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
Trajectory Tracking Control of Car-like Mobile Robots Based on Extended State Observer and Backstepping Control
Electronics 2024, 13(8), 1563; https://doi.org/10.3390/electronics13081563 (registering DOI) - 19 Apr 2024
Abstract
In this paper, a trajectory tracking control strategy for low-speed car-like mobile robots (CLMRs) based on an extended state observer (ESO) and backstepping control is proposed to address the issue of trajectory tracking accuracy degradation caused by modeling errors and external disturbances. First,
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In this paper, a trajectory tracking control strategy for low-speed car-like mobile robots (CLMRs) based on an extended state observer (ESO) and backstepping control is proposed to address the issue of trajectory tracking accuracy degradation caused by modeling errors and external disturbances. First, modeling errors and external disturbances are introduced into an ideal kinematic model of a CLMR, and a set of output equations is utilized to split the coupled, underdriven disturbance kinematic model into two mutually independent subsystems. Next, disturbances in the subsystems are estimated based on a linear ESO, and the convergence of the proposed observer is proved by the Lyapunov method. Finally, a controller with disturbance compensation is designed using backstepping control to complete the trajectory tracking task of CLMRs. Simulation and experimental results show the effectiveness of the proposed control scheme.
Full article
(This article belongs to the Special Issue Intelligent Mobile Robotic Systems: Decision, Planning and Control)
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Research on Energy Efficiency Optimization of Visible Light Communication Based on Non-Orthogonal Multiple Access
by
Yali Wu, Lei Sun, Xiaoshuang Liu and Xiaoran Lin
Electronics 2024, 13(8), 1562; https://doi.org/10.3390/electronics13081562 (registering DOI) - 19 Apr 2024
Abstract
As a contender in the competitive landscape of next-generation wireless communication technologies, visible light communication (VLC) stands out due to its potential for enhancing transmission rates and spectrum resource utilization. VLC offers various advantages, including license-free operation, high confidentiality, and cost-effectiveness. However, practical
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As a contender in the competitive landscape of next-generation wireless communication technologies, visible light communication (VLC) stands out due to its potential for enhancing transmission rates and spectrum resource utilization. VLC offers various advantages, including license-free operation, high confidentiality, and cost-effectiveness. However, practical implementation faces challenges stemming from the limited modulation bandwidth of light-emitting diodes (LEDs), constraining system capacity and VLC communication rates. To address this limitation, non-orthogonal multiple access (NOMA) emerges as a novel multiple access strategy, particularly suitable for enhancing the capacity and communication rates of downlink VLC systems through power multiplexing. This paper delves into the energy-efficient design of joint LED association and power allocation (LA–PA) for downlink NOMA-based VLC systems. Through an analysis of channel capacity, we transform the non-convex energy-efficient optimization model, accounting for signal non-negativity, per-LED optical power constraints, and user rate constraints, into a convex form. Subsequently, we propose an iterative power allocation algorithm to attain solutions for the optimization problem with pre-established LED associations. Furthermore, we derive a feasibility condition for an LED association, considering signal non-negativity, per-LED optical power constraints, power constraints for successive interference cancellation (SIC), and channel gain between transceiver signals. This condition identifies feasible LEDs capable of maximizing energy efficiency (EE) when combined with the aforementioned power allocation algorithm. Finally, we illustrate the superiority of the joint LA–PA scheme in terms of the EE, transmission reliability, and transmission capacity performance gain over NOMA in the context of VLC.
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(This article belongs to the Section Microwave and Wireless Communications)
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Scalable Multi-Robot Task Allocation Using Graph Deep Reinforcement Learning with Graph Normalization
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Zhenqiang Zhang, Xiangyuan Jiang, Zhenfa Yang, Sile Ma, Jiyang Chen and Wenxu Sun
Electronics 2024, 13(8), 1561; https://doi.org/10.3390/electronics13081561 (registering DOI) - 19 Apr 2024
Abstract
Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for large-scale multi-robot task allocation problems. This paper proposes a novel graph deep-reinforcement-learning-based approach,
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Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for large-scale multi-robot task allocation problems. This paper proposes a novel graph deep-reinforcement-learning-based approach, which solves the problem through learning. The framework leverages the graph sample and aggregate concept as the encoder to extract the node features in the context of the graph, followed by a cross-attention decoder to output the probability that each task is allocated to each robot. A graph normalization technique is also proposed prior to the input, enabling an easy adaption to real-world applications, and a deterministic solution can be guaranteed. The most important advantage of this architecture is the scalability and quick feed-forward character; regardless of whether cases have a varying number of robots or tasks, single depots, multiple depots, or even mixed single and multiple depots, solutions can be output with little computational effort. The high efficiency and robustness of the proposed method are confirmed by extensive experiments in this paper, and various multi-robot task allocation scenarios demonstrate its advantage.
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(This article belongs to the Topic Agents and Multi-Agent Systems)
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An Assessment of Receiver Algorithms for Distributed Massive MIMO Systems: Investigating Design Solutions and Performance
by
Ali Gashtasbi, Mário Marques da Silva and Rui Dinis
Electronics 2024, 13(8), 1560; https://doi.org/10.3390/electronics13081560 (registering DOI) - 19 Apr 2024
Abstract
This study investigates receiver design solutions for distributed Massive Multiple Input Multiple Output (D-m MIMO) systems, taking into account parameters such as number of access points as well as concerns related to channel estimates that use single-carrier frequency-domain equalization (SC-FDE). A significant contribution
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This study investigates receiver design solutions for distributed Massive Multiple Input Multiple Output (D-m MIMO) systems, taking into account parameters such as number of access points as well as concerns related to channel estimates that use single-carrier frequency-domain equalization (SC-FDE). A significant contribution of this research is the integration of Low-Density Parity-Check (LDPC) codes to simplify coding complexity and enhance communication efficiency. The research examines different receiver designs, such as spatial antenna correlation and sophisticated channel estimation methods. The authors propose integrating LDPC codes into the receiver architecture to simplify computations and enhance error correction and decoding. Moreover, the paper examines performance evaluation measures and approaches, highlighting the trade-offs among complexity, spectral efficiency, and error performance. The comparative analysis indicates the benefits, in terms of performance, of incorporating LDPC codes and improving system throughput and dependability. We examine four distinct receiver algorithms: zero-forcing (ZF), minimum mean square error (MMSE), maximum ratio combining (MRC), and equal gain combining (EGC). The study shows that MRC and EGC receivers work well in D-m MIMO because they make the receiver system less computationally demanding.
Full article
(This article belongs to the Special Issue Smart Communication and Networking in the 6G Era)
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Short-Term Forecasting of Wind Power Based on Error Traceability and Numerical Weather Prediction Wind Speed Correction
by
Mao Yang, Yue Jiang, Jianfeng Che, Zifen Han and Qingquan Lv
Electronics 2024, 13(8), 1559; https://doi.org/10.3390/electronics13081559 (registering DOI) - 19 Apr 2024
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Numerical weather prediction (NWP) is crucial in the current short-term wind power forecasting (STWPF) based on data, but it is difficult for STWPF to achieve high accuracy due to the limited accuracy of NWP, which poses a serious challenge to the formulation of
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Numerical weather prediction (NWP) is crucial in the current short-term wind power forecasting (STWPF) based on data, but it is difficult for STWPF to achieve high accuracy due to the limited accuracy of NWP, which poses a serious challenge to the formulation of forward generation plans. In response to the above issues, this article conducts a traceability analysis of the error of STWPF and proposes a wind power prediction method based on NWP wind speed trend correction. Firstly, the causes of existing errors are analyzed to quantify the impact of NWP on prediction accuracy. Secondly, considering the process correlation between measured and predicted wind speeds, improved complete ensemble EMD with adaptive noise (ICEEMDAN) is used to decompose historical measured wind speeds and NWP wind speeds to construct the most relevant low-frequency trend components. Thirdly, a weighted dual constraint mechanism is proposed to select the most similar historical NWP trend segments to correct NWP wind speed. Finally, the corrected wind speed is used for power prediction and completing STWPF. Through the application of this method to a wind farm in Inner Mongolia Autonomous Region, China, which effectively improves the accuracy of NWP and reduces the average RMSE by 1.39% for power prediction, the effectiveness of this method is verified.
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Open AccessArticle
Lightweight UAV Object-Detection Method Based on Efficient Multidimensional Global Feature Adaptive Fusion and Knowledge Distillation
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Jian Sun, Hongwei Gao, Zhiwen Yan, Xiangjing Qi, Jiahui Yu and Zhaojie Ju
Electronics 2024, 13(8), 1558; https://doi.org/10.3390/electronics13081558 (registering DOI) - 19 Apr 2024
Abstract
Unmanned aerial vehicles (UAVs) equipped with remote-sensing object-detection devices are increasingly employed across diverse domains. However, the detection of small, densely-packed objects against complex backgrounds and at various scales presents a formidable challenge to conventional detection algorithms, exacerbated by the computational constraints of
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Unmanned aerial vehicles (UAVs) equipped with remote-sensing object-detection devices are increasingly employed across diverse domains. However, the detection of small, densely-packed objects against complex backgrounds and at various scales presents a formidable challenge to conventional detection algorithms, exacerbated by the computational constraints of UAV-embedded systems that necessitate a delicate balance between detection speed and accuracy. To address these issues, this paper proposes the Efficient Multidimensional Global Feature Adaptive Fusion Network (MGFAFNET), an innovative detection method for UAV platforms. The novelties of our approach are threefold: Firstly, we introduce the Dual-Branch Multidimensional Aggregation Backbone Network (DBMA), an efficient architectural innovation that captures multidimensional global spatial interactions, significantly enhancing feature distinguishability for complex and occluded targets. Simultaneously, it reduces the computational burden typically associated with processing high-resolution imagery. Secondly, we construct the Dynamic Spatial Perception Feature Fusion Network (DSPF), which is tailored specifically to accommodate the notable scale variances encountered during UAV operation. By implementing a multi-layer dynamic spatial fusion coupled with feature-refinement modules, the network adeptly minimizes informational redundancy, leading to more efficient feature representation. Finally, our novel Localized Compensation Dual-Mask Distillation (LCDD) strategy is devised to adeptly translate the rich local and global features from the higher-capacity teacher network to the more resource-constrained student network, capturing both low-level spatial details and high-level semantic cues with unprecedented efficacy. The practicability and superior performance of our MGFAFNET are corroborated by a dedicated UAV detection platform, showcasing remarkable improvements over state-of-the-art object-detection methods, as demonstrated by rigorous evaluations conducted using the VisDrone2021 benchmark and a meticulously assembled proprietary dataset.
Full article
(This article belongs to the Special Issue Bridging the Gap between Deep Learning and Probabilistic Inference for Advancements in Robotics)
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A Power-Efficient High-Drive Current Mirror Combining a Regulated Cascode Topology with a Non-Linear CCII-Based Feedback
by
Mohan Julien, Serge Bernard, Fabien Soulier, Vincent Kerzérho and Guy Cathébras
Electronics 2024, 13(8), 1556; https://doi.org/10.3390/electronics13081556 (registering DOI) - 19 Apr 2024
Abstract
This brief presents a continuously regulated current mirror topology capable of providing a wide range of currents with high-precision and speed control features. The circuit combines a non-linear current-mode feedback solution for fast and energy-efficient operation with an input-referred regulated-cascode configuration for precise
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This brief presents a continuously regulated current mirror topology capable of providing a wide range of currents with high-precision and speed control features. The circuit combines a non-linear current-mode feedback solution for fast and energy-efficient operation with an input-referred regulated-cascode configuration for precise current mirroring. The proposed implementation has an output current ranging from 100 A to 2 , exhibits a fast response time of ≈100 for the full range steps, while ensuring a high power efficiency (>90%) and low current copy errors (<0.5%).
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(This article belongs to the Topic Advances in Microelectronics and Semiconductor Engineering)
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Population Game-Assisted Multi-Agent Reinforcement Learning Method for Dynamic Multi-Vehicle Route Selection
by
Liping Yan and Yu Cai
Electronics 2024, 13(8), 1555; https://doi.org/10.3390/electronics13081555 (registering DOI) - 19 Apr 2024
Abstract
To address urban traffic congestion, researchers have made various efforts to mitigate issues such as prolonged travel time, fuel wastage, and pollutant emissions. These efforts primarily involve microscopic route selection from the vehicle perspective, multi-vehicle route optimization based on traffic flow information and
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To address urban traffic congestion, researchers have made various efforts to mitigate issues such as prolonged travel time, fuel wastage, and pollutant emissions. These efforts primarily involve microscopic route selection from the vehicle perspective, multi-vehicle route optimization based on traffic flow information and historical data, and coordinated route optimization that models vehicle interaction as a game behavior. However, existing route selection algorithms suffer from limitations such as a lack of heuristic, low dynamicity, lengthy learning cycles, and vulnerability to multi-vehicle route conflicts. To further alleviate traffic congestion, this paper presents a Period-Stage-Round Route Selection Model (PSRRSM), which utilizes a population game between vehicles at each intersection to solve the Nash equilibrium. Additionally, a Period Learning Algorithm for Route Selection (PLA-RS) is proposed, which is based on a multi-agent deep deterministic policy gradient. The algorithm allows the agents to learn from the population game and eventually transition into autonomous learning, adapting to different decision-making roles in different periods. The PSRRSM is experimentally validated using the traffic simulation platform SUMO (Simulation of Urban Mobility) in both artificial and real road networks. The experimental results demonstrate that PSRRSM outperforms several comparative algorithms in terms of network throughput and average travel cost. This is achieved through the coordination of multi vehicle route optimization, facilitated by inter-vehicle population games and communication among road agents during training, enabling the vehicle strategies to reach a Nash equilibrium.
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(This article belongs to the Topic Cooperative Localization, Optimization and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)
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Advanced Analytics and Data Management in the Procurement Function: An Aviation Industry Case Study
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Andrea Altundag and Martin Wynn
Electronics 2024, 13(8), 1554; https://doi.org/10.3390/electronics13081554 (registering DOI) - 19 Apr 2024
Abstract
The company’s strategic procurement function makes a significant contribution to overall corporate success, and yet remains under-researched in terms of digitalisation and digital maturity. This research adopts an inductive case study approach, using qualitative data from in-depth interviews with industry practitioners to develop
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The company’s strategic procurement function makes a significant contribution to overall corporate success, and yet remains under-researched in terms of digitalisation and digital maturity. This research adopts an inductive case study approach, using qualitative data from in-depth interviews with industry practitioners to develop and apply a digital maturity model for the deployment of strategic procurement analytics. The case study company is a multinational aerospace corporation with almost 150,000 employees worldwide. The research presents a snapshot of the digital maturity of the strategic procurement function of this global aircraft manufacturer and finds that the current exploitation of analytics remains constrained by a range of factors, including the need for close compliance with regulatory norms. Thematic analysis of the interview material provides the basis for the development of the maturity model, which—although geared to a specific industry context—is nevertheless of relevance in other business environments. The research thus contributes to the existing literature in this field, and will also be of interest to procurement professionals. However, the research clearly has its limitations, not least in that it is based on just one industry case, and cross-industry generalisations from the findings must therefore be treated with caution.
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(This article belongs to the Special Issue Advanced Research in Technology and Information Systems)
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A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles
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Feng Zhao, Ruimin Cheng, Chunhai Li, Zhaoyu Su, Guoling Liang and Changsong Yang
Electronics 2024, 13(8), 1553; https://doi.org/10.3390/electronics13081553 (registering DOI) - 19 Apr 2024
Abstract
Blockchain technology provides a reliable information access environment for the Internet of Vehicles, but the high latency and complex computing consensus mechanism in blockchain make it difficult to port to onboard devices. Recently, there are many methods to reduce the time cost of
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Blockchain technology provides a reliable information access environment for the Internet of Vehicles, but the high latency and complex computing consensus mechanism in blockchain make it difficult to port to onboard devices. Recently, there are many methods to reduce the time cost of consensus by optimizing node grouping or reducing redundant calculations, but this would lower the security level of the blockchain. To address these issues and reduce the adverse effects of frequently changing channel quality on consensus results, a consensus mechanism based on vehicle comprehensive state factors for nodes selection (PoMS) is proposed. Firstly, the vehicle nodes utilize the machine learning model to predict local driving parameters and broadcast the predicted results to the other nodes. Secondly, each node uses interactive data to calculate the state values, and the leader comprehensively evaluates the nodes participating in the consensus and selects the nodes as relays. Finally, we also adopted a double-layer blockchain structure to accelerate the selection process of relay nodes. In order to verify the performance of the proposed consensus algorithm, we conducted tests on transmission time and communication quality. The experimental results show that compared to traditional consensus mechanisms, the algorithm proposed in this paper can reduce time overhead by an average of 12.7% and maintain a good transmission rates under a certain number of malicious nodes.
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(This article belongs to the Special Issue Data Privacy and Cybersecurity in Mobile Crowdsensing)
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Design of Multi-Band Bandstop Filters Based on Mixed Electric and Magnetic Coupling Resonators
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Jie Luo, Jinhao Zhang and Shanshan Gao
Electronics 2024, 13(8), 1552; https://doi.org/10.3390/electronics13081552 - 19 Apr 2024
Abstract
In this paper, multi-band bandstop filters (BSFs) based on mixed electric and magnetic coupling resonators are proposed. These proposed structures include a multimode resonator based on symmetrical open-circuit branches, including upper- and lower-branch filter circuits. Through this design, the center frequencies of the
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In this paper, multi-band bandstop filters (BSFs) based on mixed electric and magnetic coupling resonators are proposed. These proposed structures include a multimode resonator based on symmetrical open-circuit branches, including upper- and lower-branch filter circuits. Through this design, the center frequencies of the stopbands can be flexibly and autonomously adjusted. In addition, the filters proposed in this paper have excellent characteristics, such as miniature dimensions and abrupt roll-off skirts. Finally, these tri-band to sext-band bandstop filters were fabricated and the measured results agreed well with the simulated ones. The proposed structures can be applied in the fields of communication, information, and coal automation.
Full article
(This article belongs to the Special Issue Advances in the System of Higher-Dimension-Valued Neural Networks)
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Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning
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Eyal Weiss, Shir Caplan, Kobi Horn and Moshe Sharabi
Electronics 2024, 13(8), 1551; https://doi.org/10.3390/electronics13081551 - 19 Apr 2024
Abstract
This paper introduces a pioneering method for real-time image processing in electronic component assembly, revolutionizing quality control in manufacturing. By promptly capturing images from pick-and-place machines during the interval between component pick-up and mounting, defects are identified and promptly addressed in line. This
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This paper introduces a pioneering method for real-time image processing in electronic component assembly, revolutionizing quality control in manufacturing. By promptly capturing images from pick-and-place machines during the interval between component pick-up and mounting, defects are identified and promptly addressed in line. This proactive approach ensures that defective components are rejected before mounting, effectively preventing issues from ever occurring, thus significantly enhancing efficiency and reliability. Leveraging rapid network protocols such as gRPC and orchestration via Kubernetes, in conjunction with C++ programming and TensorFlow, this approach achieves an impressive average turnaround time of less than 5 milli-seconds. Rigorously tested on 20 operational production machines, it not only ensures adherence to IPC-A-610 and IPC-STD-J-001 standards but also optimizes production efficiency and reliability.
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(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
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Leveraging Neighbor Attention Initialization (NAI) for Efficient Training of Pretrained LLMs
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Qiao Tan and Jingjing Zhang
Electronics 2024, 13(8), 1550; https://doi.org/10.3390/electronics13081550 - 19 Apr 2024
Abstract
In the realm of pretrained language models (PLMs), the exponential increase in computational resources and time required for training as model sizes expand presents a significant challenge. This paper proposes an innovative approach named neighbor attention initialization (NAI) to expedite the training process
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In the realm of pretrained language models (PLMs), the exponential increase in computational resources and time required for training as model sizes expand presents a significant challenge. This paper proposes an innovative approach named neighbor attention initialization (NAI) to expedite the training process of larger PLMs by leveraging smaller PLMs through parameter initialization. Our methodology hinges on the hypothesis that smaller PLMs, having already learned fundamental language structures and patterns, can provide a robust foundational knowledge base for larger models, which is called function preserving. Specifically, we present a comprehensive framework detailing the process of transferring learned features on transformer-based language models mainly using the neighbor attention head and neighbor layer. We conducted experiments in GPT-2 and demonstrated that our method yields considerable savings in training costs compared to standard approaches, including learning from scratch and bert2BERT, indicating a notable improvement in training efficiency for large PLMs.
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(This article belongs to the Section Artificial Intelligence)
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Evaluations of Virtual and Augmented Reality Technology-Enhanced Learning for Higher Education
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Jia Yi Wong, Abu Bakr Azam, Qi Cao, Lihui Huang, Yuan Xie, Ingrid Winkler and Yiyu Cai
Electronics 2024, 13(8), 1549; https://doi.org/10.3390/electronics13081549 (registering DOI) - 18 Apr 2024
Abstract
Virtual reality (VR) has good potential to promote technology-enhanced learning. Students can benefit from immersive visualization and intuitive interaction in their learning of abstract concepts, complex structures, and dynamic processes. This paper is interested in evaluating the effects of VR learning games in
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Virtual reality (VR) has good potential to promote technology-enhanced learning. Students can benefit from immersive visualization and intuitive interaction in their learning of abstract concepts, complex structures, and dynamic processes. This paper is interested in evaluating the effects of VR learning games in a Virtual and Augmented Reality Technology-Enhanced Learning (VARTeL) environment within an engineering education setting. A VARTeL flipped classroom is established in the HIVE learning hub at Nanyang Technological University (NTU) Singapore for the immersive and interactive learning. Experiments are designed for the university students conducting the learning, with three interactive and immersive VR games related to science, technology, engineering and mathematics (STEM), i.e., virtual cells, a virtual F1 racing car, and vector geometry. These VR games are a part of the VARTeL apps designed in-house at NTU for STEM education. Quantitative and qualitative analyses are performed. A total of 156 students from Mechanical Engineering participated in the experiment. There are 15 participants selected for an interview after the experiment. Pre-tests and post-tests are performed using two different models, the developed VARTeL and the modified Technology-Rich Outcome-Focused Learning Environment Inventory (TROFLEI), in order to measure the efficiency of the VARTeL environment in Higher Education. Significant improvements of about 24.8% are observed for the post-tests over the pre-tests, which illustrate the effectiveness of the VARTeL for Engineering education. Details of the VR simulation games, methods of data collection, data analyses, as well as the experiment results are discussed. It is observed from the results that all the underlying scales of the modified TROFLEI are above the threshold for the ‘Good’ category, indicating that a very reliable questionnaire is designed in this research. The mean ‘Ideal’ values are about 0.7–2.6% higher than the mean ‘Actual’ values. The limitations of the experiment and future works with recommendations are also presented in this paper.
Full article
(This article belongs to the Special Issue Emerging Immersive Learning Technologies: Augmented and Virtual Reality)
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Orchestrating Isolated Network Slices in 5G Networks
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Ali Esmaeily and Katina Kralevska
Electronics 2024, 13(8), 1548; https://doi.org/10.3390/electronics13081548 - 18 Apr 2024
Abstract
Sharing resources through network slicing in a physical infrastructure facilitates service delivery to various sectors and industries. Nevertheless, ensuring security of the slices remains a significant hurdle. In this paper, we investigate the utilization of State-of-the-Art (SoA) Virtual Private Network (VPN) solutions in
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Sharing resources through network slicing in a physical infrastructure facilitates service delivery to various sectors and industries. Nevertheless, ensuring security of the slices remains a significant hurdle. In this paper, we investigate the utilization of State-of-the-Art (SoA) Virtual Private Network (VPN) solutions in 5G networks to enhance security and performance when isolating slices. We deploy and orchestrate cloud-native network functions to create multiple scenarios that emulate real-life cellular networks. We evaluate the performance of the WireGuard, IPSec, and OpenVPN solutions while ensuring confidentiality and data protection within 5G network slices. The proposed architecture provides secure communication tunnels and performance isolation. Evaluation results demonstrate that WireGuard provides slice isolation in the control and data planes with higher throughput for enhanced Mobile Broadband (eMBB) and lower latency for Ultra-Reliable Low-Latency Communications (URLLC) slices compared to IPSec and OpenVPN. Our developments show the potential of implementing WireGuard isolation, as a promising solution, for providing secure and efficient network slicing, which fulfills the 5G key performance indicator values.
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(This article belongs to the Special Issue Advances in Low-Latency Communications: Protocols, Applications, Challenges, and Opportunities)
Open AccessArticle
Harnessing Test-Oriented Knowledge Graphs for Enhanced Test Function Recommendation
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Kaiqi Liu, Ji Wu, Qing Sun, Haiyan Yang and Ruiyuan Wan
Electronics 2024, 13(8), 1547; https://doi.org/10.3390/electronics13081547 - 18 Apr 2024
Abstract
Application Programming Interfaces (APIs) have become common in contemporary software development. Many automated API recommendation methods have been proposed. However, these methods suffer from a deficit of using domain knowledge, giving rise to challenges like the “cold start” and “semantic gap” problems. Consequently,
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Application Programming Interfaces (APIs) have become common in contemporary software development. Many automated API recommendation methods have been proposed. However, these methods suffer from a deficit of using domain knowledge, giving rise to challenges like the “cold start” and “semantic gap” problems. Consequently, they are unsuitable for test function recommendation, which recommends test functions for test engineers to implement test cases formed with various test steps. This paper introduces an approach named TOKTER, which recommends test functions leveraging test-oriented knowledge graphs. Such a graph contains domain concepts and their relationships related to the system under test and the test harness, which is constructed from the corpus data of the concerned test project. TOKTER harnesses the semantic associations between test steps (or queries) and test functions by considering literal descriptions, test function parameters, and historical data. We evaluated TOKTER with an industrial dataset and compared it with three state-of-the-art approaches. Results show that TOKTER significantly outperformed the baseline by margins of at least 36.6% in mean average precision (MAP), 19.6% in mean reciprocal rank (MRR), and 1.9% in mean recall (MR) for the top-10 recommendations.
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(This article belongs to the Section Computer Science & Engineering)
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Research on Two Improved High–Voltage–Transfer–Ratio Space–Vector Pulse–Width–Modulation Strategies Applied to Five–Phase Inverter
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Mingchen Jing, Yihui Xia and Bin Zhang
Electronics 2024, 13(8), 1546; https://doi.org/10.3390/electronics13081546 - 18 Apr 2024
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Considering that the defects of traditional nearest–two–vector SVPWM (NTV–SVPWM) have a low voltage transfer ratio (VTR) and those of nearest–four–vector SVPWM (NFV–SVPWM) have a high output current harmonic, two improved space–voltage pulse–width–modulation (SVPWM) strategies are proposed in this paper, based on analyzing the
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Considering that the defects of traditional nearest–two–vector SVPWM (NTV–SVPWM) have a low voltage transfer ratio (VTR) and those of nearest–four–vector SVPWM (NFV–SVPWM) have a high output current harmonic, two improved space–voltage pulse–width–modulation (SVPWM) strategies are proposed in this paper, based on analyzing the harmonic characteristics of traditional NTV–SVPWM and NFV–SVPWM. The first strategy is to synthesize the referenced voltage vector according to the different weight factors by NTV–SVPWM and NFV–SVPWM. The second strategy is to synthesize the referenced voltage vector according to the different weight factors of NFV–SVPWM and the large vector. Compared to NTV–SVPWM, the simulation results show that the two proposed SVPWM strategies have lower output voltage errors and THDs. Compared to NFV–SVPWM, the simulation results show that the two proposed SVPWM strategies have higher VTRs and THDs. Compared to the two proposed SVPWM strategies, proposed SVPWM strategy one has a lower output voltage error and THD. The experimental results verify that the proposed modulation strategy is correct and feasible.
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Open AccessReview
On the Use of Indirect Measurements in Virtual Sensors for Renewable Energies: A Review
by
Abderraouf Benabdesselam, Quentin Dollon, Ryad Zemouri, Francis Pelletier, Martin Gagnon and Antoine Tahan
Electronics 2024, 13(8), 1545; https://doi.org/10.3390/electronics13081545 - 18 Apr 2024
Abstract
In the dynamic landscape of renewable energy, the primary goal continues to be the enhancement of competitiveness through the implementation of cutting-edge technologies. This requires a strategic focus on reducing energy costs and maximizing system performance. Within this framework, the continuous online monitoring
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In the dynamic landscape of renewable energy, the primary goal continues to be the enhancement of competitiveness through the implementation of cutting-edge technologies. This requires a strategic focus on reducing energy costs and maximizing system performance. Within this framework, the continuous online monitoring of assets is essential for efficient operations, by conducting measurements that describe the condition of various components. However, the execution of these measurements can present technical and economic obstacles. To overcome these challenges, the implementation of indirect measurement techniques emerges as a viable solution. By leveraging measurements obtained in easily accessible areas, these methods enable the estimation of quantities in regions that would otherwise be inaccessible. This approach improves the monitoring process’s efficiency and provides previously unattainable information. Adopting indirect measurement techniques is also cost-effective, allowing the replacement of expensive sensors with existing infrastructure, which cuts down on installation costs and labor. This paper offers a detailed state-of-the-art review by providing an in-depth examination and classification of indirect measurement techniques and virtual sensing methods applied in the field of renewable energies. It also identifies and discusses the existing challenges and limitations within this topic and explores potential future developments.
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(This article belongs to the Special Issue Advanced Fault Detection, Diagnosis and Prognosis in a Context of Renewable Power Generation)
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Open AccessArticle
Control of Threshold Voltage in ZnO/Al2O3 Thin-Film Transistors through Al2O3 Growth Temperature
by
Dongki Baek, Se-Hyeong Lee, So-Young Bak, Hyeongrok Jang, Jinwoo Lee and Moonsuk Yi
Electronics 2024, 13(8), 1544; https://doi.org/10.3390/electronics13081544 - 18 Apr 2024
Abstract
Ultra-thin ZnO thin-film transistors with a channel thickness of <10 nm have disadvantages of a high threshold voltage and a low carrier mobility due to a low carrier concentration. Although these issues can be addressed by utilizing the strong reducing power of tri-methyl-aluminum,
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Ultra-thin ZnO thin-film transistors with a channel thickness of <10 nm have disadvantages of a high threshold voltage and a low carrier mobility due to a low carrier concentration. Although these issues can be addressed by utilizing the strong reducing power of tri-methyl-aluminum, a method is required to control parameters such as the threshold voltage. Therefore, we fabricated a ZnO/Al2O3 thin-film transistor with a thickness of 6 nm and adjusted the threshold voltage and carrier mobility through the modulation of carrier generation by varying the growth temperature of Al2O3. As the growth temperature of Al2O3 increased, oxygen vacancies generated at the hetero–oxide interface increased, supplying a free carrier into the channel and causing the threshold voltage to shift in the negative direction. The optimized device, a ZnO/Al2O3 thin-film transistor with a growth temperature of 140 °C, exhibited a μsat of 12.26 cm2/V∙s, Vth of 8.16 V, SS of 0.65 V/decade, and ION/OFF of 3.98 × 106. X-ray photoelectron spectroscopy was performed to analyze the properties of ZnO/Al2O3 thin films.
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(This article belongs to the Section Semiconductor Devices)
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Open AccessArticle
TL-YOLO: Foreign-Object Detection on Power Transmission Line Based on Improved Yolov8
by
Yeqin Shao, Ruowei Zhang, Chang Lv, Zexing Luo and Meiqin Che
Electronics 2024, 13(8), 1543; https://doi.org/10.3390/electronics13081543 - 18 Apr 2024
Abstract
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Foreign objects on power transmission lines carry a significant risk of triggering large-scale power interruptions which may have serious consequences for daily life if they are not detected and handled in time. To accurately detect foreign objects on power transmission lines, this paper
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Foreign objects on power transmission lines carry a significant risk of triggering large-scale power interruptions which may have serious consequences for daily life if they are not detected and handled in time. To accurately detect foreign objects on power transmission lines, this paper proposes a TL-Yolo method based on the Yolov8 framework. Firstly, we design a full-dimensional dynamic convolution (ODConv) module as a backbone network to enhance the feature extraction capability, thus retaining richer semantic content and important visual features. Secondly, we present a feature fusion framework combining a weighted bidirectional feature pyramid network (BiFPN) and multiscale attention (MSA) module to mitigate the degradation effect of multiscale feature representation in the fusion process, and efficiently capture the high-level feature information and the core visual elements. Thirdly, we utilize a lightweight GSConv cross-stage partial network (GSCSP) to facilitate efficient cross-level feature fusion, significantly reducing the complexity and computation of the model. Finally, we employ the adaptive training sample selection (ATSS) strategy to balance the positive and negative samples, and dynamically adjust the selection process of the training samples according to the current state and performance of the model, thus effectively reducing the object misdetection and omission. The experimental results show that the average detection accuracy of the TL-Yolo method reaches 91.30%, which is 4.20% higher than that of the Yolov8 method. Meanwhile, the precision and recall metrics of our method are 4.64% and 3.53% higher than those of Yolov8. The visualization results also show the superior detection performance of the TL-Yolo algorithm in real scenes. Compared with the state-of-the-art methods, our method achieves higher accuracy and speed in the detection of foreign objects on power transmission lines.
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