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 (Control and Systems 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
A Three-Port DC-DC Converter with Partial Power Regulation for a Photovoltaic Generator Integrated with Energy Storage
Electronics 2024, 13(12), 2304; https://doi.org/10.3390/electronics13122304 (registering DOI) - 12 Jun 2024
Abstract
A novel integrated DC-DC converter is proposed for the first stage of two-stage grid connected photovoltaic (PV) systems with energy storage systems. The proposed three-port converter (TPC) consists of a buck–boost converter, interposed between the battery storage system and the DC-AC inverter, in
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A novel integrated DC-DC converter is proposed for the first stage of two-stage grid connected photovoltaic (PV) systems with energy storage systems. The proposed three-port converter (TPC) consists of a buck–boost converter, interposed between the battery storage system and the DC-AC inverter, in series with PV modules. The buck–boost converter in the proposed TPC is utilized for maximum power point tracking by regulating two power switches. The output power of the proposed converter is regulated by controlling the DC-AC converter. During the battery-charging mode, partial power regulation is employed with a direct power flow path (the series-connection of the PV panel, the battery and the output). As resistances in this path are almost negligible, the power conversion efficiency is higher than existing topologies. During battery-discharging mode, the power conversion is processed through a buck–boost converter with only two active power switches and one inductor. With fewer components, higher power conversion efficiency is also achieved. The circuit operation and analysis are presented in detail. To illustrate the simplicity of the converter control, the performance of the converter is tested with a straightforward maximum power point tracking on a PV system with battery cells. Simulation and experimental tests are carried out to demonstrate circuit operation and power conversion efficiency.
Full article
(This article belongs to the Special Issue Optimal Integration of Energy Storage and Conversion in Smart Grids)
Open AccessArticle
A Study of the Key Factors Influencing Young Users’ Continued Use of the Digital Twin-Enhanced Metaverse Museum
by
Ronghui Wu, Lin Gao, Hyemin Lee, Junping Xu and Younghwan Pan
Electronics 2024, 13(12), 2303; https://doi.org/10.3390/electronics13122303 (registering DOI) - 12 Jun 2024
Abstract
This research investigates the key factors influencing young users’ continuous use of digital twin-enhanced metaverse museums. Attracting young users to use the metaverse museum for a more extended period consistently contributes to increasing the frequency of visits and content usage and promoting its
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This research investigates the key factors influencing young users’ continuous use of digital twin-enhanced metaverse museums. Attracting young users to use the metaverse museum for a more extended period consistently contributes to increasing the frequency of visits and content usage and promoting its sustainable development and innovation. However, there is a lack of research on the key factors influencing young users’ continuous use of digital twin-enhanced metaverse museums, which makes the theoretical basis for the in-depth design of user motivation for metaverse museums insufficient. This study constructed a model covering four dimensions—hedonic, utilitarian, social, and technological—based on communication’s uses and gratification theory (UGT). It was validated in the Metaverse Digital Twin Museum (MDTM). Using Spatial.io’s IES Goya Museum as the experimental platform, the research team conducted Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4.0 software through experiments and surveys with 307 participants aged 18 to 30. Quantitative analyses revealed that MDTM’s hedonic gratification (hope), utilitarian gratification (information and self-presentation), social gratification (social interaction and social presence), and technological gratification (immersion) significantly influenced young users’ continued intention. The findings reveal that these six key factors can be the focus of MDTM’s future development to enhance user experience. This study fills the gap in applying UGT in the field of metaverse museums, provides metaverse museum managers with references to the key factors that can prolong users’ continued intention to use, and points out the key factors that need further attention in future research and practice.
Full article
(This article belongs to the Special Issue Metaverse and Digital Twins, 2nd Edition)
Open AccessArticle
Enhancing Communication in CPS Using Graph-Based Reply Relationship Identification in Multi-Party Conversations
by
Bingwei Zhu, Jinzhu Yang, Lirong Qiu, Weichun Sun and Bin Hou
Electronics 2024, 13(12), 2302; https://doi.org/10.3390/electronics13122302 (registering DOI) - 12 Jun 2024
Abstract
To enhance communication and collaborative work efficiency in cyber–physical systems (CPSs) within the Industry 4.0 environment, this study investigates a graph-based machine learning approach aimed at optimizing information interaction during multi-party conversations. Devices within CPSs must efficiently exchange information in real time to
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To enhance communication and collaborative work efficiency in cyber–physical systems (CPSs) within the Industry 4.0 environment, this study investigates a graph-based machine learning approach aimed at optimizing information interaction during multi-party conversations. Devices within CPSs must efficiently exchange information in real time to synchronize operations and responses. This research treats these interactions as intricate graph structures and uses graph learning techniques to accurately identify communication links and dependencies among devices. This improvement leads to more accurate decision-making and smoother operations. Our methodology involves a real-time analysis of structural patterns and node attributes within conversations, improving information flow and comprehension. The empirical findings demonstrate that this approach significantly enhances production efficiency, system adaptability, and minimizes delays attributed to communication misunderstandings. Our method can effectively identify the communication relationships between devices, significantly improving the efficiency and accuracy of information transmission. This improved communication capability leads to an enhanced production efficiency of the entire system.
Full article
(This article belongs to the Special Issue Cyber-Physical Systems in Industrial IoT)
Open AccessArticle
The Development of Fast DST-II Algorithms for Short-Length Input Sequences
by
Krystian Bielak, Aleksandr Cariow and Mateusz Raciborski
Electronics 2024, 13(12), 2301; https://doi.org/10.3390/electronics13122301 - 12 Jun 2024
Abstract
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The subject of this work is the development of fast algorithms for the discrete sinusoidal transformation of the second type (DST-II) for sequences of input data of small length N = 2, 3, 4, 5, 6, 7, 8. The starting point for the
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The subject of this work is the development of fast algorithms for the discrete sinusoidal transformation of the second type (DST-II) for sequences of input data of small length N = 2, 3, 4, 5, 6, 7, 8. The starting point for the development of algorithms is the well-known possibility of representing any discrete transformation in the form of a matrix–vector product. Due to the remarkable structural properties of the matrices of the DST-II transformation base, these matrices can be successfully factorized, which should lead to a reduction in the computational complexity of the procedure as a whole. You can factorize matrices in different ways. The art of designing fast algorithms is to find the factorization that produces the maximum effect. We justified the correctness of the obtained algorithmic solutions theoretically, using strict mathematical derivations of each of them. The developed algorithms were then further tested using MATLAB R2023b software to finally confirm their performance. Finally, we presented estimates of the computational complexity for each solution obtained and compared them with direct computational methods that rely on the direct calculation of matrix–vector products.
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Open AccessArticle
Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm
by
Xihuai Wang and Ying Zhou
Electronics 2024, 13(12), 2300; https://doi.org/10.3390/electronics13122300 - 12 Jun 2024
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Aimed at reducing the redundancy of parameters for the power system stabilizer (PSS) and static var compensator (SVC), this paper proposes a method for coordinated control and optimization based on the neighborhood rough set and improved whale optimization algorithm (NRS-IWOA). The neighborhood rough
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Aimed at reducing the redundancy of parameters for the power system stabilizer (PSS) and static var compensator (SVC), this paper proposes a method for coordinated control and optimization based on the neighborhood rough set and improved whale optimization algorithm (NRS-IWOA). The neighborhood rough set (NRS) is first utilized to simplify the redundant parameters of the controller to improve efficiency. Then, the methods of the Sobol sequence initialization population, nonlinear convergence factor, adaptive weight strategy, and random differential mutation strategy are introduced to improve the traditional whale optimization algorithm (WOA) algorithm. Finally, the improved whale optimization algorithm (IWOA) is utilized to optimize the remaining controller parameters. The simulation results show that the optimization parameters were reduced from 12 and 18 to 3 and 4 in the single-machine infinity bus system and dual-machine power system, and the optimization time was reduced by 74.5% and 42.8%, respectively. In addition, the proposed NRS-IWOA method exhibits more significant advantages in optimizing parameters and improving stability than other algorithms.
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Open AccessArticle
Revealing GLCM Metric Variations across a Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications
by
Masud Kabir, Fatih Unal, Tahir Cetin Akinci, Alfredo A. Martinez-Morales and Sami Ekici
Electronics 2024, 13(12), 2299; https://doi.org/10.3390/electronics13122299 - 12 Jun 2024
Abstract
This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through
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This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through a comprehensive examination of publicly available plant disease datasets, focusing on their performance as measured by GLCM metrics, this research identified dataset_2 (D2), a database of leaf images, as the top performer across all GLCM analyses. These datasets were then utilized to train the DarkNet19 deep learning model, with D2 exhibiting superior performance in both GLCM analysis and DarkNet19 training (achieving about 91% testing accuracy) according to performance metrics such as accuracy, precision, recall, and F1-score. The datasets other than dataset_1 and 2 exhibited significantly low classification performance, particularly in supporting GLCM analysis. The findings underscore the need for transparency and rigor in dataset selection, particularly given the abundance of similar datasets in the literature and the growing trend of utilizing deep learning methods in future scientific research.
Full article
(This article belongs to the Special Issue Machine Learning Techniques for Image Processing)
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Open AccessArticle
Colorectal Polyp Detection Model by Using Super-Resolution Reconstruction and YOLO
by
Shaofang Wang, Jun Xie, Yanrong Cui and Zhongju Chen
Electronics 2024, 13(12), 2298; https://doi.org/10.3390/electronics13122298 - 12 Jun 2024
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Colonoscopy is the primary method to prevent CRC. However, traditional polyp detection methods face problems such as low image resolution and the possibility of missing polyps. In recent years, deep learning
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Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Colonoscopy is the primary method to prevent CRC. However, traditional polyp detection methods face problems such as low image resolution and the possibility of missing polyps. In recent years, deep learning techniques have been extensively employed in the detection of colorectal polyps. However, these algorithms have not yet addressed the issue of detection in low-resolution images. In this study, we propose a novel YOLO-SRPD model by integrating SRGAN and YOLO to address the issue of low-resolution colonoscopy images. Firstly, the SRGAN with integrated ACmix is used to convert low-resolution images to high-resolution images. The generated high-resolution images are then used as the training set for polyp detection. Then, the C3_Res2Net is integrated into the YOLOv5 backbone to enhance multiscale feature extraction. Finally, CBAM modules are added before the prediction head to enhance attention to polyp information. The experimental results indicate that YOLO-SRPD achieves a mean average precision (mAP) of 94.2% and a precision of 95.2%. Compared to the original model (YOLOv5), the average accuracy increased by 1.8% and the recall rate increased by 5.6%. These experimental results confirm that YOLO-SRPD can address the low-resolution problem during colorectal polyp detection and exhibit exceptional robustness.
Full article
(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)
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Open AccessReview
Prospective Directions in the Computer Systems Industry Foundation Classes (IFC) for Shaping Data Exchange in the Sustainability and Resilience of Cities
by
Ebere Donatus Okonta, Vladimir Vukovic and Ezri Hayat
Electronics 2024, 13(12), 2297; https://doi.org/10.3390/electronics13122297 - 12 Jun 2024
Abstract
Sustainability and resilience in addressing construction’s environmental, social, and economic challenges rely on interoperability. A model-centred approach using standardised information structures like industry foundation classes (IFC) is essential for data sharing in architecture, engineering, construction, and facility management. Achieving complete interoperability across domains
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Sustainability and resilience in addressing construction’s environmental, social, and economic challenges rely on interoperability. A model-centred approach using standardised information structures like industry foundation classes (IFC) is essential for data sharing in architecture, engineering, construction, and facility management. Achieving complete interoperability across domains requires further research. This review paper focuses on IFC schema, highlighting upcoming developments like IFC 5 and “IFC x”, with a core emphasis on modularisation to enhance domain interoperability, improved links between building information modelling (BIM) and geographic information systems (GIS), along with IoT integration into BIM, cloud-based collaboration, and support for other advanced technologies such as augmented reality (AR), virtual reality (VR), artificial intelligence (AI), and digital twins. Through a critical examination of the IFC and an outlook towards its future enhancements, the research has the potential to offer valuable insights into shaping the trajectory of future advancements within the AEC and facility management sectors. The study’s discoveries could aid in establishing standardised data exchange protocols in these industries, promoting uniformity across projects, facilitating smoother communication, and mitigating errors and inefficiencies. Anticipating enhancements in the IFC could catalyse innovation, fostering the adoption of emerging technologies and methodologies. Consequently, this could drive the creation of more sophisticated tools and procedures, ultimately enhancing project outcomes and operational effectiveness.
Full article
(This article belongs to the Special Issue Advances in Smart Technologies - Selected Papers from 3rd EAI International Conference on Smart Technologies and Innovation Management)
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Open AccessArticle
A Chua’s Chaotic Chirp Spread-Spectrum Power Spectral Homogenization Strategy Based on Distribution Transformation
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Zaixue Yang, Bing Chen, Bin Liu, Yao Zhang, Qian Liang and Yanming Chen
Electronics 2024, 13(12), 2296; https://doi.org/10.3390/electronics13122296 - 12 Jun 2024
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When utilizing high-dimensional chaotic signals for frequency modulation, achieving a uniformly distributed power spectrum is a challenging task. This paper addresses this challenge by proposing a power spectrum homogenization strategy based on distribution transformation. The strategy transforms the task of achieving a uniformly
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When utilizing high-dimensional chaotic signals for frequency modulation, achieving a uniformly distributed power spectrum is a challenging task. This paper addresses this challenge by proposing a power spectrum homogenization strategy based on distribution transformation. The strategy transforms the task of achieving a uniformly distributed power spectrum in frequency modulation of high-dimensional chaotic signals to solve and equalize the probability density function of the chaotic signals, thereby further enhancing the ability of high-dimensional chaotic signals to suppress electromagnetic interference. Firstly, the difficulty of obtaining a smooth probability density function of chaotic modulation signals is solved using the kernel density estimation algorithm. Then, a distribution transformation algorithm is proposed to convert non-uniformly distributed chaotic modulation signals into uniformly distributed chaotic modulation signals. By using uniformly distributed chaotic modulation signals for frequency modulation, the objective of power spectrum equalization is achieved. Finally, taking the Chua’s chaotic signal as an example, the effectiveness of the proposed strategy is verified using an experimental platform based on a digital signal processor-controlled active clamping flyback converter.
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Open AccessReview
Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature Review
by
Rasoul Kiani and Victor S. Sheng
Electronics 2024, 13(12), 2295; https://doi.org/10.3390/electronics13122295 - 12 Jun 2024
Abstract
In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques.
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In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively.
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(This article belongs to the Special Issue Current Trends on Data Management)
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Open AccessFeature PaperArticle
Comparison Analysis of Multimodal Fusion for Dangerous Action Recognition in Railway Construction Sites
by
Otmane Amel, Xavier Siebert and Sidi Ahmed Mahmoudi
Electronics 2024, 13(12), 2294; https://doi.org/10.3390/electronics13122294 - 12 Jun 2024
Abstract
The growing demand for advanced tools to ensure safety in railway construction projects highlights the need for systems that can smoothly integrate and analyze multiple data modalities, such as multimodal learning algorithms. The latter, inspired by the human brain’s ability to integrate many
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The growing demand for advanced tools to ensure safety in railway construction projects highlights the need for systems that can smoothly integrate and analyze multiple data modalities, such as multimodal learning algorithms. The latter, inspired by the human brain’s ability to integrate many sensory inputs, has emerged as a promising field in artificial intelligence. In light of this, there has been a rise in research on multimodal fusion approaches, which have the potential to outperform standard unimodal solutions. However, the integration of multiple data sources presents significant challenges to be addressed. This work attempts to apply multimodal learning to detect dangerous actions using RGB-D inputs. The key contributions include the evaluation of various fusion strategies and modality encoders, as well as identifying the most effective methods for capturing complex cross-modal interactions. The superior performance of the MultConcat multimodal fusion method was demonstrated, achieving an accuracy of 89.3%. Results also underscore the critical need for robust modality encoders and advanced fusion techniques to outperform unimodal solutions.
Full article
(This article belongs to the Special Issue Recent Advances and Applications of Computational Intelligence)
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Open AccessArticle
Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification
by
Jinhua Zhang, Xin Liu and Jie Yan
Electronics 2024, 13(12), 2293; https://doi.org/10.3390/electronics13122293 - 12 Jun 2024
Abstract
The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind
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The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind processes under different incoming currents and different weather are very different, and the spatio-temporal correlation law of offshore wind processes is highly complex, which leads to traditional power prediction not being able to accurately predict the short-term power of offshore wind farms. Therefore, aiming at the characteristics and complexity of offshore wind power, this paper proposes an innovative short-term power prediction method for offshore wind farms based on a Gaussian mixture model (GMM). This method considers the correlation between units according to the characteristics of the measured data of units, and it divides units with high correlation into a category. The Bayesian information criterion (BIC) and contour coefficient method (SC) were used to obtain the optimal number of groups. The average intra-group correlation coefficient (AICC) was used to evaluate the reliability of measurements for the same quantized feature to select the representative units for each classification. Practical examples show that the short-term power prediction accuracy of the model after unit classification is 2.12% and 1.1% higher than that without group processing, and the mean square error and average absolute error of the short-term power prediction accuracy are reduced, respectively, which provides a basis for the optimization of prediction accuracy and economic operation of offshore wind farms.
Full article
(This article belongs to the Special Issue New Insights in Industrial Electronics: Advanced Devices and Intelligent Systems)
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Open AccessArticle
Research on Fixed-Slope On-Chip Soft-Start Method Applied to Buck DC–DC Converter
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Zhongjie Guo, Ziyi Qiu, Yuan Yang and Ningmei Yu
Electronics 2024, 13(12), 2292; https://doi.org/10.3390/electronics13122292 - 12 Jun 2024
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A fixed-slope soft-start method applicable to Buck converters for on-chip integration is proposed to address the issue of varying power stresses (device voltage, current stress) during start-up with different output voltages. The main mechanism involves combining feedback coefficient sampling with a fixed-slope reference
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A fixed-slope soft-start method applicable to Buck converters for on-chip integration is proposed to address the issue of varying power stresses (device voltage, current stress) during start-up with different output voltages. The main mechanism involves combining feedback coefficient sampling with a fixed-slope reference voltage to achieve a slow rise in the reference voltage by equating the soft-start charging current to a pulse current through the on-chip integration of a small capacitor. This allows for fixed-slope start-ups for different set output voltages. Spike elimination techniques are employed to address charging current spikes caused by pulse periods, enhancing precise control over the soft-start time. By replacing the traditional resistor divider network with a capacitive divider network in the soft-start method, DC power consumption is minimized. Upon completion of the soft start, a smooth transition to a steady-state operation occurs, with the automatic shutdown of the soft-start module reducing static power consumption. A specific circuit design and layout verification based on 0.18 μm high-voltage BCD technology demonstrates that the proposed method maintains a fixed-slope start-up of approximately 5 mV/μs within the chip’s output range of 0.9 V to 4 V, with a slope control accuracy of up to 98%. The soft-start circuit effectively eliminates surge currents generated during start-up under full load conditions of 3 A and no-load conditions of 0 A, reducing the overall surge current by 44% and enabling a stable voltage rise and the smooth transition to a steady-state operation.
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Open AccessArticle
An Architecture as an Alternative to Gradient Boosted Decision Trees for Multiple Machine Learning Tasks
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Lei Du, Haifeng Song, Yingying Xu and Songsong Dai
Electronics 2024, 13(12), 2291; https://doi.org/10.3390/electronics13122291 - 12 Jun 2024
Abstract
Deep networks-based models have achieved excellent performances in various applications for extracting discriminative feature representations by convolutional neural networks (CNN) or recurrent neural networks (RNN). However, CNN or RNN may not work when handling data without temporal/spatial structures. Therefore, finding a new technique
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Deep networks-based models have achieved excellent performances in various applications for extracting discriminative feature representations by convolutional neural networks (CNN) or recurrent neural networks (RNN). However, CNN or RNN may not work when handling data without temporal/spatial structures. Therefore, finding a new technique to extract features instead of CNN or RNN is a necessity. Gradient Boosted Decision Trees (GBDT) can select the features with the largest information gain when building trees. In this paper, we propose an architecture based on the ensemble of decision trees and neural network (NN) for multiple machine learning tasks, e.g., classification, regression, and ranking. It can be regarded as an extension of the widely used deep-networks-based model, in which we use GBDT instead of CNN or RNN. This architecture consists of two main parts: (1) the decision forest layers, which focus on learning features from the input data, (2) the fully connected layers, which focus on distilling knowledge from the decision forest layers. Powered by these two parts, the proposed model could handle data without temporal/spatial structures. This model can be efficiently trained by stochastic gradient descent via back-propagation. The empirical evaluation results of different machine learning tasks demonstrate the the effectiveness of the proposed method.
Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, Volume II)
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Open AccessArticle
GraphSensor: A Graph Attention Network for Time-Series Sensor
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Jiaqi Ge, Gaochao Xu, Jianchao Lu, Xu Xu and Xiangyu Meng
Electronics 2024, 13(12), 2290; https://doi.org/10.3390/electronics13122290 - 11 Jun 2024
Abstract
Our work focuses on the exploration of the internal relationships of signals in an individual sensor. In particular, we address the problem of not being able to evaluate such intrasensor relationships due to missing rich and explicit feature representation. To solve this problem,
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Our work focuses on the exploration of the internal relationships of signals in an individual sensor. In particular, we address the problem of not being able to evaluate such intrasensor relationships due to missing rich and explicit feature representation. To solve this problem, we propose GraphSensor, a graph attention network, with a shared-weight convolution feature encoder to generate the signal segments and learn the internal relationships between them. Furthermore, we enrich the representation of the features by utilizing a multi-head approach when creating the internal relationship graph. Compared with traditional multi-head approaches, we propose a more efficient convolution-based multi-head mechanism, which only requires of model parameters compared with the best multi-head baseline as demonstrated in the experiments. Moreover, GraphSensor is capable of achieving state-of-the-art performance in the electroencephalography dataset and improving the accuracy by compared to the best baseline in an inertial measurement unit (IMU) dataset.
Full article
(This article belongs to the Special Issue Intelligent IoT Systems with Mobile/Multi-Access Edge Computing (MEC))
Open AccessArticle
Hierarchical Inverse Distance Transformer for Enhanced Localization in Dense Crowds
by
Xiangfeng Qiu, Jin Ye, Siyu Chen and Jinhe Su
Electronics 2024, 13(12), 2289; https://doi.org/10.3390/electronics13122289 - 11 Jun 2024
Abstract
Achieving precise individual localization within densely crowded scenes poses a significant challenge due to the intricate interplay of occlusions and varying density patterns. Traditional methods for crowd localization often rely on convolutional neural networks (CNNs) to generate density maps. However, these approaches are
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Achieving precise individual localization within densely crowded scenes poses a significant challenge due to the intricate interplay of occlusions and varying density patterns. Traditional methods for crowd localization often rely on convolutional neural networks (CNNs) to generate density maps. However, these approaches are prone to inaccuracies stemming from the extensive overlaps inherent in dense populations. To overcome this challenge, our study introduces the Hierarchical Inverse Distance Transformer (HIDT), a novel framework that harnesses the multi-scale global receptive fields of Pyramid Vision Transformers. By adapting to the multi-scale characteristics of crowds, HIDT significantly enhances the accuracy of individual localization. Incorporating Focal Inverse Distance techniques, HIDT adeptly addresses issues related to scale variation and dense overlaps, prioritizing local small-scale features within the broader contextual understanding of the scene. Rigorous evaluation on standardized benchmarks has unequivocally validated the superiority of our approach. HIDT exhibits outstanding performance across various datasets. Notably, on the JHU-Crowd++ dataset, our method demonstrates significant improvements over the baseline, with MAE and MSE metrics decreasing from 66.6 and 253.6 to 59.1 and 243.5, respectively. Similarly, on the UCF-QNRF dataset, performance metrics increase from 89.0 and 153.5 to 83.6 and 138.7, highlighting the effectiveness and versatility of our approach.
Full article
(This article belongs to the Section Computer Science & Engineering)
Open AccessReview
Research Trends in Artificial Intelligence and Security—Bibliometric Analysis
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Luka Ilić, Aleksandar Šijan, Bratislav Predić, Dejan Viduka and Darjan Karabašević
Electronics 2024, 13(12), 2288; https://doi.org/10.3390/electronics13122288 - 11 Jun 2024
Abstract
This paper provides a bibliometric analysis of current research trends in the field of artificial intelligence (AI), focusing on key topics such as deep learning, machine learning, and security in AI. Through the lens of bibliometric analysis, we explore publications published from 2020
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This paper provides a bibliometric analysis of current research trends in the field of artificial intelligence (AI), focusing on key topics such as deep learning, machine learning, and security in AI. Through the lens of bibliometric analysis, we explore publications published from 2020 to 2024, using primary data from the Clarivate Analytics Web of Science Core Collection. The analysis includes the distribution of studies by year, the number of studies and citation rankings in journals, and the identification of leading countries, institutions, and authors in the field of AI research. Additionally, we investigate the distribution of studies by Web of Science categories, authors, affiliations, publication years, countries/regions, publishers, research areas, and citations per year. Key findings indicate a continued growth of interest in topics such as deep learning, machine learning, and security in AI over the past few years. We also identify leading countries and institutions active in researching this area. Awareness of data security is essential for the responsible application of AI technologies. Robust security frameworks are important to mitigate risks associated with AI integration into critical infrastructure such as healthcare and finance. Ensuring the integrity and confidentiality of data managed by AI systems is not only a technical challenge but also a societal necessity, demanding interdisciplinary collaboration and policy development. This analysis provides a deeper understanding of the current state of research in the field of AI and identifies key areas for further research and innovation. Furthermore, these findings may be valuable to practitioners and decision-makers seeking to understand current trends and innovations in AI to enhance their business processes and practices.
Full article
(This article belongs to the Special Issue Machine Learning for Cybersecurity: Threat Detection and Mitigation)
Open AccessArticle
Investigation of Ferromagnetic Nanoparticles’ Behavior in a Radio Frequency Electromagnetic Field for Medical Applications
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Katarzyna Wojtera, Lukasz Pietrzak, Lukasz Szymanski and Slawomir Wiak
Electronics 2024, 13(12), 2287; https://doi.org/10.3390/electronics13122287 - 11 Jun 2024
Abstract
This work raises the hypothesis that it is possible to use ferromagnetic carbon nanotubes filled with iron to hyperthermally destroy cancer cells in a radiofrequency electromagnetic field. This paper describes the synthesis process of iron-filled multi-walled carbon nanotubes (Fe-MWCNTs) and presents a study
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This work raises the hypothesis that it is possible to use ferromagnetic carbon nanotubes filled with iron to hyperthermally destroy cancer cells in a radiofrequency electromagnetic field. This paper describes the synthesis process of iron-filled multi-walled carbon nanotubes (Fe-MWCNTs) and presents a study of their magnetic properties. Fe-MWCNTs were synthesized by catalytic chemical vapor deposition (CCVD). Appropriate functionalization properties of the nanoparticles for biomedical applications were used, and their magnetic properties were studied to determine the heat generation efficiency induced by exposure of the particles to an external electromagnetic field. The response of the samples was measured for 45 min of exposure. The results showed an increase in sample temperature that was proportional to concentration. The results of laboratory work were compared to the simulation using COMSOL software.
Full article
(This article belongs to the Section Microelectronics)
Open AccessArticle
The Use of Business Intelligence Software to Monitor Key Performance Indicators (KPIs) for the Evaluation of a Computerized Maintenance Management System (CMMS)
by
Paola Picozzi, Umberto Nocco, Andrea Pezzillo, Adriana De Cosmo and Veronica Cimolin
Electronics 2024, 13(12), 2286; https://doi.org/10.3390/electronics13122286 - 11 Jun 2024
Abstract
The increasing use of electromedical equipment in hospital care services necessitates effective management of complex devices often unsupported by existing control systems. This paper focuses on developing a pool of evaluation indices for the Clinical Engineering Department (CED) of the ASST Grande Ospedale
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The increasing use of electromedical equipment in hospital care services necessitates effective management of complex devices often unsupported by existing control systems. This paper focuses on developing a pool of evaluation indices for the Clinical Engineering Department (CED) of the ASST Grande Ospedale Metropolitano Niguarda in Milano (Italy), aiming to enhance awareness of the economic value, assess operational units, and optimize maintenance processes. Leveraging business intelligence, this study identifies 18 key performance indicators (KPIs) across logistics, technical, and equipment management categories. An interactive dashboard, implemented using Power BI, facilitates dynamic analysis and visualization of these KPIs, providing insights into the maintenance efficiency and obsolescence of medical devices. It offers a comprehensive framework for ongoing monitoring and decision-making. The results showcase the potential of the developed KPIs and dashboard to enhance operational insights and guide improvements in the healthcare facility’s maintenance processes.
Full article
(This article belongs to the Special Issue Internet of Things, Embedded Solutions, and Edge Intelligence for Smart Health)
Open AccessArticle
Parameter Design of a Self-Generated Power Current Transformer of an Intelligent Miniature Circuit Breaker Based on COMSOL
by
Yao Wang, Guanghui Chang, Kefan Han, Xiaopeng Qian, Zhizhou Bao and Dejie Sheng
Electronics 2024, 13(12), 2285; https://doi.org/10.3390/electronics13122285 - 11 Jun 2024
Abstract
With the deep penetration of renewable energy and power electronic equipment, the overcurrent protection of an intelligent miniature circuit breaker faces new challenges. The electronic controller of an intelligent miniature circuit breaker is typically powered by the bus current rather than the phase
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With the deep penetration of renewable energy and power electronic equipment, the overcurrent protection of an intelligent miniature circuit breaker faces new challenges. The electronic controller of an intelligent miniature circuit breaker is typically powered by the bus current rather than the phase voltage to ensure a robust overcurrent protection response under all conditions, including severe short-circuit faults. So, the performance of the current transformer serving as an energy harvesting unit and the corresponding direct current to direct current convention circuit is one of the critical issues due to the limited volume of an intelligent miniature circuit breaker. In this research, a finite element model of a current transformer for an intelligent miniature circuit breaker is constructed by COMSOL to evaluate the impact of the core material, the core size, and the number of coil turns on the energy harvesting capability of the current transformer. Meanwhile, the relationship between the output of the power supply and its design parameters is investigated by circuit simulation. As a result, a novel type of current transformer is proposed based on well-designed parameters. Finally, experimental tests have been conducted to verify the hysteresis characteristics, output characteristics, and energy harvesting effect. The results demonstrate that the hysteresis properties of the transformer align with the simulation results. The power supply can work with a minimum current of 8 amperes, which is 23.08% better than before.
Full article
(This article belongs to the Section Power Electronics)
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