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
Artificial Intelligence in Social Media Forensics: A Comprehensive Survey and Analysis
Electronics 2024, 13(9), 1671; https://doi.org/10.3390/electronics13091671 (registering DOI) - 26 Apr 2024
Abstract
Social media platforms have completely revolutionized human communication and social interactions. Their positive impacts are simply undeniable. What has also become undeniable is the prevalence of harmful antisocial behaviors on these platforms. Cyberbullying, misinformation, hate speech, radicalization, and extremist propaganda have caused significant
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Social media platforms have completely revolutionized human communication and social interactions. Their positive impacts are simply undeniable. What has also become undeniable is the prevalence of harmful antisocial behaviors on these platforms. Cyberbullying, misinformation, hate speech, radicalization, and extremist propaganda have caused significant harms to society and its most vulnerable populations. Thus, the social media forensics field was born to enable investigators and law enforcement agents to better investigate and prosecute these cybercrimes. This paper surveys the latest research works in the field to explore how artificial intelligence (AI) techniques are being utilized in social media forensics investigations. We examine how natural language processing can be used to identify extremist ideologies, detect online bullying, and analyze deceptive profiles. Additionally, we explore the literature on GNNs and how they are applied in social network modeling for forensic purposes. We conclude by discussing the key challenges in the field and suggest future research directions.
Full article
(This article belongs to the Special Issue Network and Mobile Systems Security, Privacy and Forensics)
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Open AccessArticle
A Nonlinear Subspace Predictive Control Approach Based on Locally Weighted Projection Regression
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Xinwei Wu and Xuebo Yang
Electronics 2024, 13(9), 1670; https://doi.org/10.3390/electronics13091670 (registering DOI) - 26 Apr 2024
Abstract
Subspace predictive control (SPC) is a widely recognized data-driven methodology known for its reliability and convenience. However, effectively applying SPC to complex industrial process systems remains a challenging endeavor. To address this, this paper introduces a nonlinear subspace predictive control approach based on
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Subspace predictive control (SPC) is a widely recognized data-driven methodology known for its reliability and convenience. However, effectively applying SPC to complex industrial process systems remains a challenging endeavor. To address this, this paper introduces a nonlinear subspace predictive control approach based on locally weighted projection regression (NSPC-LWPR). By projecting the input space into localized regions, constructing precise local models, and aggregating them through weighted summation, this approach handles the nonlinearity effectively. Additionally, it dynamically adjusts the control strategy based on online process data and model parameters, while eliminating the need for offline process data storage, greatly enhancing the adaptability and efficiency of the approach. The parameter determination criteria and theoretical analysis encompassing feasibility and stability assessments provide a robust foundation for the proposed approach. To illustrate its efficacy and feasibility, the proposed approach is applied to a continuous stirred tank heater (CSTH) benchmark system. Comparative results highlight its superiority over SPC and adaptive subspace predictive control (ASPC) methods, evident in enhanced tracking precision and predictive accuracy. Overall, the proposed NSPC-LWPR approach presents a promising solution for nonlinear control challenges in industrial process systems.
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(This article belongs to the Special Issue High Performance Control and Industrial Applications)
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Continual Monitoring of Respiratory Disorders to Enhance Therapy via Real-Time Lung Sound Imaging in Telemedicine
by
Murdifi Muhammad, Minghui Li, Yaolong Lou and Chang-Sheng Lee
Electronics 2024, 13(9), 1669; https://doi.org/10.3390/electronics13091669 (registering DOI) - 26 Apr 2024
Abstract
This work presents a configurable Internet of Things architecture for acoustical sensing and analysis for frequent remote respiratory assessments. The proposed system creates a foundation for enabling real-time therapy and patient feedback adjustment in a telemedicine setting. By allowing continuous remote respiratory monitoring,
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This work presents a configurable Internet of Things architecture for acoustical sensing and analysis for frequent remote respiratory assessments. The proposed system creates a foundation for enabling real-time therapy and patient feedback adjustment in a telemedicine setting. By allowing continuous remote respiratory monitoring, the system has the potential to give clinicians access to assessments from which they could make decisions about modifying therapy in real-time and communicate changes directly to patients. The system comprises a wearable wireless microphone array interfaced with a programmable microcontroller with embedded signal conditioning. Experiments on the phantom model were conducted to demonstrate the feasibility of reconstructing acoustic lung images for detecting obstructions in the airway and provided controlled validation of noise resilience and imaging capabilities. An optimized denoising technique and design innovations provided 7 dB more SNR and 7% more imaging accuracy for the proposed system, benchmarked against digital stethoscopes. While further clinical studies are warranted, initial results suggest potential benefits over single-point digital stethoscopes for internet-enabled remote lung monitoring needing noise immunity and regional specificity. The flexible architecture aims to bridge critical technical gaps in frequent and connected respiratory function at home or in busy clinical settings challenged by ambient noise interference.
Full article
(This article belongs to the Special Issue Smart Communication and Networking in the 6G Era)
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Design and Implementation of Reconfigurable Array Adaptive Optoelectronic Hybrid Interconnect Shunting Network
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Bowen Yang, Yong Li, Chao Xi, Rui Shan, Yu Feng and Jiaying Luo
Electronics 2024, 13(9), 1668; https://doi.org/10.3390/electronics13091668 (registering DOI) - 26 Apr 2024
Abstract
Addressing challenges regarding Hybrid Optoelectronic Network-on-Chip systems, such as congestion control, their limited adaptability, and their inability to facilitate optoelectronic co-simulation, this study introduces an adaptive hybrid optoelectronic interconnection shunt structure tailored for reconfigurable array processors. Within this framework, an adaptive shunt routing
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Addressing challenges regarding Hybrid Optoelectronic Network-on-Chip systems, such as congestion control, their limited adaptability, and their inability to facilitate optoelectronic co-simulation, this study introduces an adaptive hybrid optoelectronic interconnection shunt structure tailored for reconfigurable array processors. Within this framework, an adaptive shunt routing algorithm and a low-loss non-blocking five-port optical router are developed. Furthermore, an adaptive hybrid optoelectronic interconnection simulation model and a performance statistical model, established using SystemVerilog and Verilog, complement these designs. The experimental results showcase promising enhancements: the designed routing algorithm demonstrates an average 17.5% improvement in mitigating congestion at network edge nodes; substantial reductions in the required number of cross waveguides and micro-ring resonators for optical routers lead to an average path insertion loss of only 0.522 dB. Moreover, the hybrid optoelectronic interconnection performance statistical model supports the design of routing strategies and topology structures, enabling resource usage, power consumption, insertion loss, and other performance metrics to be accurately assessed.
Full article
(This article belongs to the Special Issue Configurable Computing Systems for Enhanced Industrial Communication)
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FATE: A Flexible FPGA-Based Automatic Test Equipment for Digital ICs
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Jin Zhang, Zhenghui Liu, Xiao Hu, Peixin Liu, Zhiling Hu and Lidan Kuang
Electronics 2024, 13(9), 1667; https://doi.org/10.3390/electronics13091667 (registering DOI) - 26 Apr 2024
Abstract
The limits of chip technology are constantly being pushed with the continuous development of integrated circuit manufacturing processes and equipment. Currently, chips contain several billion, and even tens of billions, of transistors, making chip testing increasingly challenging. The verification of very large-scale integrated
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The limits of chip technology are constantly being pushed with the continuous development of integrated circuit manufacturing processes and equipment. Currently, chips contain several billion, and even tens of billions, of transistors, making chip testing increasingly challenging. The verification of very large-scale integrated circuits (VLSI) requires testing on specialized automatic test equipment (ATE), but their cost and size significantly limit their applicability. The current FPGA-based ATE is limited in its scalability and support for few test channels and short test vector lengths. As a result, it is only suitable for testing specific chips in small-scale circuits and cannot be used to test VLSI. This paper proposes a low-cost hardware and software solution for testing digital integrated circuits based on design for testability (DFT) on chips, which enables the functional and performance test of the chip. The solution proposed can effectively use the resources within the FPGA to provide additional test channels. Furthermore, the round-robin data transmission mode can also support test vectors of any length and it can satisfy different types of chip test projects through the dynamic configuration of each test channel. The experiment successfully tested a digital signal processor (DSP) chip with 72 scan test pins (theoretically supporting 160 test pins). Compared to our previous work, the work in this paper increases the number of test channels by four times while reducing resource utilization per channel by 37.5%, demonstrating good scalability and versatility.
Full article
(This article belongs to the Topic Advances in Microelectronics and Semiconductor Engineering)
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Open AccessArticle
Optimizing Network Service Continuity with Quality-Driven Resource Migration
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Chaofan Chen, Yubo Song, Yu Jiang and Mingming Zhang
Electronics 2024, 13(9), 1666; https://doi.org/10.3390/electronics13091666 (registering DOI) - 25 Apr 2024
Abstract
Despite advances in security technology, it is impractical to entirely prevent intrusion threats. Consequently, developing effective service migration strategies is crucial to maintaining the continuity of network services. Current service migration strategies initiate the migration process only upon detecting a loss of service
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Despite advances in security technology, it is impractical to entirely prevent intrusion threats. Consequently, developing effective service migration strategies is crucial to maintaining the continuity of network services. Current service migration strategies initiate the migration process only upon detecting a loss of service functionality in the nodes, which increases the risk of service interruptions. Moreover, the migration decision-making process has not adequately accounted for the alignment between tasks and node resources, thereby amplifying the risk of system overload. To address these shortcomings, we introduce a Quality-Driven Resource Migration Strategy (QD-RMS). Specifically, QD-RMS initiates the migration process at an opportune moment, determined through an analysis of service quality. Subsequently, it employs a method combining Pareto optimality and the simulated annealing algorithm to identify the node most suitable for migration. This approach not only guarantees seamless service continuity but also ensures optimal resource distribution and load balancing. The experiments demonstrate that, in comparison with conventional migration strategies, QD-RMS achieves superior service quality and an approximate 20% increase in maximum task capacity. This substantiates the strategic superiority and technological advancement of the proposed strategy.
Full article
(This article belongs to the Special Issue Knowledge Information Extraction Research)
Open AccessArticle
Thermal Safety Assessment Method for Power Devices in Natural Air-Cooled Converters
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Weichao He, Yiming Zhu, Zijian Liu, Jiaxue Lin, Fengshuo Bao, Wei Zu and Zhanfeng Ying
Electronics 2024, 13(9), 1665; https://doi.org/10.3390/electronics13091665 (registering DOI) - 25 Apr 2024
Abstract
The junction temperature of a power device in a natural air-cooled power converter fluctuates randomly due to the variation in airflow rate in ambient environments. Most of the existing thermal analysis methods do not pay attention to the uncertain influence of airflow on
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The junction temperature of a power device in a natural air-cooled power converter fluctuates randomly due to the variation in airflow rate in ambient environments. Most of the existing thermal analysis methods do not pay attention to the uncertain influence of airflow on the heat-dissipation capacity of such converters, making it difficult to accurately evaluate the thermal safety of these devices. To address this issue, a thermal safety assessment method for power devices in natural air-cooled converters is proposed in this paper. In the proposed method, convective heat resistance samples of converter housing are extracted with an equivalent thermal network model and the historical operation temperature of the converter. Wavelet packet transform is used to analyze the time–frequency domain characteristics of the convective heat resistance, and Monte Carlo simulation is employed to simulate the random influence of the airflow rate on the device junction temperature. The thermal safety of power devices is assessed in the form of over-temperature probability, which is expressed by a two-variable growth function. An experimental platform is designed to validate the effectiveness of the proposed method. The results show that the proposed method can accurately estimate the over-temperature risk of a power device in a natural air-cooled converter under different ambient temperature and current levels, thus effectively improving the thermal reliability of converters.
Full article
(This article belongs to the Section Power Electronics)
Open AccessArticle
Characterizing Lossy Dielectric Materials in Shock Physics by Millimeter-Wave Interferometry Using One-Dimensional Convolutional Neural Networks and Nonlinear Optimization
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Ngoc Tuan Pham, Alexandre Lefrançois and Hervé Aubert
Electronics 2024, 13(9), 1664; https://doi.org/10.3390/electronics13091664 (registering DOI) - 25 Apr 2024
Abstract
When a dielectric material undergoes mechanical impact, it generates a shock wave, causing changes in its refractive index. Recent demonstrations have proven that the modified refractive index can be determined remotely using a millimeter-wave interferometer. However, these demonstrations are based on the resolution
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When a dielectric material undergoes mechanical impact, it generates a shock wave, causing changes in its refractive index. Recent demonstrations have proven that the modified refractive index can be determined remotely using a millimeter-wave interferometer. However, these demonstrations are based on the resolution of an inverse electromagnetic problem, which assumes that the losses in the material are negligible. This restrictive assumption is overcome in this article, in which a new approach is proposed to solve the inverse electromagnetic problem in lossy and shocked dielectric materials. Our methodology combines a one-dimensional convolutional neural network architecture, namely Inverse problem of Lossless or Lossy Shocked Wavefront Network (ILSW-Net), with a nonlinear optimization technique based on the Nelder–Mead algorithm to estimate losses within dielectric materials under a mechanical impact. Experimental results for both simulated and real signals show that our method can successfully predict the velocities and the refractive index while accurately estimating the shock wavefront.
Full article
(This article belongs to the Topic Computational Intelligence in Remote Sensing: 2nd Edition)
Open AccessArticle
A Data–Physics-Driven Modeling Approach of Key Equipment for Large-Scale Distribution Network Simulation
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Rui Qiu, Hao Bai, Ruotian Yao, Chengxi Liu, Min Xu, Qi Chen and Weichen Yang
Electronics 2024, 13(9), 1663; https://doi.org/10.3390/electronics13091663 (registering DOI) - 25 Apr 2024
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Fueled by pressing global climate concerns, the integration of large-scale renewable distributed generation sources, including distributed wind power and photovoltaics, along with electricity substitution loads into the distribution network has been accelerated to diminish carbon emissions. This shift introduces significant challenges and necessitates
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Fueled by pressing global climate concerns, the integration of large-scale renewable distributed generation sources, including distributed wind power and photovoltaics, along with electricity substitution loads into the distribution network has been accelerated to diminish carbon emissions. This shift introduces significant challenges and necessitates the advanced operation and control of distribution systems to accommodate these changes effectively. Against this backdrop, there is a growing expectation for an open and scalable central control mode, equipped with compatible interfaces, to offer a visionary development platform for the grid. This platform is anticipated to meet the evolving needs of future distribution system development, ensuring adaptability and forward compatibility. The aforementioned platform requires open, scalable, and interface-compatible models of key distribution network equipment as its foundation. To address the challenges presented, this paper proposes a data–physics-driven modeling approach for automating simulations in distribution systems. This method employs a simplified and standardized system of linear differential equations with undetermined coefficients to capture the common physical characteristics of specific device types. The models designed through this approach are notably open, allowing for real-time data to refine undetermined coefficients and accurately depict the dynamic behavior of equipment over various periods. Their scalability also stands out, rendering them apt for large-scale distribution network simulations. The paper elaborates on models for distributed photovoltaic, wind turbine, energy storage, and electric vehicle, and demonstrates their application within an IEEE-33 node distribution network topology built on Python.
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Open AccessArticle
A Hybrid CNN-LSTM Approach for Precision Deepfake Image Detection Based on Transfer Learning
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Omar Alfarouk Hadi Hasan Al-Dulaimi and Sefer Kurnaz
Electronics 2024, 13(9), 1662; https://doi.org/10.3390/electronics13091662 (registering DOI) - 25 Apr 2024
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The detection of deepfake images and videos is a critical concern in social communication due to the widespread utilization of deepfake techniques. The prevalence of these methods poses risks to trust and authenticity across various domains, emphasizing the importance of identifying fake faces
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The detection of deepfake images and videos is a critical concern in social communication due to the widespread utilization of deepfake techniques. The prevalence of these methods poses risks to trust and authenticity across various domains, emphasizing the importance of identifying fake faces for security and preventing socio-political issues. In the digital media era, deep learning outperforms traditional image processing methods in deepfake detection, underscoring its significance. This research introduces an innovative approach for detecting deepfake images by employing transfer learning in a hybrid architecture that combines convolutional neural networks (CNNs) and long short-term memory (LSTM). The hybrid CNN-LSTM model exhibits promise in combating deep fakes by merging the spatial awareness of CNNs with the temporal context understanding of LSTMs. Demonstrating effective performance on open-source datasets like “DFDC” and “Ciplab”, the proposed method achieves an impressive precision of 98.21%, indicating its capability to accurately identify deepfake images with a limited false-positive rate. The model’s error rate is 0.26%, emphasizing the challenges and intricacies inherent in deepfake detection tasks. These findings underscore the potential of hybrid deep learning techniques for addressing the urgent issue of deepfake image detection.
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Open AccessArticle
A Knowledge Graph Completion Algorithm Based on the Fusion of Neighborhood Features and vBiLSTM Encoding for Network Security
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Wenbo Zhang, Mengxuan Wang, Guangjie Han, Yongxin Feng and Xiaobo Tan
Electronics 2024, 13(9), 1661; https://doi.org/10.3390/electronics13091661 (registering DOI) - 25 Apr 2024
Abstract
Knowledge graphs in the field of network security can integrate diverse, heterogeneous, and fragmented network security data, further explore the relationships between data, and provide support for deep analysis. Currently, there is sparse security information in the field of network security knowledge graphs.
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Knowledge graphs in the field of network security can integrate diverse, heterogeneous, and fragmented network security data, further explore the relationships between data, and provide support for deep analysis. Currently, there is sparse security information in the field of network security knowledge graphs. The limited information provided by traditional text encoding models leads to insufficient reasoning ability, greatly restricting the development of this field. Starting from text encoding, this paper first addresses the issue of the inadequate capabilities of traditional models using a deep learning model for assistance. It designs a vBiLSTM model based on a word2vec and BiLSTM combination to process network security texts. By utilizing word vector models to retain semantic information in entities and extract key features to input processed data into BiLSTM networks for extracting higher-level features that better capture and express their deeper meanings, this design significantly enhances understanding and expression capabilities toward complex semantics in long sentences before inputting final feature vectors into the KGC-N model. The KGC-N model uses feature vectors combined with graph structure information to fuse forward and reverse domain features and then utilizes a Transformer decoder to decode predictions and complete missing information within the network security knowledge map. Compared with other models using evaluation metrics such as MR, MRR demonstrates that employing our proposed method effectively improves performance on completion tasks and increases comprehension abilities toward complex relations, thereby enhancing accuracy and efficiency when completing knowledge graphs.
Full article
(This article belongs to the Special Issue Security and Trust in Internet of Things and Edge Computing)
Open AccessArticle
Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval
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Teng Ma, Daniel Organisciak, Wenbao Ma and Yang Long
Electronics 2024, 13(9), 1660; https://doi.org/10.3390/electronics13091660 (registering DOI) - 25 Apr 2024
Abstract
The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can seamlessly integrate into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this
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The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can seamlessly integrate into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual–semantic gap, steering the model towards enhanced performance in Zero-Shot Instance Retrieval. Our models achieve state-of-the-art performance on both the SUN (92.8% and 82.2%) and CUB datasets (59.92% and 48.82%) for bi-directional image-attribute retrieval accuracy. This work not only benchmarks the cognition alignment of AI but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.
Full article
(This article belongs to the Special Issue Zero-Shot Learning in Natural Language Processing and It’s Applications)
Open AccessArticle
Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting
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Jinyeong Oh, Dayeong So, Jaehyeok Jo, Namil Kang, Eenjun Hwang and Jihoon Moon
Electronics 2024, 13(9), 1659; https://doi.org/10.3390/electronics13091659 (registering DOI) - 25 Apr 2024
Abstract
Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability to effectively learn unstable environmental variables and their complex interactions. However, NNs are limited in their practical industrial application in the energy sector because the optimization
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Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability to effectively learn unstable environmental variables and their complex interactions. However, NNs are limited in their practical industrial application in the energy sector because the optimization of the model structure or hyperparameters is a complex and time-consuming task. This paper proposes a two-stage NN optimization method for robust solar PV power forecasting. First, the solar PV power dataset is divided into training and test sets. In the training set, several NN models with different numbers of hidden layers are constructed, and Optuna is applied to select the optimal hyperparameter values for each model. Next, the optimized NN models for each layer are used to generate estimation and prediction values with fivefold cross-validation on the training and test sets, respectively. Finally, a random forest is used to learn the estimation values, and the prediction values from the test set are used as input to predict the final solar PV power. As a result of experiments in the Incheon area, the proposed method is not only easy to model but also outperforms several forecasting models. As a case in point, with the New-Incheon Sonae dataset—one of three from various Incheon locations—the proposed method achieved an average mean absolute error (MAE) of 149.53 kW and root mean squared error (RMSE) of 202.00 kW. These figures significantly outperform the benchmarks of attention mechanism-based deep learning models, with average scores of 169.87 kW for MAE and 232.55 kW for RMSE, signaling an advance that is expected to make a significant contribution to South Korea's energy industry.
Full article
(This article belongs to the Special Issue Trends in Photovoltaic Systems for Enhanced Power Generation and Energy Efficiency)
Open AccessArticle
Improving Real-Time Performance of Micro-ROS with Priority-Driven Chain-Aware Scheduling
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Zilong Wang, Songran Liu, Dong Ji and Wang Yi
Electronics 2024, 13(9), 1658; https://doi.org/10.3390/electronics13091658 (registering DOI) - 25 Apr 2024
Abstract
Micro-ROS is widely used to bridge the performance gap between resource-constrained microcontrollers and powerful computing devices in ROS-based robotic applications. After modeling the callback scheduling module and the communication module in micro-ROS, we found that there are some design flaws that significantly impact
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Micro-ROS is widely used to bridge the performance gap between resource-constrained microcontrollers and powerful computing devices in ROS-based robotic applications. After modeling the callback scheduling module and the communication module in micro-ROS, we found that there are some design flaws that significantly impact the real-time performance of micro-ROS. To improve the timing predictability and run-time efficiency of micro-ROS, we design and implement a priority-driven chain-aware scheduling system (PoDS) based on the existing micro-ROS architecture. The experimental results demonstrate that our proposed PoDS exhibits significantly improved real-time performance compared to the default micro-ROS.
Full article
(This article belongs to the Special Issue Embedded Systems: Fundamentals, Design and Practical Applications)
Open AccessArticle
Smart Water ATM with Arduino Integration, RFID Authentication, and Dynamic Dispensing for Enhanced Hydration Practices
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Yit Yan Koh, Chiang Liang Kok, Navas Ibraahim and Chin Guan Lim
Electronics 2024, 13(9), 1657; https://doi.org/10.3390/electronics13091657 (registering DOI) - 25 Apr 2024
Abstract
This proposal outlines the development of a comprehensive solution to address hydration challenges through the creation of a Smart Water ATM with Arduino integration, RFID authentication, and dynamic dispensing capabilities. Traditional water dispensers often fall short in monitoring water intake and promoting optimal
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This proposal outlines the development of a comprehensive solution to address hydration challenges through the creation of a Smart Water ATM with Arduino integration, RFID authentication, and dynamic dispensing capabilities. Traditional water dispensers often fall short in monitoring water intake and promoting optimal hydration practices. In response, our project aims to revolutionize hydration practices by integrating Arduino Mega and Uno boards into a Smart Water ATM with a Bottle Dispenser. This innovative system is designed to dispense specific water quantities based on user preferences, encourage the use of personal water bottles, display water temperature, and operate independently without the need for a water line connection. Prior to prototype fabrication, an extensive literature review and survey were conducted to understand existing water dispenser shortcomings and gather public perspectives in Singapore. The Smart Water ATM addresses identified issues by incorporating sensors to monitor water levels, dispense specific quantities, and measure water temperatures. The prototype fabrication involves designing a stainless-steel housing, 3D printing the Smart Water dispenser unit, and installing tanks, tubing, and electronic components. To enhance user interaction, the Smart Water ATM requires RFID authentication through Identity Cards, tracking daily water consumption. An LCD screen displays the dispensed water volume over the ATM’s lifespan, motivating users to be mindful of their water consumption and minimize wastage. Adjustments will be made for deployment in public spaces, such as train stations, where access to permanent water dispensers is limited. This proposal presents an innovative solution to promote enhanced hydration practices, encouraging users to adopt mindful water consumption habits.
Full article
Open AccessArticle
A Modified SVPWM Strategy for Reducing PWM Voltage Noise and Balancing Neutral Point Potential
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Renxi Gong, Hao Wu, Jing Tang and Xingyuan Wan
Electronics 2024, 13(9), 1656; https://doi.org/10.3390/electronics13091656 (registering DOI) - 25 Apr 2024
Abstract
PWM (pulse width modulation) is the most widely applied current conversion technology, but the high-frequency harmonics it causes have a significant negative impact on inverter system performance. This paper focuses on the three-phase T-type three-level inverter as the research object and addresses existing
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PWM (pulse width modulation) is the most widely applied current conversion technology, but the high-frequency harmonics it causes have a significant negative impact on inverter system performance. This paper focuses on the three-phase T-type three-level inverter as the research object and addresses existing PWM voltage noise and midpoint potential imbalance issues by proposing an improved random SVPWM strategy, named Neutral Point Potential Balance Random Space Vector PWM (NPB−RSVPWM). The NPB−RSVPWM strategy includes three main steps: (1) introducing a midpoint potential balancing control loop to adjust the synthesis timing of the effective vectors to generate pulse signals, optimizing midpoint potential balance; (2) employing a randomly varying carrier frequency in place of the carrier used in the SVPWM strategy to generate the driving signals for switching devices; and (3) controlling the inverter through the driving pulse signals. This strategy optimizes the synthesis sequence of traditional SVPWM strategy vectors and incorporates random frequency modulation techniques. The mathematical model analyzes PWM harmonic expressions corresponding to fixed switching frequencies, and a random frequency carrier is chosen to suppress these PWM harmonics. The effective vector’s equivalent circuit is analyzed, proposing a technique for optimized vector synthesis timing. The simulation and experimental results verify that the NPB−RSVPWM technique can disperse PWM harmonic energy, reduce voltage noise, and optimize midpoint potential balance. Under the NPB−RSVPWM strategy, the line voltage spectrum becomes uniform, the maximum harmonic content is greatly reduced, and the fluctuation in the DC side midpoint potential is significantly improved. Compared with the traditional SVPWM strategy and random PWM strategy, the NPB−RSVPWM strategy has a lower voltage noise, smaller total harmonic distortion, and a more stable midpoint potential. The effectiveness and feasibility of the NPB−RSVPWM strategy are verified by simulation and experimental results.
Full article
Open AccessArticle
Fault Diagnosis Based on Tensor Computing and Meta-Learning for Smart Grid and Power Communication Network
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Qiusheng Yu, Ti Guan, Anqi Tian, Mingyue Si, Bin Qi, Yingjie Jiang, Yan Zhang, Li Li and Wensheng Zhang
Electronics 2024, 13(9), 1655; https://doi.org/10.3390/electronics13091655 (registering DOI) - 25 Apr 2024
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Fault diagnosis (FD) is a critical challenge for the smart grid and the power communication network, especially when both heterogeneous networks are exponentially becoming enormous and complicated. Consequently, some conventional FD schemes based on labor seem inefficient, even disabled, because they usually cannot
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Fault diagnosis (FD) is a critical challenge for the smart grid and the power communication network, especially when both heterogeneous networks are exponentially becoming enormous and complicated. Consequently, some conventional FD schemes based on labor seem inefficient, even disabled, because they usually cannot efficiently utilize multi-dimensional and heterogeneous big data from both networks. To deal with this challenging technical problem, a novel FD scheme based on tensor computing and meta-learning is proposed for the smart grid and the power communication network. In the proposed scheme, tensor computing is used to process tensor big data from both networks, and a new data fusion scheme is designed to complete and analyze the incomplete and sparse big data. Based on the fused data, a meta-learning approach is used to construct the FD scheme, especially when the target fault samples are inadequate and sparse. In meta-learning, the convolutional neural network is employed as a base learner to generate an FD training model, and the model-agnostic meta-learning algorithm is utilized to fine-tune and further train the pre-trained model. Simulation results and theoretical analysis indicate that the proposed DF scheme based on tensor computing can efficiently process sparse and heterogeneous big data from both networks. Furthermore, the meta-learning-based FD scheme provides an efficient way to diagnose faults with inadequate target samples. The proposed FD scheme based on tensor computing and meta-learning provides a novel solution to detect and analyze the potential faults for smart grid and power communication networks.
Full article
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Open AccessArticle
Active Visual Perception Enhancement Method Based on Deep Reinforcement Learning
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Zhonglin Yang, Hao Fang, HuanYu Liu, Junbao Li, Yutong Jiang and Mengqi Zhu
Electronics 2024, 13(9), 1654; https://doi.org/10.3390/electronics13091654 (registering DOI) - 25 Apr 2024
Abstract
Traditional object detection methods using static cameras are constrained by their limited perspectives, hampering the effective detection of low-confidence targets. To address this challenge, this study introduces a deep reinforcement learning-based visual perception enhancement technique. This approach leverages pan–tilt–zoom (PTZ) cameras to achieve
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Traditional object detection methods using static cameras are constrained by their limited perspectives, hampering the effective detection of low-confidence targets. To address this challenge, this study introduces a deep reinforcement learning-based visual perception enhancement technique. This approach leverages pan–tilt–zoom (PTZ) cameras to achieve active vision, enabling them to autonomously make decisions and actions tailored to the current scene and object detection outcomes. This optimization enhances both the object detection process and information acquisition, significantly boosting the intelligent perception capabilities of PTZ cameras. Experimental findings demonstrate the robust generalization capabilities of this method across various object detection algorithms, resulting in an average confidence level improvement of 23.80%.
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(This article belongs to the Special Issue Recent Advances in Image and Video Processing Using Artificial Intelligence)
Open AccessArticle
A Hybrid Scheme for TX I/Q Imbalance Self-Calibration in a Direct-Conversion Transceiver
by
Ruhao Wang, Peng Gao, Jiarui Liu, Zhiyu Wang, Chenge Wang and Faxin Yu
Electronics 2024, 13(9), 1653; https://doi.org/10.3390/electronics13091653 (registering DOI) - 25 Apr 2024
Abstract
A generic transmitter (TX) I/Q imbalance self-calibration method, which was designed based on a hybrid analog and digital structure, is proposed in this paper. The whole calibration scheme was implemented using low-complexity digital–analog circuits based on a zero-force feedback loop. In order to
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A generic transmitter (TX) I/Q imbalance self-calibration method, which was designed based on a hybrid analog and digital structure, is proposed in this paper. The whole calibration scheme was implemented using low-complexity digital–analog circuits based on a zero-force feedback loop. In order to eliminate the negative effect of local oscillator (LO) harmonics on the calibration, we used a variable-delay line (VDL) in the analog domain instead of the digital phase compensator. The prototype chip was fabricated within a 0.2∼5.0 GHz direct-conversion transmitter in a 65 nm CMOS process, and measurements found an image rejection ratio (IRR) of 65 dBc.
Full article
(This article belongs to the Section Circuit and Signal Processing)
Open AccessArticle
Edge-Enhanced Dual-Stream Perception Network for Monocular Depth Estimation
by
Zihang Liu and Quande Wang
Electronics 2024, 13(9), 1652; https://doi.org/10.3390/electronics13091652 - 25 Apr 2024
Abstract
Estimating depth from a single RGB image has a wide range of applications, such as in robot navigation and autonomous driving. Currently, Convolutional Neural Networks based on encoder–decoder architecture are the most popular methods to estimate depth maps. However, convolutional operators have limitations
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Estimating depth from a single RGB image has a wide range of applications, such as in robot navigation and autonomous driving. Currently, Convolutional Neural Networks based on encoder–decoder architecture are the most popular methods to estimate depth maps. However, convolutional operators have limitations in modeling large-scale dependence, often leading to inaccurate depth predictions at object edges. To address these issues, a new edge-enhanced dual-stream monocular depth estimation method is introduced in this paper. ResNet and Swin Transformer are combined to better extract global and local features, which benefits the estimation of the depth map. To better integrate the information from the two branches of the encoder and the shallow branch of the decoder, we designed a lightweight decoder based on the multi-head Cross-Attention Module. Furthermore, in order to improve the boundary clarity of objects in the depth map, a loss function with an additional penalty for depth estimation error on the edges of objects is presented. The results on three datasets, NYU Depth V2, KITTI, and SUN RGB-D, show that the method presented in this paper achieves better performance for monocular depth estimation. Additionally, it has good generalization capabilities for various scenarios and real-world images.
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(This article belongs to the Special Issue Applications of Artificial Intelligence in Computer Vision)
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