Search Results (1,998)

Power-frequency overexcitation increases the voltage-to-frequency ratio applied to a transformer core and may drive the core into saturation, resulting in substantial distortion of the no-load current. This paper presents a duality-derived electromagnetic model for an on-board traction transformer by combining a sixth-order Foster network identified through vector fitting with a Jiles–Atherton hysteresis operator. The proposed model preserves the physical correspondence between magnetic-flux paths and equivalent-circuit elements while accounting for history-dependent core hysteresis and frequency-dependent core impedance. The model is implemented and numerically evaluated in MATLAB/Simulink R2024a, and experimental validation is performed on a 250 kVA prototype under rated power-frequency excitation and power-frequency overexcitation. Under rated excitation, the RMS relative error and NRMSE of the no-load current are 0.09% and 0.96%, respectively, with a maximum relative error of 2.90%. Under power-frequency overexcitation, the NRMSE is 4.14% and the maximum relative error is 9.37%. The results show that the proposed model accurately reproduces the nonlinear no-load current response associated with core saturation and hysteresis within the investigated power-frequency excitation range. Full article

Electrochemical parameter identification is crucial for evaluating the electrochemical processes in proton exchange membrane water electrolysis (PEMWE). Conventional characterization techniques-including polarization-curve fitting, electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and current interruption (CI)-face significant limitations for rapid diagnostics under high-current dynamic operation, arising from constraints in instrument current rating, measurement time, zero-current control, and noise amplification in numerical differentiation. In this study, we present a simple current step (CS) method to accurately identify key electrochemical parameters and perform overpotential breakdown by using a simplified equivalent circuit model with a current source. To address the numerical instability in derivative calculation caused by sampling noise during voltage transient analysis, a physics-informed neural network (PINN) is introduced to enhance signal smoothness while guaranteeing physical consist ency. Compared with standard characterization, the proposed CS-PINN method demonstrates high accuracy, with an error of less than 2% in overpotential breakdown, less than 5.3% in ohmic resistance, and 2.8% in the Tafel slope (at 5 A/cm²). These results confirm that the CS-PINN method provides a fast, accurate, and equipment-friendly route for rapid electrochemical parameter identification in PEMWE. Full article

Permeability prediction and hydraulic rock typing in carbonate reservoirs remain challenging because similar porosity values may correspond to markedly different flow capacities controlled by pore throat size, connectivity, and capillary accessibility. This study aims to develop an integrated workflow for permeability prediction and petrophysical–hydraulic rock typing of a heterogeneous dolomite reservoir using parameters that directly characterize the drainable pore throat network. Routine core analysis, centrifuge-derived capillary pressure curves, nuclear magnetic resonance $T_2$ spectra, electrical measurements, petrographic and SEM observations, and fractal descriptors were jointly analyzed. Capillary pressure curves were fitted with the Li–Horne model and transformed into equivalent pore throat radius distributions; characteristic radii $R_q$, Swanson and Capillary-Parachor parameters, irreducible water saturation, and fractal characteristics were calculated for subsequent regression analysis and rock typing. The conventional $k-\varphi$ relationship showed limited predictive capability, whereas models incorporating $R_{15}-R_{21}$ provided a more reliable permeability estimate. The best-performing relationship was close to $k\propto R^2\varphi$, supporting the interpretation of $R_{20}$ as a centrifuge-derived analog of the effective hydraulic radius. Comparison with FZI, Winland R35, NMR groups, and electrofacies showed that $R_{20}$-based typing produced a compact separation of samples by hydraulic quality. The proposed workflow is presented as a single-well proof of concept and requires validation in independent wells before application to field-scale geological and hydrodynamic models. Full article

With the increasing penetration of wind and photovoltaic generation, microgrids are playing an increasingly important role in promoting renewable energy accommodation, enhancing operational flexibility, and enabling low-carbon energy management. However, the strong uncertainty of renewable generation and load demand, together with the coupling effects of electricity, hydrogen, and carbon markets, poses significant challenges to the optimal planning and operation of microgrid energy storage systems. To address these issues, this paper proposes a bi-level optimal sizing framework for an electric–hydrogen hybrid energy storage system (EHH-ESS) in a microgrid under multi-market coupling. First, typical wind–solar–load scenarios are generated using a Wasserstein generative adversarial network with gradient penalty (WGAN-GP), so as to capture the stochastic characteristics and temporal correlations of renewable generation and load demand. Then, a multi-market coupling index (MCI), integrating electricity price, hydrogen price, and carbon price signals, is constructed to characterize time-varying economic and low-carbon operating incentives and to guide coordinated dispatch decisions. On this basis, a bi-level multi-objective optimization model is established. The upper level determines the optimal capacities of battery storage, electrolyzers, fuel cells, and hydrogen tanks, while the lower level performs hourly coordinated operation of the microgrid under multi-market conditions. The model considers annual equivalent total cost, renewable energy curtailment rate, and carbon emissions as objective functions, and is solved using the NSGA-III algorithm. Compared with the no-storage benchmark, the proposed scheme improves the annual operating economics and renewable-energy accommodation under the studied market conditions. The proposed method significantly reduces annual operating cost and improves renewable energy accommodation. However, under the current carbon price and grid emission factor settings, the optimal economic solution increases carbon emissions relative to the baseline, indicating a trade-off between economic arbitrage and low-carbon operation. Full article

Multilayer perceptrons (MLPs) are considered as a singular model of learning machines. Singularities cause local minima and plateaus in the learning process. I/O-equivalence, where two different MLPs are regarded as the same multivariable function, is an important concept for understanding singularities in neural networks. In this paper, I/O-equivalence is extended to T-equivalence, which is a concept where two MLPs yield the same results through a transformation of input and output. We provide constructive families and procedures for obtaining T-equivalent networks of real-, complex-, and quaternion-valued neural networks. In particular, T-equivalence of quaternion-valued neural networks is much more complicated than that of the others. Full article

Background/Objectives: Transcranial magnetic stimulation (TMS) initiates a cascade from intracranial electric-field exposure through neural recruitment and plasticity to distributed network responses. Neuronavigation improves the geometric reproducibility of delivery but does not guarantee equivalent cortical exposure or target engagement. This narrative review integrates these levels within an operational framework for precision TMS. Methods: Six domain-specific PubMed searches covering 1 January 1985 to 31 July 2026 were supplemented by Google Scholar and citation tracking. A documented rerun on 17 August 2026 yielded 6430 records (5617 unique after cross-query deduplication). Evidence was synthesized narratively; no quantitative synthesis or formal risk-of-bias assessment was performed. Results: Neuronavigation improves geometric precision by stabilizing target definition and coil pose, whereas individualized electric-field models estimate intracranial exposure. Neither establishes biological precision, which also depends on neuronal orientation, brain state, circuit architecture, medication, and behavior. Motor-system measures are not validated as universal biomarkers for nonmotor cortex, and no single validated biomarker captures TMS-induced plasticity. Convergent, controlled multimodal evidence may strengthen inference about target engagement; adaptive and closed-loop approaches remain experimental. Conclusions: Geometric delivery, modeled exposure, biological engagement, and durable functional or clinical benefit require separate validation. Spatial accuracy alone does not establish clinical value. Full article

Wireless power transfer (WPT) can eliminate moving power-supply harnesses in automotive power-seat systems, but seat travel and nearby metallic structures cause substantial variations in magnetic coupling and electromagnetic loss. This paper proposes a structure-aware, partially overlapped segmented transmitter and evaluates two predefined excitation states according to receiver position. In the single-segment state, only the reference segment CP1 is energized; in the simultaneous dual-segment state, CP1 and the adjacent segment CP2 are energized together. Three-dimensional finite element method (FEM) simulations compare candidate transmitter structures and evaluate the electromagnetic influence of the aluminum lower rail, steel upper rail, and steel seat frame. The transmitter geometry is determined by considering mutual inductance, winding loss, structural eddy-current loss, and partial-overlap characteristics. A three-coil equivalent circuit clarifies the branch-current distribution, and a two-state switched-capacitor network accommodates the different equivalent transmitter impedances. A 100 W, 110 kHz prototype separately evaluates representative states at x = 0 and 80 mm; automatic position-based state switching is not implemented. At x = 0 mm, CP1-only excitation achieves 78.79% efficiency. At x = 80 mm, CP1 + CP2 excitation produces 32.13 V and 72.15%, compared with 18.78 V and 67.84% under CP1-only excitation, thereby satisfying the 30 V minimum output requirement. Full article

Building Information Modeling (BIM)-enabled performance assessment workflows for rural constructions remain relatively unexplored. This is even more important for buildings implementing process-oriented systems, such as airflow networks. This study presents a BIM-integrated framework for the early-stage design and performance assessment of rural constructions, with application to solar barn dryers and their ventilation systems through reduced-order airflow-network modeling. The proposed workflow combines parametric BIM-based geometry generation with lumped-parameter fluid-dynamic modeling to evaluate the influence of airflow-network topology on pressure losses, airflow distribution, fan power demand, and energy consumption. Nine BIM-generated design alternatives and ten geometric parameter sets were investigated under equivalent operating conditions. The airflow system was represented as a pressure-driven network including solar air panels, ducts, collectors, fan chambers, ventilation channels, and drying cells, accounting for both localized and distributed pressure losses. Results show that airflow-network geometry significantly affects system performance. Configurations characterized by more compact and aerodynamically efficient layouts reduced cumulative pressure losses by approximately 10–20% compared with less optimized solutions. More efficient designs enable reductions in required airflow rates of ~22% and in fan power demand of up to ~40% (≈11–18 kW). The most efficient configurations also exhibited lower annual energy consumption while maintaining the minimum overpressure required for effective hay drying. The study demonstrates how BIM environments can support physics-informed comparative evaluation of alternative ventilation layouts during the early design stage, extending BIM applications toward performance-oriented design and digital management of agricultural building systems. The proposed methodology provides a computationally efficient design-support framework that may also apply to other controlled-environment agricultural infrastructures governed by airflow-network dynamics. Full article

Quality control in green coffee bean production is critical for food safety and economic sustainability. A persistent gap in existing automated inspection systems is their inability to capture both sides of each bean, which leads to systematic under-detection of surface defects. This work presents three contributions: (i) a novel mechatronic apparatus that mechanically guarantees dual-sided imaging of every bean, (ii) a public 12-class dataset of green coffee bean defects, and (iii) an embedded, real-time inspection pipeline validated on low-cost hardware. The apparatus sequentially presents each bean, from a standard 350 g sample, to two 16-megapixel cameras under controlled LED illumination. A dataset of 9600 images spanning 12 classes (11 defects and 1 normal) was generated from expert-classified samples and enriched through data augmentation. Four convolutional neural network (CNN) architectures, VGG-16, VGG-19, ResNet-50 and YOLOv8, were trained and benchmarked using precision, recall, F1-score and mean average precision. YOLOv8 achieved the best overall performance, with a precision of 97.4%, a recall of 99.6%, an F1-score of 0.930 and a mean average precision of 96.5%, outperforming VGG-16 (accuracy 86.07%), VGG-19 (accuracy 67.03%) and ResNet-50 (accuracy 87.76%). Dual-sided acquisition raised mean per-class detection accuracy from 0.727 to 0.908, a relative gain of 25.7% over an equivalent single-sided configuration. Deployed in real-time “track” mode on a Raspberry Pi 4, the system simultaneously classifies defects and counts beans by category, processing a 350 g sample in approximately 38 min. Combining mechanical innovation with lightweight deep learning enables practical, scalable, and cost-effective quality control for laboratories specialized in coffee analysis. Full article

The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data (GRACE, GLDAS, TerraClimate, and MODIS) with machine learning and explanatory artificial intelligence techniques for the long-term assessment and interpretation of GWS anomalies in the data-poor Iğdır Basin. Three different modeling approaches were developed: XGBoost, Long Short-Term Memory (LSTM) networks, and their combined model, and interpreted using the Shapley Additive Explanations (SHAP) method. The results showed a significant long-term decreasing trend in groundwater storage anomalies at a rate of −0.87 mm per month during the 2002–2016 period, indicating continuous depletion. The LSTM model demonstrated the best performance with R² of 0.59, RMSE of 19.5 mm, and MAE of 15.1 mm, revealing the dominant role of temporal dependencies in groundwater systems. SHAP analysis identified lagged groundwater anomalies (especially GWS_lag3) as the most effective predictors; this may reflect the memory effect and lagged response specific to semi-arid aquifer systems, but this interpretation needs to be validated in different study areas. Snow water equivalent and total water storage anomalies also emerged as significant determinants, while the direct effect of instantaneous precipitation was found to be limited. This study addresses significant gaps in the literature by combining sequence-based modeling with model interpretability in a semi-arid closed basin. The findings highlight the necessity of using system memory and explainable artificial intelligence together for reliable groundwater prediction. While the proposed hybrid approach has the potential for application in other semi-arid regions, its broader usability needs to be supported by independent validation studies under different hydrogeological and climatic conditions. Full article

In order to enable accurate and efficient rice quality evaluation through full-surface defect detection of brown rice, a detection system based on convolutional neural network (CNN) was developed. A dataset of images of five categories—unhulled, normal, broken, cracked, and insect-bitten brown rice—was collected. Three CNN models, YOLOv5s, YOLOv7, and Faster R-CNN, were evaluated and compared with traditional algorithms including support vector machine (SVM) and back propagation (BP) neural networks. Experimental results showed that CNN-based methods in the present database significantly outperformed traditional approaches, with the YOLOv5s model achieving the best comprehensive performance: 95.80% detection accuracy, 10.90 ms inference time per image, and 92 frames/s processing speed. An improved Rice-YOLOv5s algorithm was further proposed and validated through batch detection experiments, achieving an average recognition accuracy of 96.44% and a processing time of 9.2 ms per image, which is equivalent to approximately 108.7 FPS. This study demonstrates the feasibility of CNN-based brown rice defect detection, with future work directed toward lightweight deployment and multimodal fusion for production-line application. Full article

This work presents the first complete system for automated six degrees of freedom (6DOF) satellite pose estimation from spatially resolved, ground-based, adaptive optics (AO)-corrected imagery, addressing a key challenge in Space Domain Awareness (SDA). The approach mitigates the need for human labeling by directly regressing satellite orientation and position from blurry, noisy, and deeply shadowed imagery. A multi-stage deep neural network pipeline localizes the satellite, predicts pose, and optionally applies temporal filtering. Networks are trained exclusively on fully synthetic imagery generated from a CAD model, yet generalize effectively to real data, bridging the Sim2Real domain gap. On 137 real, human-labeled test images of Seasat, the model achieved a mean rotation error of $5^{°}$ and a mean image-plane translation error of 21 cm. Slant range error was quantitatively evaluated on synthetic data due to unknown real-sensor parameters. Qualitative evaluation of additional real Seasat imagery rated 177 of 199 predicted poses as “ground truth equivalent” or “high-confidence match,” with zero catastrophic failures. The system was extended to seven degrees of freedom (7DOF) for satellites with articulating components and demonstrated on real Hubble Space Telescope (HST) imagery, achieving ${5.5}^{°}$ rotation error, 51 cm image-plane translation error, and $8^{°}$ symmetry-adjusted solar array error on a 249-frame pass with causal temporal filtering. Across 586 real test images from Seasat and HST (captured over multiple decades under diverse conditions) the system consistently performed well. Full 6DOF performance was quantified on a high-fidelity wave optics (HFWO) synthetic test set of Seasat, where the model achieved ${8.4}^{°}$ mean rotation error, 34 cm image-plane translation error, and 1.4% line-of-sight range error at $r_0=6$ cm and 1031 km range. In a limited 200-image benchmark, the model demonstrated 48% lower mean rotation error than a single human labeler while operating ∼800× faster. It required <40 h and a single A100 GPU to generate data and train. The approach was also demonstrated for ARGOS, a smaller satellite with highly symmetric geometry. An exploratory General Image-Quality Equation-based image quality metric (AO-IQ) was introduced as an empirical correlate for pose accuracy. General-purpose models like GPT-4o and Depth Anything V2 failed across most SDA tasks, but rapid gains in vision-language models warrant continued monitoring. These results establish a new operational baseline for practical, real-time satellite pose estimation from AO SDA imagery. Full article

Fan-Out Panel-Level Packaging (FO-PLP) enables high area utilization and manufacturing efficiency, but process-induced warpage caused by the coefficient of thermal expansion (CTE) mismatch and polymer shrinkage remains a major challenge. This study presents a classifier-gated hybrid machine-learning framework for the rapid and accurate FO-PLP warpage prediction using a database generated from a validated three-dimensional finite element process model. A Random Forest classifier first estimates the probability of each global warpage mode, while cluster analysis reduces the spatial training dataset. Two mode-specific artificial neural networks are then combined through probability-weighted fusion to predict the full warpage field and enable warpage prediction for previously unseen geometry layouts. The framework was evaluated on 16 independent finite element designs spanning both warpage modes. Compared with an equivalent single-network model, the proposed approach consistently achieved lower mean and maximum prediction errors across all designs, with the greatest improvements at the panel edges and corners where the prediction is most challenging. In addition, the clustering strategy reduced the training-set size and computational cost. These results demonstrate that integrating warpage-mode classification with mode-specific learning improves both the prediction accuracy and training efficiency, providing a practical tool for the fast warpage assessment of new FO-PLP layout designs. Full article

With the increasing integration of distributed photovoltaics, energy storage systems, electric vehicle chargers, and intelligent terminals, accurate identification of heterogeneous edge-end devices in distribution networks has become challenging due to their diverse operating characteristics and similar current signatures. This paper proposes an identification method based on current time–frequency features and Random Forest. Equivalent grid-connected current models are developed for five types of edge-end devices, considering different capacity levels, operating states, ripple characteristics, and transient behaviors. A 13-dimensional feature set is extracted from time-domain, frequency-domain, and time–frequency characteristics, covering 11 device subclasses. Feature analysis is conducted to evaluate the separability of the extracted features, and Random Forest is employed for multi-class device identification. The results show that the proposed method achieves an overall accuracy above 98% on independent test samples and 96.91% in the IEEE 33-bus validation case, demonstrating its effectiveness for distribution-network edge-end device identification. Full article

Transportation networks are subject to multiple sources of uncertainty, ranging from stochastic fluctuations in demand and travel times to epistemic indeterminacy in infrastructure condition, disruption risk, and user behavior. A diverse body of mathematical frameworks has emerged in response, including stochastic programming and probabilistic reliability analysis, fuzzy and possibilistic approaches, Liu’s uncertainty theory and uncertain programming, and robust or distributionally robust optimization. This article delivers a comprehensive, mathematically oriented synthesis of these paradigms for transportation networks, with emphasis on network-level structures—paths, flows, spanning trees, and network design problems—and on reliability notions including connectivity, travel-time, capacity, and max-type reliability. A central theme is that modelling choices about uncertainty representation and reliability indices are inseparable from questions of stability and sensitivity: how robust are optimal or near-optimal configurations when parameters vary within plausible ranges? Building on deterministic post-optimal analysis, this paper reviews tolerance-based stability concepts for uncertain most reliable paths, maximum reliable transmission paths, and uncertain minimum spanning trees under Liu-type uncertainty and demonstrates how inverse-distribution mappings yield exact deterministic equivalents and belief-based robustness margins. A dedicated comparative framework is developed, summarizing the data requirements, core advantages, typical limitations, and suitable engineering scenarios of each uncertainty paradigm to guide model selection in practice. The discussion extends to practical applications in post-disaster planning, infrastructure investment prioritization, and supply chain network design and identifies open research directions including network-wide travel-time reliability under belief-based uncertainty, unified stability frameworks across paradigms, and the integration of machine learning for uncertainty distribution elicitation. The emphasis throughout is on conceptual structure, modelling assumptions, and interpretability of reliability and stability indices, thereby positioning uncertain transportation networks as a rich interface between applied mathematics, operations research, and infrastructure planning. Full article