Electronics

19 pages, 21217 KiB

Open AccessArticle

Air Traffic Flow Prediction in Aviation Networks Using a Multi-Dimensional Spatiotemporal Framework

by Cong Wu, Hui Ding, Zhongwang Fu and Ning Sun

Electronics 2024, 13(19), 3803; https://doi.org/10.3390/electronics13193803 (registering DOI) - 25 Sep 2024

A novel, multi-dimensional, spatiotemporal prediction framework is proposed to enhance air traffic flow prediction in increasingly complex aviation networks. This framework incorporates graph convolutional networks (GCNs) with multi-dimensional Long Short-Term Memory (LSTM) networks and multi-scale, temporal convolution, employing an attention mechanism to effectively [...] Read more.

A novel, multi-dimensional, spatiotemporal prediction framework is proposed to enhance air traffic flow prediction in increasingly complex aviation networks. This framework incorporates graph convolutional networks (GCNs) with multi-dimensional Long Short-Term Memory (LSTM) networks and multi-scale, temporal convolution, employing an attention mechanism to effectively capture spatiotemporal dependencies. By addressing irregular topologies and dynamic temporal trends, the framework models local air traffic patterns with improved accuracy. The experimental results demonstrate significant predictive accuracy improvements over traditional methods, particularly in accounting for the complex nature of air traffic flows. The model’s scalability and adaptability extend its application to various aviation networks, encompassing all airspace units within three local networks, rather than focusing solely on airport traffic. These findings contribute to the development of more intelligent, accurate, and adaptive air traffic management systems, ultimately enhancing both operational efficiency and safety. Full article

(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)

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8 pages, 3336 KiB

Open AccessCommunication

Analysis of Influencing Factors on Multilevel Storage Performance in Phase-Change Random Access Memory

by Zhiyu Wang and Daolin Cai

Electronics 2024, 13(19), 3802; https://doi.org/10.3390/electronics13193802 (registering DOI) - 25 Sep 2024

Abstract

In response to the growing demand for advanced memory technologies, this study investigates a 4 Mb phase-change memory (PCRAM) chip employing a carbon-doped Ge₂Sb₂Te₅ (C-GST) dielectric material to achieve multistage storage. The Partial-RESET programming and verification (P&V) method [...] Read more.

In response to the growing demand for advanced memory technologies, this study investigates a 4 Mb phase-change memory (PCRAM) chip employing a carbon-doped Ge₂Sb₂Te₅ (C-GST) dielectric material to achieve multistage storage. The Partial-RESET programming and verification (P&V) method was utilized to effectively create intermediate-resistance states, facilitating multilevel storage. The study focuses on optimizing the key parameters affecting the P&V method to enhance the precision and efficiency of reaching intermediate resistance values. Through comprehensive experimentation on the PCRAM array, this work evaluates the performance of multilevel storage, providing insights into the potential for scalable, high-density memory applications. Full article

(This article belongs to the Special Issue Advanced CMOS Devices and Applications, 2nd Edition)

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18 pages, 1142 KiB

Open AccessArticle

Design of Environmental Sensor Board for Energy Harvesting: Integration of Conventional and Eco-Friendly Sensors with Power Generation Sources

by Giovanni Landi, Guerino Avallone, Carlo Barone and Sergio Pagano

Electronics 2024, 13(19), 3801; https://doi.org/10.3390/electronics13193801 (registering DOI) - 25 Sep 2024

Abstract

A platform for indoor monitoring inside buildings, integrating both conventional and environmentally friendly devices with energy-harvesting sources, is proposed. Biomaterials such as gelatin and chitosan, derived from renewable resources, have been utilized to fabricate hydrogel and active layers for sensors and supercapacitors. These [...] Read more.

A platform for indoor monitoring inside buildings, integrating both conventional and environmentally friendly devices with energy-harvesting sources, is proposed. Biomaterials such as gelatin and chitosan, derived from renewable resources, have been utilized to fabricate hydrogel and active layers for sensors and supercapacitors. These devices enhance the environmental profile of the proposed solution by employing sustainable materials and optimizing energy consumption. The developed electronic board prototype provides a versatile platform for testing various sensor configurations while accommodating different energy-harvesting sources. The article details the design of an energy harvesting system for indoor monitoring, covering various aspects regarding energy sources, power management circuits, and low-power microcontroller units. It examines energy storage devices and sensors, including both eco-friendly and commercial ones, as well as radio transceivers with different communication technologies. Additionally, an energy analysis to evaluate the performance and energy efficiency of the platform is presented. Full article

(This article belongs to the Section Circuit and Signal Processing)

18 pages, 743 KiB

Open AccessArticle

Multi-Objective Optimal Configuration of Hydrogen Fuel Cell-Based Multi-Energy Microgrid System Considering Exergy

by Ji Li, Lei Xu, Yang Kou, Weile Liang, Yunshan Wang and Zhi Yuan

Electronics 2024, 13(19), 3800; https://doi.org/10.3390/electronics13193800 (registering DOI) - 25 Sep 2024

Abstract

Relying solely on electrical energy storage for energy regulation makes it difficult to provide a stable and efficient energy supply for microgrid systems currently. Additionally, the economic cost of microgrids and the rate of energy use present a challenge that must be addressed. [...] Read more.

Relying solely on electrical energy storage for energy regulation makes it difficult to provide a stable and efficient energy supply for microgrid systems currently. Additionally, the economic cost of microgrids and the rate of energy use present a challenge that must be addressed. A strategy for allocating capacity for multi-energy microgrids that takes energy efficiency and hydrogen energy into account is offered as a solution to the aforementioned issues. Initially, the construction of the multi-energy microgrid system takes into account the thermoelectric coupling properties of hydrogen energy devices. Second, the system’s energy utilization level is measured using the exergy efficiency analysis. Next, the multi-objective capacity optimization allocation model of the multi-energy microgrid system is established, with the exergy efficiency and system economic cost serving as the objective functions. Lastly, the multi-objective model is solved using the ε-constraint approach to find the Pareto frontier, and Technique for Order Preference by Similarity to an Ideal Solution is employed for decision-making. The example results demonstrate that, when compared to a traditional microgrid using electric energy storage, the proposed model can effectively lower the system’s economic cost and improve exergy efficiency. Additionally, multi-objective capacity optimization can be used to strike a balance between exergy efficiency and the system’s economic cost. For relevant studies on the capacity allocation of multi-energy microgrids, this work can be a helpful resource. Full article

(This article belongs to the Special Issue Hydrogen and Fuel Cells: Innovations and Challenges)

17 pages, 4088 KiB

Open AccessArticle

A Blockchain-Based Security Framework for East-West Interface of SDN

by Hamad Alrashede, Fathy Eassa, Abdullah Marish Ali, Faisal Albalwy and Hosam Aljihani

Electronics 2024, 13(19), 3799; https://doi.org/10.3390/electronics13193799 - 25 Sep 2024

Abstract

Software-Defined Networking (SDN) has emerged as a revolutionary architecture in computer networks, offering comprehensive network control and monitoring capabilities. However, securing the east–west interface, which is crucial for communication between distributed SDN controllers, remains a significant challenge. This study proposes a novel blockchain-based [...] Read more.

Software-Defined Networking (SDN) has emerged as a revolutionary architecture in computer networks, offering comprehensive network control and monitoring capabilities. However, securing the east–west interface, which is crucial for communication between distributed SDN controllers, remains a significant challenge. This study proposes a novel blockchain-based security framework that integrates Ethereum technology with customized blockchain algorithms for authentication, encryption, and access control. The framework introduces decentralized mechanisms to protect against diverse attacks, including false data injection, man-in-the-middle (MitM), and unauthorized access. Experimental results demonstrate the effectiveness of this framework in securing distributed controllers while maintaining high network performance and low latency, paving the way for more resilient and trustworthy SDN infrastructures. Full article

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20 pages, 27260 KiB

Open AccessArticle

An Improved Product Defect Detection Method Combining Centroid Distance and Textural Information

by Haorong Wu, Xiaoxiao Li, Fuchun Sun, Limin Huang, Tao Yang, Yuechao Bian and Qiurong Lv

Electronics 2024, 13(19), 3798; https://doi.org/10.3390/electronics13193798 - 25 Sep 2024

Abstract

In order to solve the problems of a high mismatching rate and being easily affected by noise and gray transformation, an improved product defect detection method combining centroid distance and textural information is proposed in this paper. Based on image preprocessing, the improved [...] Read more.

In order to solve the problems of a high mismatching rate and being easily affected by noise and gray transformation, an improved product defect detection method combining centroid distance and textural information is proposed in this paper. Based on image preprocessing, the improved fuzzy C-means clustering method is used to extract the closed contour features. Then, the contour center distance description operator is used for bidirectional matching, and a robust coarse matching contour pair is obtained. After the coarse matching contour pair is screened, the refined matching result is obtained by using the improved local binary pattern operator. Finally, by comparing whether the number of fine matching pairs is consistent with the number of template outlines, the detection of good and bad industrial products is realized, and the closed contour extraction experiment, the anti-rotation matching experiment, the anti-gray difference matching experiment, and the defect detection experiment of three different products are designed. The experimental results show that the improved product defect detection method has good performance in relation to anti-rotation transformation and anti-gray difference, the detection accuracy can reach more than 90%, and the detection time is up to 362.6 ms, which can meet the requirements of industrial real-time detection. Full article

(This article belongs to the Special Issue Digital Twins of Complex Systems: Data, Modeling, Algorithm and Application)

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21 pages, 957 KiB

Open AccessArticle

Social Trust Confirmation-Based Selfish Node Detection Algorithm in Socially Aware Networks

by Xiaowen Chen, Ying Rao, Zenggang Xiong, Yuan Li, Xuemin Zhang, Delin Hou, Qiangqiang Lou and Jing Li

Electronics 2024, 13(19), 3797; https://doi.org/10.3390/electronics13193797 - 25 Sep 2024

Abstract

Nodes in socially aware networks (SANs) may act selfishly on individual bases due to resource constraints and socially selfish behavior arising from the social preferences of nodes. In response to such selfish behaviors exhibited by nodes, this paper proposes a social trust confirmation-based [...] Read more.

Nodes in socially aware networks (SANs) may act selfishly on individual bases due to resource constraints and socially selfish behavior arising from the social preferences of nodes. In response to such selfish behaviors exhibited by nodes, this paper proposes a social trust confirmation-based selfish node detection algorithm (STCDA). This algorithm first utilizes a subjective forwarding willingness detection mechanism to discern selfishness. If a node’s energy is insufficient or its message rejection rate is too high—that is, the node cannot or is unwilling to forward messages—it indicates that the node is selfish. Otherwise, it is evaluated more thoroughly through the node’s social trust detection mechanisms. It calculates the social trust level of nodes based on the benefits of forwarding messages, thereby distinguishing between individually selfish nodes and socially selfish nodes in the network. If further evaluation is needed, the final judgment will be made using the message confirmation feedback detection mechanism. This checks the message information forwarded by nodes in the network. If nodes fail to forward messages after receiving them—excluding reasons such as message expiration or temporary insufficient cache space—it indicates that the nodes are selfish. Results from experimental simulations show that this algorithm performs better than traditional algorithms. Under conditions of 80% selfish nodes, a message TTL of 300 min, and 10 MB of cache space, it improves the message delivery rate by 5.87% and reduces the average delay by 6.2% compared to the existing comprehensive confirmation-based selfish node detection algorithm. Full article

(This article belongs to the Section Computer Science & Engineering)

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16 pages, 3278 KiB

Open AccessArticle

Real-Time Wild Horse Crossing Event Detection Using Roadside LiDAR

by Ziru Wang, Hao Xu, Fei Guan and Zhihui Chen

Electronics 2024, 13(19), 3796; https://doi.org/10.3390/electronics13193796 - 25 Sep 2024

Abstract

Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging [...] Read more.

Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging (LiDAR) technology. The developed LiDAR data processing procedure includes background filtering, object clustering, object tracking, and object classification. Considering that the background information collected by LiDAR may change over time, an automatic background filtering method that updates the background in real-time has been developed to subtract the background effectively over time. After a standard object clustering and a fast object tracking method, eight features were extracted from the clustering group, including a feature developed to specifically identify wild horses, and a vertical point distribution was used to describe the objects. The classification results of the four classifiers were compared, and the experiments showed that the support vector machine (SVM) had more reliable results. The field test results showed that the developed method could accurately detect a wild horse within the detection range of LiDAR. The wild horse crossing information can warn drivers about the risks of wild horse–vehicle collisions in real-time. Full article

(This article belongs to the Special Issue Advances, Applications, and Challenges of Deep Learning-Aided Computer Vision and Pattern Recognition in Intelligent Transportation)

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26 pages, 7190 KiB

Open AccessArticle

Takagi–Sugeno Fuzzy Parallel Distributed Compensation Control for Low-Frequency Oscillation Suppression in Wind Energy-Penetrated Power Systems

by Ruikai Song, Sunhua Huang, Linyun Xiong, Yang Zhou, Tongkun Li, Pizheng Tan and Zhaozun Sun

Electronics 2024, 13(19), 3795; https://doi.org/10.3390/electronics13193795 - 25 Sep 2024

Abstract

In this paper, a Takagi–Sugeno fuzzy parallel distributed compensation control (TS-PDCC) is proposed for low-frequency oscillation (LFO) suppression in wind energy-penetrated power systems. Firstly, the fuzzy C-mean algorithm (FCMA) is applied to cluster the daily average wind speed of the wind farm, and [...] Read more.

In this paper, a Takagi–Sugeno fuzzy parallel distributed compensation control (TS-PDCC) is proposed for low-frequency oscillation (LFO) suppression in wind energy-penetrated power systems. Firstly, the fuzzy C-mean algorithm (FCMA) is applied to cluster the daily average wind speed of the wind farm, and the obtained wind speed clustering center is used as the premise variable of TS-PDCC, which increases the freedom of parameter setting of the TS fuzzy model and is closer to the actual working environment. Secondly, based on the TS fuzzy model, the TS-PDCC is designed to adjust the active power output of the wind turbine for LFO suppression. To facilitate the computation of controller parameters, the stability conditions are transformed into a set of Linear Matrix Inequalities (LMIs) via the Schur complement. Subsequently, a Lyapunov function is designed to verify the stability of the wind energy-penetrated power system and obtain the parameter ranges. Simulation cases are conducted to verify the validity and superior performance of the proposed TS-PDCC under different operating conditions. Full article

(This article belongs to the Section Power Electronics)

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16 pages, 3654 KiB

Open AccessArticle

A Graph Similarity Algorithm Based on Graph Partitioning and Attention Mechanism

by Fengyu Miao, Xiuzhuang Zhou, Shungen Xiao and Shiliang Zhang

Electronics 2024, 13(19), 3794; https://doi.org/10.3390/electronics13193794 - 25 Sep 2024

Abstract

In recent years, graph similarity algorithms have been extensively developed based on neural networks. However, with an increase in the node count in graphs, these models either suffer from a reduced representation ability or face a significant increase in the computational cost. To [...] Read more.

In recent years, graph similarity algorithms have been extensively developed based on neural networks. However, with an increase in the node count in graphs, these models either suffer from a reduced representation ability or face a significant increase in the computational cost. To address this issue, a graph similarity algorithm based on graph partitioning and attention mechanisms was proposed in this study. Our method first divided each input graph into the subgraphs to directly extract the local structural features. The residual graph convolution and multihead self-attention mechanisms were employed to generate node embeddings for each subgraph, extract the feature information from the nodes, and regenerate the subgraph embeddings using varying attention weights. Initially, rough cosine similarity calculations were performed on all subgraph pairs from the two sets of subgraphs, with highly similar pairs selected for precise similarity computation. These results were then integrated into the similarity score for the input graph. The experimental results indicated that the proposed learning algorithm outperformed the traditional algorithms and similar computing models in terms of graph similarity computation performance. Full article

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25 pages, 21622 KiB

Open AccessArticle

Advanced Design and Implementation of a 2-Channel, Multi-Functional Therapeutic Electrical Stimulator

by Rujira Lakatem, Suttipong Boontaklang and Chow Chompoo-inwai

Electronics 2024, 13(19), 3793; https://doi.org/10.3390/electronics13193793 - 24 Sep 2024

Abstract

This research introduces the design, implementation, and rigorous evaluation of a novel 2-channel, multi-functional therapeutic electrical stimulator, meticulously engineered to meet the stringent demands of contemporary clinical applications. The device integrates a high-speed R-2R ladder DAC and a sophisticated pulse generator unit, capable [...] Read more.

This research introduces the design, implementation, and rigorous evaluation of a novel 2-channel, multi-functional therapeutic electrical stimulator, meticulously engineered to meet the stringent demands of contemporary clinical applications. The device integrates a high-speed R-2R ladder DAC and a sophisticated pulse generator unit, capable of producing twelve essential current waveforms with fully adjustable parameters, including pulse amplitude, pulse duration, and pulse repetitive frequency. The proposed driving stage unit ensures precise voltage-to-current conversion, delivering stable and accurate output currents even under varying load conditions, which effectively simulate the diverse impedance characteristics of human tissue. Extensive testing confirmed the compliance with international medical standards, notably IEC 60601-1, IEC 60601-1-2, and IEC 60601-2-10. The experimental results underscore the device’s consistent operation within prescribed safety and performance thresholds, with all deviations in pulse parameters remaining well below the permissible limits. Furthermore, the proposed electrical stimulator demonstrated exceptional stability across variable load conditions, as evidenced by minimal amplitude errors and high correlation between waveform characteristics. These findings highlight the proposed device’s robustness and its potential as a versatile tool for a wide range of therapeutic applications, including pain management, muscle stimulation, and nerve rehabilitation, thus marking a significant advancement in the field of therapeutic electrical stimulation. Full article

(This article belongs to the Section Industrial Electronics)

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21 pages, 4710 KiB

Open AccessArticle

TWPT: Through-Wall Position Detection and Tracking System Using IR-UWB Radar Utilizing Kalman Filter-Based Clutter Reduction and CLEAN Algorithm

by Jinlong Zhang, Xiaochao Dang and Zhanjun Hao

Electronics 2024, 13(19), 3792; https://doi.org/10.3390/electronics13193792 - 24 Sep 2024

Abstract

Against the backdrop of rapidly advancing Artificial Intelligence of Things (AIOT) and sensing technologies, there is a growing demand for indoor location-based services (LBSs). This paper proposes a through-the-wall localization and tracking (TWPT) system based on an improved ultra-wideband (IR-UWB) radar to achieve [...] Read more.

Against the backdrop of rapidly advancing Artificial Intelligence of Things (AIOT) and sensing technologies, there is a growing demand for indoor location-based services (LBSs). This paper proposes a through-the-wall localization and tracking (TWPT) system based on an improved ultra-wideband (IR-UWB) radar to achieve more accurate localization of indoor moving targets. The TWPT system overcomes the limitations of traditional localization methods, such as multipath effects and environmental interference, using the high penetration and high accuracy of IR-UWB radar based on multi-sensor fusion technology. In the study, an improved Kalman filter (KF) algorithm is used for clutter reduction, while the CLEAN algorithm, combined with a compensation mechanism, is utilized to increase the target detection probability. Finally, a three-point localization estimation algorithm based on multi-IR-UWB radar is applied for the precise position and trajectory estimation of the target. Experimental validation indicates the TWPT system achieves a high positioning accuracy of 96.91%, with a root mean square error (RMSE) of 0.082 m and a Maximum Position Error (MPE) of 0.259 m. This study provides a highly accurate and precise solution for indoor TWPT based on IR-UWB radar. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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25 pages, 7956 KiB

Open AccessArticle

A Novel Feedforward Scheme for Enhancing Dynamic Performance of Vector-Controlled Dual Active Bridge Converter with Dual Phase Shift Modulation for Fast Battery Charging Systems

by Armel Asongu Nkembi, Danilo Santoro, Fawad Ahmad, Iñigo Kortabarria, Paolo Cova, Emilio Sacchi and Nicola Delmonte

Electronics 2024, 13(19), 3791; https://doi.org/10.3390/electronics13193791 - 24 Sep 2024

Abstract

This paper proposes a novel feedforward control scheme to achieve a very smooth transition from Constant Current (CC) to Constant Voltage (CV) charging modes, the commonly used method for electric vehicle charging applications. Furthermore, a three-loop model-independent Linear Active Disturbance Rejection Control (LADRC)-based [...] Read more.

This paper proposes a novel feedforward control scheme to achieve a very smooth transition from Constant Current (CC) to Constant Voltage (CV) charging modes, the commonly used method for electric vehicle charging applications. Furthermore, a three-loop model-independent Linear Active Disturbance Rejection Control (LADRC)-based system is proposed, replacing the traditional two-loop Proportional-Integral (PI) control system. The extra loop performs a decoupled dq vector control of the inductor current, which is typically not used in single-phase Dual Active Bridge (DAB) systems. This additional loop not only facilitates the optimal determination of both internal and external phase shift angles of a Dual-Phase Shift (DPS) modulator but also lowers the peak input current of the converter, allowing for lower-rated switches. Numerical simulations using MATLAB/Simulink demonstrate the robustness of the proposed control strategy against both input voltage disturbances and load disturbances during the transition from CC to CV charging modes. Hence, the dynamic performance of the charging system is significantly improved with minimal controller effort. Full article

(This article belongs to the Special Issue Advanced DC-DC Converter Topology Design, Control, Application)

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19 pages, 17909 KiB

Open AccessArticle

Nighttime Pothole Detection: A Benchmark

by Min Ling, Quanjun Shi, Xin Zhao, Wenzheng Chen, Wei Wei, Kai Xiao, Zeyu Yang, Hao Zhang, Shuiwang Li, Chenchen Lu and Yufan Zeng

Electronics 2024, 13(19), 3790; https://doi.org/10.3390/electronics13193790 - 24 Sep 2024

Abstract

In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can [...] Read more.

In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can easily lead to vehicle skidding, loss of control, and even traffic accidents, especially when water has pooled in or submerged the potholes. Therefore, the detection and recognition of road potholes can significantly reduce vehicle damage and the incidence of safety incidents. However, research on road pothole detection lacks high-quality annotated datasets, particularly under low-light conditions at night. To address this issue, this study introduces a novel Nighttime Pothole Dataset (NPD), independently collected and comprising 3831 images that capture diverse scene variations. The construction of this dataset aims to counteract the insufficiency of existing data resources and strives to provide a richer and more realistic benchmark. Additionally, we develop a baseline detector, termed WT-YOLOv8, for the proposed dataset, based on YOLOv8. We also evaluate the performance of the improved WT-YOLOv8 method and eight state-of-the-art object detection methods on the NPD and the COCO dataset. The experimental results on the NPD demonstrate that WT-YOLOv8 achieves a 2.3% improvement in mean Average Precision (mAP) over YOLOv8. In terms of the key metrics—[email protected] and [email protected]—it shows enhancements of 1.5% and 2.8%, respectively, compared to YOLOv8. The experimental results provide valuable insights into each method’s strengths and weaknesses under low-light conditions. This analysis highlights the importance of a specialized dataset for nighttime pothole detection and shows variations in accuracy and robustness among methods, emphasizing the need for improved nighttime pothole detection techniques. The introduction of the NPD is expected to stimulate further research, encouraging the development of advanced algorithms for nighttime pothole detection, ultimately leading to more flexible and reliable road maintenance and road safety. Full article

(This article belongs to the Special Issue New Trends in AI-Assisted Computer Vision)

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17 pages, 4501 KiB

Open AccessArticle

Model Assisted Extended State Observer-Based Deadbeat Predictive Current Control for Modular Multilevel Converter

by Xiaowei Yang, Yongqiang Zhang, Yang Liu and Sheng Jiang

Electronics 2024, 13(19), 3789; https://doi.org/10.3390/electronics13193789 - 24 Sep 2024

Abstract

Aiming at the issues of control delay and circuit parameter mismatch in three-phase modular multilevel converters (MMCs), this paper proposes a model assisted extended state observer-based deadbeat predictive current control (MAESO-based DPCC) strategy to regulate the AC-side current and internal circulating current. The [...] Read more.

Aiming at the issues of control delay and circuit parameter mismatch in three-phase modular multilevel converters (MMCs), this paper proposes a model assisted extended state observer-based deadbeat predictive current control (MAESO-based DPCC) strategy to regulate the AC-side current and internal circulating current. The model assisted ESO (MAESO) is employed to estimate the predicted values of the d- and q-axis components of the AC-side current, the internal circulating current, and system disturbance caused by the other certain and uncertain factors (including circuit parameter changes) of MMC at the time instant k + 1, and the required control input at the time instant k + 1 is then calculated based on the deadbeat control principle. The proposed control strategy not only maintains excellent steady-state performance and fast dynamic response characteristics similar to those of the traditional deadbeat predictive current control (DPCC) strategy but also has stronger robustness in the case of circuit parameter changes. The proposed control strategy was ultimately compared with the traditional DPCC strategy via experiments, and the experimental results verify the feasibility and effectiveness of the proposed control strategy. Full article

(This article belongs to the Special Issue Energy Management and Control in Emerging Electrified Transportation Systems)

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17 pages, 1334 KiB

Open AccessArticle

A Detailed Analysis and Gain Derivation of Reconfigurable Voltage Rectifier-Based LLC Converter

by Fahad Alaql, Walied Alfraidi, Abdullah Alhatlani, Abdullrahman A. Al-Shamma’a, Hassan M. Hussein Farh and Ahmed Allehyani

Electronics 2024, 13(19), 3788; https://doi.org/10.3390/electronics13193788 - 24 Sep 2024

Abstract

In this paper, a complete analysis of an LLC resonant converter with a customized rectifier structure is presented. The converter is intended for wide, low-input, high-output voltage DC bus applications. The performance of the converter is assessed using comprehensive time-domain and fundamental harmonic [...] Read more.

In this paper, a complete analysis of an LLC resonant converter with a customized rectifier structure is presented. The converter is intended for wide, low-input, high-output voltage DC bus applications. The performance of the converter is assessed using comprehensive time-domain and fundamental harmonic approximation (FHA), which demonstrates its capacity to operate across an ample range of voltages by precisely adjusting the rectifier structure. The converter’s capability is illustrated by deriving and discussing detailed mode operation, steady-state analysis, and DC gain equations. In order to verify the theoretical analysis, a prototype with a power output of 250 watts is constructed and subjected to testing. The results of the testing demonstrate that the converter is both feasible and effective. The experimental findings illustrate its capacity to manage vast voltage ranges while upholding high efficiency. In addition, the converter utilizes a frequency switching modulation (FSM) to connect with a photovoltaic (PV) panel and control the high output voltage. This demonstrates its adaptability in renewable energy applications. The validation is in accordance with theoretical predictions, demonstrating the converter’s high-efficiency performance and versatility. Full article

(This article belongs to the Special Issue Power Electronics and Renewable Energy System)

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22 pages, 494 KiB

Open AccessArticle

Vehicle Trajectory Prediction Based on Adaptive Edge Generation

by He Ren and Yanyan Zhang

Electronics 2024, 13(18), 3787; https://doi.org/10.3390/electronics13183787 - 23 Sep 2024

Abstract

With the rapid evolution of intelligent driving technology, vehicle trajectory prediction has become a pivotal technique for enhancing road safety and traffic efficiency. In this domain, high-definition vector maps and graph neural networks (GNNs) play a vital role, supporting precise vehicle positioning and [...] Read more.

With the rapid evolution of intelligent driving technology, vehicle trajectory prediction has become a pivotal technique for enhancing road safety and traffic efficiency. In this domain, high-definition vector maps and graph neural networks (GNNs) play a vital role, supporting precise vehicle positioning and optimizing path planning, thereby improving the performance of intelligent driving systems. However, high-definition vector maps and traditional GNNs still encounter several challenges in trajectory prediction, such as high computational resource demands, long training times, and limited modeling capabilities for dynamic traffic environments and complex interactions. To address these challenges, this paper proposes an adaptive edge generator method, this method dynamically constructs and optimizes the connections between nodes in the GNN architecture, effectively enhancing the accuracy and efficiency of trajectory prediction. Specifically, we classify nodes into dynamic and static nodes based on their attributes, and devise differentiated edge construction strategies accordingly. For dynamic nodes, we introduce a relative angle factor, enabling the attention model to comprehensively consider the distance and intersection status between nodes, resulting in more accurate computation of edge weights. For static nodes, we utilize a length threshold to assess the feasibility of establishing connections between vehicles and lane lines, determining whether a connection should be established. Through this approach, we successfully reduce the algorithmic complexity, increase computational speed, and maintain high trajectory prediction accuracy. Tests on the Argoverse motion prediction dataset demonstrate that trajectory prediction utilizing the adaptive edge generator achieves an average displacement error (ADE) of 0.6681, a final displacement error (FDE) of 0.9864, and a miss rate (MR) of 0.0952. Furthermore, the model parameters are significantly reduced, validating the effectiveness of the proposed vehicle trajectory prediction method based on the adaptive edge generator. Full article

(This article belongs to the Section Artificial Intelligence)

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23 pages, 1115 KiB

Open AccessArticle

A Smart Contract Vulnerability Detection Method Based on Heterogeneous Contract Semantic Graphs and Pre-Training Techniques

by Jie Zhang, Gehao Lu and Jia Yu

Electronics 2024, 13(18), 3786; https://doi.org/10.3390/electronics13183786 - 23 Sep 2024

Abstract

The use of smart contracts in areas such as finance, supply chain management, and the Internet of Things has significantly advanced blockchain technology. However, once deployed on the blockchain, smart contracts cannot be modified or revoked. Any vulnerabilities can lead to severe economic [...] Read more.

The use of smart contracts in areas such as finance, supply chain management, and the Internet of Things has significantly advanced blockchain technology. However, once deployed on the blockchain, smart contracts cannot be modified or revoked. Any vulnerabilities can lead to severe economic losses and data breaches, making pre-deployment vulnerability detection critically important. Traditional smart contract vulnerability detection methods suffer from low accuracy and limited reusability across different scenarios. To enhance detection capabilities, this paper proposes a smart contract vulnerability detection method based on heterogeneous contract semantic graphs and pre-training techniques. Compared to the conventional graph structures used in existing methods, heterogeneous contract semantic graphs contain richer contract information. By integrating these with pre-trained models, our method exhibits stronger vulnerability capture and generalization capabilities. Experimental results show that this method has improved the accuracy, recall, precision, and F1 value in the detection of four widely existing and harmful smart contract vulnerabilities compared with existing methods, which greatly improves the detection ability of smart contract vulnerabilities. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)

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12 pages, 4318 KiB

Open AccessArticle

Contact State Recognition for Dual Peg-in-Hole Assembly of Tightly Coupled Dual Manipulator

by Jiawei Zhang, Chengchao Bai, Jifeng Guo, Zhengai Cheng and Ying Chen

Electronics 2024, 13(18), 3785; https://doi.org/10.3390/electronics13183785 - 23 Sep 2024

Abstract

Contact state recognition is a critical technology for enhancing the robustness of robotic assembly tasks. There have been many studies on contact state recognition for single-manipulator, single peg-in-hole assembly tasks. However, as the number of pegs and holes increases, the contact state becomes [...] Read more.

Contact state recognition is a critical technology for enhancing the robustness of robotic assembly tasks. There have been many studies on contact state recognition for single-manipulator, single peg-in-hole assembly tasks. However, as the number of pegs and holes increases, the contact state becomes significantly more complex. Additionally, when a tightly coupled multi-manipulator is required, the estimation errors in the contact forces between pegs and holes make contact state recognition challenging. The current state recognition methods have not been tested in such tasks. This paper tested Support Vector Machine (SVM) and several neural network models on these tasks and analyzed the recognition accuracy, precision, recall, and F1 score. An ablation experiment was carried out to test the contributions of force, image, and position to the recognition performance. The experimental results show that SVM has better performance than the neural network models. However, when the size of the dataset is limited, SVM still faces generalization issues. By applying heuristic action, this paper proposes a two-stage recognition strategy that can improve the recognition success rate of the SVM. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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