Electronics

25 pages, 2495 KB

Open AccessArticle

Low-Frequency Oscillation Suppression Strategy for Ship Microgrid Based on Virtual PSS Adaptive Damping Control with Supercapacitor

by Yue Ding, Ke Zhao, Jiandong Duan and Li Sun

Electronics 2026, 15(2), 390; https://doi.org/10.3390/electronics15020390 - 15 Jan 2026

Abstract

A virtual power system stabilizer (PSS) adaptive damping control strategy based on a supercapacitor is used to suppress oscillations in a ship microgrid. The energy transmission path of the proposed strategy is to apply the equivalent damping power to the rotor by varying [...] Read more.

A virtual power system stabilizer (PSS) adaptive damping control strategy based on a supercapacitor is used to suppress oscillations in a ship microgrid. The energy transmission path of the proposed strategy is to apply the equivalent damping power to the rotor by varying the electromagnetic power of the generator. Compared with conventional PSSs based on supercapacitors, storage devices not only enhance the capacity of damping power injected into the microgrid but also have more flexible configurations applicable to the size constraints of the ship microgrid. In addition, the adaptive control ensures that the DC bus voltage of the converter of the energy storage device is controlled within the neighborhood of the steady-state operating point, ensuring the asymptotic stability of the damping system. Finally, an experimental platform was built to verify the correctness and validity of the above theory. Full article

(This article belongs to the Special Issue Cyber-Physical System Applications in Smart Power and Microgrids)

22 pages, 1592 KB

Open AccessArticle

NOMA-Enabled Cooperative Two-Way Communications for Both Primary and Secondary Systems

by Dong-Hua Chen and Kaiwei Ruan

Electronics 2026, 15(2), 389; https://doi.org/10.3390/electronics15020389 (registering DOI) - 15 Jan 2026

Abstract

With the aid of non-orthogonal multiple access (NOMA), this paper investigates simultaneous two-way communications for cooperative cognitive radio networks, where a group of secondary access points (APs) scattered over a primary cell not only serve their own users but also help the primary [...] Read more.

With the aid of non-orthogonal multiple access (NOMA), this paper investigates simultaneous two-way communications for cooperative cognitive radio networks, where a group of secondary access points (APs) scattered over a primary cell not only serve their own users but also help the primary cell-edge users

^{'}

transmissions cooperatively. As a reward for the cooperation, these APs are granted full access to the primary frequency spectrum. To coordinate the two-way transmissions of the primary and secondary networks, we propose a spectrum-efficient cooperative scheme that only involves two transmission phases, and particularly, the two variable-length transmission phases endow the system with the capability of adapting to possible DL and UL traffic asymmetry. For the system design, we formulate a power minimization problem subject to the bidirectional transmission rate constraints of both networks. The formulated problem is shown to be nonlinear and nonconvex, and for the numerically efficient solution, we propose an iterative algorithm facilitated by the successive convex approximation technique. Simulation results show that the proposed design algorithm has fast convergence speed and is superior to the hybrid orthogonal multiple access and NOMA schemes. Full article

(This article belongs to the Special Issue Recent Advances in Radio Resource Management for Future Cellular Networks)

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9 pages, 6264 KB

Open AccessArticle

A 4.7–8.8 GHz Wideband Switched Coupled Inductor VCO for Dielectric Spectroscopy Sensors

by Kiho Lee, Hapsah Aulia Azzahra, Muhammad Fakhri Mauludin, Dong-Ho Lee, Jusung Kim and Songcheol Hong

Electronics 2026, 15(2), 388; https://doi.org/10.3390/electronics15020388 - 15 Jan 2026

Abstract

The miniaturization of dielectric sensing has driven the development of both oscillator- and receiver-based sensors. Wide-frequency-range and low-power-consumption voltage-controlled oscillators (VCOs) are required as a reference clock for receiver-based dielectric spectroscopy. In this paper, we propose a switched coupled inductor VCO offering sufficiently [...] Read more.

The miniaturization of dielectric sensing has driven the development of both oscillator- and receiver-based sensors. Wide-frequency-range and low-power-consumption voltage-controlled oscillators (VCOs) are required as a reference clock for receiver-based dielectric spectroscopy. In this paper, we propose a switched coupled inductor VCO offering sufficiently wide bandwidth in a power-efficient manner. The proposed switched coupled inductor offers higher coupling factor and mutual inductance compared to direct switched inductor schemes along with a higher quality factor and tuning range. The proposed switched coupled inductor improved the frequency tuning range by 21% compared to the conventional VCO. The measurement results show that the proposed VCO oscillates from 4.7 to 8.8 GHz frequency, suitable for dielectric spectroscopy sensors. With only 4.5 mW power consumption, the proposed VCO can achieve −103.3 dBc/Hz phase noise at 1 MHz offset, with a resulting tuning range figure-of-merit (FOMT) of −187.4 dBc/Hz. Full article

(This article belongs to the Special Issue Microwave Engineering and Applied Electromagnetism in Modern Engineering)

33 pages, 4457 KB

Open AccessFeature PaperArticle

Resilience-Oriented Energy Management of Networked Microgrids: A Case Study from Lombok, Indonesia

by Mahshid Javidsharifi, Hamoun Pourroshanfekr Arabani, Najmeh Bazmohammadi, Juan C. Vasquez and Josep M. Guerrero

Electronics 2026, 15(2), 387; https://doi.org/10.3390/electronics15020387 - 15 Jan 2026

Abstract

Building resilient and sustainable energy systems is a critical challenge for disaster-prone regions in the Global South. This study investigates the energy management of a networked microgrid (NMG) system on Lombok Island, Indonesia, a region frequently exposed to natural disasters (NDs) and characterized [...] Read more.

Building resilient and sustainable energy systems is a critical challenge for disaster-prone regions in the Global South. This study investigates the energy management of a networked microgrid (NMG) system on Lombok Island, Indonesia, a region frequently exposed to natural disasters (NDs) and characterized by vulnerable grid infrastructure. A multi-objective optimization framework is developed to jointly minimize operational costs, load-not-served, and environmental impacts under both normal and abnormal operating conditions. The proposed strategy employs the Multi-objective JAYA (MJAYA) algorithm to coordinate photovoltaic generation, diesel generators, battery energy storage systems, and inter-microgrid power exchanges within a 20 kV distribution network. Using real load, generation, and electricity price data, we evaluate the NMG’s performance under five representative fault scenarios that emulate ND-induced outages, including grid disconnection and loss of inter-microgrid links. Results show that the interconnected NMG structure significantly enhances system resilience, reducing load-not-served from 366.3 kWh in fully isolated operation to only 31.7 kWh when interconnections remain intact. These findings highlight the critical role of cooperative microgrid networks in strengthening community-level energy resilience in vulnerable regions. The proposed framework offers a practical decision-support tool for planners and governments seeking to enhance energy security and advance sustainable development in disaster-affected areas. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence in Modern Power and Energy Systems, 2nd Edition)

21 pages, 8269 KB

Open AccessArticle

RTDNet: Modulation-Conditioned Attention Network for Robust Denoising of LPI Radar Signals

by Min-Wook Jeon, Do-Hyun Park and Hyoung-Nam Kim

Electronics 2026, 15(2), 386; https://doi.org/10.3390/electronics15020386 - 15 Jan 2026

Abstract

Accurate processing of low-probability-of-intercept (LPI) radar signals poses a critical challenge in electronic warfare support (ES). These signals are often transmitted at very low signal-to-noise ratios (SNRs), making reliable analysis difficult. Noise interference can lead to misinterpretation, potentially resulting in strategic errors and [...] Read more.

Accurate processing of low-probability-of-intercept (LPI) radar signals poses a critical challenge in electronic warfare support (ES). These signals are often transmitted at very low signal-to-noise ratios (SNRs), making reliable analysis difficult. Noise interference can lead to misinterpretation, potentially resulting in strategic errors and jeopardizing the safety of friendly forces. Accordingly, effective noise suppression techniques that preserve the original waveform shape are crucial for reliable analysis and accurate parameter estimation. In this study, we propose the recognize-then-denoise network (RTDNet), which effectively removes noise while minimizing signal distortion. The proposed approach first employs a modulation recognition network to infer the modulation scheme and then feeds the inferred label to an attention-based denoiser to guide feature extraction. By leveraging prior information, the attention mechanism preserves key features and reconstructs challenging patterns such as polytime and polyphase codes. Simulation results indicate that RTDNet more effectively removes noise while maintaining the waveform shape and salient signal structures compared with existing techniques. Furthermore, RTDNet improves modulation classification accuracy and parameter estimation performance. Finally, its compact model size and fast inference meet the performance and efficiency requirements of ES missions. Full article

(This article belongs to the Section Artificial Intelligence)

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51 pages, 3714 KB

Open AccessArticle

Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review

by Carlos Álvarez-López, Alfonso González-Briones and Tiancheng Li

Electronics 2026, 15(2), 385; https://doi.org/10.3390/electronics15020385 - 15 Jan 2026

Abstract

This paper reviews Artificial Intelligence techniques for distributed energy management, focusing on integrating machine learning, reinforcement learning, and multi-agent systems within IoT-Edge-Cloud architectures. As energy infrastructures become increasingly decentralized and heterogeneous, AI must operate under strict latency, privacy, and resource constraints while remaining [...] Read more.

This paper reviews Artificial Intelligence techniques for distributed energy management, focusing on integrating machine learning, reinforcement learning, and multi-agent systems within IoT-Edge-Cloud architectures. As energy infrastructures become increasingly decentralized and heterogeneous, AI must operate under strict latency, privacy, and resource constraints while remaining transparent and auditable. The study examines predictive models ranging from statistical time series approaches to machine learning regressors and deep neural architectures, assessing their suitability for embedded deployment and federated learning. Optimization methods—including heuristic strategies, metaheuristics, model predictive control, and reinforcement learning—are analyzed in terms of computational feasibility and real-time responsiveness. Explainability is treated as a fundamental requirement, supported by model-agnostic techniques that enable trust, regulatory compliance, and interpretable coordination in multi-agent environments. The review synthesizes advances in MARL for decentralized control, communication protocols enabling interoperability, and hardware-aware design for low-power edge devices. Benchmarking guidelines and key performance indicators are introduced to evaluate accuracy, latency, robustness, and transparency across distributed deployments. Key challenges remain in stabilizing explanations for RL policies, balancing model complexity with latency budgets, and ensuring scalable, privacy-preserving learning under non-stationary conditions. The paper concludes by outlining a conceptual framework for explainable, distributed energy intelligence and identifying research opportunities to build resilient, transparent smart energy ecosystems. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

32 pages, 8181 KB

Open AccessArticle

Advanced Energy Management and Dynamic Stability Assessment of a Utility-Scale Grid-Connected Hybrid PV–PSH–BES System

by Sharaf K. Magableh, Mohammad Adnan Magableh, Oraib M Dawaghreh and Caisheng Wang

Electronics 2026, 15(2), 384; https://doi.org/10.3390/electronics15020384 - 15 Jan 2026

Abstract

Despite the growing adoption of hybrid energy systems integrating solar photovoltaic (PV), pumped storage hydropower (PSH), and battery energy storage (BES), comprehensive studies on their dynamic stability and interaction mechanisms remain limited, particularly under weak grid conditions. Due to the high impedance of [...] Read more.

Despite the growing adoption of hybrid energy systems integrating solar photovoltaic (PV), pumped storage hydropower (PSH), and battery energy storage (BES), comprehensive studies on their dynamic stability and interaction mechanisms remain limited, particularly under weak grid conditions. Due to the high impedance of weak grids, ensuring stability across varied operating scenarios is crucial for advancing grid resilience and energy reliability. This paper addresses these research gaps by examining the interaction dynamics between PV, PSH, and BES on the DC side and the utility grid on the AC side. The study identifies operating-region-dependent instability mechanisms arising from negative incremental resistance behavior and weak grid interactions and proposes a virtual-impedance-based active damping control strategy to suppress poorly damped oscillatory modes. The proposed controller effectively reshapes the converter output impedance, shifts unstable eigenmodes into the left-half plane, and improves phase margins without requiring additional hardware components or introducing steady-state power losses. System stability is analytically assessed using root-locus, Bode, and Nyquist criteria within a developed small-signal state-space model, and further validated through large-signal real-time simulations on an OPAL-RT platform. The main contributions of this study are threefold: (i) a comprehensive stability analysis of a utility-scale grid-connected hybrid PV–PSH–BES system under weak grid conditions, (ii) identification of operating-region-dependent instability mechanisms associated with DC–link interactions, and (iii) development and real-time validation of a practical virtual-impedance-based active damping strategy for enhancing system stability and grid integration reliability. Full article

(This article belongs to the Special Issue Advances in Power Electronics Converters for Modern Power Systems)

16 pages, 3131 KB

Open AccessArticle

DOVCII-Based Notch Filter Employing a Single Tunable Active Inductor

by Riccardo Olivieri, Tobia Carini, Gianluca Barile, Vincenzo Stornelli and Giuseppe Ferri

Electronics 2026, 15(2), 383; https://doi.org/10.3390/electronics15020383 - 15 Jan 2026

Abstract

This work presents a notch filter architecture based on a dual-output second-generation voltage conveyor, designed with a current-mode approach. The proposed topology employs a single frequency-selective LC branch and directly uses the two voltage outputs of the DOVCII to generate a notch response [...] Read more.

This work presents a notch filter architecture based on a dual-output second-generation voltage conveyor, designed with a current-mode approach. The proposed topology employs a single frequency-selective LC branch and directly uses the two voltage outputs of the DOVCII to generate a notch response without additional active stages. Analytical expressions for the transfer function, notch frequency, and quality factor are derived, highlighting independent control of the passband gain and notch parameters. A sensitivity analysis demonstrates that the notch frequency depends exclusively on the LC product with half-order sensitivities, while the quality factor is predominantly controlled by a single resistor, resulting in predictable tuning and improved tolerance to passive component variations. Transistor-level analysis of the proposed filter was carried out using a standard AMS 0.35 μm CMOS process and has been validated through both circuit-level simulations and experimental measurements using a DOVCII implementation based on the AD844 current-feedback amplifier. Prototypes operating at 100 kHz and 50 Hz notch frequencies have been implemented, the latter employing a current-mode inductance simulator to avoid bulky passive inductors. Full article

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

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22 pages, 2181 KB

Open AccessArticle

Design and Manufacturability-Aware Optimization of a 30 GHz Gap Waveguide Bandpass Filter Using Resonant Posts

by Antero Ccasani-Davalos, Erwin J. Sacoto-Cabrera, L. Walter Utrilla Mego, Julio Cesar Herrera-Levano, Roger Jesus Coaquira-Castillo, Yesenia Concha-Ramos and Edison Moreno-Cardenas

Electronics 2026, 15(2), 382; https://doi.org/10.3390/electronics15020382 - 15 Jan 2026

Abstract

This paper presents the design and optimization, based on electromagnetic simulation, of a fifth-order bandpass filter centered at 30 GHz, implemented using Gap Waveguide (GWG) technology and pole-type cylindrical resonators, intended for applications in 5G communication systems and high-frequency satellite links. Unlike conventional [...] Read more.

This paper presents the design and optimization, based on electromagnetic simulation, of a fifth-order bandpass filter centered at 30 GHz, implemented using Gap Waveguide (GWG) technology and pole-type cylindrical resonators, intended for applications in 5G communication systems and high-frequency satellite links. Unlike conventional Chebyshev designs reported in the literature, this study proposes an integrated methodology that combines theoretical synthesis, full-wave electromagnetic modeling, and multivariable optimization, while accounting for manufacturing constraints. The developed method encompasses the electromagnetic characterization of individual resonators, the extraction of cavity–cavity coupling parameters, and the complete implementation of the filter using full-wave electromagnetic simulations. A distinctive aspect of the proposed approach is the explicit incorporation of practical manufacturing effects, such as rounded corners induced by machining processes, alongside analytical and numerical geometric compensation procedures that preserve the device’s electrical response. Furthermore, a combination of gradient-based optimization algorithms and evolutionary strategies is employed to align the electromagnetic response with the target theoretical behavior. The results obtained through electromagnetic simulation indicate that the designed filter achieves return losses exceeding 20 dB and a fractional bandwidth of 4.95%, consistent with the reference Chebyshev response. Finally, the potential extension of the proposed approach to higher frequency bands is discussed conceptually, laying the groundwork for future work that includes experimental validation. Full article

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26 pages, 2749 KB

Open AccessArticle

Deep-Learning-Driven Adaptive Filtering for Non-Stationary Signals: Theory and Simulation

by Manuel J. Cabral S. Reis

Electronics 2026, 15(2), 381; https://doi.org/10.3390/electronics15020381 - 15 Jan 2026

Abstract

Adaptive filtering remains a cornerstone of modern signal processing but faces fundamental challenges when confronted with rapidly changing or nonlinear environments. This work investigates the integration of deep learning into adaptive-filter architectures to enhance tracking capability and robustness in non-stationary conditions. After reviewing [...] Read more.

Adaptive filtering remains a cornerstone of modern signal processing but faces fundamental challenges when confronted with rapidly changing or nonlinear environments. This work investigates the integration of deep learning into adaptive-filter architectures to enhance tracking capability and robustness in non-stationary conditions. After reviewing and analyzing classical algorithms—LMS, NLMS, RLS, and a variable step-size LMS (VSS-LMS)—their theoretical stability and mean-square error behavior are formalized under a slow-variation system model. Comprehensive simulations using drifting autoregressive (AR(2)) processes, piecewise-stationary FIR systems, and time-varying sinusoidal signals confirm the classical trade-off between performance and complexity: RLS achieves the lowest steady-state error, at a quadratic cost, whereas LMS remains computationally efficient with slower adaptation. A stabilized VSS-LMS algorithm is proposed to balance these extremes; the results show that it maintains numerical stability under abrupt parameter jumps while attaining steady-state MSEs that are comparable to RLS (approximately 3 × 10⁻²) and superior robustness to noise. These findings are validated by theoretical tracking-error bounds that are derived for bounded parameter drift. Building on this foundation, a deep-learning-driven adaptive filter is introduced, where the update rule is parameterized by a neural function,

U_{θ}

, that generalizes the classical gradient descent. This approach offers a pathway toward adaptive filters that are capable of self-tuning and context-aware learning, aligning with emerging trends in AI-augmented system architectures and next-generation computing. Future work will focus on online learning and FPGA/ASIC implementations for real-time deployment. Full article

(This article belongs to the Special Issue Advanced System Architectures and AI-Driven Innovations for Next-Generation Computing)

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16 pages, 762 KB

Open AccessArticle

RAE: A Role-Based Adaptive Framework for Evaluating Automatically Generated Public Opinion Reports

by Jinzheng Yu, Yang Xu, Yifan Feng, Ligu Zhu, Hao Shen and Lei Shi

Electronics 2026, 15(2), 380; https://doi.org/10.3390/electronics15020380 - 15 Jan 2026

Abstract

Public Opinion Reports are essential tools for crisis management, yet their evaluation remains a critical bottleneck that often delays response actions. Recently, dominant Large Language Model (LLM)-based evaluators often overlook a critical challenge: highly open-ended dimensions such as “innovation” and “feasibility” require synthesizing [...] Read more.

Public Opinion Reports are essential tools for crisis management, yet their evaluation remains a critical bottleneck that often delays response actions. Recently, dominant Large Language Model (LLM)-based evaluators often overlook a critical challenge: highly open-ended dimensions such as “innovation” and “feasibility” require synthesizing diverse stakeholder perspectives, as different groups judge these qualities from fundamentally different perspectives. Motivated by this, we propose the Role-based Adaptive Evaluation (RAE) framework. This framework employs an adaptive mechanism leveraging multi-perspective evaluation insights through role-based analysis, and further introduces dynamically generated roles tailored to specific contexts for these dimensions. RAE further incorporates multi-role reasoning aggregation to minimize individual biases and enhance evaluation robustness. Extensive experiments demonstrate that RAE significantly improves alignment with human expert judgments, especially on challenging highly open-ended dimensions. Full article

(This article belongs to the Topic Advances in Integrative AI, Machine Learning, and Big Data for Transformative Applications)

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21 pages, 5194 KB

Open AccessArticle

A Typhoon Clustering Model for the Western Pacific Coast Based on Interpretable Machine Learning

by Yanhe Wang, Yinzhen Lv, Lei Zhang, Tianrun Gao, Ruiqi Feng, Yihan Zhou and Wei Zhang

Electronics 2026, 15(2), 379; https://doi.org/10.3390/electronics15020379 - 15 Jan 2026

Abstract

As a complex and destructive natural disaster, the characteristics of typhoons are closely related to human activities, and their accurate categorization is of vital significance for improving disaster warning and management capabilities. This study highlights the key role of typhoon clustering in analyzing [...] Read more.

As a complex and destructive natural disaster, the characteristics of typhoons are closely related to human activities, and their accurate categorization is of vital significance for improving disaster warning and management capabilities. This study highlights the key role of typhoon clustering in analyzing typhoon behaviors, aiming to provide reliable support for disaster prevention and control. Based on the NOAA meteorological dataset from 2003 to 2024, this study firstly adopts the K-means clustering algorithm to classify typhoons into seven categories and then utilizes eight machine learning models to train and validate the classification results, and introduces the Shapley’s additive interpretation (SHAP) algorithm to enhance the interpretability of the models. The study data covers a variety of features such as air temperature, wind speed, atmospheric pressure, and weather station observations, etc. After a systematic preprocessing process, a feature matrix containing key variables such as typhoon intensity and moving speed is constructed. The results show that the XGBoost model outperforms others across multiple evaluation metrics (Accuracy: 0.992, Precision: 0.989, Recall: 0.992, F1.5 Score: 0.990), highlighting its exceptional capability in managing complex weather classification tasks. The seven categories of typhoon types classified by K-means exhibit different feature patterns, while the SHAP analysis further reveals the effects of each feature on the classification and its potential interactions. This study not only verifies the effectiveness of K-means combined with machine learning in typhoon classification but also lays a solid scientific foundation for accurate prediction, risk assessment and optimization of management strategies for typhoon disasters through the in-depth analysis of feature impacts. Full article

(This article belongs to the Special Issue AI-Driven Data Analytics and Mining)

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23 pages, 11947 KB

Open AccessArticle

Geometry-Consistency-Guided Unsupervised Domain Adaptation Framework for Cross-Voltage Transmission-Line Point-Cloud Semantic Segmentation

by Kun Ji, Hongwu Tan, Dabing Yang, Pu Wang, Di Cao, Yuan Gao and Zhou Yang

Electronics 2026, 15(2), 378; https://doi.org/10.3390/electronics15020378 - 15 Jan 2026

Abstract

Semantic segmentation of transmission-line point clouds is fundamental to intelligent power inspection and grid asset management, as segmentation accuracy directly influences defect detection and facility assessment tasks. However, transmission-line point clouds collected across different voltage levels often show significant variations in density and [...] Read more.

Semantic segmentation of transmission-line point clouds is fundamental to intelligent power inspection and grid asset management, as segmentation accuracy directly influences defect detection and facility assessment tasks. However, transmission-line point clouds collected across different voltage levels often show significant variations in density and geometric structure due to heterogeneous LiDAR sensors and flight configurations. Combined with the high cost of large-scale manual annotation, these factors limit the scalability of existing supervised segmentation methods. To overcome these challenges, we propose a geometry-consistency-guided unsupervised domain adaptation framework tailored for cross-voltage transmission-line point-cloud segmentation. The framework employs KPConvX as the backbone and integrates three progressive components. First, a geometric consistency constraint enhances robustness to spatial variations and enables extraction of structural features invariant across voltage levels. Second, a domain feature alignment module reduces distribution shifts through global feature transformation. Third, a minimum-entropy-based pseudo-label refinement strategy improves the reliability of pseudo-labels during self-training. Experiments on a multi-voltage transmission-line dataset demonstrate the effectiveness of the proposed method. With the KPConvX backbone, the framework achieves 66.1% mean Intersection over Union (mIoU) and 94.3% overall accuracy on the unlabeled 110 kV target domain, exceeding the source-only baseline by 15.6% mIoU and outperforming several state-of-the-art UDA methods. This work provides an efficient, annotation-friendly solution for cross-voltage point-cloud segmentation and offers a promising direction for domain adaptation in complex power-grid environments. Full article

(This article belongs to the Special Issue Advances in 3D Computer Vision and 3D Data Processing)

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19 pages, 8046 KB

Open AccessArticle

Instruction Fine-Tuning Through the Lens of Verbatim Memorization

by Jie Zhang, Chi-Ho Lin and Suan Lee

Electronics 2026, 15(2), 377; https://doi.org/10.3390/electronics15020377 - 15 Jan 2026

Abstract

Supervised fine-tuning is key for model alignment, but its mechanisms are debated, with conflicting evidence supporting either a superficial alignment hypothesis or significant task improvements. This paper examines supervised fine-tuning’s impact from the perspective of verbatim memorization. Using the open-source OLMo-2 model series [...] Read more.

Supervised fine-tuning is key for model alignment, but its mechanisms are debated, with conflicting evidence supporting either a superficial alignment hypothesis or significant task improvements. This paper examines supervised fine-tuning’s impact from the perspective of verbatim memorization. Using the open-source OLMo-2 model series and test datasets (instruction format, safety-sensitive, and factual knowledge) constructed from its pre-training corpus, we analyzed changes across memorization, linguistic styles, and task performance. We found that supervised fine-tuning significantly weakens the model’s verbatim memorization of pre-training data. Simultaneously, it improves generated text in terms of alignment objectives, such as polite expression and structured organization. However, this process also leads to performance degradation on knowledge-intensive downstream tasks. Further representation analysis reveals that these changes are mainly concentrated in the later layers of the model. We conclude that supervised fine-tuning acts as a continuation of the learning process on new data. By adjusting model representations, supervised fine-tuning induces a learning tilt toward the styles and content of the instruction-tuning dataset. This inclination successfully instills alignment objectives while consequently reducing the effective accessibility of previously learned knowledge, which indicates the observed degradation in both pre-training data memorization and factual task performance. The source code is publicly available. Full article

(This article belongs to the Special Issue Innovative Applications of Large Language Models in Natural Language Processing (NLP))

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14 pages, 1872 KB

Open AccessArticle

An AI-Driven Trainee Performance Evaluation in XR-Based CPR Training System for Enhancing Personalized Proficiency

by Junhyung Kwon and Won-Tae Kim

Electronics 2026, 15(2), 376; https://doi.org/10.3390/electronics15020376 - 15 Jan 2026

Abstract

Cardiac arrest is a life-threatening emergency requiring immediate intervention, with bystander-initiated Cardiopulmonary resuscitation (CPR) being critical for survival, especially in out-of-hospital situations where medical help is often delayed. Given that over 70% of out-of-hospital cases occur in private residences, there is a growing [...] Read more.

Cardiac arrest is a life-threatening emergency requiring immediate intervention, with bystander-initiated Cardiopulmonary resuscitation (CPR) being critical for survival, especially in out-of-hospital situations where medical help is often delayed. Given that over 70% of out-of-hospital cases occur in private residences, there is a growing imperative to provide widespread CPR training to the public. However, conventional instructor-led CPR training faces inherent limitations regarding spatiotemporal constraints and the lack of personalized feedback. To address these issues, this paper proposes an AI-integrated XR-based CPR training system designed as an advanced auxiliary tool for skill acquisition. The system integrates vision-based pose estimation with multimodal sensor data to assess the trainee’s posture and compression metrics in accordance with Korean regional CPR guidelines. Moreover, it utilizes a Large Language Model to evaluate verbal protocols, including requesting an emergency call that aligns with the guidelines. Experimental validation of the proof-of-concept reveals a verbal evaluation accuracy of 88% and a speech recognition accuracy of approximately 95%. Furthermore, the optimized concurrent architecture provides a real-time response latency under 0.5 s, and the automated marker-based tracking ensures precise spatial registration without manual calibration. These results confirm the technical feasibility of the system as a complementary solution for basic life support education. Full article

(This article belongs to the Special Issue Virtual Reality Applications in Enhancing Human Lives)

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24 pages, 3870 KB

Open AccessArticle

A Variable Frequency Agricultural Sensing Method Based on Deep Prediction

by Rihong Zhang, Zhaokang Gong, Xiaoming Li, Guichao Ling, Binger Zhu, Shu Chen and Baoe Wang

Electronics 2026, 15(2), 375; https://doi.org/10.3390/electronics15020375 - 15 Jan 2026

Abstract

To address the challenges of low data validity and limited energy efficiency in agricultural IoT, we propose a deep predictive agricultural variable-frequency sensing method. First, we construct a hybrid prediction model, denoted as SVMD-TCN-R-GRU-T (STRGT). This model integrates successive variational mode decomposition (SVMD) [...] Read more.

To address the challenges of low data validity and limited energy efficiency in agricultural IoT, we propose a deep predictive agricultural variable-frequency sensing method. First, we construct a hybrid prediction model, denoted as SVMD-TCN-R-GRU-T (STRGT). This model integrates successive variational mode decomposition (SVMD) with an optimized TCN-GRU architecture, thereby improving prediction accuracy. Building on this framework, we design a frequency conversion sampling method under dual detection analysis (FCSDDA). This approach employs wavelet transform to determine the minimum sampling rate and incorporates dynamic time warping evaluate data variation. The dual detection mechanism enables real-time adjustment of sensor acquisition frequency. Experimental results demonstrate that the proposed model significantly outperforms conventional models in terms of RMSE, MAE, and MAPE. When the STRGT outputs are applied as inputs to the FCSDDA algorithm, the system achieved optimal improvements in energy efficiency improvement rate (84.61%) and data value density (0.3368), exceeding the performance of other prediction model variants. These findings confirm that prediction accuracy directly influences adaptive sensing performance. This indicates that the method can effectively achieve dual optimization of energy saving and data validity in testing scenarios. In the future, more agricultural sensing scenarios can be validated. Full article

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13 pages, 6118 KB

Open AccessCommunication

A Bidirectional Right-Hand Circularly Polarized Endfire Antenna Array for 5G Tunnel Communications

by Wenbo Li, Haitao Lu, Peng Xu and Xiao Cai

Electronics 2026, 15(2), 374; https://doi.org/10.3390/electronics15020374 - 15 Jan 2026

Abstract

For 5G tunnel communications, antennas often face critical challenges arising from severe path loss and multipath fading in confined environments, as well as polarization mismatch under dynamic propagation conditions. This paper proposes a 3.5-GHz circularly polarized (CP) endfire antenna array with bidirectional right-hand [...] Read more.

For 5G tunnel communications, antennas often face critical challenges arising from severe path loss and multipath fading in confined environments, as well as polarization mismatch under dynamic propagation conditions. This paper proposes a 3.5-GHz circularly polarized (CP) endfire antenna array with bidirectional right-hand CP radiation, featuring high gain, low profile, and compact configuration. The array is implemented on a single-layer F4B substrate and integrates eight pairs of electric and magnetic dipoles to synthesize orthogonal linear field components required for CP radiation. By applying the extended method of maximum power transmission efficiency, constraints on the amplitude and phase are introduced to maximize the CP gain in the endfire direction. A 16-element linear array prototype is fabricated and tested for validation. Measurement results show that the proposed array achieves a bidirectional right-hand CP endfire gain exceeding 12.2 dBic, an impedance bandwidth from 3.1 to 3.78 GHz, and a 3 dB axial ratio bandwidth of 19.5%, demonstrating its suitability for 5G tunnel communication applications. Full article

(This article belongs to the Special Issue Recent Advancements in Antenna Technologies: Novel Design Techniques and Emerging Applications)

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25 pages, 3803 KB

Open AccessArticle

Enhanced Frequency Dynamic Support for PMSG Wind Turbines via Hybrid Inertia Control

by Jian Qian, Yina Song, Gengda Li, Ziyao Zhang, Yi Wang and Haifeng Yang

Electronics 2026, 15(2), 373; https://doi.org/10.3390/electronics15020373 - 14 Jan 2026

Abstract

High penetration of wind farms into the power grid lowers system inertia and compromises stability. This paper proposes a grid-forming control strategy for Permanent Magnet Synchronous Generator (PMSG) wind turbines based on DC-link voltage matching and virtual inertia. First, a relationship between grid [...] Read more.

High penetration of wind farms into the power grid lowers system inertia and compromises stability. This paper proposes a grid-forming control strategy for Permanent Magnet Synchronous Generator (PMSG) wind turbines based on DC-link voltage matching and virtual inertia. First, a relationship between grid frequency and DC-link voltage is established, replacing the need for a phase-locked loop. Then, DC voltage dynamics are utilized to trigger a real-time switching of the power tracking curve, releasing the rotor’s kinetic energy for inertia response. This is further coordinated with a de-loading control that maintains active power reserves through over-speeding or pitch control. Finally, the MATLAB/Simulink simulation results and RT-LAB hardware-in-the-loop experiments demonstrate the capability of the proposed control strategy to provide rapid active power support during grid disturbances. Full article

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

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22 pages, 1380 KB

Open AccessArticle

Decision-Making in Complex Systems Using AI-Based Decision Support: The Role of Trust, Transparency, and Data Quality

by Georgiana-Tatiana Bondac, Sorina-Geanina Stanescu, Constantin Aurelian Ionescu, Anisoara Duica and Marilena Carmen Uzlău

Electronics 2026, 15(2), 372; https://doi.org/10.3390/electronics15020372 - 14 Jan 2026

Abstract

In the context of accelerated digital transformation, organizations increasingly operate as complex systems in which strategic decision-making is challenged by uncertainty, data heterogeneity, and bounded rationality. The integration of artificial intelligence (AI) into organizational processes is therefore redefining how decisions are supported and [...] Read more.

In the context of accelerated digital transformation, organizations increasingly operate as complex systems in which strategic decision-making is challenged by uncertainty, data heterogeneity, and bounded rationality. The integration of artificial intelligence (AI) into organizational processes is therefore redefining how decisions are supported and enacted. This study develops and validates an integrated conceptual model that explains how trust in AI-based decision support systems (AI-DSSs), data transparency and quality, perceived usefulness, and ease of use influence decision-making efficiency and the intention to adopt AI-DSS in complex organizational contexts. The empirical analysis is based on a questionnaire survey administered to 324 respondents from Romanian organizations operating in IT, services, industry, and public administration. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) implemented in SmartPLS 4. The results show that data transparency and quality strongly enhance trust in AI-DSS (β = 0.784, p < 0.001). Trust positively influences both perceived usefulness (β = 0.229, p < 0.01) and perceived ease of use (β = 0.482, p < 0.001), confirming its role as a key psychological enabler of favorable technology perceptions. Furthermore, perceived ease of use significantly affects perceived usefulness (β = 0.597, p < 0.001). Regarding adoption-related attitudes, perceived usefulness (β = 0.352, p < 0.001), trust (β = 0.311, p < 0.001), and perceived ease of use (β = 0.135, p < 0.05) exert significant positive effects on the intention to adopt AI-DSS, which in turn demonstrates a strong association with decision-making efficiency (β = 0.544, p < 0.001). By extending traditional technology acceptance models (TAM) with AI-specific dimensions—namely transparency, data quality, and trust—this study contributes to the literature on decision-making in complex systems and offers practical insights for organizations seeking to improve decision effectiveness through AI-based support. Full article

(This article belongs to the Special Issue Advances in Decision Making for Complex Systems)

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