Electronics

23 pages, 2046 KB

Open AccessArticle

Secure and Recoverable RGB-Colored Two-Dimensional Barcodes: A Hybrid Framework Combining Lightweight Cryptography and Pretrained Vision Models

by Heider A. M. Wahsheh

Electronics 2026, 15(9), 1855; https://doi.org/10.3390/electronics15091855 (registering DOI) - 27 Apr 2026

Abstract

Two-dimensional (2D) barcodes are now embedded in payment platforms, authentication workflows, industrial traceability, smart packaging, and mobile information services. Their ubiquity has simultaneously increased the incentive for phishing, tampering, and malicious redirection, while recent RGB-colored barcode designs have introduced a second challenge: maintaining [...] Read more.

Two-dimensional (2D) barcodes are now embedded in payment platforms, authentication workflows, industrial traceability, smart packaging, and mobile information services. Their ubiquity has simultaneously increased the incentive for phishing, tampering, and malicious redirection, while recent RGB-colored barcode designs have introduced a second challenge: maintaining reliable payload recovery under non-ideal capture conditions. This study presents a unified framework for secure and recoverable RGB-colored 2D barcodes across QR Code, Data Matrix, Aztec, and PDF417 symbologies. The framework combines channel-separated RGB encoding, lightweight hybrid cryptographic protection, and pretrained vision-based validation to jointly improve confidentiality, authenticity, and operational trust. A recoverability-oriented evaluation protocol is introduced to quantify robustness under distance variation, angular distortion, illumination change, blur, and color shift. Experimental results show that compact schemes based on ChaCha20-Poly1305 and Ed25519 achieve the most favorable trade-off between security overhead and decoding reliability, while EfficientNet-B0 offers the best deployment balance among the evaluated vision backbones. Data Matrix and Aztec exhibit the strongest maximum reliable distance under the tested conditions. The results indicate that secure barcode design cannot be treated as a purely cryptographic or purely visual problem; instead, practical deployment benefits from a layered architecture in which cryptography, computer vision, and recoverability metrics are optimized together. Full article

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32 pages, 4508 KB

Open AccessArticle

Silicon Carbide Potential for Railway Traction Applications: Efficiency, Loadability, Life Cycle Energy Analysis, and Cost Assessment Comparison to Si-Based Inverter Topologies

by Lucas Barroso Spejo, Timon Briner, Thiago Batista Soeiro and Renato Amaral Minamisawa

Electronics 2026, 15(9), 1854; https://doi.org/10.3390/electronics15091854 (registering DOI) - 27 Apr 2026

Abstract

Silicon carbide (SiC) power devices are emerging as an alternative for electrical transportation systems to improve energy efficiency, reduce carbon emissions, increase power density, and enable long-term cost savings throughout the product life cycle. Thus, a fair comparison with state-of-the-art Silicon (Si) technology [...] Read more.

Silicon carbide (SiC) power devices are emerging as an alternative for electrical transportation systems to improve energy efficiency, reduce carbon emissions, increase power density, and enable long-term cost savings throughout the product life cycle. Thus, a fair comparison with state-of-the-art Silicon (Si) technology is required to justify the productization of SiC devices. This work performs a systematic investigation of both technologies at the device and system levels for distinct power module voltage classes (3.3 and 6.5 kV) and circuit topologies. Initially, experimental characterization of state-of-the-art power modules is performed, followed by energy efficiency characterizations at the power converter level. Then, an electrothermal simulation model was built and validated based on experimental results. Accurate system simulations of commercial two- and three-level traction topologies were developed, focusing on efficiency over the entire load range, loadability, potential energy savings under realistic train drive cycles, and a financial comparison of inverter prices per kW. SiC exhibits lower loadability degradation at high switching frequencies (>500 Hz) than Si technology. Energy-saving potentials of 40–70% in the traction inverter with a guaranteed return on investment during the converter’s lifetime are achieved by substituting Si with SiC inverters. In addition, massive energy savings of up to 200 MWh per inverter lifetime can effectively reduce the carbon footprint of railway systems (up to ~76 t CO₂-eq saved during the inverter lifetime). This paper provides essential information for distinct stakeholders to support the decision-making process and design considerations for future railway power conversion technologies. Full article

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

25 pages, 10694 KB

Open AccessArticle

by Junjun Yang and Chunguang Ren

Electronics 2026, 15(9), 1853; https://doi.org/10.3390/electronics15091853 (registering DOI) - 27 Apr 2026

Abstract

In high-frequency 400 V/48 V matrix planar LLC resonant converters for data center power supplies, enlarged interwinding parasitic capacitance can induce significant transformer-related common-mode (CM) displacement currents. However, the effects of secondary-side rectifier commutation and local winding position on the resulting CM spikes [...] Read more.

In high-frequency 400 V/48 V matrix planar LLC resonant converters for data center power supplies, enlarged interwinding parasitic capacitance can induce significant transformer-related common-mode (CM) displacement currents. However, the effects of secondary-side rectifier commutation and local winding position on the resulting CM spikes have not been sufficiently clarified. This paper establishes a unified analytical expression for the transformer-related CM current in a converter with a half-bridge primary and a full-bridge synchronous-rectifier (SR) secondary. The analysis shows that asynchronous SR commutation shifts the secondary reference potential and introduces additional excitation through the interwinding parasitic capacitances, thereby producing double-pulse CM current spikes. The unequal spike amplitudes among different secondary-side rectifier units are further explained by the combined effects of local winding position and distributed parasitic coupling. Based on these findings, a shielding-layer scheme was then proposed and verified on a 400 V/48 V, 300 kHz, 3 kW prototype. The experimental results show average reductions of about 15 dB over 150 kHz–800 kHz and 20 dB over 800 kHz–6.5 MHz in the CM voltage spectrum, whereas the prototype achieves a peak efficiency of 97.78%. Full article

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44 pages, 36503 KB

Open AccessArticle

A Dual-Branch ST-GCN System for Joint Recognition of OOW Unsafe Behaviors and Facial Fatigue Features

by Rui Qi, Shengwei Xing, Kairen Chen, Zijian Zhang and Xiaoyu He

Electronics 2026, 15(9), 1852; https://doi.org/10.3390/electronics15091852 (registering DOI) - 27 Apr 2026

Abstract

The Officer on Watch (OOW) is critical to ensuring the safety of the vessel, cargo, and crew during navigation. To reduce maritime accidents caused by unsafe behaviors or fatigue, this paper proposes a dual-branch detection system based on Spatial–Temporal Graph Convolutional Networks (ST-GCN): [...] Read more.

The Officer on Watch (OOW) is critical to ensuring the safety of the vessel, cargo, and crew during navigation. To reduce maritime accidents caused by unsafe behaviors or fatigue, this paper proposes a dual-branch detection system based on Spatial–Temporal Graph Convolutional Networks (ST-GCN): BODY-ST-GCN for pose-based behavior recognition and FACE-ST-GCN for facial state analysis. For spatial modeling, a Triple Graph Fusion (TGF) strategy is introduced to integrate static, adaptive, and attention graphs, enhancing the representation of skeletal and facial keypoints. For temporal modeling, BODY-ST-GCN incorporates a Three-Scale Parallel Temporal Convolutional Network (TSP-TCN) to capture multi-scale motion dynamics, while FACE-ST-GCN uses a Temporal Adaptive Module (TAM) to extract stable facial state features. Furthermore, a joint risk classification mechanism categorizes OOW duty states into four hierarchical levels: Safe, Early Fatigue Warning, High Fatigue Risk, and Emergency. This mechanism enables continuous, real-time monitoring and dynamic assessment. Experiments demonstrate that BODY-ST-GCN and FACE-ST-GCN achieve macro average precisions of 0.969 and 0.947, respectively, outperforming the baseline ST-GCN by 6.4% and 14.9%, providing reliable technical support for onboard safety management. Full article

24 pages, 32801 KB

Open AccessArticle

Age-Invariant Face Retrieval Based on Hybrid Metric Learning Framework (HMLF)

by Jingtian Cao, Tingshuo Zhang, Ziyi Wang and Bobo Lian

Electronics 2026, 15(9), 1851; https://doi.org/10.3390/electronics15091851 (registering DOI) - 27 Apr 2026

Abstract

Cross-age face analysis has emerged as an important topic in biometric recognition due to substantial facial appearance variations caused by aging. Nevertheless, most existing approaches primarily focus on face verification (1:1 matching) and frequently rely on explicit age annotations, which limit their applicability [...] Read more.

Cross-age face analysis has emerged as an important topic in biometric recognition due to substantial facial appearance variations caused by aging. Nevertheless, most existing approaches primarily focus on face verification (1:1 matching) and frequently rely on explicit age annotations, which limit their applicability in large-scale retrieval scenarios. In this study, large-scale cross-age face retrieval (1:N matching) is investigated, and a Hybrid Metric Learning Framework (HMLF) is proposed to learn age-invariant and retrieval-oriented facial representations without requiring age labels. The proposed framework integrates Additive Angular Margin Loss (ArcFace) with supervised contrastive learning to enhance feature discriminability. Furthermore, a mixed triplet mining strategy is introduced to improve the effectiveness of hard sample selection. A memory bank-based InfoNCE formulation is incorporated to provide a large number of negative samples, and an uncertainty-based adaptive weighting scheme is designed to automatically balance multiple loss components during optimization. To better simulate realistic retrieval scenarios, an extended cross-age retrieval evaluation protocol is established. Extensive experimental results demonstrate that the proposed framework achieves superior retrieval performance across different backbone architectures. The results further provide systematic insights into the influence of backbone design, loss formulation, and optimization strategies on cross-age retrieval accuracy. Full article

29 pages, 1192 KB

Open AccessArticle

Robust Dynamic State Estimation and Collaborative Control of Distribution Networks Considering Measurement Outliers

by Ming Zhou, Qiang Wu, Hongwei Su, Yiwei Cui and Zhuangxi Tan

Electronics 2026, 15(9), 1850; https://doi.org/10.3390/electronics15091850 (registering DOI) - 27 Apr 2026

Abstract

Active distribution networks require precise real-time monitoring and control despite measurement outliers and rapid load dynamics. Conventional robust estimators frequently fail to distinguish between transient measurement corruption and genuine physical state mutations, leading to estimation lag or erroneous control actions. To address this, [...] Read more.

Active distribution networks require precise real-time monitoring and control despite measurement outliers and rapid load dynamics. Conventional robust estimators frequently fail to distinguish between transient measurement corruption and genuine physical state mutations, leading to estimation lag or erroneous control actions. To address this, we propose a resilient cyber–physical framework that jointly optimizes robust dynamic state estimation and collaborative voltage control. At the estimation layer, a novel Persistence-Based Robust Extended Kalman Filter (PB-REKF) is developed, which employs a temporal persistence counter to adaptively switch between Huber M-estimation for sporadic outlier suppression and covariance inflation for rapid tracking of persistent state mutations. At the control layer, a chance-constrained Second-Order Cone Programming (SOCP) strategy directly embeds the real-time posterior covariance from the PB-REKF into the voltage safety constraints, creating a data-quality-adaptive security buffer that provides a

95 %

probabilistic voltage guarantee. Simulations on 5-bus and IEEE 33-bus systems demonstrate that the proposed framework achieves a

29.5 %

reduction in global RMSE and a

72.8 %

reduction in peak outlier-window estimation error relative to the standard EKF, while reducing the voltage violation rate from

8.8 %

to

3.8 %

. The complete estimation and control pipeline requires

1.341

ms per update step, confirming real-time feasibility. Full article

19 pages, 16801 KB

Open AccessArticle

Point Cloud Semantic Segmentation Network Based on Serialized Attention

by Chieh-Yuan Teng, Yi-Hao Hsu, Wei-Hao Chen, Chih-Lung Lin and Chi-Hung Chuang

Electronics 2026, 15(9), 1849; https://doi.org/10.3390/electronics15091849 (registering DOI) - 27 Apr 2026

Abstract

With Transformers achieving breakthrough results in natural language processing and computer vision, researchers have attempted to leverage their powerful modeling capabilities in 3D point cloud processing. However, the inherent unordered and unstructured nature of point cloud data poses significant challenges to directly applying [...] Read more.

With Transformers achieving breakthrough results in natural language processing and computer vision, researchers have attempted to leverage their powerful modeling capabilities in 3D point cloud processing. However, the inherent unordered and unstructured nature of point cloud data poses significant challenges to directly applying Transformer architectures. This research proposes a novel point cloud processing method by introducing point cloud serialization and a serialization-based attention mechanism to enhance the performance of the PointNeXt model in semantic segmentation tasks. Traditional point cloud processing methods typically treat point clouds as unstructured data collections, resulting in low computational efficiency and scalability limitations. Our proposed approach breaks through the constraints of point cloud data’s unordered nature by serializing point clouds into a structured format. We employ spatial filling curves (such as Z-order and Hilbert curves) to sort point clouds, enabling efficient grouping of points into non-overlapping patches and applying more efficient attention mechanisms on these patches. Based on the serialization point cloud, we incorporate the segment attention mechanism from Point Transformer V3 (PTv3), which leverages the ordered characteristics of Serialization. By designing segment interactions (such as sequential shifting and sequential random mixing), we expand the model’s receptive field while maintaining computational efficiency. Full article

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15 pages, 4149 KB

Open AccessArticle

LRNet: A Lightweight Detection Model for Foreign Objects on Coal Mine Conveyor

by Lili Xu, Youli Yao and Airan Zhang

Electronics 2026, 15(9), 1848; https://doi.org/10.3390/electronics15091848 (registering DOI) - 27 Apr 2026

Abstract

Coal mine conveyor belt foreign objects detection is critical in conveyor belt transportation of coal. Aiming at the problems that the existing coal mine conveyor belt foreign objects detection model has a large number of parameters, occupies more computer resources, and detects fewer [...] Read more.

Coal mine conveyor belt foreign objects detection is critical in conveyor belt transportation of coal. Aiming at the problems that the existing coal mine conveyor belt foreign objects detection model has a large number of parameters, occupies more computer resources, and detects fewer types of foreign objects, the original YOLOv13 object detection algorithm is optimized to achieve lightweight design and high precision. Therefore, a sophisticated lightweight YOLO network named LRNet is proposed based on the original YOLOv13, which is tailored for foreign objects detection on coal mine conveyor belts. First, lightweight ShuffleNetv2 is used as the backbone network for YOLOv13 to reduce computational cost and the number of parameters, and to improve the network parallelism. Second, the Bidirectional Feature Pyramid Network (BIFPN) is used as a feature fusion network to effectively fuse global deep and shallow key detail information. Finally, the Coordinate Attention (CA) mechanism is added to enhance the extraction capability of key features and strengthen the foreign objects target attention to improve the network model detection accuracy. The experimental results show that the average detection accuracy of LRNet reaches 91.0%, the number of parameters is 3.6 M. The proposed method can quickly and accurately detect foreign objects in coal mine conveyor belts with less computational resources, and at the same time, it shows strong adaptability and anti-interference ability, which reflects the effectiveness and advancedness of the LRNet model. Full article

39 pages, 10297 KB

Open AccessArticle

On Memorization and Generalization in Compact Transformers

by Aki Härmä, Ali Al-Saeedi, Anton Changalidis, Dumitru Verşebeniuc, Marcin Pietrasik and Anna Wilbik

Electronics 2026, 15(9), 1847; https://doi.org/10.3390/electronics15091847 (registering DOI) - 27 Apr 2026

Abstract

Large language models (LLMs) seem to demonstrate human-like understanding and generalization of language content. These arise from the capabilities of the models to memorize and generalize the training content. In this paper, we review the recent literature and theories on the mechanisms in [...] Read more.

Large language models (LLMs) seem to demonstrate human-like understanding and generalization of language content. These arise from the capabilities of the models to memorize and generalize the training content. In this paper, we review the recent literature and theories on the mechanisms in self-attention neural networks. We also report three computational experiments that give insights into the underlying mechanisms and capabilities of the models. We also report three computational experiments showing that memorization capacity in compact transformers can be empirically linked to architectural parameters, that structured domain knowledge can be retained in small decoder-only models, and that in-context abstraction requires sufficient architectural depth. These findings suggest that the current models are superfluous for many specific applications, especially in on-edge use cases. A better understanding of application requirements and architecture details can be expected to help in building new LLM architectures that can be efficiently implemented on dedicated on-edge circuits. Full article

(This article belongs to the Special Issue The Future of LLM Architectures)

26 pages, 2904 KB

Open AccessArticle

Cross-Modal Semantic Alignment and Dynamic Routing Enhancement for Inspection and Supervision Scenarios

by Changhua Hu, Jianfeng Liu, Zheng Cheng, Hu Han, Yuetian Huang, Qingguo Shi and Yi Su

Electronics 2026, 15(9), 1846; https://doi.org/10.3390/electronics15091846 (registering DOI) - 27 Apr 2026

Abstract

Traditional inspection and supervision in power grid operations suffer from heterogeneous multi-source data (text, tables, and images), low policy retrieval efficiency, difficult issue characterization, non-standardized reporting, and weak closed-loop rectification. To address these challenges in Guangdong Power Grid scenarios, this paper proposes CSA-DR, [...] Read more.

Traditional inspection and supervision in power grid operations suffer from heterogeneous multi-source data (text, tables, and images), low policy retrieval efficiency, difficult issue characterization, non-standardized reporting, and weak closed-loop rectification. To address these challenges in Guangdong Power Grid scenarios, this paper proposes CSA-DR, a Cross-modal Semantic Alignment and Dynamic Routing enhancement method. CSA-DR retrieves relevant policy documents, structured tables, and inspection-related images from an external regulatory knowledge base and encodes them via a tri-modal into a unified semantic space, achieving precise cross-modal alignment between inspection descriptions and supporting evidence. A dynamic routing mechanism is introduced to adaptively allocate modality importance according to task requirements, significantly improving key information extraction, violation detection, and causal analysis. Additionally, the framework integrates an external regulatory knowledge base. For each inspection task, relevant policy documents, structured tables, and evidence images are retrieved from this knowledge base and used as the tri-modal input to the model. This knowledge-grounded design enables cross-modal semantic alignment, evidence traceability, and standardized inspection report generation. Experiments on a real multi-source inspection dataset from Guangdong Power Grid show that CSA-DR consistently outperforms the compared baseline methods and ablation variant across all applicable metrics, with notable improvements in cross-modal MRR and image-to-image Recall@5. Full article

(This article belongs to the Special Issue AI-Powered Natural Language Processing Applications)

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23 pages, 824 KB

Open AccessArticle

An LLM-Based Multi-Path Question Answering System with XGBoost Routing and Threshold-Based Refusal

by Bo Dai, Caiyun Li, Yiyun Cao, Jie Ling and Xiaowen Liu

Electronics 2026, 15(9), 1845; https://doi.org/10.3390/electronics15091845 (registering DOI) - 27 Apr 2026

Abstract

In conventional question answering systems, general-purpose large language models (LLMs), despite their strong capabilities in language understanding and generation, exhibit notable limitations in scenarios with stringent factuality requirements. Their outputs often lack explicit evidential grounding, making them prone to hallucinations and inconsistent responses. [...] Read more.

In conventional question answering systems, general-purpose large language models (LLMs), despite their strong capabilities in language understanding and generation, exhibit notable limitations in scenarios with stringent factuality requirements. Their outputs often lack explicit evidential grounding, making them prone to hallucinations and inconsistent responses. Moreover, LLMs do not inherently guarantee determinism when performing operations over structured data—such as aggregation, conditional filtering, and cross-field constraints—thereby undermining result consistency and reliability. To address these issues, we propose an internal-data-first framework for controllable question answering in high-risk scenarios. The framework categorizes knowledge sources into unstructured documents and structured data, enabling evidence-constrained generation via retrieval-augmented generation (RAG) and database-backed, verifiable query execution via restricted, read-only structured queries. In addition, an UNK branch is introduced as a safe degradation mechanism that triggers refusal when inputs lack sufficient evidence, exceed system capability boundaries, or fail to meet confidence requirements, thereby suppressing hallucinations and unauthorized generation. To enable controlled selection among the two execution pathways (RAG/SQL) and the safety degradation branch (UNK) at the system level, we design a learned router based on XGBoost with confidence-thresholded selective prediction, which preferentially activates UNK refusals for low-confidence or out-of-distribution inputs. We validate the proposed framework using a graduate admissions consultation system as an exemplar application, constructing both a document knowledge base and structured score tables, and conducting controlled comparisons across multiple system variants with multi-metric evaluations. Experimental results indicate that, under the current controlled evaluation setting, the proposed framework exhibits relatively stable behavior under complex query formulations and demonstrates practical engineering potential in high-risk vertical-domain question answering scenarios. Full article

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19 pages, 1856 KB

Open AccessArticle

Coordinated Optimization Method for Post-Disaster Transmission Line Repair and System Restoration Against Ice and Snow Disasters

by Liang Yang, Wenchao Zhang, Yong Zhai, Yu Chen and Qing Wan

Electronics 2026, 15(9), 1844; https://doi.org/10.3390/electronics15091844 (registering DOI) - 27 Apr 2026

Abstract

A coordinated optimization method for emergency repair scheduling and system operation restoration is proposed to address large-scale transmission line outages caused by extreme weather events such as ice and snow disasters. First, an active outage scenario for transmission lines is constructed based on [...] Read more.

A coordinated optimization method for emergency repair scheduling and system operation restoration is proposed to address large-scale transmission line outages caused by extreme weather events such as ice and snow disasters. First, an active outage scenario for transmission lines is constructed based on the Jones icing thickness model and an exponential failure probability model, while incorporating the spatial distribution characteristics of ice disasters. Subsequently, a bi-level optimization model with repair resource constraints is developed. The upper-level model determines the transmission line repair schedule with the objective of minimizing the total repair time while taking system power supply restoration efficiency into account. Based on the completion times of line repairs, the lower-level model optimizes the system restoration process by considering power flow constraints, generator start-up processes, and load restoration characteristics. To address the challenges posed by discrete operational states and strongly coupled bi-level constraints that are difficult to solve using conventional approaches, a logic-based Integer L-shaped coordinated solution method is proposed. Finally, the effectiveness of the proposed method is validated through case studies based on the IEEE New England 10-unit 39-bus system. The results demonstrate that the proposed method can significantly improve system load restoration levels while maintaining high repair efficiency. Full article

(This article belongs to the Special Issue Security Defense Technologies for the New-Type Power System)

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24 pages, 2148 KB

Open AccessArticle

Evaluation of the Locational Value of Diverse Non-Wires Alternative Portfolios for Network Investment Deferral: From Individual DERs to Integrated Controllable Microgrids

by Juwon Park, San Kim and Sung-Kwan Joo

Electronics 2026, 15(9), 1843; https://doi.org/10.3390/electronics15091843 (registering DOI) - 27 Apr 2026

Abstract

Increasing load demand and localized constraints are driving the need for cost-effective alternatives to traditional network reinforcement. However, existing Non-Wires Alternative (NWA) planning approaches often rely on simplified assumptions or computationally intensive full-year optimization, limiting their practical applicability. This study proposes a planning-oriented [...] Read more.

Increasing load demand and localized constraints are driving the need for cost-effective alternatives to traditional network reinforcement. However, existing Non-Wires Alternative (NWA) planning approaches often rely on simplified assumptions or computationally intensive full-year optimization, limiting their practical applicability. This study proposes a planning-oriented method integrating 8760-h Direct Load Flow (DLF)-based assessment, worst-case screening, and Mixed-Integer Linear Programming (MILP)-based resource sizing for the coordinated deployment of Energy Storage Systems (ESSs), Demand Response (DR), and Photovoltaic (PV) resources, along with building-scale microgrid candidates. The proposed microgrid candidates are modeled as grid-connected, building-scale configurations in which PV, ESSs, and DR are co-located at a single node, representing integrated resource units within the distribution system. The results show that voltage constraints are the dominant limiting factor and that NWAs primarily function as an investment deferral strategy rather than a full replacement for traditional reinforcement, delaying constraint violations by approximately 2 to 14 years. An ESS provides the most direct contribution to constraint mitigation, while DR and PV offer complementary support. The results also highlight the importance of locational deployment. In particular, a co-located microgrid configuration (MG_111) is selected as the optimal portfolio under moderate load growth conditions (Case B, 2%), demonstrating the practical feasibility of integrated DER deployment at a single node. Economic feasibility is found to be highly sensitive to incentive design, with profitability achieved only under favorable compensation conditions. These results demonstrate that coordinated DER portfolios can effectively extend deferral periods and provide practical insights into cost-effective NWA planning under realistic operating conditions. Full article

(This article belongs to the Special Issue Application of Microgrids in Power System)

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19 pages, 961 KB

Open AccessArticle

A Physics-Guided Residual Correction Framework for Four-Hour-Ahead Photovoltaic Power Forecasting

by Yihang Ou Yang, Yufeng Guo, Dazhi Yang, Junci Tang, Qun Yang, Yuxin Jiang, Lichaozheng Qin and Lai Jiang

Electronics 2026, 15(9), 1842; https://doi.org/10.3390/electronics15091842 (registering DOI) - 27 Apr 2026

Abstract

Accurate ultra-short-term photovoltaic (PV) power forecasting is essential for secure grid dispatch and renewable-rich system operation, yet it remains difficult because of rapid weather fluctuations and error accumulation in multi-step prediction. This paper proposes a decoupled physics-guided residual-correction framework, built on an attention-based [...] Read more.

Accurate ultra-short-term photovoltaic (PV) power forecasting is essential for secure grid dispatch and renewable-rich system operation, yet it remains difficult because of rapid weather fluctuations and error accumulation in multi-step prediction. This paper proposes a decoupled physics-guided residual-correction framework, built on an attention-based sequence-to-sequence (Seq2Seq) architecture, for deterministic 4 h ahead rolling PV forecasting at 15 min resolution. In the first stage, a physical model maps numerical weather prediction (NWP) inputs to a deterministic baseline trajectory while preserving physical bounds. In the second stage, an Attention-Seq2Seq network learns the structured residual trajectory from historical sequences. The global attention mechanism allows the decoder to focus on the most informative historical states, helping reduce information loss and error accumulation over extended horizons. Experiments on a 22-month real-world PV dataset show that the proposed framework outperforms conventional linear and nonlinear benchmarks, reducing root mean square error (RMSE) and mean absolute error (MAE) by 23.79% and 39.17%, respectively, relative to the physical baseline. The framework also maintains robust instantaneous tracking under rapidly changing cloud conditions and preserves a 30–40% error reduction rate at Steps 12–16, supporting reliable intraday scheduling. Full article

(This article belongs to the Special Issue Design and Control of Renewable Energy Systems in Smart Cities)

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19 pages, 2161 KB

Open AccessArticle

TLA-SleepNet: A Transformer–BiLSTM–Attention Network for Automatic Sleep Staging Using Single-Channel Ballistocardiogram Signals

by Jianfeng Wu, Banteng Liu and Ke Wang

Electronics 2026, 15(9), 1841; https://doi.org/10.3390/electronics15091841 (registering DOI) - 27 Apr 2026

Abstract

Traditional sleep staging studies typically rely on signals collected using contact-based sensors, which may interfere with the natural sleep state of subjects and thus affect the authenticity and reliability of the recorded data. To address this limitation, this study proposes an automatic sleep [...] Read more.

Traditional sleep staging studies typically rely on signals collected using contact-based sensors, which may interfere with the natural sleep state of subjects and thus affect the authenticity and reliability of the recorded data. To address this limitation, this study proposes an automatic sleep staging method based on non-contact single-channel ballistocardiogram (BCG) signals. First, band-pass filtering is applied to the raw BCG signals to separate the heart rate and respiratory components. Heart rate variability (HRV) and respiratory rate variability (RRV) features are then extracted, and mutual information is used to select key feature subsets that exhibit strong correlations with different sleep stages. Considering the complexity and prominent temporal characteristics of real-world sleep data, a temporal modeling network named TLA-SleepNet is constructed to enhance the model’s capability in capturing complex sequential features and improving robustness. Experiments conducted on 10 independent sleep recordings containing a total of 10,614 sleep epochs demonstrate that, under subject non-independent testing conditions with five-fold cross-validation, the proposed method achieves an accuracy of 87.1% in the sleep staging task, with precision, kappa coefficient, and F1-score reaching 92.4%, 81.9%, and 88.7%, respectively. The results indicate that the proposed method can achieve a reliable sleep staging performance without direct contact between sensors and the human body, providing a feasible solution for non-contact sleep monitoring in home-based and mobile healthcare applications. Full article

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35 pages, 5703 KB

Open AccessArticle

An Interpretable Agent-Assisted Pipeline for Statistical Anomaly Detection in IoT Temperature Time Series

by Luis Miguel Pires and José Braga de Vasconcelos

Electronics 2026, 15(9), 1840; https://doi.org/10.3390/electronics15091840 (registering DOI) - 27 Apr 2026

Abstract

The research presents an interpretable framework which detects anomalies in IoT temperature time-series data with low complexity for use in edge environments that lack resources. The proposed solution uses three traditional statistical filters which include Hampel and Interquartile Range (IQR) and Z-Score to [...] Read more.

The research presents an interpretable framework which detects anomalies in IoT temperature time-series data with low complexity for use in edge environments that lack resources. The proposed solution uses three traditional statistical filters which include Hampel and Interquartile Range (IQR) and Z-Score to build an agent-assisted decision layer which selects the best method through a multi-criteria cost function. The framework runs tests on a structured synthetic dataset which contains seven different anomaly tests and on an actual IoT dataset which was gathered from eight separate sensor points. The researchers use standard anomaly detection metrics which include precision and recall and F1-score and false positive rate to conduct their complete evaluation. The proposed method is tested against two machine learning baseline methods which are Isolation Forest and One-Class Support Vector Machine (OC-SVM). The results show that the agent-assisted method achieves detection results which match industry standards while showing high interpretability and low processing needs. The framework demonstrates its ability to function in actual IoT environments through its use of authentic real-world data, and also basic statistical techniques together with an adjustable decision system create a strong and understandable method to detect anomalies in IoT sensing systems. Full article

(This article belongs to the Section Artificial Intelligence)

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14 pages, 1630 KB

Open AccessArticle

Broadband Stepped-Impedance Wilkinson Power Divider with Improved Performance

by Stelios Tsitsos, Maria Prousali and Hristos T. Anastassiu

Electronics 2026, 15(9), 1839; https://doi.org/10.3390/electronics15091839 (registering DOI) - 26 Apr 2026

Abstract

Herein, we present the analysis, design, optimization, and fabrication of a broadband, stepped-impedance Wilkinson power divider. The proposed structure employs stepped-impedance transmission lines and open-circuited stubs, achieving a simple and compact implementation while maintaining a wideband frequency response. Initially, transmission-line-based circuit analysis was [...] Read more.

Herein, we present the analysis, design, optimization, and fabrication of a broadband, stepped-impedance Wilkinson power divider. The proposed structure employs stepped-impedance transmission lines and open-circuited stubs, achieving a simple and compact implementation while maintaining a wideband frequency response. Initially, transmission-line-based circuit analysis was performed to extract the design equations, followed by simulation and optimization to enhance impedance matching and output-port isolation over a broad bandwidth. Finally, the proposed divider was fabricated using microstrip-line technology, and experimental measurements were conducted using the Agilent E5071C vector network analyzer. The simulation and measurement results showed efficient wideband operation over the 1–4 GHz frequency range. Specifically, the measured return loss at the input port was <−10 dB; the corresponding return loss at the output ports was <−15 dB. The measured insertion loss was −3.73 ± 0.42 dB. The isolation between the output ports was <−10 dB, reaching approximately −30 dB at 2.1 GHz and −25 dB at the center operating frequency (f₀ = 2.5 GHz). The amplitude and phase imbalances were 0 ± 0.2 dB and 0^o ± 0.8^o, respectively. Furthermore, the overall size of the proposed wideband Wilkinson power divider was 0.35λg × 0.21λg. Compared to previous designs, the divider proposed in this study exhibits an improved and more symmetric frequency response, as well as a substantially reduced size, making it suitable for several modern wireless technologies such as Wi-Fi, Bluetooth, GPS, DCS, WCDMA, and sub-6 GHz 5G communication systems. Full article

(This article belongs to the Special Issue High-Frequency Electromagnetic Technologies for 5G/6G Communications: Antenna Design and Signal Optimization)

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30 pages, 6414 KB

Open AccessArticle

Research on Distracted and Fatigue-Related Driving Behavior Detection Based on YOLOv12-LAD

by Xiyao Liu, Zhiwei Guan, Qiang Chen and Yi Ren

Electronics 2026, 15(9), 1838; https://doi.org/10.3390/electronics15091838 (registering DOI) - 26 Apr 2026

Abstract

Distracted and fatigue-related driving behaviors are major causes of road traffic accidents, creating an urgent need for reliable driver monitoring systems. Vision-based detection methods have garnered widespread attention due to their low cost of deployment and practical applicability. However, existing lightweight models often [...] Read more.

Distracted and fatigue-related driving behaviors are major causes of road traffic accidents, creating an urgent need for reliable driver monitoring systems. Vision-based detection methods have garnered widespread attention due to their low cost of deployment and practical applicability. However, existing lightweight models often suffer from limited global contextual perception and insufficient preservation of fine details. Motivated by these challenges, this study introduces an improved distracted and fatigue-related driving behavior detection model, YOLOv12-LAD, built on the YOLOv12 architecture. The proposed framework integrates a Large Separable Kernel Attention module (LSKA) to enhance global contextual perception, an Adaptive Downsampling module (ADown) to mitigate information loss during feature compression, and a Dynamic Sampling module (DySample) to enable content-adaptive feature reconstruction and improve multi-scale behavior representation. Experimental results show that YOLOv12-LAD achieved 97.5% precision, 96.3% recall, and 98.4% mAP@50 with only 2.5 million parameters, 6.2 GFLOPs, and an inference speed of 249 FPS. Ablation studies, comparisons with representative models, cross-dataset evaluation, and real-vehicle tests further verify the effectiveness and robustness of the proposed method. The proposed method demonstrates strong performance while maintaining computational efficiency, making it suitable for real-time vision-based driver monitoring applications. Full article

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33 pages, 2618 KB

Open AccessArticle

Bridging Cross-Modal Semantic Gaps with Multi-Source Semantic Anchors in Knowledge-Based Visual Question Answering

by Junming Hu, Jinxiong Zhang, Feng Zhan and Yiran Huang

Electronics 2026, 15(9), 1837; https://doi.org/10.3390/electronics15091837 (registering DOI) - 26 Apr 2026

Abstract

Knowledge-based visual question answering (KB-VQA) requires leveraging external knowledge relevant to the image to assist reasoning. Existing methods typically convert images into a single textual description for knowledge retrieval or directly rely on the implicit knowledge within large language models to generate answers. [...] Read more.

Knowledge-based visual question answering (KB-VQA) requires leveraging external knowledge relevant to the image to assist reasoning. Existing methods typically convert images into a single textual description for knowledge retrieval or directly rely on the implicit knowledge within large language models to generate answers. However, a single textual description struggles to preserve fine-grained visual information such as object attributes and scene text, limiting retrieval quality. Meanwhile, naively fusing multi-source information tends to introduce modality noise, undermining reasoning accuracy. To address these issues, we propose a unified framework that constructs multi-source semantic anchors to bridge the cross-modal semantic gaps among vision, questions, and external knowledge. Specifically, we unify image captions, object tags, and optical character recognition (OCR) text as semantic anchors. These anchors serve as shared intermediaries to pre-align visual and textual features, avoiding direct interaction between heterogeneous modalities. During cross-modal fusion, a cross-residual gating mechanism adaptively suppresses modality noise by leveraging the semantic anchors as stable references. The framework further integrates contrastive learning to strengthen cross-modal alignment and employs a retrieve-then-read pipeline for open-domain answer reasoning. Experiments on the OK-VQA, FVQA, and A-OKVQA datasets demonstrate that the proposed framework outperforms state-of-the-art methods across multiple metrics, validating the effectiveness and robustness of the proposed framework. Full article

(This article belongs to the Topic Generative AI and Interdisciplinary Applications)

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