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
Where Are We Now?—Exploring the Metaverse Representations to Find Digital Twins
Electronics 2024, 13(10), 1984; https://doi.org/10.3390/electronics13101984 (registering DOI) - 19 May 2024
Abstract
The Metaverse promises to change our lives and how we usually interact with the world. However, it can only evolve with technological development and entertainment engagement advances. To investigate more leads regarding this concept, we have a main search question: How are the
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The Metaverse promises to change our lives and how we usually interact with the world. However, it can only evolve with technological development and entertainment engagement advances. To investigate more leads regarding this concept, we have a main search question: How are the Metaverse, gaming, and digital twins represented in Academia? To answer it, we need to verify and determine how the Metaverse is defined, how gaming, as an entertainment industry, is represented, and how Digital Twins are defined by scientific knowledge. It will also be important to analyze how these concepts are intercorrelated. Here, we present a documental study—meta-analysis—of the most relevant indexed scientific papers published in the last ten years, according to predefined inclusion and exclusion criteria. Leximancer software will help us determine the main concepts and themes extracted from these articles—namely from the Keywords, Abstracts, Methodologies, and Conclusions sections. This study allows us to understand how these concepts are perceived, contribute to a scientific discussion, and give suggestions for future research and new leads on approaching these concepts.
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(This article belongs to the Special Issue Perception and Interaction in Mixed, Augmented, and Virtual Reality)
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Scaled Model for Studying the Propagation of Radio Waves Diffracted from Tunnels
by
Ori Glikstein, Gad A. Pinhasi and Yosef Pinhasi
Electronics 2024, 13(10), 1983; https://doi.org/10.3390/electronics13101983 (registering DOI) - 18 May 2024
Abstract
One of the major challenges in designing a wireless indoor–outdoor communication network operating in tunnels and long corridors is to identify the optimal location of the outside station for attaining a proper coverage. It is required to formulate a combined model, describing the
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One of the major challenges in designing a wireless indoor–outdoor communication network operating in tunnels and long corridors is to identify the optimal location of the outside station for attaining a proper coverage. It is required to formulate a combined model, describing the propagation along the tunnel and the resulting diffracted outdoor pattern from its exit. An integrated model enables estimations of the radiation patterns at the rectangular tunnel exit, as well as in the free space outside of the tunnel. The tunnel propagation model is based on a ray-tracing image model, while the free-space diffraction model is based on applying the far-field Fraunhofer diffraction equation. The model predictions of sensing the radiation intensity at the tunnel end and at a plane located at a distance ahead were compared with experimental data obtained using a down-scaled tunnel model and shorter radiation wavelength correspondingly. This down-scaling enabled detailed measurements of the radiation patterns at the tunnel exit and at the far field. The experimental measurements for the scaled tunnel case fit the theoretical model predictions. The presented model accurately described the multi-path effects emerging from inside the tunnel and the resulting outdoor diffracted pattern at a distance from the tunnel exit.
Full article
(This article belongs to the Special Issue Next-Generation Indoor Wireless Communication)
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A New Method for Anti-Interference Measurement of Capacitance Parameters of Long-Distance Transmission Lines Based on Harmonic Components
by
Kaibai Wang, Zihao Zhang, Xingwei Xu, Zhijian Hu, Zhengwei Sun, Jiahao Tan, Xiang Yao and Jingfu Tian
Electronics 2024, 13(10), 1982; https://doi.org/10.3390/electronics13101982 (registering DOI) - 18 May 2024
Abstract
In the context of strong electromagnetic interference environments, the measurement accuracy of the capacitance parameters of transmission lines under power frequency measurement methods is not high. In this paper, a capacitance parameter anti-interference measurement method for transmission lines based on harmonic components is
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In the context of strong electromagnetic interference environments, the measurement accuracy of the capacitance parameters of transmission lines under power frequency measurement methods is not high. In this paper, a capacitance parameter anti-interference measurement method for transmission lines based on harmonic components is proposed to overcome the impact of power frequency interference. When applying this method, it is first necessary to open-circuit the end of the line under test. Subsequently, apply voltage to the head end of the tested line through a step-up transformer. Due to the saturation of the transformer during no-load conditions, a large number of harmonics are generated, primarily third harmonic. The third harmonic components of voltage and current on the tested transmission line are extracted using the Fourier transform. The proposed method addresses the influence of line distribution effects by establishing a distributed parameter model for long-distance transmission lines. The relevant transmission matrix for the zero-sequence distributed parameters is obtained by combining Laplace transform and similarity transform to solve the transmission line equations. Using synchronous measurement data from the third harmonic components of voltage and current at both ends of the transmission line, combined with the transmission matrix, this method accurately measures the zero-sequence capacitance parameters. The PSCAD/EMTDC simulation results and field test outcomes have demonstrated the feasibility and accuracy of the proposed method for measuring line capacitance parameters under strong electromagnetic interference.
Full article
Open AccessArticle
Parallel Spatio-Temporal Attention Transformer for Video Frame Interpolation
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Xin Ning, Feifan Cai, Yuhang Li and Youdong Ding
Electronics 2024, 13(10), 1981; https://doi.org/10.3390/electronics13101981 (registering DOI) - 18 May 2024
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Traditional video frame interpolation methods based on deep convolutional neural networks face challenges in handling large motions. Their performance is limited by the fact that convolutional operations cannot directly integrate the rich temporal and spatial information of inter-frame pixels, and these methods rely
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Traditional video frame interpolation methods based on deep convolutional neural networks face challenges in handling large motions. Their performance is limited by the fact that convolutional operations cannot directly integrate the rich temporal and spatial information of inter-frame pixels, and these methods rely heavily on additional inputs such as optical flow to model motion. To address this issue, we develop a novel framework for video frame interpolation that uses Transformer to efficiently model the long-range similarity of inter-frame pixels. Furthermore, to effectively aggregate spatio-temporal features, we design a novel attention mechanism divided into temporal attention and spatial attention. Specifically, spatial attention is used to aggregate intra-frame information, integrating both attention and convolution paradigms through the simple mapping approach. Temporal attention is used to model the similarity of pixels on the timeline. This design achieves parallel processing of these two types of information without extra computational cost, aggregating information in the space–time dimension. In addition, we introduce a context extraction network and multi-scale prediction frame synthesis network to further optimize the performance of the Transformer. Our method and state-of-the-art methods are extensively quantitatively and qualitatively experimented on various benchmark datasets. On the Vimeo90K and UCF101 datasets, our model achieves improvements of 0.09 dB and 0.01 dB in the PSNR metrics over UPR-Net-large, respectively. On the Vimeo90K dataset, our model outperforms FLAVR by 0.07 dB, with only 40.56% of its parameters. The qualitative results show that for complex and large-motion scenes, our method generates sharper and more realistic edges and details.
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Open AccessArticle
Waveform Optimization Control of an Active Neutral Point Clamped Three-Level Power Converter System
by
Jinghua Zhou and Jin Li
Electronics 2024, 13(10), 1980; https://doi.org/10.3390/electronics13101980 (registering DOI) - 18 May 2024
Abstract
Currently, the escalating integration of renewable energy sources is causing a steady weakening of grid strength. When grid strength is weak, interactions between inverters or those between inverters and grid line impedance can provoke widespread oscillations in the power system. Additionally, the diverse
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Currently, the escalating integration of renewable energy sources is causing a steady weakening of grid strength. When grid strength is weak, interactions between inverters or those between inverters and grid line impedance can provoke widespread oscillations in the power system. Additionally, the diverse DC voltage application characteristics of power converter systems (PCS) may lead to over-modulation, generating narrow pulse issues that further impact control of the midpoint potential balance. Existing dead-time elimination methods are highly susceptible to current polarity judgments, rendering them ineffective in practical use. PCS, due to inherent dead-time effects, midpoint potential imbalances in three-level topologies, and narrow pulses, can elevate low-order harmonic content in the output voltage, ultimately distorting grid-connected currents. This is particularly susceptible to causing resonance in weak grids. To enhance the output voltage waveform of PCS, this article introduces a comprehensive compensation control strategy that combines dead-time elimination, midpoint potential balance, and narrow pulse suppression, all based on an active neutral point clamped (ANPC) three-level topology. This strategy gives precedence to dead-time elimination and calculates the upper and lower limits of the zero-sequence available for midpoint potential balance while fully compensating for narrow pulses. By prioritizing dead-time elimination, followed by narrow pulse suppression and finally midpoint potential balance, this method decouples the coupling between these three factors. The effectiveness of the proposed method is validated through semi-physical simulations.
Full article
(This article belongs to the Section Power Electronics)
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A Survey on AI-Empowered Softwarized Industrial IoT Networks
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Elisa Rojas, David Carrascal, Diego Lopez-Pajares, Joaquin Alvarez-Horcajo, Juan A. Carral, Jose Manuel Arco and Isaias Martinez-Yelmo
Electronics 2024, 13(10), 1979; https://doi.org/10.3390/electronics13101979 (registering DOI) - 18 May 2024
Abstract
The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related
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The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related works in this field, with a particular focus on the specific role of network softwarization. Furthermore, the survey delves into their context and trends, categorizing works into several types and comparing them based on their contribution to the advancement of the state of the art. Since our analysis yields a lack of integrated practical implementations and a potential desynchronization with current standards, we finalize our study with a summary of challenges and future research ideas.
Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and the Future of Communication)
Open AccessArticle
A Novel Approach to Managing System-on-Chip Sub-Blocks Using a 16-Bit Real-Time Operating System
by
Boisy Pitre and Martin Margala
Electronics 2024, 13(10), 1978; https://doi.org/10.3390/electronics13101978 (registering DOI) - 18 May 2024
Abstract
Embedded computers are ubiquitous in products across various industries, including the automotive and medical industries, and in consumer goods such as appliances and entertainment devices. These specialized computing systems utilize Systems on Chips (SoCs), devices that are made up of one or more
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Embedded computers are ubiquitous in products across various industries, including the automotive and medical industries, and in consumer goods such as appliances and entertainment devices. These specialized computing systems utilize Systems on Chips (SoCs), devices that are made up of one or more main microprocessor cores. SoCs are augmented with sub-blocks that perform dedicated tasks to support the system. Sub-blocks contain custom logic or small-footprint microprocessors, depending upon their complexity, and perform support functions such as clock generation, device testing, phase-locked loop synchronization and peripheral management for interfaces such as a Universal Serial Bus (USB) or Serial Peripheral Interface (SPI). SoC designers have traditionally obtained sub-blocks from commercial vendors. While these sub-blocks have well-defined interfaces, their internal implementations are opaque. Without visibility of the specifics of the implementation, SoC designers are limited to the degree to which they can optimize these off-the-shelf sub-blocks. The result is that power and area constraints are dictated by the design of a third-party vendor. This work introduces a novel idea: using an open-source, small, multitasking, real-time operating system inside an SoC sub-block to manage multiple processes, thereby conserving code space. This OS is TurbOS, a new operating system whose primary goal is to provide the highest performance using the least amount of space. It is written in the assembly language of a new pipelined 16-bit microprocessor developed at the University of Florida, the Turbo9. TurbOS is derived from and incorporates the design benefits of an existing operating system called NitrOS-9, and reduces the code size from its progenitor by nearly 20%. Furthermore, it is over 80% smaller than the popular FreeRTOS operating system. TurbOS delivers a rich feature set for managing memory and process resources that are useful in SoC sub-block applications in an extremely small footprint of only 3 kilobytes.
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(This article belongs to the Special Issue Progress and Future Development of Real-Time Systems on Chip)
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Leveraging Self-Distillation and Disentanglement Network to Enhance Visual–Semantic Feature Consistency in Generalized Zero-Shot Learning
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Xiaoming Liu, Chen Wang, Guan Yang, Chunhua Wang, Yang Long, Jie Liu and Zhiyuan Zhang
Electronics 2024, 13(10), 1977; https://doi.org/10.3390/electronics13101977 (registering DOI) - 18 May 2024
Abstract
Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based
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Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based on semantic information, all of which rely on the correct alignment of visual–semantic features. However, they often overlook the inconsistency between original visual features and semantic attributes. Additionally, due to the existence of cross-modal dataset biases, the visual features extracted and synthesized by the model may also mismatch with some semantic features, which could hinder the model from properly aligning visual–semantic features. To address this issue, this paper proposes a GZSL framework that enhances the consistency of visual–semantic features using a self-distillation and disentanglement network (SDDN). The aim is to utilize the self-distillation and disentanglement network to obtain semantically consistent refined visual features and non-redundant semantic features to enhance the consistency of visual–semantic features. Firstly, SDDN utilizes self-distillation technology to refine the extracted and synthesized visual features of the model. Subsequently, the visual–semantic features are then disentangled and aligned using a disentanglement network to enhance the consistency of the visual–semantic features. Finally, the consistent visual–semantic features are fused to jointly train a GZSL classifier. Extensive experiments demonstrate that the proposed method achieves more competitive results on four challenging benchmark datasets (AWA2, CUB, FLO, and SUN).
Full article
(This article belongs to the Special Issue Deep/Machine Learning in Visual Recognition and Anomaly Detection)
Open AccessArticle
Experimental Approach for Reliability Analysis of Medium-Power Zener Diodes under DC Switching Surge Degradation
by
Daniel van Niekerk and Johan Venter
Electronics 2024, 13(10), 1976; https://doi.org/10.3390/electronics13101976 (registering DOI) - 18 May 2024
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This study investigated the reliability of Zener diodes subjected to a gradually increasing DC switching surge amplitude with delay internals between surges to avoid thermal degradation from different manufacturers with similar specifications. The analysis involved applying occasional 3 ms direct current (DC) switching
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This study investigated the reliability of Zener diodes subjected to a gradually increasing DC switching surge amplitude with delay internals between surges to avoid thermal degradation from different manufacturers with similar specifications. The analysis involved applying occasional 3 ms direct current (DC) switching surges with a gradual increasing surge voltage, followed by a constant current test to verify device functionality for three different selected manufacturer 5.1 V Zener diodes. This experimental approach was used to identify the maximum surge current that each Zener diode could handle before failing to clamp the surge voltage at the specified Zener reference voltage. Statistical analysis revealed significant differences in the maximum average surge current between different manufacturers. The maximum average surge current findings just before failure were 1.98 A, 3.18 A, and 3.33 A, respectively, and associated 95% confidence interval ranges can be used as a reliable metric to compare Zener diode population reliability against occasional DC switching surges. The findings revealed variations in the DC switching surge current handling capabilities between Zener diodes from different manufacturers with similar electrical specifications. The statistically measured maximum average surge current just before device failure can be considered an effective metric to compare the reliability of Zener diodes against DC switching surge degradation.
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A New Symmetrical Source-Based DC/AC Converter with Experimental Verification
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Kailash Kumar Mahto, Bidyut Mahato, Bikramaditya Chandan, Durbanjali Das, Priyanath Das, Georgios Fotis, Vasiliki Vita and Michael Mann
Electronics 2024, 13(10), 1975; https://doi.org/10.3390/electronics13101975 - 17 May 2024
Abstract
This research paper introduces a new topology for multilevel inverters, emphasizing the reduction of harmonic distortion and the optimization of the component count. The complexity of an inverter is determined by the number of power switches, which is significantly reduced in the presented
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This research paper introduces a new topology for multilevel inverters, emphasizing the reduction of harmonic distortion and the optimization of the component count. The complexity of an inverter is determined by the number of power switches, which is significantly reduced in the presented topology, as fewer switches require fewer driver circuits. In this proposed topology, a new single-phase generalized multilevel inverter is analyzed with an equal magnitude of voltage supply. A 9-level, 11-level, or 13-level symmetrical inverter with RL load is analyzed in MATLAB/Simulink 2019b and then experimentally validated using the dSPACE-1103 controller. The experimental verification of the load voltage and current with different modulation indices is also presented. The analysis of the proposed topology concludes that the total required number of components is lower than that necessary for the classical inverter topologies, as well as for some new proposed multilevel inverters that are also compared with the proposed topology in terms of gate driver circuits, power switches, and DC sources, which thereby enhances the goodness of the proposed topology. Thus, a comparison of this inverter with the other topologies validates its acceptance.
Full article
(This article belongs to the Special Issue Electrical Power Systems Quality)
Open AccessArticle
Fractional-Order Least-Mean-Square-Based Active Control for an Electro–Hydraulic Composite Engine Mounts
by
Lida Wang, Rongjun Ding, Kan Liu, Jun Yang, Xingwu Ding and Renping Li
Electronics 2024, 13(10), 1974; https://doi.org/10.3390/electronics13101974 - 17 May 2024
Abstract
For the vibration of automobile powertrain, this paper designs electro–hydraulic composite engine mounts. Subsequently, the dynamic characteristics of the hydraulic mount and the electromagnetic actuator were analyzed and experimentally studied separately. Due to the strong nonlinearity of the hybrid electromechanical engine mount, a
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For the vibration of automobile powertrain, this paper designs electro–hydraulic composite engine mounts. Subsequently, the dynamic characteristics of the hydraulic mount and the electromagnetic actuator were analyzed and experimentally studied separately. Due to the strong nonlinearity of the hybrid electromechanical engine mount, a Fractional-Order Least-Mean-Square (FGO-LMS) algorithm was proposed to model its secondary path identification. To validate the vibration reduction effect, a rapid control prototype test platform was established, and vibration active control experiments were conducted based on the Multiple–Input Multiple–Output Filter-x Least-Mean-Square (MIMO-FxLMS) algorithm. The results indicate that, under various operating conditions, the vibration transmitted to the chassis from the powertrain was significantly suppressed.
Full article
(This article belongs to the Special Issue Control and Optimization of Power Converters and Drives)
Open AccessReview
Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles
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Seyed Mahdi Miraftabzadeh, Michela Longo, Andrea Di Martino, Alessandro Saldarini and Roberto Sebastiano Faranda
Electronics 2024, 13(10), 1973; https://doi.org/10.3390/electronics13101973 - 17 May 2024
Abstract
The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical
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The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.
Full article
(This article belongs to the Special Issue Advanced Energy Supply and Storage Systems for Electric Vehicles)
Open AccessArticle
Enhancing Learning of 3D Model Unwrapping through Virtual Reality Serious Game: Design and Usability Validation
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Bruno Rodriguez-Garcia, José Miguel Ramírez-Sanz, Ines Miguel-Alonso and Andres Bustillo
Electronics 2024, 13(10), 1972; https://doi.org/10.3390/electronics13101972 - 17 May 2024
Abstract
Given the difficulty of explaining the unwrapping process through traditional teaching methodologies, this article presents the design, development, and validation of an immersive Virtual Reality (VR) serious game, named Unwrap 3D Virtual: Ready (UVR), aimed at facilitating the learning of unwrapping 3D models.
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Given the difficulty of explaining the unwrapping process through traditional teaching methodologies, this article presents the design, development, and validation of an immersive Virtual Reality (VR) serious game, named Unwrap 3D Virtual: Ready (UVR), aimed at facilitating the learning of unwrapping 3D models. The game incorporates animations to aid users in understanding the unwrapping process, following Mayer’s Cognitive Theory of Multimedia Learning and Gamification principles. Structured into four levels of increasing complexity, users progress through different aspects of 3D model unwrapping, with the final level allowing for result review. A sample of 53 students with experience in 3D modeling was categorized based on device (PC or VR) and previous experience (XP) in VR, resulting in Low-XP, Mid-XP, and High-XP groups. Hierarchical clustering identified three clusters, reflecting varied user behaviors. Results from surveys assessing game experience, presence, and satisfaction show higher immersion reported by VR users despite greater satisfaction being observed in the PC group due to a bug in the VR version. Novice users exhibited higher satisfaction, which was attributed to the novelty effect, while experienced users demonstrated greater control and proficiency.
Full article
(This article belongs to the Special Issue Serious Games and Extended Reality (XR))
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Volt-VAR Control in Active Distribution Networks Using Multi-Agent Reinforcement Learning
by
Shi Su, Haozhe Zhan, Luxi Zhang, Qingyang Xie, Ruiqi Si, Yuxin Dai, Tianlu Gao, Linhan Wu, Jun Zhang and Lei Shang
Electronics 2024, 13(10), 1971; https://doi.org/10.3390/electronics13101971 - 17 May 2024
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With the advancement of power systems, the integration of a substantial portion of renewable energy often leads to frequent voltage surges and increased fluctuations in distribution networks (DNs), significantly affecting the safety of DNs. Active distribution networks (ADNs) can address voltage issues arising
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With the advancement of power systems, the integration of a substantial portion of renewable energy often leads to frequent voltage surges and increased fluctuations in distribution networks (DNs), significantly affecting the safety of DNs. Active distribution networks (ADNs) can address voltage issues arising from a high proportion of renewable energy by regulating distributed controllable resources. However, the conventional mathematical optimization-based approach to voltage reactive power control has certain limitations. It heavily depends on precise DN parameters, and its online implementation requires iterative solutions, resulting in prolonged computation time. In this study, we propose a Volt-VAR control (VVC) framework in ADNs based on multi-agent reinforcement learning (MARL). To simplify the control of photovoltaic (PV) inverters, the ADNs are initially divided into several distributed autonomous sub-networks based on the electrical distance of reactive voltage sensitivity. Subsequently, the Multi-Agent Soft Actor-Critic (MASAC) algorithm is employed to address the partitioned cooperative voltage control problem. During online deployment, the agents execute distributed cooperative control based on local observations. Comparative tests involving various methods are conducted on IEEE 33-bus and IEEE 141-bus medium-voltage DNs. The results demonstrate the effectiveness and versatility of this method in managing voltage fluctuations and mitigating reactive power loss.
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Open AccessArticle
Machine Learning-Based Hand Pose Generation Using a Haptic Controller
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Jongin Choi, Jaehong Lee, Daniel Oh and Eung-Joo Lee
Electronics 2024, 13(10), 1970; https://doi.org/10.3390/electronics13101970 - 17 May 2024
Abstract
In this study, we present a novel approach to derive hand poses from data input via a haptic controller, leveraging machine learning techniques. The input values received from the haptic controller correspond to the movement of five fingers, each assigned a value between
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In this study, we present a novel approach to derive hand poses from data input via a haptic controller, leveraging machine learning techniques. The input values received from the haptic controller correspond to the movement of five fingers, each assigned a value between 0.0 and 1.0 based on the applied pressure. The wide array of possible finger movements requires a substantial amount of motion capture data, making manual data integration difficult. This challenge is primary due to the need to process and incorporate large volumes of diverse movement information. To tackle this challenge, our proposed method automates the process by utilizing machine learning algorithms to convert haptic controller inputs into hand poses. This involves training a machine learning model using supervised learning, where hand poses are matched with their corresponding input values, and subsequently utilizing this trained model to generate hand poses in response to user input. In our experiments, we assessed the accuracy of the generated hand poses by analyzing the angles and positions of finger joints. As the quantity of training data increased, the margin of error decreased, resulting in generated poses that closely emulated real-world hand movements.
Full article
(This article belongs to the Special Issue Multi-robot Systems: Collaboration, Control, and Path Planning)
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Open AccessArticle
Robotic Manipulator in Dynamic Environment with SAC Combing Attention Mechanism and LSTM
by
Xinghong Kuang and Sucheng Zhou
Electronics 2024, 13(10), 1969; https://doi.org/10.3390/electronics13101969 - 17 May 2024
Abstract
The motion planning task of the manipulator in a dynamic environment is relatively complex. This paper uses the improved Soft Actor Critic Algorithm (SAC) with the maximum entropy advantage as the benchmark algorithm to implement the motion planning of the manipulator. In order
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The motion planning task of the manipulator in a dynamic environment is relatively complex. This paper uses the improved Soft Actor Critic Algorithm (SAC) with the maximum entropy advantage as the benchmark algorithm to implement the motion planning of the manipulator. In order to solve the problem of insufficient robustness in dynamic environments and difficulty in adapting to environmental changes, it is proposed to combine Euclidean distance and distance difference to improve the accuracy of approaching the target. In addition, in order to solve the problem of non-stability and uncertainty of the input state in the dynamic environment, which leads to the inability to fully express the state information, we propose an attention network fused with Long Short-Term Memory (LSTM) to improve the SAC algorithm. We conducted simulation experiments and present the experimental results. The results prove that the use of fused neural network functions improved the success rate of approaching the target and improved the SAC algorithm at the same time, which improved the convergence speed, success rate, and avoidance capabilities of the algorithm.
Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Engineering)
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Open AccessArticle
Novel Waveform Design with a Reduced Cyclic Prefix in MIMO Systems
by
Huanhuan Yin, Jiehao Luo, Baobing Wang, Bing Zhang, Shuang Luo and Dejin Kong
Electronics 2024, 13(10), 1968; https://doi.org/10.3390/electronics13101968 - 17 May 2024
Abstract
For well-known orthogonal frequency division multiplexing (OFDM), the cyclic prefix (CP) is essential for coping with multipath channels. Nevertheless, CP is a pure redundant signal, which wastes valuable time–frequency resources. We propose a novel waveform based on symbol repetition, which is presented to
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For well-known orthogonal frequency division multiplexing (OFDM), the cyclic prefix (CP) is essential for coping with multipath channels. Nevertheless, CP is a pure redundant signal, which wastes valuable time–frequency resources. We propose a novel waveform based on symbol repetition, which is presented to cut down the CP overhead in OFDM. In the presented OFDM with symbol repetition (SR-OFDM), one CP is inserted in the front of several transmitted symbols, instead of only one symbol, as in the conventional way. As a result, it can save the overhead created by CP. Furthermore, due to the existence of the remaining CP, the multipath channel can still be converted into the frequency domain, and single-tap equalization can still be used to equalize information free from interference. In addition, we also extend the proposed SR-OFDM into multiple input–multiple output (MIMO) systems. Finally, the proposed schemes are validated by computer simulations under the various channels.
Full article
(This article belongs to the Special Issue Advanced Digital Signal Processing for Future Digital Communications)
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Open AccessArticle
OcularSeg: Accurate and Efficient Multi-Modal Ocular Segmentation in Non-Constrained Scenarios
by
Yixin Zhang, Caiyong Wang, Haiqing Li, Xianyun Sun, Qichuan Tian and Guangzhe Zhao
Electronics 2024, 13(10), 1967; https://doi.org/10.3390/electronics13101967 - 17 May 2024
Abstract
Multi-modal ocular biometrics has recently garnered significant attention due to its potential in enhancing the security and reliability of biometric identification systems in non-constrained scenarios. However, accurately and efficiently segmenting multi-modal ocular traits (periocular, sclera, iris, and pupil) remains challenging due to noise
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Multi-modal ocular biometrics has recently garnered significant attention due to its potential in enhancing the security and reliability of biometric identification systems in non-constrained scenarios. However, accurately and efficiently segmenting multi-modal ocular traits (periocular, sclera, iris, and pupil) remains challenging due to noise interference or environmental changes, such as specular reflection, gaze deviation, blur, occlusions from eyelid/eyelash/glasses, and illumination/spectrum/sensor variations. To address these challenges, we propose OcularSeg, a densely connected encoder–decoder model incorporating eye shape prior. The model utilizes Efficientnetv2 as a lightweight backbone in the encoder for extracting multi-level visual features while minimizing network parameters. Moreover, we introduce the Expectation–Maximization attention (EMA) unit to progressively refine the model’s attention and roughly aggregate features from each ocular modality. In the decoder, we design a bottom-up dense subtraction module (DSM) to amplify information disparity between encoder layers, facilitating the acquisition of high-level semantic detailed features at varying scales, thereby enhancing the precision of detailed ocular region prediction. Additionally, boundary- and semantic-guided eye shape priors are integrated as auxiliary supervision during training to optimize the position, shape, and internal topological structure of segmentation results. Due to the scarcity of datasets with multi-modal ocular segmentation annotations, we manually annotated three challenging eye datasets captured in near-infrared and visible light scenarios. Experimental results on newly annotated and existing datasets demonstrate that our model achieves state-of-the-art performance in intra- and cross-dataset scenarios while maintaining efficient execution.
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(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects)
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Open AccessArticle
An Enhanced Power Allocation Strategy for Microgrids Considering Frequency and Voltage Restoration
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Chunguang Yang, Xue Wu, Qichao Song, Haoyu Wu and Yixin Zhu
Electronics 2024, 13(10), 1966; https://doi.org/10.3390/electronics13101966 - 17 May 2024
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In a microgrid, load power should be properly shared among multiple distributed generation (DG) units, not only for fundamental power but also for negative sequence and harmonic power. In this paper, the operation of a microgrid under imbalance and nonlinear load conditions is
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In a microgrid, load power should be properly shared among multiple distributed generation (DG) units, not only for fundamental power but also for negative sequence and harmonic power. In this paper, the operation of a microgrid under imbalance and nonlinear load conditions is studied, and a consensus algorithm-based distributed control strategy is proposed for the microgrid power allocation, frequency, and voltage restoration. First of all, the output current of DG unit is decomposed by second-order generalized integrator (SOGI) modules to obtain the fundamental power and harmonic power through the power calculation formula. Then, state values of DG units, such as local power, frequency, and voltage, are transmitted on a sparse communication network. Under the action of a consensus algorithm, the real power of DG units is allocated following the equal increment principle; the reactive power, imbalance, and harmonic power are allocated according to the capacities of DG units; and the frequency of the microgrid and the voltage at the point of common coupling (PCC) are rated. In the consensus-based strategy, DG units only communicate with their neighbor units; thus, the “plug and play” function is reserved. Compared with the centralized control strategy, the proposed strategy with a distributed consensus protocol can simplify the maintenance and possible expansions of the system, making the microgrid more flexible. Moreover, as the structure of the detailed network is not required, it is easy to apply in practice. Simulation and experiment results are presented to verify the proposed method.
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Open AccessReview
Exploring Innovative Approaches to Synthetic Tabular Data Generation
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Eugenia Papadaki, Aristidis G. Vrahatis and Sotiris Kotsiantis
Electronics 2024, 13(10), 1965; https://doi.org/10.3390/electronics13101965 - 17 May 2024
Abstract
The rapid advancement of data generation techniques has spurred innovation across multiple domains. This comprehensive review delves into the realm of data generation methodologies, with a keen focus on statistical and machine learning-based approaches. Notably, novel strategies like the divide-and-conquer (DC) approach and
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The rapid advancement of data generation techniques has spurred innovation across multiple domains. This comprehensive review delves into the realm of data generation methodologies, with a keen focus on statistical and machine learning-based approaches. Notably, novel strategies like the divide-and-conquer (DC) approach and cutting-edge models such as GANBLR have emerged to tackle a spectrum of challenges, spanning from preserving intricate data relationships to enhancing interpretability. Furthermore, the integration of generative adversarial networks (GANs) has sparked a revolution in data generation across sectors like healthcare, cybersecurity, and retail. This review meticulously examines how these techniques mitigate issues such as class imbalance, data scarcity, and privacy concerns. Through a meticulous analysis of evaluation metrics and diverse applications, it underscores the efficacy and potential of synthetic data in refining predictive models and decision-making software. Concluding with insights into prospective research trajectories and the evolving role of synthetic data in propelling machine learning and data-driven solutions across disciplines, this work provides a holistic understanding of the transformative power of contemporary data generation methodologies.
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(This article belongs to the Special Issue Advances in Data Science and Machine Learning)
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