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
Development of an Uneven Terrain Decision-Aid Landing System for Fixed-Wing Aircraft Based on Computer Vision
Electronics 2024, 13(10), 1946; https://doi.org/10.3390/electronics13101946 (registering DOI) - 15 May 2024
Abstract
This paper presents a computer vision-based standalone decision-aid landing system for light fixed-wing aircraft, aiming to enhance safety during emergency landings. Current landing assistance systems in airports, such as Instrument Landing Systems (ILSs) and Precision Approach Path Indicators (PAPIs), often rely on costly
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This paper presents a computer vision-based standalone decision-aid landing system for light fixed-wing aircraft, aiming to enhance safety during emergency landings. Current landing assistance systems in airports, such as Instrument Landing Systems (ILSs) and Precision Approach Path Indicators (PAPIs), often rely on costly and location-specific ground equipment, limiting their utility for low-payload light aircraft. Especially in emergency conditions, the pilot may be forced to land on an arbitrary runway where the road flatness and glide angle cannot be ensured. To address these issues, a stereo vision-based auxiliary landing system is proposed, which is capable of estimating an appropriate glide slope based on the terrain, to assist pilots in safe landing decision-making. Moreover, in real-world scenarios, challenges with visual-based methods arise when attempting emergency landings on complex terrains with diverse objects, such as roads and buildings. This study solves this problem by employing the Gaussian Mixture Model (GMM) to segment the color image and extract ground points, while the iterative weighted plane fitting (IWPF) algorithm is introduced to mitigate the interference of outlier feature points, reaching a highly robust plane normal estimation. With the aid of the proposed system, the pilot is able to evaluate the landing glide angle/speed with respect to the uneven terrain. Simulation results demonstrate that the proposed system can successfully achieve landing guidance in unknown environments by providing glide angle estimations with an average error of less than 1 degree.
Full article
(This article belongs to the Special Issue UAV (Unmanned Aerial Vehicles) Networks: Recent Developments and Emerging Trends)
Open AccessArticle
Optimization Decomposition of Monthly Contracts for Integrated Energy Service Provider Considering Spot Market Bidding Equilibria
by
Chen Wu, Zhinong Wei, Xiangchen Jiang, Yizhen Huang and Donglou Fan
Electronics 2024, 13(10), 1945; https://doi.org/10.3390/electronics13101945 - 15 May 2024
Abstract
Under the current power trading model, especially in the context of the large-scale penetration of renewable energy and the rapid integration of renewable energy into the power system, reasonable medium- and long-term decomposition can reduce the fluctuation in the energy price when the
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Under the current power trading model, especially in the context of the large-scale penetration of renewable energy and the rapid integration of renewable energy into the power system, reasonable medium- and long-term decomposition can reduce the fluctuation in the energy price when the integrated energy service provider (IESP) participates in the spot market. It helps to avoid the price risk of the spot market. Additionally, it promotes the optimization of the operation of the regional energy day-ahead scheduling. At the present stage, most of the medium- and long-term contract decomposition methods focus on the decomposition of a single power and take less consideration of the bidding space in the spot market. This limitation makes it challenging to achieve efficient interaction and interconnection among multi-energy resources and smooth integration between the medium- and long-term market and the spot market. To address these issues, this paper proposes an optimal monthly contract decomposition method for IESPs that takes into account the equilibrium of spot bidding. First, the linking process and rolling framework of multi-energy transactions between the medium- and long-term market and the spot market are designed. Second, an optimal decomposition model for monthly contracts is constructed, and a daily decomposition method for monthly medium- and long-term contracts that accounts for the spot bidding equilibrium is proposed. Then, the daily preliminary decomposition result of medium- and long-term multi-energy contracts is used as the boundary condition of the day-ahead scheduling model, and the coupling characteristics of the multi-energy networks of electricity, gas, and heat are taken into account, as well as the operational characteristics. Then, considering the coupling characteristics and operating characteristics of electricity, gas, and heat networks, the optimal scheduling model of a multi-energy network is constructed to minimize the sum of cumulative daily operating costs, and the monthly final contract decomposition value and daily spot bidding space are derived. Finally, examples are calculated to verify the validity of the decomposition model, and the examples show that the proposed method can reduce the variance in spot energy purchase by about 4.64%, and, at the same time, reduce the cost of contract decomposition by about USD 0.33 million.
Full article
(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)
Open AccessArticle
Semantic Augmentation in Chinese Adversarial Corpus for Discourse Relation Recognition Based on Internal Semantic Elements
by
Zheng Hua, Ruixia Yang, Yanbin Feng and Xiaojun Yin
Electronics 2024, 13(10), 1944; https://doi.org/10.3390/electronics13101944 - 15 May 2024
Abstract
This paper proposes incorporating linguistic semantic information into discourse relation recognition and constructing a Semantic Augmented Chinese Discourse Corpus (SACA) comprising 9546 adversative complex sentences. In adversative complex sentences, we suggest a quadruple (P, Q, R, )
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This paper proposes incorporating linguistic semantic information into discourse relation recognition and constructing a Semantic Augmented Chinese Discourse Corpus (SACA) comprising 9546 adversative complex sentences. In adversative complex sentences, we suggest a quadruple (P, Q, R, ) representing internal semantic elements, where the semantic opposition between Q and forms the basis of the adversative relationship. P denotes the premise, and R represents the adversative reason. The overall annotation approach of this corpus follows the Penn Discourse Treebank (PDTB), except for the classification of senses. We combined insights from the Chinese Discourse Treebank (CDTB) and obtained eight sense categories for Chinese adversative complex sentences. Based on this corpus, we explore the relationship between sense classification and internal semantic elements within our newly proposed Chinese Adversative Discourse Relation Recognition (CADRR) task. Leveraging deep learning techniques, we constructed various classification models and the model that utilizes internal semantic element features, demonstrating their effectiveness and the applicability of our SACA corpus. Compared with pre-trained models, our model incorporates internal semantic element information to achieve state-of-the-art performance.
Full article
(This article belongs to the Special Issue Data Mining Applied in Natural Language Processing)
Open AccessArticle
Design of Series-Connected Novel Large-Scale Offshore Wind Power All-DC System with Fault Blocking Capability
by
Yalun Ru, Haiyun Wang and Zhanlong Li
Electronics 2024, 13(10), 1943; https://doi.org/10.3390/electronics13101943 - 15 May 2024
Abstract
The utilization of wind power all-DC systems with DC collection and transmission is an effective solution for the extensive development of wind power in deep-sea areas. However, in the event of faults occurring in wind power all-DC systems, the fault propagation speed is
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The utilization of wind power all-DC systems with DC collection and transmission is an effective solution for the extensive development of wind power in deep-sea areas. However, in the event of faults occurring in wind power all-DC systems, the fault propagation speed is extremely rapid, with a wide-ranging impact, and to date, there are no complete DC engineering references available. It is crucial to research the topology and fault isolation methods applicable to large-scale offshore wind power all-DC systems in deep-sea areas. This paper proposes a novel series-connected all-DC system topology and presents corresponding fault isolation methods for internal faults in wind turbine units and faults in high-voltage DC transmission lines. The system simulation model was constructed using PSCAD/EMTDC (v4.6.3), and simulations were conducted for internal faults in the wind turbine units and DC transmission line short-circuit faults. The simulation results demonstrate that the proposed system can isolate various DC faults while maintaining stable operation, thereby validating the effectiveness of the control strategies and fault isolation methods proposed in this paper.
Full article
(This article belongs to the Section Flexible Electronics)
Open AccessArticle
CE-PBFT: An Optimized PBFT Consensus Algorithm for Microgrid Power Trading
by
Xu Ding, Haihua Lu and Lanxian Cheng
Electronics 2024, 13(10), 1942; https://doi.org/10.3390/electronics13101942 - 15 May 2024
Abstract
Currently, in the blockchain-based distributed microgrid trading system, there are some problems, such as low throughput, high delay, and a high communication overhead. To this end, an improved Practical Byzantine Fault Tolerance (PBFT) algorithm (CE-PBFT) suitable for microgrid power trading is proposed. First,
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Currently, in the blockchain-based distributed microgrid trading system, there are some problems, such as low throughput, high delay, and a high communication overhead. To this end, an improved Practical Byzantine Fault Tolerance (PBFT) algorithm (CE-PBFT) suitable for microgrid power trading is proposed. First, a node credit value calculation model is introduced, and nodes are divided into consensus, supervisory, and propagation nodes according to their credit values, forming a hierarchical network structure to ensure the efficiency and reliability of consensus. Secondly, the consensus process is optimized by adopting a segmented consensus mechanism. This approach calculates the consensus rounds for nodes and selects the methods for node-type switching and consensus based on these calculations, reaching dynamic changes in node states and credit values, effectively reducing the communication overhead of node consensus. Finally, the experiments show that compared with the IMPBFT and PBFT algorithms, the CE-PBFT algorithm has better performance in throughput, delay, and communication overhead, with a 22% higher average throughput and 15% lower average delay than the IMPBFT algorithm and a 118% higher average throughput and 87% lower average delay than the PBFT algorithm.
Full article
(This article belongs to the Special Issue Blockchain Technology Is Applied in the IoT System)
Open AccessArticle
Unstructured Document Information Extraction Method with Multi-Faceted Domain Knowledge Graph Assistance for M2M Customs Risk Prevention and Screening Application
by
Fengchun Tian, Haochen Wang, Zhenlong Wan, Ran Liu, Ruilong Liu, Di Lv and Yingcheng Lin
Electronics 2024, 13(10), 1941; https://doi.org/10.3390/electronics13101941 - 15 May 2024
Abstract
As a crucial national security defense line, the existing risk prevention and screening system of customs falls short in terms of intelligence and diversity for risk identification factors. Hence, the urgent issues to be addressed in the risk identification system include intelligent extraction
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As a crucial national security defense line, the existing risk prevention and screening system of customs falls short in terms of intelligence and diversity for risk identification factors. Hence, the urgent issues to be addressed in the risk identification system include intelligent extraction technology for key information from Customs Unstructured Accompanying Documents (CUADs) and the reliability of the extraction results. In the customs scenario, OCR is employed for M2M interactions, but current models have difficulty adapting to diverse image qualities and complex customs document content. We propose a hybrid mutual learning knowledge distillation (HMLKD) method for optimizing a pre-trained OCR model’s performance against such challenges. Additionally, current models lack effective incorporation of domain-specific knowledge, resulting in insufficient text recognition accuracy for practical customs risk identification. We propose a customs domain knowledge graph (CDKG) developed using CUAD knowledge and propose an integrated CDKG post-OCR correction method (iCDKG-PostOCR) based on CDKG. The results on real data demonstrate that the accuracies improve for code text fields to 97.70%, for character type fields to 96.55%, and for numerical type fields to 96.00%, with a confidence rate exceeding 99% for each. Furthermore, the Customs Health Certificate Extraction System (CHCES) developed using the proposed method has been implemented and verified at Tianjin Customs in China, where it has showcased outstanding operational performance.
Full article
Open AccessFeature PaperReview
Communications and Data Science for the Success of Vehicle-to-Grid Technologies: Current State and Future Trends
by
Noelia Uribe-Pérez, Amaia Gonzalez-Garrido, Alexander Gallarreta, Daniel Justel, Mikel González-Pérez, Jon González-Ramos, Ane Arrizabalaga, Francisco Javier Asensio and Peru Bidaguren
Electronics 2024, 13(10), 1940; https://doi.org/10.3390/electronics13101940 - 15 May 2024
Abstract
Vehicle-to-grid (V2G) technology has emerged as a promising solution for enhancing the integration of electric vehicles (EVs) into the electric grid, offering benefits, such as distributed energy resource (DER) integration, grid stability support, and peak demand management, among others, as well as environmental
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Vehicle-to-grid (V2G) technology has emerged as a promising solution for enhancing the integration of electric vehicles (EVs) into the electric grid, offering benefits, such as distributed energy resource (DER) integration, grid stability support, and peak demand management, among others, as well as environmental advantages. This study provides a comprehensive review of V2G systems, with a specific focus on the role of the communication, as they have been identified as key enablers, as well as the challenges that V2G must face. It begins by introducing the fundamentals of V2G systems, including their architecture, operation, and a description of the benefits for different sectors. It then delves into the communication technologies and protocols in V2G systems, highlighting the key requirements in achieving reliable and efficient communication between EVs and the different agents involved. A comprehensive review of communication standards is described, as well as the main communication technologies, which are evaluated in terms of their suitability for V2G applications. Furthermore, the study discusses the challenges and environmental implications of V2G technology, emphasizing the importance of addressing strong and reliable communications to maximize its potential benefits. Finally, future research directions and potential solutions for overcoming challenges in V2G systems are outlined, offering useful insights for researchers, policymakers, and administrations as well as related industry stakeholders.
Full article
(This article belongs to the Section Power Electronics)
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Open AccessArticle
A Drone-Assisted Anonymous Authentication and Key Agreement Protocol with Access Control for Accident Rescue in the Internet of Vehicles
by
Jihu Zheng, Haixin Duan, Chenyu Wang, Qiang Cao, Guoai Xu and Rui Fang
Electronics 2024, 13(10), 1939; https://doi.org/10.3390/electronics13101939 - 15 May 2024
Abstract
The drone-assisted Internet of Vehicles (DIoV) displays great potential in the punctual provision of rescue services without geographical limitations. To ensure data security in accident response and rescue services, authentication schemes with access control are employed. These schemes ensure that only specific rescue
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The drone-assisted Internet of Vehicles (DIoV) displays great potential in the punctual provision of rescue services without geographical limitations. To ensure data security in accident response and rescue services, authentication schemes with access control are employed. These schemes ensure that only specific rescue vehicle operators acting within a valid period can achieve mutual authentication from a designated processor, while access for mismatched, revoked, or expired users is denied. However, the current alternatives fail to ensure session key forward secrecy, entities’ mutual authentication, and user anonymity, thereby compromising users’ privacy and the security of communications. Moreover, executing too many time-consuming operations on vehicles’ resource-constrained devices inevitably degrades the performance of the authentication protocol. Balancing security and performance in the design of an authentication protocol with access control presents a significant challenge. To address this, a more efficient and robust authentication with access control has been designed. The proposed protocol ensures user anonymity through dynamic pseudonym allocation, achieves forward secrecy by excluding the long-term key from session key generation, and obtains mutual authentication by verifying the integrity of the messages exchanged. According to the security and performance analysis, it is demonstrated that the proposal is a robust, efficient, and cost-effective solution. In particular, the proposal can reduce the computational overhead by 66% compared to recent alternatives.
Full article
(This article belongs to the Special Issue Cryptography in Network Security)
Open AccessArticle
Detection of False Data Injection Attacks on Smart Grids Based on A-BiTG Approach
by
Wei He, Weifeng Liu, Chenglin Wen and Qingqing Yang
Electronics 2024, 13(10), 1938; https://doi.org/10.3390/electronics13101938 - 15 May 2024
Abstract
A false data injection attack (FDIA) is the main attack method that threatens the security of smart grids. FDIAs mislead the control center to make wrong judgments by modifying the measurement data of the power grid system. Therefore, the effective and accurate detection
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A false data injection attack (FDIA) is the main attack method that threatens the security of smart grids. FDIAs mislead the control center to make wrong judgments by modifying the measurement data of the power grid system. Therefore, the effective and accurate detection of FDIAs is crucial for the safe operation of smart grids. However, the current deep learning-based methods do not fully exploit the short-term local characteristics and long-term dependencies of power grid data and have poor correlation with past and future time series information, resulting in a lack of credibility in the detection results. In view of this, an FDIA detection model combining a bidirectional temporal convolutional network and bidirectional gated recurrent unit with an attention mechanism (A-BiTG) was proposed. The proposed model utilizes a bidirectional time convolutional network (BiTCN) and bidirectional gated recurrent unit (BiGRU) to consider past and future temporal information in the grid. This enhances the ability of the model to capture long-term dependencies and extract features, while also solving the model’s problem of exploding and vanishing gradients. In addition, an attention mechanism (AM) was added to dynamically assign weights to the extracted feature information and retain the most valuable features to improve the detection accuracy of the model. Finally, the proposed method was compared with existing methods on the IEEE 14-bus and IEEE 118-bus test systems. The results show that the proposed detection model is more robust and superior under different noise environments and FDIA signals with different intensities.
Full article
Open AccessArticle
Algebraic Speed Estimation for Sensorless Induction Motor Control: Insights from an Electric Vehicle Drive Cycle
by
Jorge Neira-García, Andrés Beltrán-Pulido and John Cortés-Romero
Electronics 2024, 13(10), 1937; https://doi.org/10.3390/electronics13101937 - 15 May 2024
Abstract
Induction motors (IMs) must meet high reliability and safety standards in mission-critical applications, such as electric vehicles (EVs), where sensorless control strategies are fundamental. However, sensorless rotor speed estimation demands improvements to overcome filtering distortions, tuning complexities, and sensitivity to IM model mismatch.
[...] Read more.
Induction motors (IMs) must meet high reliability and safety standards in mission-critical applications, such as electric vehicles (EVs), where sensorless control strategies are fundamental. However, sensorless rotor speed estimation demands improvements to overcome filtering distortions, tuning complexities, and sensitivity to IM model mismatch. Algebraic methods offer inherent filtering capabilities and design flexibility to address these challenges without introducing additional dynamics into the control system. The objective of this paper is to provide an algebraic estimation strategy that yields an accurate rotor speed estimate for sensorless IM control. The strategy includes an algebraic estimator with single-parameter tuning and inherent filtering action. We propose an EV case study to experimentally evaluate and compare its performance with a typical drive cycle and a dynamic torque load that emulates a small-scale EV power train. The algebraic estimator exhibited a signal-to-noise ratio (SNR) of 43 dB. The closed-loop experiment for the EV case study showed average tracking errors below 1 rad/s and similar performance compared to a well-known sensorless strategy. Our results show that the proposed algebraic estimation strategy works effectively in a nominal speed range for a practical IM sensorless application. The algebraic estimator only requires single-parameter tuning and potentially facilitates IM model updates using a resetting scheme.
Full article
(This article belongs to the Section Systems & Control Engineering)
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Open AccessArticle
Vignetting Compensation Method for CMOS Camera Based on LED Spatial Array
by
Shuo Huang, Xifeng Zheng, Xinyue Mao, Yufeng Chen and Yu Chen
Electronics 2024, 13(10), 1936; https://doi.org/10.3390/electronics13101936 - 15 May 2024
Abstract
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To solve the problem of pixel light intensity information distortion caused by camera vignetting in optical devices such as CMOS or CCD cameras, existing studies mainly focus on small spatial light fields and point light sources and adopt an integrating sphere and function
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To solve the problem of pixel light intensity information distortion caused by camera vignetting in optical devices such as CMOS or CCD cameras, existing studies mainly focus on small spatial light fields and point light sources and adopt an integrating sphere and function model for vignetting correction, but it is not suitable for large LED optical composite display devices. Under this background, this paper innovatively proposes a camera vigneting compensation method based on an LED spatial array, independently develops a two-dimensional translation device driven by a high-precision guide rail, uses spatial array technology to obtain the brightness distribution of the corrected display screen to quantify its camera vigneting distortion characteristics, and adopts systematic mathematical operations and iterative compensation strategies. Industry standard tests show that the brightness uniformity of the display has been improved by 5.06%. The above research results have been applied to mass production and industrialization.
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Open AccessArticle
Exploring Reddit Community Structure: Bridges, Gateways and Highways
by
Jan Sawicki and Maria Ganzha
Electronics 2024, 13(10), 1935; https://doi.org/10.3390/electronics13101935 - 15 May 2024
Abstract
Multiple research directions have been proposed to study the information structure of Reddit. One of them is to model inter-subreddit relations but modeling user interactions in the form of a graph. Building upon prior work centered on political subreddits using pre-2020 data, we
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Multiple research directions have been proposed to study the information structure of Reddit. One of them is to model inter-subreddit relations but modeling user interactions in the form of a graph. Building upon prior work centered on political subreddits using pre-2020 data, we expand this investigation to include a more extensive dataset spanning 2022 and encompassing diverse topic areas. Employing NLP techniques such as text embeddings, we model subreddit content directly and construct a subreddit graph network based on cosine similarity. Community detection using the Louvain method reveals distinct subreddits and allows the analysis of inter-community connections via previous works’ concepts of “bridges” and “gateways”. Surprisingly, our findings indicate redundancy between bridges and gateways in the utilized dataset. Therefore, we introduce a new concept, “highways”. Highways, representing the most traversed paths between subreddits, unveil insights not captured by previous analyses, underscoring the significance of novel conceptual frameworks in uncovering latent knowledge within Reddit’s online community structures.
Full article
(This article belongs to the Special Issue Advances in Graph-Based Data Mining)
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Open AccessArticle
Wideband Low Phase-Noise Signal Generation Using Coaxial Resonator in Cascaded Phase Locked Loop
by
Aljaž Blatnik and Boštjan Batagelj
Electronics 2024, 13(10), 1934; https://doi.org/10.3390/electronics13101934 - 15 May 2024
Abstract
The generation of high-quality wideband frequency sweeps presents a significant challenge, particularly in modern telecommunication, radar, and measurement systems where miniaturization is paramount. While phase-locked loops (PLLs) have become the dominant technique for signal generation, their application in broadband sweeps necessitates fractional-N operation.
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The generation of high-quality wideband frequency sweeps presents a significant challenge, particularly in modern telecommunication, radar, and measurement systems where miniaturization is paramount. While phase-locked loops (PLLs) have become the dominant technique for signal generation, their application in broadband sweeps necessitates fractional-N operation. This, in turn, degrades phase noise and introduces unwanted spurs. This paper proposes a novel approach for broadband signal generation. By cascading two PLLs and utilizing a coaxial resonator, we achieve a high-performance oscillator that operates without the excessive fractional spurs, maintaining their level below −80 dBc across the entire frequency band. The prototype demonstrates non-degraded phase noise performance, reaching −102 dBc/Hz at 100 kHz offset and −121 dBc/Hz at 1 MHz offset for signals at 10 GHz. Despite significant frequency jumps, our design achieves lock times below 41 µs. These results, supported by theoretical analysis, validate the proposed method’s effectiveness in generating low-noise broadband frequency sweeps, ideal for local oscillator applications.
Full article
(This article belongs to the Special Issue Feature Papers in Microwave and Wireless Communications Section)
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Open AccessArticle
Reinforcement of DC Electrified Railways by a Modular Battery Energy Storage System
by
Erick Matheus da Silveira Brito, Philippe Ladoux, Joseph Fabre and Benoit Sonier
Electronics 2024, 13(10), 1933; https://doi.org/10.3390/electronics13101933 - 15 May 2024
Abstract
DC railway electrification was deployed at the beginning of the 20th century in several countries in Europe. Today, this power system is no longer adapted to the demands of increased rail traffic. Due to the relatively low voltage level, the current consumed by
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DC railway electrification was deployed at the beginning of the 20th century in several countries in Europe. Today, this power system is no longer adapted to the demands of increased rail traffic. Due to the relatively low voltage level, the current consumed by the trains reaches several kAs. So, in the worst case, the locomotives cannot operate at their rated power due to the voltage drop along the contact line. Conventional solutions to reduce the voltage drop consist of increasing the cross-section of overhead lines or reducing the length of sectors by installing additional substations. Nevertheless, these solutions are expensive and not always feasible. The implementation of a Modular Battery Energy Storage System (MBESS) can be an alternative solution to reinforce the railway power supply. This paper first presents an MBESS based on elementary blocks associating Full-SiC Isolated DC-DC converter and battery racks. The electrical models of a railway sector and an elementary block are described, and simulations are performed considering real railroad traffic on two sectors of the French National Rail Network, electrified at 1.5 kV. The results show that the installation of an MBESS in the railway sector boosts the locomotive’s voltage while also increasing overall system efficiency.
Full article
(This article belongs to the Special Issue Railway Traction Power Supply, 2nd Edition)
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Open AccessArticle
A Multiplexing Optical Temperature Sensing System for Induction Motors Using Few-Mode Fiber Spatial Mode Diversity
by
Feng Liu, Tianle Gu and Weicheng Chen
Electronics 2024, 13(10), 1932; https://doi.org/10.3390/electronics13101932 - 15 May 2024
Abstract
Induction motors are widely applied in motor drive systems. Effective temperature monitoring is one of the keys to ensuring the reliability and optimal performance of the motors. Therefore, this paper introduces a multiplexed optical temperature sensing system for induction motors based on few-mode
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Induction motors are widely applied in motor drive systems. Effective temperature monitoring is one of the keys to ensuring the reliability and optimal performance of the motors. Therefore, this paper introduces a multiplexed optical temperature sensing system for induction motors based on few-mode fiber (FMF) spatial mode diversity. By using the spatial mode dimension of FMF, fiber Bragg grating (FBG) carried by different spatial modes of optical paths is embedded in different positions of the motor to realize multipoint synchronous multiplexing temperature monitoring. The paper establishes and demonstrates a photonic lantern-based mode division sensing system for motor temperature monitoring. As a proof of concept, the system demonstrates experiments in multiplexed temperature sensing for motor stators using the fundamental mode LP01 and high-order spatial modes LP11, LP21, and LP02. The FBG sensitivity carried by the above mode is 0.0107 nm/°C, 0.0106 nm/°C, 0.0097 nm/°C, and 0.0116 nm/°C, respectively. The dynamic temperature changes in the stator at different positions of the motor under speeds of 1k rpm, 1.5k rpm, 2k rpm with no load, 3 kg load, and 5 kg load, as well as at three specific speed–load combinations of 1.5k rpm_3 kg, 1k rpm_0kg, 2k rpm_5 kg and so on are measured, and the measured results of different spatial modes are compared and analyzed. The findings indicate that different spatial modes can accurately reflect temperature variations at various positions in motor stator winding.
Full article
(This article belongs to the Special Issue Sensing Technology and Intelligent Application)
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Open AccessArticle
Coordinated Reconfiguration with Energy Storage System for Load Restoration in Integrated Electric and Heating Systems
by
Ke Wang, Jing Wang, Pengfei Su and Song Zhang
Electronics 2024, 13(10), 1931; https://doi.org/10.3390/electronics13101931 - 15 May 2024
Abstract
Coordinated load restoration of integrated electric and heating systems (IEHSs) has become indispensable following natural disasters due to the increasingly relevant integration between power distribution systems (PDS) and district heating systems (DHS). In this paper, a coordinated reconfiguration with an energy storage system
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Coordinated load restoration of integrated electric and heating systems (IEHSs) has become indispensable following natural disasters due to the increasingly relevant integration between power distribution systems (PDS) and district heating systems (DHS). In this paper, a coordinated reconfiguration with an energy storage system is introduced to optimize load restoration in the aftermath of natural catastrophes. By modifying the DHS network topology, it is possible to maintain an uninterrupted energy supply in unfaulty zones by shifting heat loads among sources and adjusting the operation of coupled devices. Additionally, energy storage systems with rapid response times are implemented to enhance load restoration efficiency, especially when working in conjunction with multiple energy sources. Comprehensive case analyses have been systematically conducted to demonstrate the impact of coordinated reconfiguration with energy storage systems on improving load restoration.
Full article
(This article belongs to the Special Issue Hydrogen and Fuel Cells: Innovations and Challenges)
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Open AccessArticle
Enhanced Wild Horse Optimizer with Cauchy Mutation and Dynamic Random Search for Hyperspectral Image Band Selection
by
Tao Chen, Yue Sun, Huayue Chen and Wu Deng
Electronics 2024, 13(10), 1930; https://doi.org/10.3390/electronics13101930 - 15 May 2024
Abstract
The high dimensionality of hyperspectral images (HSIs) brings significant redundancy to data processing. Band selection (BS) is one of the most commonly used dimensionality reduction (DR) techniques, which eliminates redundant information between bands while retaining a subset of bands with a high information
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The high dimensionality of hyperspectral images (HSIs) brings significant redundancy to data processing. Band selection (BS) is one of the most commonly used dimensionality reduction (DR) techniques, which eliminates redundant information between bands while retaining a subset of bands with a high information content and low noise. The wild horse optimizer (WHO) is a novel metaheuristic algorithm widely used for its efficient search performance, yet it tends to become trapped in local optima during later iterations. To address these issues, an enhanced wild horse optimizer (IBSWHO) is proposed for HSI band selection in this paper. IBSWHO utilizes Sobol sequences to initialize the population, thereby increasing population diversity. It incorporates Cauchy mutation to perturb the population with a certain probability, enhancing the global search capability and avoiding local optima. Additionally, dynamic random search techniques are introduced to improve the algorithm search efficiency and expand the search space. The convergence of IBSWHO is verified on commonly used nonlinear test functions and compared with state-of-the-art optimization algorithms. Finally, experiments on three classic HSI datasets are conducted for HSI classification. The experimental results demonstrate that the band subset selected by IBSWHO achieves the best classification accuracy compared to conventional and state-of-the-art band selection methods, confirming the superiority of the proposed BS method.
Full article
(This article belongs to the Section Computer Science & Engineering)
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Open AccessArticle
Selecting the Best Permanent Magnet Synchronous Machine Design for Use in a Small Wind Turbine
by
Marcin Lefik, Anna Firych-Nowacka, Michal Lipian, Malgorzata Brzozowska and Tomasz Smaz
Electronics 2024, 13(10), 1929; https://doi.org/10.3390/electronics13101929 - 15 May 2024
Abstract
The article describes the selection of a permanent magnet synchronous machine design that could be implemented in a small wind turbine designed by the GUST student organization together with researchers working at the Technical University of Lodz. Based on measurements of the characteristics
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The article describes the selection of a permanent magnet synchronous machine design that could be implemented in a small wind turbine designed by the GUST student organization together with researchers working at the Technical University of Lodz. Based on measurements of the characteristics of available machines, eight initial designs of machines with different rotor designs were proposed. The size of the stator, the number of pairs of poles, and the dimensions of the magnets were used as initial parameters of the designed machines. The analysis was carried out about the K-index, the so-called index of benefits. The idea was to make the selected design as efficient as possible while keeping production costs and manufacturing time low. This paper describes how to select the best design of a permanent magnet synchronous generator intended to work with a small wind turbine. All generator parameters were selected keeping in mind the competition requirements, as the designed generator will be used in the author’s wind turbine. Based on the determined characteristics of the generator variants and the value of the K-index, a generator with a latent magnet rotor was selected as the best solution. The aforementioned K-index is a proprietary concept developed for the selection of the most suitable generator design. This paper did not use optimization methods; the analysis was only supported by the K-index.
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(This article belongs to the Special Issue Innovative Numerical Methods for Advanced Computation of Electromagnetic Devices and Microsystems)
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Open AccessArticle
A New Machine-Learning-Driven Grade-Point Average Prediction Approach for College Students Incorporating Psychological Evaluations in the Post-COVID-19 Era
by
Tiantian Zhang, Zhidan Zhong, Wentao Mao, Zhihui Zhang and Zhe Li
Electronics 2024, 13(10), 1928; https://doi.org/10.3390/electronics13101928 - 15 May 2024
Abstract
With the rapid development of artificial intelligence in recent years, intelligent evaluation of college students’ growth by means of the monitoring data from training processes is becoming a promising technique in the field intelligent education. Current studies, however, tend to utilize course grades,
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With the rapid development of artificial intelligence in recent years, intelligent evaluation of college students’ growth by means of the monitoring data from training processes is becoming a promising technique in the field intelligent education. Current studies, however, tend to utilize course grades, which are objective, to predict students’ grade-point averages (GPAs), but usually neglect subjective factors like psychological resilience. To solve this problem, this paper takes mechanical engineering as the research object, and proposes a new machine-learning-driven GPA prediction approach to evaluate the academic performance of engineering students by incorporating psychological evaluation data into basic course scores. Specifically, this paper adopts SCL-90 psychological assessment data collected in the freshman year, including key mental health indicators such as somatization, depression, hostility, and interpersonal sensitivity indicators, as well as professional basic course scores, including mechanical principles, mechanical design, advanced mathematics, and engineering drawing. Four representative machine learning algorithms, Support Vector Machine (SVM), CNN-CBAM, Extreme Gradient Boosting (XGBoost) and Classification and Regression Tree (CART) that include deep and shallow models, respectively, are then employed to build a classification model for GPA prediction. This paper designs a validation experiment by tracking 229 students from the 2020 class from the School of Mechanical and Electrical Engineering of Henan University of Science and Technology, China. The students’ academic performance in senior grades is divided into five classes to use as the prediction labels. It is verified that psychological data and course data can be effectively integrated into GPA prediction for college students, with an accuracy rate of 83.64%. Meanwhile, this paper also reveals that anxiety indicators in the psychological assessment data have the greatest impact on college students’ academic performance, followed by interpersonal sensitivity. The experimental results also show that, for predicting junior year GPAs, psychological factors play more important role than they do in predicting sophomore GPAs. Suggestions are therefore given: the current practice in existing undergraduate teaching, i.e., only conducting psychological assessments in the initial freshman year, should be updated by introducing follow-up psychological assessments in each academic year.
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(This article belongs to the Special Issue Innovations and Challenges of Higher Education Institutions in the Post-COVID-19 Era)
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Open AccessArticle
KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph
by
Jiquan Peng, Jibing Gong, Chao Zhou, Qian Zang, Xiaohan Fang, Kailun Yang and Jing Yu
Electronics 2024, 13(10), 1927; https://doi.org/10.3390/electronics13101927 - 15 May 2024
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Traditional collaborative filtering (CF)-based recommendation systems are often challenged by data sparsity. The recent research has recognized the potential of integrating new information sources, such as knowledge graphs, to address this issue. However, a common drawback is the neglect of the interplay between
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Traditional collaborative filtering (CF)-based recommendation systems are often challenged by data sparsity. The recent research has recognized the potential of integrating new information sources, such as knowledge graphs, to address this issue. However, a common drawback is the neglect of the interplay between user–item interaction data and knowledge graph information, resulting in insufficient model performance due to coarse-grained feature fusion. To bridge this gap, in this paper, we propose a novel graph neural network (GNN) model called KGCFRec, which leverages both Knowledge Graph and user–item Collaborative Filtering information for an enhanced Recommender system. KGCFRec employs a dual-channel information propagation and aggregation mechanism to generate distinct representations for the collaborative knowledge graph and the user–item interaction graph. This is followed by an attention mechanism that adaptively fuses the knowledge graph with collaborative information, thereby refining the representations and narrowing the gap between them. The experiments conducted on three real-world datasets demonstrate that KGCFRec outperforms state-of-the-art methods. These promising results underscore the capability of KGCFRec to enhance recommendation accuracy by integrating knowledge graph information.
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