Search Results (3,387)

The integration of distributed generation (DG) changes the fault current path, magnitude, direction, and transient characteristics of distribution networks, which increases the difficulty of fault type identification. In particular, weak fault features and high-frequency transient components may reduce the reliability of traditional feature-based diagnosis methods. To improve the representation and classification capability of fault signals, this paper proposes a fault type identification method based on wavelet packet transform and an improved CNN-BiGRU model with a channel attention mechanism. First, three-phase voltage, three-phase current, and zero-sequence voltage signals are decomposed by wavelet packet transform, and the corresponding time–frequency matrices are constructed. Then, these matrices are integrated and converted into time-frequency images, so that multi-source fault information can be represented in a unified form. On this basis, CNN is used to extract local spatial features from the time-frequency images, while BiGRU is employed to capture bidirectional dependency information of fault features. Furthermore, a channel attention mechanism is introduced to enhance informative feature channels and suppress redundant information, thereby improving the fault classification performance. Simulation results based on a 10 kV DG-integrated distribution network show that the proposed method achieves high recognition accuracy under different DG capacities and access configurations. Compared with CNN, BiGRU, and CNN-BiGRU models, the proposed CNN-BiGRU-Attention model shows better classification accuracy and adaptability, demonstrating its effectiveness for fault type identification in active distribution networks. Full article

Artificial intelligence (AI) is transforming higher education, although most research addresses its integration in terms of frequency of use or technological acceptance, without examining how it translates into specific curricular and instructional decisions. That is why this study has a dual aim: to develop and validate the AI Instructional Design Questionnaire for Critical Thinking (AID-CTQ) and to analyze how university faculty integrate AI into instructional design practices in higher education. The sample included 144 faculty members from a university in Madrid, selected by convenience. Exploratory and confirmatory factor analyses of the questionnaire supported a three-factor structure: Activity Design (F1), Critical Thinking Assessment (F2), and Self-Regulation and Reflection (F3). The final 12-item model shows good model fit (CFI = 0.98, TLI = 0.98, RMSEA = 0.05, SRMR = 0.05) and adequate overall reliability (α = 0.86). At the item level, responses related to assessment and reflective practices showed consistently high agreement, whereas items linked to activity design displayed greater variability. Faculty members with more than 10 years of experience obtained significantly higher scores, indicating that the educational value of AI depends less on the tools used and more on the quality of instructional decisions. Reported use of AI was high, with ChatGPT and Copilot being the most frequently used tools. Overall, the findings indicate that the integration of AI in higher education is evolving from predominantly instrumental uses toward more pedagogical and curriculum-oriented forms of implementation. Accordingly, the educational value of AI lies less in the tool itself than in the quality of the instructional decisions through which it is meaningfully embedded in the curriculum. Full article

Ultrasonic thickness resonance can be effectively used to detect and quantify the level of corrosion in steel nuclear storage containers as well as other corrosion-prone thin-walled structures, such as pipes and storage tanks. Electro-Magnetic Acoustic Transducers (EMATs) have several advantages over more traditional piezoelectric-based transducers; namely, they can be used in a non-contact fashion on robotic platforms, allowing for measurements regardless of surface conditions or temperature. The major challenge of EMAT application is the power required to counteract the low actuation efficiency, which is achieved with a high-power ultrasonic pulse generator and a transformer circuit. Resonance techniques, in which most of the energy is concentrated near structural resonance frequencies, are preferable to improve efficiency of electro-magnetic acoustic measurements. This methodology was applied to 316L stainless steel thin plates subjected to uniform corrosion as well as pitting corrosion imitating different damage scenarios in a nuclear waste container. The resonant peak frequency shift was found to be proportional to the severity of corrosion for minimally corroded samples. However, the complete disappearance of the resonance peak was observed in the samples with severe corrosion damage. The EMAT liftoff distance was studied to quantify its effect on the amplitude, spread, and frequency of resonant peaks. Recommendations for use of EMATs for assessing corrosion damage are presented. The study demonstrates the success of frequency-based detection of corrosion damage in 316L stainless steel used in fabrication of nuclear waste storage containers. Full article

Wave overtopping is one of the most complex coastal hazards to characterize in field conditions due to its high non-linearity and the interaction between unsteady hydrodynamics and wave–structure processes. To get insights into the underlying occurrence and persistence of overtopping, this study proposes an integration of numerical and data-driven models. Multi-month field observations made at a breakwater are used to investigate the hydro-meteorological parameters causing overtopping initiation and persistence. High-frequency video-derived overtopping detections are combined with coupled ADCIRC–UnSWAN (ADvanced CIRCulation–Unstructured Simulating WAves Nearshore) hindcasts to construct near-structure hydro-meteorological conditions. The results reveal a clear dynamical asymmetry showing that overtopping initiation corresponds to exceedance of crest elevation at individual wave-scale associated with elevated wave height, water level, wave steepness, and wind characteristics, whereas overtopping persistence depends on short-term temporal effects associated with wave energy, direction, and sustained water levels. Gradient-boosted decision trees, temporal convolutional networks, and Transformer models are employed, demonstrating that persistence cannot be inferred from instantaneous sea-states alone, indicating a separation of timescales between triggering and sustained overtopping dynamics. These findings provide field-scale evidence of distinct hydrodynamic regimes governing overtopping processes, highlighting the importance of temporal characteristics for understanding overtopping dynamics and developing predictive coastal hazard frameworks. Full article

Instance segmentation of surface defects is one of the research hotspots in the field of image segmentation. Due to limitations such as restricted receptive fields or the loss of fine-grained details, traditional neural network models still struggle to achieve sufficiently high-segmentation accuracy for surface defects. To meet the demand for high precision segmentation of surface defects on copper strips and plates in industrial quality inspection, this paper proposes a feature fusion segmentation network, termed DSFFNet. First, a dual-branch structure is designed in DSFFNet to fuse spatial-domain features with discrete cosine transform (DCT)-domain features, thereby obtaining richer feature information. Second, a 2D-DCT frequency feature extraction module is developed to more effectively capture the edge information of targets. Third, a triplet attention mechanism is introduced into the backbone network to form an attention-centric network. Finally, a bidirectional fusion module and a multi-scale fusion network are designed to capture finer-grained feature information. Comparative experiments conducted on the KUST-SEG-Dataset demonstrate that DSFFNet achieves 94.66% ± 1.07% (mask)mAP${}_{50}$ and 95.38% ± 0.06% (box)mAP${}_{50}$, outperforming several classic image segmentation methods. Furthermore, generalization experiments on the public NEU-Seg dataset yield a (mask)mAP${}_{50}$ of 86.27% ± 0.01%. The generalization results indicate that DSFFNet is robust to datasets with similar defect types. Full article

The trend in the power electronics industry toward higher power density and efficiency has brought high-frequency transformers (HFTs) to the forefront of critical applications, including isolated DC–DC converters, electric vehicle chargers, and solid-state transformers. This paper focuses on the leakage inductance of HFTs and presents a systematic comparative framework that evaluates five surrogate modeling and hybrid optimization approaches for the rapid and accurate estimation of leakage inductance. A comprehensive parametric dataset was constructed, comprising 1210 finite element analysis simulations conducted via finite element analysis in the ANSYS Maxwell 2024 R1 environment, varying the number of winding turns, primary winding thickness, and secondary winding thickness of the HFT. All five methods were trained and evaluated on the same dataset under identical conditions. The comparative evaluation demonstrates that the proposed hybrid Gray Wolf optimizer–artificial neural network (GWO-ANN) framework achieved the highest prediction accuracy (R² = 0.9832, MSE = 0.01780, MAE = 0.0935 µH) and the fastest convergence among all tested approaches. The generalization capability of the proposed model was confirmed through blind validation tests across six geometric configurations spanning the full range of the design space, yielding a maximum prediction error of 8.15% and an average error of 2.14%. The functional validity of the proposed parameters was further tested in a third validation layer using MATLAB/Simulink R2024b transformer circuit studies, demonstrating a theoretical efficiency of 96.06%. This three-layer validation approach proves both the parametric and functional reliability of the proposed framework for HFT designs. Full article

Stock index price series are composed of superimposed multi-frequency components, including long-term trends, cyclical fluctuations, and stochastic noise. Effectively decoupling these heterogeneous components and modeling them separately is key to improving forecasting accuracy. Existing methods under the “decomposition–prediction” paradigm mostly employ fixed-scale decomposition, and the forecasting models are not specifically adapted to the non-stationary and high-noise characteristics of financial data, resulting in limitations in adaptivity and local dynamic capture. This paper proposes a frequency-aware adaptive multi-scale decomposition Transformer hybrid model (FAMS-Transformer). At the decomposition level, the fast Fourier transform is used to dynamically identify dominant cycles, thereby adaptively decoupling trends and fluctuations, overcoming the limitations of fixed-scale decomposition. At the forecasting level, a lightweight depthwise separable convolution is embedded between the self-attention and feedforward network of the Transformer encoder, enhancing the model’s ability to capture local temporal dynamics and achieving collaborative modeling of global dependencies and local information. Comparative experiments with 15 baseline models including LSTM, Transformer, TimesNet, and FreTS on three representative Chinese market indices—Shanghai Composite Index, Shenzhen Component Index, and Small and Medium Enterprises 100 Index—across four prediction horizons from one step to 15 steps demonstrate that FAMS-Transformer achieves the best forecasting accuracy in all scenarios. The coefficient of determination for 15-step prediction remains stably between 0.730 and 0.928. Moreover, the model still performs well on the S & P 500 dataset. Ablation studies and significance tests further validate the effectiveness of each core module and the statistical significance of the performance improvements. Full article

Near-fault pulse-like ground motions (NFPLGMs) impose concentrated energy demands that can severely damage bridges, yet their scarcity and the influence of structural parameter uncertainties are often neglected in seismic fragility assessments. This study proposed a synthesis method for NFPLGMs by superposing low-frequency pulse components (extracted via the Gabor wavelet transform and low-pass filtering) with high-frequency stochastic components based on an evolutionary power spectrum. A three-span reinforced concrete bridge was modeled in OpenSeesPy, and Incremental Dynamic Analysis (IDA), together with a quadratic response surface model, were used to plot seismic fragility curves. The damping ratio (ξ), elastic modulus of steel reinforcement (E_s), yield strength of steel reinforcement (f_y), diameter of longitudinal reinforcement (D), and peak ground acceleration (PGA) were treated as random variables. Sensitivity indices were computed using Monte Carlo sampling (n = 10,000). Results show that ξ most strongly affects the displacement ductility ratio of the bridge pier (u_d) (variation of up to 32.6%), while E_s dominates the shear deformation of the bridge bearing (d) (variation of up to 43.8%). Neglecting structural parameter uncertainties overestimates median PGA thresholds (m_R) for different damage states by 1.5%–36.1%, and replacing NFPLGMs with ordinary ground motions overestimates seismic capacity by 1.7%–36.6%. The bridge bearing is consistently more vulnerable than the pier, with a collapse probability of 0.9566 at PGA = 1.0 g. These findings highlight the necessity of incorporating both NFPLGM characteristics and structural parameter uncertainties into bridge seismic fragility assessment. On the other hand, when seismic retrofitting of bridges is carried out using coating materials, priority should be given to more vulnerable components, such as bridge bearings, to improve the utilization efficiency of limited resources. Full article

Slamming-induced whipping has been recognized as a key contributor to fatigue damage of large ships operating under severe sea states. However, accurate prediction of whipping responses remains challenging because of complex nonlinear fluid–structure interactions. This study aims to investigate the characteristics of slamming-induced whipping and quantitatively analyze its influence on the fatigue damage of an ultra-large container ship. A three-dimensional fully nonlinear time-domain hydroelastic method, in which the boundary element model is coupled with a Timoshenko beam model, is employed to predict the slamming-induced whipping responses. Segmented model tests in long-crested irregular waves are conducted to provide wave loads of hull girders under severe sea states. The total and wave-frequency vertical bending moments are separated by the fast Fourier transform, and their statistical characteristics are evaluated through probability distributions. Fatigue damage is assessed on the basis of the rainflow counting method and the Palmgren–Miner cumulative damage rule. The contribution of high-frequency whipping responses to fatigue damage is quantitatively evaluated using a fatigue damage factor. It is demonstrated that slamming-induced whipping can significantly amplify fatigue damage by increasing stress amplitudes and cycle counts, particularly under high forward speeds and severe sea conditions. The findings provide a reliable reference for the fatigue design and safety assessment of ultra-large container ships. Full article

To address the issues of severe noise interference, limited classification capability of linear classifiers, and difficulty in adaptively optimizing classifier parameters in rolling bearing fault diagnosis, this paper proposes a hybrid diagnostic model integrating the multi−scale convolutional neural network and rime ice optimization algorithm optimized kernel extreme learning machine. The method first employs the synchrosqueezed wavelet transform to convert raw vibration signals into high−resolution time−frequency images, effectively enhancing the visualization of fault impact features. Then, the multi−scale convolutional neural network is used to extract preliminary features from the time−frequency images, and the kernel extreme learning machine is introduced to replace the Softmax linear classifier in traditional convolutional neural networks, thereby constructing a nonlinear decision boundary to more effectively separate complex fault patterns. Finally, the rime algorithm is introduced to optimize the regularization coefficient and kernel parameters of the kernel extreme learning machine, enabling the kernel extreme learning machine to perform fault classification with an optimal nonlinear decision boundary. Experimental results on the bearing datasets from Huazhong University of Science and Technology and Case Western Reserve University show that the proposed method achieves classification accuracies of 99.75% and 99.83%, respectively, outperforming several comparison models. Furthermore, noise robustness experiments demonstrate that the proposed model maintains an accuracy of approximately 90% under low signal−to−noise ratio (SNR) conditions, outperforming all comparison models and demonstrating high classification accuracy under strong noise. Full article

This study investigates physics-informed and data-driven hybrid modeling strategies for an automotive-grade electrohydraulic (EH) semi-active damper system. Although deep sequence learning architectures such as Long Short-Term Memory (LSTM) networks and Transformers can provide high predictive accuracy, purely data-driven approaches may struggle to preserve physical consistency and maintain robustness under unseen operating conditions. These limitations become more pronounced for EH dampers, whose hysteretic characteristics exhibit highly nonlinear and non-proportional variations under different current and frequency excitations, unlike the more scalable behavior commonly observed in magnetorheological (MR) dampers. To address these challenges, two physics-informed integration strategies are investigated. The first strategy combines physical and data-driven models through parallel loss-function synthesis. The second strategy introduces a learnable physics layer (PINN-Hybrid), in which the coefficients of the extended hyperbolic tangent formulation are adaptively learned within the neural network architecture. In this framework, the physical model acts as a structural regularization mechanism that guides the learning process while preserving the flexibility of data-driven sequence modeling. The proposed models are evaluated under abrupt valve-control operating conditions. Comparative results indicate that the proposed physics-informed architectures improve hysteresis continuity, physical plausibility, and robustness compared with purely data-driven approaches, particularly in low-velocity and transition regions. The proposed framework therefore demonstrates the potential of physics-informed learning strategies for reliable real-time modeling of nonlinear automotive EH damper systems. Full article

Electric dipole antennas can be designed in a variety of geometries and applied across a wide range of configurations. Appropriately designed dipole antennas can provide deep tissue penetration and low radiofrequency (RF) power deposition in magnetic resonance imaging (MRI), making them attractive for applications requiring safe and effective RF transmission in deep regions. On clinical 3 T MRI systems, however, conventional dipoles are too large in size for practical imaging of the head. Inspired by telecommunications designs, the present work adapts meandered dipoles (where the conductor is folded to shorten the antenna) with the resonance frequency controlled through trace geometry. Additionally, multi-channel configurations are considered to improve RF power transmission. A straight dipole was progressively transformed into meandered geometries and characterized using benchtop measurements and electromagnetic simulations. Analyses evaluated frequency response, near-field behavior, power-flow directionality, and distributions of local tissue heating and transmitted RF magnetic field in multi-channel arrays. A four-channel parallel-transmit (pTx) prototype was also used to show the feasibility of dipole-based head imaging at 3 T. The present work demonstrates a practical implementation of compact, low-heating dipole arrays for head MRI, with potential for extension to ultra-high-field or multinuclear imaging. Full article

This paper presents a mathematical modeling-based vision-guided triple-loop control method for lane tracking of an underactuated bicycle robot. To describe the coupling between lateral balance and path tracking, a reaction-wheel-based inverted-pendulum model is established using the Lagrange formulation. Based on the linearized dynamics, the transfer function between the flywheel rotational speed and the motor torque is derived, providing a mathematical basis for designing the gain-scheduled triple-loop PID controller. To generate continuous control inputs under practical visual disturbances, an improved Hough transform, a near-field multi-layer sliding window detector, and a multi-scenario finite-state-machine strategy are incorporated for lateral deviation estimation and path reconstruction. A cascaded smoothing filter is further introduced to reduce high-frequency command fluctuations and improve the closed-loop control response. Real-vehicle experiments on an STM32F407-based underactuated bicycle robot demonstrate that the proposed framework achieves stable dynamic balance and robust lane tracking. Compared with a conventional Hough-transform and sliding window method, the lateral RMSE is reduced by 40.2%, 39.85%, and 32.35% in straight, left-turn, and right-turn scenarios, respectively. Full article

Probabilistic risk assessment of complex offshore oil and gas systems is often challenged by scarce statistical data and multiple uncertainties. Traditional point-value probability and standard Bayesian networks cannot fully represent and propagate these uncertainties, which may mislead high-risk security decision-making. To address this issue, this paper proposes a new hybrid risk assessment framework that combines interval-valued T-spherical fuzzy sets (IVTSFS) with credal networks (CN). First, IVTSFS is used to quantify the subjective risk perception of multiple experts, effectively capturing hesitancy, fuzziness, and group disagreement. An improved probability mapping mechanism is introduced to align linguistic evaluations with objective failure frequency spaces, thereby avoiding systemic transformation biases. Subsequently, the interval conditional probability table is constructed using the imprecise leakage noise-OR model, which alleviates the problem of parameter dimension explosion in complex causal structure and explicitly retains the parameter uncertainty. The 2U algorithm is then applied to perform accurate interval inference in CN. The feasibility and comparative advantages of the method are illustrated in the actual case of the single-point mooring system. The results clearly output the upper and lower bounds of the system failure risk, and identify the key vulnerable nodes through diagnostic reasoning and sensitivity analysis. This study has theoretical contributions in fuzzy decision-making and uncertainty modeling. By unifying advanced fuzzy cognitive quantification and imprecise probability propagation, it provides a structured uncertainty representation tool for expert-informed risk screening under data scarcity. Full article

Multimodal image fusion aims to integrate complementary information from heterogeneous imaging modalities into a single informative image. However, many deep learning-based fusion methods rely on complex feature extractors, leading to high computational cost and limited suitability for real-time deployment on resource-constrained devices. To address this issue, this paper proposes a lightweight Mamba-INN dual-branch network for efficient multimodal image fusion. The proposed model decouples global structure modeling from local detail preservation. A simplified Mamba-inspired branch is designed to capture long-range contextual dependencies, while a lightweight invertible neural network branch preserves high-frequency textures and edge information through information-preserving transformations. The lightweight INN branch preserves high-frequency texture and edge information during the forward feature transformation process through reversible feature partitioning, coupled transformations, and exponential scale modulation, thereby reducing the loss of detail caused by feature compression. Compact shallow feature refinement, module reuse, low-dimensional channel design, and a streamlined decoder are further introduced to reduce redundant computation. Experiments on infrared-visible and medical image fusion benchmarks, including MSRS, TNO, RoadScene, MRI-CT, MRI-PET, and MRI-SPECT datasets, demonstrate that the proposed method achieves competitive fusion quality with low model complexity. The proposed method achieves performance comparable to or better than that of methods such as CDDFuse, U2Fusion, CNN and SDNet on metrics including MI, VIF, Qabf, and SSIM for infrared-visible and medical image fusion tasks, while containing only 0.24 million parameters and requiring 24.04 GFLOPs of computational power at an input resolution of 256 × 256. Compared to CDDFuse, our method significantly reduces model complexity, enhancing the potential for lightweight deployment while maintaining fusion quality. Full article