Electronics

22 pages, 2108 KB

Open AccessArticle

Comprehensive Parameter Optimization of Composite Harmonic Injection for Capacitor Voltage Fluctuation Suppression of MMC

by Tan Li, Yingxin Wang, Bin Yuan and Yu Meng

Electronics 2026, 15(2), 359; https://doi.org/10.3390/electronics15020359 - 13 Jan 2026

Abstract

Modular multilevel converter (MMC) is widely employed in high-voltage direct current (HVDC) systems for the long-distance renewable energy transmission, where the larger submodule (SM) capacitors significantly increase its size, weight and cost. Conventional capacitor voltage fluctuation suppression methods, such as composite harmonic injection [...] Read more.

Modular multilevel converter (MMC) is widely employed in high-voltage direct current (HVDC) systems for the long-distance renewable energy transmission, where the larger submodule (SM) capacitors significantly increase its size, weight and cost. Conventional capacitor voltage fluctuation suppression methods, such as composite harmonic injection (CHI) strategies, can achieve lightweight MMC. However, these approaches often neglect the dynamic constraints between harmonic injection parameters and their coupled effect on modulation wave, which not only leads to suboptimal global solutions but also increases the risk of system overshoot. Therefore, this paper proposes a comprehensive CHI parameters optimization method to minimize capacitor voltage fluctuations, thereby allowing for a smaller SM capacitor. First, the analytical expression of SM average capacitor voltage is developed, incorporating the injected second-order harmonic circulating current and third-order harmonic voltage. On this basis, an objective function is defined to minimize the sum of the fundamental and second-order harmonic components of the average capacitor voltage, with the harmonic injection parameters and modulation index as optimization variables. Then, these parameters are optimized using a particle swarm optimization (PSO) algorithm, where their constraints are set to prevent modulation wave overshoot and additional power loss. Finally, the optimization method is validated through a ±500 kV, 1500 MW MMC-HVDC system under various power conditions in PSCAD/EMTDC (version 4.6.3). In addition, simulation results demonstrate that the proposed method can achieve a 13.33% greater reduction in SM capacitance value compared to conventional strategies. Full article

(This article belongs to the Special Issue Stability Analysis and Optimal Operation in Power Electronic Systems)

► Show Figures

Figure 1

15 pages, 51755 KB

Open AccessArticle

Underwater Acoustic Data Transmission in the Presence of Challenging Multipath Conditions and Shadow Zones: Sea Trial Analysis and Lessons Learned

by Jacopo Lazzarin, Antonio Montanari, Diego Spinosa, Davide Cosimo, Riccardo Costanzi, Filippo Campagnaro and Michele Zorzi

Electronics 2026, 15(2), 358; https://doi.org/10.3390/electronics15020358 - 13 Jan 2026

Abstract

In comparison to traditional wired and wireless communication scenarios, the underwater channel is peculiar, being significantly more difficult for communication and presenting a unique set of features and impairments, thus necessitating special care in selecting ad hoc encoding and modulation technologies to achieve [...] Read more.

In comparison to traditional wired and wireless communication scenarios, the underwater channel is peculiar, being significantly more difficult for communication and presenting a unique set of features and impairments, thus necessitating special care in selecting ad hoc encoding and modulation technologies to achieve successful transmissions. This process can be aided by simulations, which can be effectively carried out only using a good, detailed channel model validated through sea measurements. This study presents the results of a sea measurement campaign run in May 2024 off the Gulf of La Spezia, Italy, characterized by challenging shallow water conditions and the presence of shadow zones. The collected data is then used to model a simulated channel as faithful as possible to the one experienced during the sea trial. The obtained channel is then used to carry out a comparison of different forward error correction (FEC) codes, highlighting each scheme’s performance in our working context. Conclusive results show that a satisfactory simulated channel was obtained and that a different choice of FEC schemes could have improved the performance of the underwater acoustic communication. Full article

(This article belongs to the Special Issue Recent Advances in Audio, Speech and Music Processing and Analysis, 2nd Edition)

23 pages, 24039 KB

Open AccessArticle

Multi-Region Temperature Prediction in Grain Storage: Integrating WSLP Spatial Structure with LSTM–iTransformer Hybrid Framework

by Yongqi Xu, Peiru Li, Jin Qian, Limin Shi, Hui Zhang and Bangyu Li

Electronics 2026, 15(2), 357; https://doi.org/10.3390/electronics15020357 - 13 Jan 2026

Abstract

Grain security is a fundamental guarantee for social stability and sustainable development. Accurate monitoring and prediction of overall granary temperature are essential for reducing storage losses and improving warehouse management efficiency. As an integrated system, the temperature evolution of the grain pile is [...] Read more.

Grain security is a fundamental guarantee for social stability and sustainable development. Accurate monitoring and prediction of overall granary temperature are essential for reducing storage losses and improving warehouse management efficiency. As an integrated system, the temperature evolution of the grain pile is deeply affected by its inherent physical structure and heat transfer pathways. Therefore, a multi-level warehouse–surface–line–point (WSLP) structural modeling approach driven by the physical properties of the grain pile is proposed to extract the joint environmental and spatial characteristics. Building upon the WSLP framework, a dual-channel time-series prediction architecture integrating both long short-term memory (LSTM) and iTransformer through a mutual verification fusion mechanism is developed to enable synchronized temperature forecasting across different regions of the grain piles. Experiments are conducted using real granary data from Shandong, China. The results demonstrate that the proposed model achieves more than 30% improvement over baseline methods in terms of MAE and RMSE. Moreover, the WSLP-LSTM–iTransformer framework significantly improves prediction accuracy in complex warehouse environments and enhances the interpretability and applicability of deep learning models for grain condition forecasting by incorporating real environmental characteristics. Full article

► Show Figures

Figure 1

22 pages, 3834 KB

Open AccessArticle

Image-Based Spatio-Temporal Graph Learning for Diffusion Forecasting in Digital Management Systems

by Chenxi Du, Zhengjie Fu, Yifan Hu, Yibin Liu, Jingwen Cao, Siyuan Liu and Yan Zhan

Electronics 2026, 15(2), 356; https://doi.org/10.3390/electronics15020356 - 13 Jan 2026

Abstract

With the widespread application of high-resolution remote sensing imagery and unmanned aerial vehicle technologies in agricultural scenarios, accurately characterizing spatial pest diffusion from multi-temporal images has become a critical issue in intelligent agricultural management. To overcome the limitations of existing machine learning approaches [...] Read more.

With the widespread application of high-resolution remote sensing imagery and unmanned aerial vehicle technologies in agricultural scenarios, accurately characterizing spatial pest diffusion from multi-temporal images has become a critical issue in intelligent agricultural management. To overcome the limitations of existing machine learning approaches that focus mainly on static recognition and lack effective spatio-temporal diffusion modeling, a UAV-based pest diffusion prediction and simulation framework is proposed. Multi-temporal UAV RGB and multispectral imagery are jointly modeled using a graph-based representation of farmland parcels, while temporal modeling and environmental embedding mechanisms are incorporated to enable simultaneous prediction of diffusion intensity and propagation paths. Experiments conducted on two real agricultural regions, Bayan Nur and Tangshan, demonstrate that the proposed method consistently outperforms representative spatio-temporal baselines. Compared with ST-GCN, the proposed framework achieves approximately 17–22% reductions in MAE and MSE, together with 8–12% improvements in PMR, while maintaining robust classification performance with precision, recall, and F1-score exceeding 0.82. These results indicate that the proposed approach can provide reliable support for agricultural information systems and diffusion-aware decision generation. Full article

(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)

► Show Figures

Figure 1

25 pages, 44733 KB

Open AccessArticle

Small-Sample Thermal Fault Diagnosis Using Knowledge Graph and Generative Adversarial Networks

by Ke Chen, Gang Xu, Yunjie Zhang and Yi Wang

Electronics 2026, 15(2), 355; https://doi.org/10.3390/electronics15020355 - 13 Jan 2026

Abstract

The scarcity of fault samples significantly impedes the generalization of data-driven diagnosis models for local thermal imbalances in integrated energy systems. To overcome this limitation, this paper proposes a novel knowledge graph-guided conditional generative adversarial network (KG-GAN) framework. The approach begins by constructing [...] Read more.

The scarcity of fault samples significantly impedes the generalization of data-driven diagnosis models for local thermal imbalances in integrated energy systems. To overcome this limitation, this paper proposes a novel knowledge graph-guided conditional generative adversarial network (KG-GAN) framework. The approach begins by constructing a dynamically updatable fault knowledge graph for district heating systems, which explicitly encapsulates pipeline topology, thermodynamic principles, and fault propagation mechanisms. The derived knowledge embeddings are then fused with physics-based constraints into the adversarial learning process, effectively alleviating the issue of physically implausible sample generation that plagues conventional data-centric models. Experimental validation on a district heating platform, involving four common fault types, demonstrates the superiority of our method. With only 100 samples per fault category, a diagnostic model trained on KG-GAN-generated data achieves a classification accuracy of 91.7%, outperforming a GAN-based baseline by 8.3%. Furthermore, t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization reveals a 92.3% feature distribution consistency between generated and real samples, confirming the method’s capability to enhance diagnostic robustness and physical interpretability under small-sample conditions. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence (AI) for Engineering Applications)

19 pages, 1607 KB

Open AccessArticle

Real-Time Bird Audio Detection with a CNN-RNN Model on a SoC-FPGA

by Rodrigo Lopes da Silva, Gustavo Jacinto, Mário Véstias and Rui Policarpo Duarte

Electronics 2026, 15(2), 354; https://doi.org/10.3390/electronics15020354 - 13 Jan 2026

Abstract

Monitoring wildlife has become increasingly important for understanding the evolution of species and ecosystem health. Acoustic monitoring offers several advantages over video-based approaches, enabling continuous 24/7 observation and robust detection under challenging environmental conditions. Deep learning models have demonstrated strong performance in audio [...] Read more.

Monitoring wildlife has become increasingly important for understanding the evolution of species and ecosystem health. Acoustic monitoring offers several advantages over video-based approaches, enabling continuous 24/7 observation and robust detection under challenging environmental conditions. Deep learning models have demonstrated strong performance in audio classification. However, their computational complexity poses significant challenges for deployment on low-power embedded platforms. This paper presents a low-power embedded system for real-time bird audio detection. A hybrid CNN–RNN architecture is adopted, redesigned, and quantized to significantly reduce model complexity while preserving classification accuracy. To support efficient execution, a custom hardware accelerator was developed and integrated into a Zynq UltraScale+ ZU3CG FPGA. The proposed system achieves an accuracy of 87.4%, processes up to 5 audio samples per second, and operates at only 1.4 W, demonstrating its suitability for autonomous, energy-efficient wildlife monitoring applications. Full article

(This article belongs to the Special Issue System-on-Chip (SoC) and Field-Programmable Gate Array (FPGA) Design, 2nd Edition)

► Show Figures

Figure 1

34 pages, 12645 KB

Open AccessArticle

Multimodal Intelligent Perception at an Intersection: Pedestrian and Vehicle Flow Dynamics Using a Pipeline-Based Traffic Analysis System

by Bao Rong Chang, Hsiu-Fen Tsai and Chen-Chia Chen

Electronics 2026, 15(2), 353; https://doi.org/10.3390/electronics15020353 - 13 Jan 2026

Abstract

Traditional automated monitoring systems adopted for Intersection Traffic Control still face challenges, including high costs, maintenance difficulties, insufficient coverage, poor multimodal data integration, and limited traffic information analysis. To address these issues, the study proposes a sovereign AI-driven Smart Transportation governance approach, developing [...] Read more.

Traditional automated monitoring systems adopted for Intersection Traffic Control still face challenges, including high costs, maintenance difficulties, insufficient coverage, poor multimodal data integration, and limited traffic information analysis. To address these issues, the study proposes a sovereign AI-driven Smart Transportation governance approach, developing a mobile AI solution equipped with multimodal perception, task decomposition, memory, reasoning, and multi-agent collaboration capabilities. The proposed system integrates computer vision, multi-object tracking, natural language processing, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to construct a Pipeline-based Traffic Analysis System (PTAS). The PTAS can produce real-time statistics on pedestrian and vehicle flows at intersections, incorporating potential risk factors such as traffic accidents, construction activities, and weather conditions for multimodal data fusion analysis, thereby providing forward-looking traffic insights. Experimental results demonstrate that the enhanced DuCRG-YOLOv11n pre-trained model, equipped with our proposed new activation function

β s i l u

, can accurately identify various vehicle types in object detection, achieving a frame rate of 68.25 FPS and a precision of 91.4%. Combined with ByteTrack, it can track over 90% of vehicles in medium- to low-density traffic scenarios, obtaining a 0.719 in MOTA and a 0.08735 in MOTP. In traffic flow analysis, the RAG of Vertex AI, combined with Claude Sonnet 4 LLMs, provides a more comprehensive view, precisely interpreting the causes of peak-hour congestion and effectively compensating for missing data through contextual explanations. The proposed method can enhance the efficiency of urban traffic regulation and optimizes decision support in intelligent transportation systems. Full article

(This article belongs to the Special Issue Interactive Design for Autonomous Driving Vehicles)

► Show Figures

Figure 1

30 pages, 4344 KB

Open AccessArticle

HAGEN: Unveiling Obfuscated Memory Threats via Hierarchical Attention-Gated Explainable Networks

by Mahmoud E. Farfoura, Mohammad Alia and Tee Connie

Electronics 2026, 15(2), 352; https://doi.org/10.3390/electronics15020352 - 13 Jan 2026

Abstract

Memory resident malware, particularly fileless and heavily obfuscated types, continues to pose a major problem for endpoint defense tools, as these threats often slip past traditional signature-based detection techniques. Deep learning has shown promise in identifying such malicious activity, but its use in [...] Read more.

Memory resident malware, particularly fileless and heavily obfuscated types, continues to pose a major problem for endpoint defense tools, as these threats often slip past traditional signature-based detection techniques. Deep learning has shown promise in identifying such malicious activity, but its use in real Security Operations Centers (SOCs) is still limited because the internal reasoning of these neural network models is difficult to interpret or verify. In response to this challenge, we present HAGEN, a hierarchical attention architecture designed to combine strong classification performance with explanations that security analysts can understand and trust. HAGEN processes memory artifacts through a series of attention layers that highlight important behavioral cues at different scales, while a gated mechanism controls how information flows through the network. This structure enables the system to expose the basis of its decisions rather than simply output a label. To further support transparency, the final classification step is guided by representative prototypes, allowing predictions to be related back to concrete examples learned during training. When evaluated on the CIC-MalMem-2022 dataset, HAGEN achieved 99.99% accuracy in distinguishing benign programs from major malware classes such as spyware, ransomware, and trojans, all with modest computational requirements suitable for live environments. Beyond accuracy, HAGEN produces clear visual and numeric explanations—such as attention maps and prototype distances—that help investigators understand which memory patterns contributed to each decision, making it a practical tool for both detection and forensic analysis. Full article

(This article belongs to the Section Artificial Intelligence)

► Show Figures

Figure 1

29 pages, 2829 KB

Open AccessArticle

Real-Time Deterministic Lane Detection on CPU-Only Embedded Systems via Binary Line Segment Filtering

by Shang-En Tsai, Shih-Ming Yang and Chia-Han Hsieh

Electronics 2026, 15(2), 351; https://doi.org/10.3390/electronics15020351 - 13 Jan 2026

Abstract

The deployment of Advanced Driver-Assistance Systems (ADAS) in economically constrained markets frequently relies on hardware architectures that lack dedicated graphics processing units. Within such environments, the integration of deep neural networks faces significant hurdles, primarily stemming from strict limitations on energy consumption, the [...] Read more.

The deployment of Advanced Driver-Assistance Systems (ADAS) in economically constrained markets frequently relies on hardware architectures that lack dedicated graphics processing units. Within such environments, the integration of deep neural networks faces significant hurdles, primarily stemming from strict limitations on energy consumption, the absolute necessity for deterministic real-time response, and the rigorous demands of safety certification protocols. Meanwhile, traditional geometry-based lane detection pipelines continue to exhibit limited robustness under adverse illumination conditions, including intense backlighting, low-contrast nighttime scenes, and heavy rainfall. Motivated by these constraints, this work re-examines geometry-based lane perception from a sensor-level viewpoint and introduces a Binary Line Segment Filter (BLSF) that leverages the inherent structural regularity of lane markings in bird’s-eye-view (BEV) imagery within a computationally lightweight framework. The proposed BLSF is integrated into a complete pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and a simplified Hough-style sliding-window fitting scheme combined with RANSAC. Experiments on a self-collected dataset of 297 challenging frames show that the inclusion of BLSF significantly improves robustness over an ablated baseline while sustaining real-time performance on a 2 GHz ARM CPU-only platform. Additional evaluations on the Dazzling Light and Night subsets of the CULane and LLAMAS benchmarks further confirm consistent gains of approximately 6–7% in F₁-score, together with corresponding improvements in IoU. These results demonstrate that interpretable, geometry-driven lane feature extraction remains a practical and complementary alternative to lightweight learning-based approaches for cost- and safety-critical ADAS applications. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles, Volume 2)

► Show Figures

Figure 1

22 pages, 2547 KB

Open AccessArticle

Hybridizing Explainable AI (XAI) for Intelligent Feature Extraction in Phishing Website Detection

by Rashed Alsakarnah, Mohammad Z. Masoud and Ahmad Ghababsheh

Electronics 2026, 15(2), 350; https://doi.org/10.3390/electronics15020350 - 13 Jan 2026

Abstract

This study proposes an explainability-driven feature selection framework for phishing website detection using a large-scale, heterogeneous dataset collected from four independent sources. The combined dataset contains approximately 500,000 samples, including 300,000 phishing pages and 200,000 legitimate pages, providing a comprehensive representation of real-world [...] Read more.

This study proposes an explainability-driven feature selection framework for phishing website detection using a large-scale, heterogeneous dataset collected from four independent sources. The combined dataset contains approximately 500,000 samples, including 300,000 phishing pages and 200,000 legitimate pages, providing a comprehensive representation of real-world web traffic. To enhance model interpretability and reduce feature redundancy, four explainable artificial intelligence (XAI) techniques—SHAP, LIME, partial dependence plots (PDPs), and permutation importance (PDI)—were applied to rank and analyze feature contributions. The union of all selected features was subsequently refined through a thresholding mechanism, forming the proposed Hybrid Explainability Random Forest Algorithm (HXRF). A Random Forest (RF) classifier was trained using the optimized feature subset and evaluated on an independently sampled set of 2000 webpages. Results demonstrate that HXRF significantly improves classification performance, achieving an accuracy of 98.2%, with balanced precision, recall, and F1 scores. The confusion matrix confirms strong generalization across both phishing and legitimate classes, with minimal false predictions. This work demonstrates that combining multi-method XAI with selective feature filtering produces a compact, interpretable, and highly discriminative feature set capable of robust phishing detection at scale. Full article

(This article belongs to the Section Artificial Intelligence)

► Show Figures

Figure 1

20 pages, 6759 KB

Open AccessArticle

Transient Voltage Support Strategy for Microgrids at the Distribution Network Edge Considering Cable Capacitance

by Shiran Cao, Ruotian Yao, Weihao Shuai, Hao Bai, Shiqi Jiang and Yawen Zheng

Electronics 2026, 15(2), 349; https://doi.org/10.3390/electronics15020349 - 13 Jan 2026

Abstract

Microgrids are commonly connected through medium-voltage cables in coastal distribution networks and other microgrids. However, a faulted microgrid may increase the collapse risk if the supporting microgrids are disconnected due to voltage sags. Conventional voltage support methods, which primarily rely on the impedance [...] Read more.

Microgrids are commonly connected through medium-voltage cables in coastal distribution networks and other microgrids. However, a faulted microgrid may increase the collapse risk if the supporting microgrids are disconnected due to voltage sags. Conventional voltage support methods, which primarily rely on the impedance characteristics of the transmission line, typically regulate the active-to-reactive current ratio (hereafter referred to as “current ratio”) to maximize positive sequence voltage while minimizing negative sequence voltage. Nevertheless, the distributed capacitance inherent in cables induces deviations in both the amplitude and phase of the transmitted current, while simultaneously intensifying the coupling between voltage and current. These effects complicate the voltage fluctuation behavior and impair the effectiveness of voltage support, thereby increasing the risk of disconnection and collapse for the faulted microgrid (hereafter referred to as “fault region”). To address this challenge, this study focuses on non-faulted microgrids (hereafter referred to as “microgrids”), proposing a method for active current correction and transient voltage support that considers the influence of cable distributed capacitance. By analyzing the voltage and current characteristics on both ends of the interconnecting cables, the method optimizes the current injection ratio. It mitigates deviation caused by cable capacitance effects, thereby enhancing the voltage support performance of the microgrid. Notably, the proposed method operates independently of real-time voltage and current measurements from the fault region, significantly reducing communication demands. Experimental results based on a practical microgrid validate the effectiveness of the proposed method, demonstrating a 27.9% improvement in voltage support performance compared to conventional methods. Full article

(This article belongs to the Topic Optimal Planning, Integration and Control of Smart Grids and Microgrids Systems, 2nd Edition)

► Show Figures

Figure 1

19 pages, 6052 KB

Open AccessArticle

SGMT-IDS: A Dual-Branch Semi-Supervised Intrusion Detection Model Based on Graphs and Transformers

by Yifei Wu and Liang Wan

Electronics 2026, 15(2), 348; https://doi.org/10.3390/electronics15020348 - 13 Jan 2026

Abstract

Network intrusion behaviors exhibit high concealment and diversity, making intrusion detection methods based on single-behavior modeling unable to accurately characterize such activities. To overcome this limitation, we propose SGMT-IDS, a dual-branch semi-supervised intrusion detection model based on Graph Neural Networks (GNNs) and Transformers. [...] Read more.

Network intrusion behaviors exhibit high concealment and diversity, making intrusion detection methods based on single-behavior modeling unable to accurately characterize such activities. To overcome this limitation, we propose SGMT-IDS, a dual-branch semi-supervised intrusion detection model based on Graph Neural Networks (GNNs) and Transformers. By constructing two views of network attacks, namely structural and behavioral semantics, the model performs collaborative analysis of intrusion behaviors from both perspectives. The model adopts a dual-branch architecture. The SGT branch captures the structural embeddings of network intrusion behaviors, and the GML-Transformer branch extracts the semantic information of intrusion behaviors. In addition, we introduce a two-stage training strategy that optimizes the model through pseudo-labeling and contrastive learning, enabling accurate intrusion detection with only a small amount of labeled data. We conduct experiments on the NF-Bot-IoT-V2, NF-ToN-IoT-V2, and NF-CSE-CIC-IDS2018-V2 datasets. The experimental results demonstrate that SGMT-IDS achieves superior performance across multiple evaluation metrics. Full article

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

► Show Figures

Figure 1

21 pages, 4794 KB

Open AccessArticle

Heat Transfer and Mechanical Performance Analysis and Optimization of Lattice Structure for Electric Vehicle Thermal Management

by Xiaokang Ye, Xiaoxia Sun, Zhixuan Liang, Ran Tian, Mingshan Wei, Panpan Song and Lili Shen

Electronics 2026, 15(2), 347; https://doi.org/10.3390/electronics15020347 - 13 Jan 2026

Abstract

With the trend toward integrated development in electric vehicles, thermal management components are becoming more compact and highly integrated. This evolution, however, leads to complex spatial layouts of high- and low-temperature fluid circuits, causing localized heat accumulation and unintended heat transfer between channels, [...] Read more.

With the trend toward integrated development in electric vehicles, thermal management components are becoming more compact and highly integrated. This evolution, however, leads to complex spatial layouts of high- and low-temperature fluid circuits, causing localized heat accumulation and unintended heat transfer between channels, which compromises cooling efficiency. Concurrently, these compact components must possess sufficient mechanical strength to withstand operational loads such as vibration. Therefore, designing structures that simultaneously suppress heat transfer and ensure mechanical intensity presents a critical challenge. This study introduces Triply Periodic Minimal Surface (TPMS) and Body-Centered Cubic (BCC) lattice structures as multifunctional solutions to address the undesired heat transfer and mechanical support requirements. Their thermal and mechanical performances are analyzed, and a feedforward neural network model is developed based on CFD simulations to map key structural parameters to thermal and mechanical outputs. A dual-objective optimization approach is then applied to identify optimal structural parameters that balance thermal and mechanical requirements. Validation via CFD confirms that the neural network-based optimization effectively achieves a trade-off between heat transfer suppression and structural strength, providing a reliable design methodology for integrated thermal management systems. Full article

(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles, 2nd Edition)

► Show Figures

Figure 1

18 pages, 3911 KB

Open AccessArticle

Wide Range Dual Active Half-Bridge Resonant Converter with PWM Control and Load-Independent Voltage Gain Characteristics

by Jingtao Xu, Sirui Huang and Lulin Zhang

Electronics 2026, 15(2), 346; https://doi.org/10.3390/electronics15020346 - 13 Jan 2026

Abstract

This paper proposes a fixed frequency pulse width modulation (PWM) for a dual active half-bridge resonant converter. The wide voltage range can be achieved without adding any additional components, and the voltage gain characteristic is independent of the load. Meanwhile, all switches can [...] Read more.

This paper proposes a fixed frequency pulse width modulation (PWM) for a dual active half-bridge resonant converter. The wide voltage range can be achieved without adding any additional components, and the voltage gain characteristic is independent of the load. Meanwhile, all switches can achieve full range zero voltage switching (ZVS). The driving logic is unified between the primary and secondary sides, allowing for the implementation of both boost and buck modes. Hence, the control logic is simple. In addition, the multiple-order harmonic analysis of the resonant tank is proposed without complex time-domain calculations. Hence, the expression of voltage gain, current characteristics, and soft switching conditions can be conveniently analyzed. Finally, a 500 W experimental prototype was built. The experimental results prove the effectiveness and superiority of the proposed solution. Full article

(This article belongs to the Special Issue Modelling, Design and Implementation of Power Electronic Converters)

► Show Figures

Figure 1

17 pages, 710 KB

Open AccessArticle

KD-SecBERT: A Knowledge-Distilled Bidirectional Encoder Optimized for Open-Source Software Supply Chain Security in Smart Grid Applications

by Qinman Li, Xixiang Zhang, Weiming Liao, Tao Dai, Hongliang Zheng, Beiya Yang and Pengfei Wang

Electronics 2026, 15(2), 345; https://doi.org/10.3390/electronics15020345 - 13 Jan 2026

Abstract

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. [...] Read more.

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. In power information networks and cyber–physical control systems, vulnerabilities in open-source components integrated into Supervisory Control and Data Acquisition (SCADA), Energy Management System (EMS), and Distribution Management System (DMS) platforms and distributed energy controllers may propagate along the supply chain, threatening system security and operational stability. In such application scenarios, large language models (LLMs) often suffer from limited semantic accuracy when handling domain-specific security terminology, as well as deployment inefficiencies that hinder their practical adoption in critical infrastructure environments. To address these issues, this paper proposes KD-SecBERT, a domain-specific semantic bidirectional encoder optimized through multi-level knowledge distillation for open-source software supply chain security in smart grid applications. The proposed framework constructs a hierarchical multi-teacher ensemble that integrates general language understanding, cybersecurity-domain knowledge, and code semantic analysis, together with a lightweight student architecture based on depthwise separable convolutions and multi-head self-attention. In addition, a dynamic, multi-dimensional distillation strategy is introduced to jointly perform layer-wise representation alignment, ensemble knowledge fusion, and task-oriented optimization under a progressive curriculum learning scheme. Extensive experiments conducted on a multi-source dataset comprising National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) entries, security-related GitHub code, and Open Web Application Security Project (OWASP) test cases show that KD-SecBERT achieves an accuracy of 91.3%, a recall of 90.6%, and an F1-score of 89.2% on vulnerability classification tasks, indicating strong robustness in recognizing both common and low-frequency security semantics. These results demonstrate that KD-SecBERT provides an effective and practical solution for semantic analysis and software supply chain risk assessment in smart grids and other critical-infrastructure environments. Full article

► Show Figures

Figure 1

22 pages, 2909 KB

Open AccessArticle

Study on Quality of AI Service Guarantee in Digital Twin Networks for XR Scenarios

by Jinfei Zhou, Yuehong Gao, Xinyao Wang, Yiran Li and Ziqi Zhao

Electronics 2026, 15(2), 344; https://doi.org/10.3390/electronics15020344 - 13 Jan 2026

Abstract

In line with the trend of “native intelligence”, artificial intelligence (AI) will be more deeply integrated into communication networks in the future. Quality of AI service (QoAIS) will become an important factor in measuring the performance of native AI wireless networks. Networks should [...] Read more.

In line with the trend of “native intelligence”, artificial intelligence (AI) will be more deeply integrated into communication networks in the future. Quality of AI service (QoAIS) will become an important factor in measuring the performance of native AI wireless networks. Networks should reasonably allocate multi-dimensional resources to ensure QoAIS for users. Extended Reality (XR) is one of the important application scenarios for future 6G networks. To ensure both the accuracy and latency requirements of users for AI services are met, this paper proposes a resource allocation algorithm called Asynchronous Multi-Agent Deep Deterministic Policy Gradient with Independent State and Action (A-MADDPG-ISA). The proposed algorithm supports agents to use different dimensional state spaces and action spaces; therefore, it enables agents to address different strategy issues separately and makes the algorithm design more flexible. The actions of different agents are executed asynchronously, enabling actions outputted earlier to be transmitted as additional information to other agents. The simulation results show that the proposed algorithm has a 10.41% improvement compared to MADDPG (Multi-Agent Deep Deterministic Policy Gradient). Furthermore, to overcome the limitations of directly applying AI or manual rule-based schemes to real networks, this research establishes a digital twin network (DTN) system and designs pre-validation functionality. The DTN system contributes to better ensuring users’ QoAIS. Full article

► Show Figures

Figure 1

21 pages, 2506 KB

Open AccessArticle

Collaborative Dispatch of Power–Transportation Coupled Networks Based on Physics-Informed Priors

by Zhizeng Kou, Yingli Wei, Shiyan Luan, Yungang Wu, Hancong Guo, Bochao Yang and Su Su

Electronics 2026, 15(2), 343; https://doi.org/10.3390/electronics15020343 - 13 Jan 2026

Abstract

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a [...] Read more.

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article

► Show Figures

Figure 1

23 pages, 4481 KB

Open AccessArticle

PathSelect: Dynamic Token Condensation and Hierarchical Attention for Accelerated T2I Diffusion

by Yan Fu, Gaolin Ye, Ou Ye, Ting Hou and Ruimin Dai

Electronics 2026, 15(2), 342; https://doi.org/10.3390/electronics15020342 - 13 Jan 2026

Abstract

Recent advancements in large language models (LLMs) have significantly improved text-to-image (T2I) generation, enabling systems to produce visually compelling and semantically meaningful images. However, preserving fine-grained semantic consistency in generated images, particularly in response to complex and region-specific textual prompts, remains a key [...] Read more.

Recent advancements in large language models (LLMs) have significantly improved text-to-image (T2I) generation, enabling systems to produce visually compelling and semantically meaningful images. However, preserving fine-grained semantic consistency in generated images, particularly in response to complex and region-specific textual prompts, remains a key challenge. In this work, we propose a context-aware hierarchical agent mechanism that integrates a semantic condensation strategy to enhance attention efficiency and maintain critical visual-textual alignment. By dynamically fusing contextual information, the method effectively balances computational efficiency and ensures semantic alignment with textual descriptions. Experimental results demonstrate improved visual coherence and semantic consistency across diverse prompts, validated through quantitative metrics and qualitative analysis. Our contributions include: (i) introducing a novel semantic condensation strategy that enhances attention efficiency while preserving critical feature information; (ii) developing a new hierarchical agent attention mechanism to enhance computation efficiency; (iii) designing an iterative feedback method based on CLIP Score to improve image diversity and overall quality. Full article

(This article belongs to the Section Artificial Intelligence)

► Show Figures

Figure 1

27 pages, 1127 KB

Open AccessReview

Evolution and Emerging Frontiers in Point Cloud Technology

by Wenjuan Wang, Haleema Ehsan, Shi Qiu, Tariq Ur Rahman, Jin Wang and Qasim Zaheer

Electronics 2026, 15(2), 341; https://doi.org/10.3390/electronics15020341 - 13 Jan 2026

Abstract

Point cloud intelligence integrates advanced technologies such as Light Detection and Ranging (LiDAR), photogrammetry, and Artificial Intelligence (AI) to transform transportation infrastructure management. This review highlights state-of-the-art advancements in denoising, registration, segmentation, and surface reconstruction. A detailed case study on three-dimensional (3D) mesh [...] Read more.

Point cloud intelligence integrates advanced technologies such as Light Detection and Ranging (LiDAR), photogrammetry, and Artificial Intelligence (AI) to transform transportation infrastructure management. This review highlights state-of-the-art advancements in denoising, registration, segmentation, and surface reconstruction. A detailed case study on three-dimensional (3D) mesh generation for railway fastener monitoring showcases how these techniques address challenges like noise and computational complexity while enabling precise and efficient infrastructure maintenance. By demonstrating practical applications and identifying future research directions, this work underscores the transformative potential of point cloud intelligence in supporting predictive maintenance, digital twins, and sustainable transportation systems. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application, 3rd Edition)

► Show Figures

Figure 1

Journal Description

Electronics

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI