.18 μm CMOS process, which is capable of self-starting and maintaining stable operations at input voltages ranging from 90 mV to 300 mV, achieving a peak efficiency of 93%.
Full article
(This article belongs to the Special Issue Micro Energy Harvesters: Modelling, Design, and Applications)
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Open AccessArticle
Reactive Shield for Reducing the Magnetic Field of a Wireless Power Transfer System with Dipole Coil Structure
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Yujun Shin and Seongho Woo
Electronics 2024, 13(9), 1712; https://doi.org/10.3390/electronics13091712 - 29 Apr 2024
Abstract
This paper proposes a reactive shield structure to reduce the leakage magnetic field of a wireless power transfer (WPT) system with a dipole coil structure. The reactive shield resonates at a frequency lower than that of the WPT system and operates in an
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This paper proposes a reactive shield structure to reduce the leakage magnetic field of a wireless power transfer (WPT) system with a dipole coil structure. The reactive shield resonates at a frequency lower than that of the WPT system and operates in an inductive region where the reactance is positive. Therefore, the magnetic field generated by the shield coil is 180° different in phase from that generated by the transmitting coil, resulting in an effective reduction in the leakage magnetic field. The methodology for designing the reactive shield for the dipole coil structure is mathematically analyzed, and the current and magnetic field phases are compared. Its effectiveness has been validated through simulations and experiments. Specifically, the proposed method is validated through a 50 W class WPT experiment, which showed that the proposed shielding structure achieves efficiency reductions ranging from 0.3% to 1.5% and has a leakage magnetic field reduction effect of up to 67% compared to the comparison groups.
Full article
(This article belongs to the Special Issue Wireless Power Transfer Systems for Biomedical Devices: Modeling, Simulation, Application)
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Open AccessArticle
Reinventing Web Security: An Enhanced Cycle-Consistent Generative Adversarial Network Approach to Intrusion Detection
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Menghao Fang, Yixiang Wang, Liangbin Yang, Haorui Wu, Zilin Yin, Xiang Liu, Zexian Xie and Zixiao Kong
Electronics 2024, 13(9), 1711; https://doi.org/10.3390/electronics13091711 - 29 Apr 2024
Abstract
Web3.0, as the link between the physical and digital domains, faces increasing security threats due to its inherent complexity and openness. Traditional intrusion detection systems (IDSs) encounter formidable challenges in grappling with the multidimensional and nonlinear traffic data characteristic of the Web3.0 environment.
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Web3.0, as the link between the physical and digital domains, faces increasing security threats due to its inherent complexity and openness. Traditional intrusion detection systems (IDSs) encounter formidable challenges in grappling with the multidimensional and nonlinear traffic data characteristic of the Web3.0 environment. Such challenges include insufficient samples of attack data, inadequate feature extraction, and resultant inaccuracies in model classification. Moreover, the scarcity of certain traffic data available for analysis by IDSs impedes the system’s capacity to document instances of malicious behavior. In response to these exigencies, this paper presents a novel approach to Web3.0 intrusion detection, predicated on the utilization of cycle-consistent generative adversarial networks (CycleGANs). Leveraging the data transformation capabilities of its generator, this method facilitates bidirectional conversion between normal Web3.0 behavioral data and potentially intrusive behavioral data. This transformative process not only augments the diversity and volume of recorded intrusive behaviors but also clandestinely simulates various attack scenarios. Furthermore, through fostering mutual competition and learning between the discriminator and generator, the approach enhances the ability to discern the defining characteristics of potential intrusive behaviors, thereby bolstering the accuracy of intrusion detection. To substantiate the efficacy of the CycleGAN-based intrusion detection method, simulation experiments were conducted utilizing public datasets, including KDD CUP 1999 (KDD), CIC-DDOS2019, CIC-IDS2018, and SR-BH 2020. The experimental findings evince the method’s remarkable accuracies across the four datasets, attaining rates of 99.81%, 97.79%, 89.25%, and 95.15%, respectively, while concurrently maintaining low false-positive rates. This research contributes novel insights and methodologies toward the advancement of Web3.0 intrusion detection through the application of CycleGAN technology, which is poised to play a pivotal role in fortifying the security landscape of Web3.0.
Full article
(This article belongs to the Special Issue Applied Cryptography and Practical Cryptoanalysis for Web 3.0)
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Open AccessArticle
Ship Network Traffic Engineering Based on Reinforcement Learning
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Xinduoji Yang, Minghui Liu, Xinxin Wang, Bingyu Hu, Meng Liu and Xiaomin Wang
Electronics 2024, 13(9), 1710; https://doi.org/10.3390/electronics13091710 - 29 Apr 2024
Abstract
This research addresses multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we used reinforcement learning-based methods to simulate traffic engineering in ship networks. We focused on three aspects—traffic balance, instruction
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This research addresses multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we used reinforcement learning-based methods to simulate traffic engineering in ship networks. We focused on three aspects—traffic balance, instruction priority, and complex network structure—to evaluate reinforcement learning performance in these scenarios. Performance: We developed a reinforcement learning framework for ship network traffic engineering that treats the routing policy as the state and the network state as the environment. The agent generates routing changes and uses actions to optimize traffic services. The experimental results show that reinforcement learning optimizes network traffic balance, reasonably arranges instruction priorities, and copes with complex network structures, greatly improving the network’s quality of service (QoS). Through an in-depth analysis of the experimental data, we noticed that network consumption was reduced by 9.1% under reinforcement learning. Reinforcement learning effectively implemented priority routing of high-priority instructions while reducing the occupancy rate of the edge with the highest occupancy rate in the network by 18.53%.
Full article
(This article belongs to the Special Issue Data-Driven Innovations in Networked Systems and Applications: Recent Developments and Emerging Trends)
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Open AccessArticle
Impact Analysis of Cyber Attacks against Energy Communities in Distribution Grids
by
Afroz Mokarim, Giovanni Battista Gaggero and Mario Marchese
Electronics 2024, 13(9), 1709; https://doi.org/10.3390/electronics13091709 - 29 Apr 2024
Abstract
With the advancement of regulations regarding the reduction in carbon emissions, renewable energy communities have come into the picture. However, many implications come with the installation of these communities from a cybersecurity point of view. The software platforms responsible for managing and controlling
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With the advancement of regulations regarding the reduction in carbon emissions, renewable energy communities have come into the picture. However, many implications come with the installation of these communities from a cybersecurity point of view. The software platforms responsible for managing and controlling them handle a lot of crucial information, and therefore, tampering with these data can lead to several impacts on the operation of these communities and, in turn, the power grid as well. This paper examines the plausible impacts that can be caused by altering certain parameters of the system that make it a potential cyber attack target. The analysis is done by observing how the grid responds to these manipulations for both low-voltage as well as medium-voltage systems. These systems are designed along with integrated energy communities and are implemented in MATLAB/Simulink R2022b software. The observations are made by plotting the grid voltage and power profiles in normal as well as attacked conditions.
Full article
(This article belongs to the Special Issue Anomaly Detection and Prevention in the Smart Grid)
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Open AccessArticle
Seamless Switching Control Strategy for a Power Conversion System in a Microgrid Based on Extended State Observer and Super-Twisting Algorithm
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Jiayu Kang, Ye Wang, Jiachen Wang and Baoquan Liu
Electronics 2024, 13(9), 1708; https://doi.org/10.3390/electronics13091708 - 29 Apr 2024
Abstract
Microgrids can operate stably in both islanded and grid-connected modes, and the transition between these modes enhances system reliability and flexibility, enabling microgrids to adapt to diverse operational requirements and environmental conditions. The switching process, however, may introduce transient voltage and frequency fluctuations,
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Microgrids can operate stably in both islanded and grid-connected modes, and the transition between these modes enhances system reliability and flexibility, enabling microgrids to adapt to diverse operational requirements and environmental conditions. The switching process, however, may introduce transient voltage and frequency fluctuations, causing voltage and current shocks to the grid and potentially damaging devices and systems connected to the microgrid. To address this issue, this study introduces a novel approach based on the Extended State Observer (ESO) and the Super-Twisting Algorithm (STA). Power conversion systems use Virtual Synchronous Generator (VSG) control and Power-Quality (PQ) control when they are connected to the grid or when the microgrid is not connected to the grid. VSG and PQ share a current loop. Transitioning the reference current generated by the outer loop achieves the switching of control strategies. A real-time observer is designed to estimate and compensate for current fluctuations, disturbances, and variations in id, iq, and system parameters during the switching process to facilitate a smooth transition of control strategies. Furthermore, to enhance the dynamic response and robustness of the system, the Proportional–Integral (PI) controller in the ESO is replaced with a novel super-twisting sliding mode controller based on a boundary layer. The Lyapunov stability principle is applied to ensure asymptotic stability under disturbances. The proposed control strategy is validated through simulation using a seamless switching model of the power conversion system developed on the Matlab/Simulink (R2021b) platform. Simulation results demonstrate that the optimized control strategy enables smooth microgrid transitions, thereby improving the overall reliability of grid operations.
Full article
(This article belongs to the Special Issue Advanced Robust and Optimal Control for Power Converters in Renewable Energy Generation Systems)
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Open AccessArticle
Attitude Calculation Method of Drilling Tools Based on Cross-Correlation Extraction and ASRUKF
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Liansheng Qin, Wenzhuo Wang, Mingjiang Shi, Yanbing Liang and Peipei Tan
Electronics 2024, 13(9), 1707; https://doi.org/10.3390/electronics13091707 - 28 Apr 2024
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As a key component of the measurement while drilling technology, the accuracy of attitude calculation is directly related to the efficiency of resource exploration. To reduce the influence of vibration, rotation, and other disturbances on the attitude sensor during drilling, a method based
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As a key component of the measurement while drilling technology, the accuracy of attitude calculation is directly related to the efficiency of resource exploration. To reduce the influence of vibration, rotation, and other disturbances on the attitude sensor during drilling, a method based on cross-correlation extraction and the adaptive square-root unscented Kalman filter (ASRUKF) is proposed to solve the attitude of the drilling tool in this paper. Firstly, the error of the signal collected by the attitude sensor is compensated, and the unscented Kalman filter (UKF) is used for filtering. Then, the effective gravitational acceleration signal is extracted by the cross-correlation method. Finally, an experimental platform for simulating the fully rotating attitude measurement system is established, and the application effects of the UKF and ASRUKF in the attitude calculation are compared. Compared with the UKF, the root mean square error of the inclination angle calculated by the ASRUKF is reduced by 12.9%, and the variance is reduced by 27.3%; the root mean square error of the azimuth angle is reduced by 29.5%, and the variance is reduced by 39.9%. The experimental results show that the attitude calculation method proposed in this paper can stably and effectively improve the accuracy of the attitude calculation of drilling tools.
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Open AccessArticle
A Benchmark for UAV-View Natural Language-Guided Tracking
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Hengyou Li, Xinyan Liu and Guorong Li
Electronics 2024, 13(9), 1706; https://doi.org/10.3390/electronics13091706 - 28 Apr 2024
Abstract
We propose a new benchmark, UAVNLT (Unmanned Aerial Vehicle Natural Language Tracking), for the UAV-view natural language-guided tracking task. UAVNLT consists of videos taken from UAV cameras from four cities for vehicles on city roads. For each video, vehicles’ bounding boxes, trajectories, and
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We propose a new benchmark, UAVNLT (Unmanned Aerial Vehicle Natural Language Tracking), for the UAV-view natural language-guided tracking task. UAVNLT consists of videos taken from UAV cameras from four cities for vehicles on city roads. For each video, vehicles’ bounding boxes, trajectories, and natural language are carefully annotated. Compared to the existing data sets, which are only annotated with bounding boxes, the natural language sentences in our data set can be more suitable for many application fields where humans take part in the system for that language, being not only more friendly for human–computer interaction but also capable of overcoming the appearance features’ low uniqueness for tracking. We tested several existing methods on our new benchmarks and found that the performance of the existing methods was not satisfactory. To pave the way for future work, we propose a baseline method suitable for this task, achieving state-of-the-art performance. We believe our new data set and proposed baseline method will be helpful in many fields, such as smart city, smart transportation, vehicle management, etc.
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(This article belongs to the Topic Theoretical and Applied Problems in Human-Computer Intelligent Systems)
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Open AccessArticle
Design and Test of Offset Quadrature Phase-Shift Keying Modulator with GF180MCU Open Source Process Design Kit
by
Emma Mascorro-Guardado, Susana Ortega-Cisneros, Emilio Isaac Baungarten-Leon, Luis A. Luna-Rodriguez, Uriel Jaramillo-Toral, Manuel Hernández-Aramburo and Emanuel Murillo-García
Electronics 2024, 13(9), 1705; https://doi.org/10.3390/electronics13091705 - 28 Apr 2024
Abstract
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This article explores the evolution of integrated circuits , highlighting the fundamental role of open source Electronic Design Automation (EDA) tools in their development. It describes the IC’s design flow, differentiating between Front-end and Back-end design stages, and
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This article explores the evolution of integrated circuits , highlighting the fundamental role of open source Electronic Design Automation (EDA) tools in their development. It describes the IC’s design flow, differentiating between Front-end and Back-end design stages, and details the process of implementing the digital stage in offset quadrature phase-shift keying (OQPSK) modulation in an IC, including its hardware description language (HDL), the implementation test in the field-programmable gate array (FPGA), and the physical layout using the first manufactured open source process design kits (PDKs) in Global Foundries’ 180 nm, as well as the use of OpenLane and Caravel. To conclude, the results of the physical tests obtained from the digital modulation are presented, as well as the performance of the raised cosine shaping filter.
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Open AccessArticle
The Genesis of AIbyAI Integrated Circuit: Where AI Creates AI
by
Emilio Isaac Baungarten-Leon, Susana Ortega-Cisneros, Mohamed Abdelmoneum, Ruth Yadira Vidana Morales and German Pinedo-Diaz
Electronics 2024, 13(9), 1704; https://doi.org/10.3390/electronics13091704 - 28 Apr 2024
Abstract
The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis
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The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AIbyAI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AIbyAI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs.
Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
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