Search Results (58)

The increasing penetration of electric vehicles (EVs) and rooftop photovoltaics (PV) is intensifying the variability and uncertainty of residential net demand, thereby challenging real-time operation in smart grids and microgrids. The purpose of this study is to develop and evaluate an accurate and operationally relevant short-term forecasting framework that jointly models household net demand and EV charging behaviour. To this end, a Residual-Normalised Multi-Task GRU (RN-MTGRU) architecture is proposed, enabling the simultaneous learning of shared temporal patterns across interdependent energy streams while maintaining robustness under highly non-stationary conditions. Using one-minute resolution measurements of household demand, PV generation, EV charging activity, and weather variables, the proposed model consistently outperforms benchmark forecasting approaches across 1–30 min horizons, with the largest performance gains observed during periods of rapid load variation. Beyond predictive accuracy, the relevance of the proposed approach is demonstrated through a demand response case study, where forecast-informed control leads to substantial reductions in daily peak demand on critical days and a measurable annual increase in PV self-consumption. These results highlight the practical significance of the RN-MTGRU as a scalable forecasting solution that enhances local flexibility, supports renewable integration, and strengthens real-time decision-making in residential smart grid environments. Full article

Micro-fault diagnosis of vehicle driving motor bearings can significantly bring safety and economic benefits in preventing major accidents and extending equipment lifespan. However, under variable operating conditions, effectively capturing and diagnosing fault-related weak current fluctuation or high-frequency noise features, presents substantial technical challenges. Regarding these issues, this paper proposes multi-residual neural networks (multi-ResNets) and an evidential reasoning rule (ER Rule)-based fault diagnosis model. The model employs a benchmark condition generalization mechanism, which selects multiple typical load conditions as diagnostic anchor points based on a multi-residual neural network structure. Furthermore, by integrating a sub-model credibility assessment mechanism to perform diagnostic condition assessment and category assessment based on ER rule. The experimental results indicate that compared to the traditional machine learning algorithms, the proposed multi-ResNets-ER Rule-based model achieves higher diagnostic accuracy and result reliability for micro-faults under variable operating conditions. Full article

Accurate prediction of the stress–strain fields in fiber-reinforced composites is crucial for performance analysis and structural design. However, due to their complex microstructures, traditional finite element analysis (FEA) entails a very high computational cost. Therefore, this study proposes a conditional generative adversarial network (cGAN) framework, named FiberGAN, to enable rapid prediction of the microscopic stress–strain fields in fiber-reinforced composites. The method employs an adaptive representative volume element (RVE) generation algorithm to construct random fiber arrangements with fiber volume fractions ranging from 30% to 50% and uses FEA to obtain the corresponding stress and strain fields as training data. A U-Net generator, combined with a PatchGAN discriminator, captures both global distribution patterns and fine local details. Under tensile and shear loading conditions, the R² values of FiberGAN predictions range from 0.96 to 0.99, while the structural similarity index (SSIM) values range from 0.95 to 0.99. The error maps show that prediction residuals are mainly concentrated in high-gradient regions with small magnitudes. These results demonstrate that the proposed deep learning model can successfully predict stress–strain field distributions for different fiber volume fractions under various loading conditions. Full article

With the increasing integration of distributed energy resources and the growing variability of multiple loads, distribution networks face significant uncertainties in measurement data, line parameters, and topology. Traditional state estimation methods, such as weighted least squares, rely on accurate network parameters and are therefore highly sensitive to measurement noise and topology variations. To address these challenges, this work proposes a comprehensive data-driven framework for ADN state estimation that features a novel integration of an improved deep residual network (i-ResNet) and transfer learning. An improved deep residual network (i-ResNet) is developed to enable fast and robust state estimation without dependence on online parameters, even under uncertain data conditions. Furthermore, a transfer learning–based model is introduced to accommodate topology changes by leveraging historical data from multiple network configurations. Experimental studies on the IEEE 33-bus and 118-bus test systems are conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method achieves higher accuracy and faster convergence than conventional techniques, with voltage magnitude errors consistently maintained below 1%. Full article

The study of wear topography in self-lubricating joint bearings is of significant importance for evaluating their service life. In this work, an image dataset was acquired using a white-light interferometer, and the topographical height and color of the images were standardized. Images of worn bearing specimens subjected to 72,000 swing cycles at a frequency of 2 Hz under loads of 100 N, 150 N, 200 N, and 250 N were optimized to construct a processed image dataset. To overcome the limitations of traditional recognition methods in fully capturing both global and local image-based metrics, an improved residual neural network (ResNet) model was proposed. Comparative results with CNN, CapsNet, and conventional ResNet models indicate that, on the processed image dataset, the proposed method improved recognition accuracy by 34% relative to traditional approaches, and by 19% compared to the conventional ResNet. This study provides a novel approach and perspective for investigating the wear topography of self-lubricating joint bearings. Full article

Understanding suspended sediment transport in macrotidal embayments is crucial for assessing water quality, ecosystem function, and long-term morphological stability. This study provides a high-resolution, localized estimate of suspended sediment flux and examines the empirical relationship between turbidity (NTU, nephelometric turbidity unit) and total suspended matter (TSM, mg·L⁻¹) in the main tidal channel of Gomso Bay, a UNESCO-designated tidal flat on the west coast of Korea. A 13 h high-resolution fixed-point observation was conducted during a semi-diurnal tidal cycle using a multi-instrument platform, including an RCM, CTD profiler, tide gauge, and water sampling for gravimetric TSM analysis. Vertical measurements at the surface, mid, and bottom layers, taken every 15–30 min, revealed a strong linear correlation (R² = 0.94) between turbidity and TSM, empirically validating the use of optical sensors for real-time sediment monitoring under the highly dynamic conditions of Korean west-coast tidal channels. The net suspended sediment transport load was estimated at approximately 5503 kg·m⁻¹, with ebb-dominant residual currents indicating a net seaward sediment flux at the observation site. Residual flows over macrotidal channels are known to vary laterally, with landward fluxes often occurring over shoals. Importantly, the results from this single-station, short-duration observation indicate a predominantly seaward suspended sediment transport during the study period, which should be interpreted as a localized and time-specific estimate rather than a bay-wide characteristic. Nevertheless, these findings provide a baseline for assessing sediment flux and contribute to future applications in digital twin modeling and coastal management. Gomso Bay is part of the UNESCO-designated ‘Getbol, Korean Tidal Flats’, underscoring the global significance of preserving and monitoring this dynamic coastal system. Full article

Internal combustion engines (ICEs) are prone to faults such as abnormal injection pressure and valve clearance, but traditional diagnosis methods struggle with feature extraction and require large data volumes, limiting real-time applications. Deep learning approaches like CNN and LSTM have improved accuracy but often fail to capture both time and frequency domain features efficiently. This study proposes a Time–Frequency Domain Diagnosis Network (TFDN) that integrates a time-domain path (using residual networks and self-attention mechanisms for sequential feature extraction) and a frequency-domain path (using CNNs for spatial feature extraction). The model employs Swish activation functions and batch normalization to enhance training efficiency. Validated on a six-cylinder diesel engine with 12 fault types, TFDN achieved an accuracy of 98.12%~99.79% in full-load conditions, outperforming baselines like CNN, ResNet, and LSTM. Under mixed operating conditions, TFDN maintained high accuracy, precision, and recall, and demonstrated robustness with limited data (60%~70% accuracy at 5 samples per fault). TFDN effectively combines time-frequency features to improve diagnostic accuracy and stability, enabling real-time fault detection with reduced data dependency. It offers a practical solution for ICE condition monitoring. Full article

Reliable load forecasting is crucial for ensuring optimal dispatch, grid security, and cost efficiency. To address limitations in prediction accuracy and generalization, this paper proposes a hybrid model, GRU-MHSAM-ResNet, which integrates a gated recurrent unit (GRU), multi-head self-attention (MHSAM), and a residual network (ResNet)block. Firstly, GRU is employed as a deep temporal encoder to extract features from historical load data, offering a simpler structure than long short-term memory (LSTM). Then, the MHSAM is used to generate adaptive representations by weighting input features, thereby strengthening the key features. Finally, the features are processed by fully connected layers, while a ResNet block is added to mitigate gradient vanishing and explosion, thus improving prediction accuracy. The experimental results on actual load datasets from systems in China, Australia, and Malaysia demonstrate that the proposed GRU-MHSAM-ResNet model exhibits superior predictive accuracy to compared models, including the CBR model and the LSTM-ResNet model. On the three datasets, the proposed model achieved a mean absolute percentage error (MAPE) of 1.65% (China), 5.52% (Australia), and 1.57% (Malaysia), representing a significant improvement over the other models. Furthermore, in five repeated experiments on the Malaysian dataset, it exhibited lower error fluctuation and greater result stability compared to the benchmark LSTM-ResNet model. Therefore, the proposed model provides a new forecasting method for power system dispatch, exhibiting high accuracy and generalization ability. Full article

Oceanic whitecaps are caused by wave breaking and are very important in air–sea interactions. Usually, whitecap coverage is considered a key factor in representing the role of whitecaps. However, the accurate identification of whitecap coverage in videos under dynamic marine conditions is a tough task. An EMA-SE-ResUNet deep learning model was proposed in this study to address this challenge. Based on a foundation of residual network (ResNet)-50 as the encoder and U-Net as the decoder, the model incorporated efficient multi-scale attention (EMA) module and squeeze-and-excitation network (SENet) module to improve its performance. By employing a dynamic weight allocation strategy and a channel attention mechanism, the model effectively strengthens the feature representation capability for whitecap edges while suppressing interference from wave textures and illumination noise. The model’s adaptability to complex sea surface scenarios was enhanced through the integration of data augmentation techniques and an optimized joint loss function. By applying the proposed model to a dataset collected by a shipborne camera system deployed during a comprehensive fishery resource survey in the northwest Pacific, the model results outperformed main segmentation algorithms, including U-Net, DeepLabv3+, HRNet, and PSPNet, in key metrics: whitecap intersection over union (IoU_W) = 73.32%, pixel absolute error (PAE) = 0.081%, and whitecap F1-score (F1_W) = 84.60. Compared to the traditional U-Net model, it achieved an absolute improvement of 2.1% in IoU_W while reducing computational load (GFLOPs) by 57.3% and achieving synergistic optimization of accuracy and real-time performance. This study can provide highly reliable technical support for studies on air–sea flux quantification and marine aerosol generation. Full article

This study, via a combination of experiments and numerical simulations, investigates the structural tensile failure mechanisms of battle-damaged aluminum alloy flat panels and stiffened panels, the variation in their residual strength with hole characteristics, and modifies the calculation formula of the net-section failure criterion for evaluating damaged panels’ residual strength. Experimental and simulation results demonstrate that hole size and position exert a significant influence on panels’ residual strength: larger hole size and greater eccentricity both diminish load-bearing capacity, stiffened panels with web damage exhibit lower load-bearing capacity than those with flange damage. Different hole positions induce edge effects that alter stress distribution at the hole cross-section. Introducing a stress averaging coefficient modifies the residual strength evaluation of flat panels, which is further extended to stiffened panels with high result accuracy. This study presents a rapid method for evaluating damaged panels’ residual strength and serves as a reference for aircraft battle damage repair (ABDR) design. Full article

To address the limitations of one-dimensional vibration signals in convolutional neural networks and the insufficient feature extraction capability of traditional single data processing methods under complex operating conditions, this paper proposes a novel fault diagnosis method that integrates dual-graph transformation and an improved residual network. Firstly, the one-dimensional vibration signals are converted into time–frequency representations using the short-time Fourier transform (STFT) and the synchrosqueezed wavelet transform (SWT). Subsequently, these dual-domain representations are fed in parallel into a customized parallel two-dimensional residual network (P2D-Sk-ResNet), which incorporates the selective kernel network (SKNet) mechanism into a ResNet architecture. This design enables adaptive multi-scale feature extraction. Finally, the features from the fully connected layer are classified using the extreme gradient boosting (XGBoost) algorithm to complete the fault diagnosis task. Comparative experiments demonstrate that the proposed STFT-SWT-P2D-Sk-ResNet-XGBoost achieves a diagnostic accuracy of 98.51% under constant load conditions, significantly outperforming several baseline models. Furthermore, the model exhibits superior generalization capability under varying load conditions and strong robustness in noisy environments. The proposed method provides a valuable and practical reference for intelligent fault diagnosis in industrial applications. Full article

The autonomous navigation of inspection robots in complex forest environments heavily relies on accurate trunk detection. However, existing detection models struggle to achieve both high accuracy and real-time performance on resource-constrained edge devices. To address this challenge, this study proposes a lightweight algorithm named YOLOv11-TrunkLight. The core innovations of the algorithm include (1) a novel StarNet_Trunk backbone network, which replaces traditional residual connections with element-wise multiplication and incorporates depthwise separable convolutions, significantly reducing computational complexity while maintaining a large receptive field; (2) the C2DA deformable attention module, which effectively handles the geometric deformation of tree trunks through dynamic relative position bias encoding; and (3) the EffiDet detection head, which improves detection speed and reduces the number of parameters through dual-path feature decoupling and a dynamic anchor mechanism. Experimental results demonstrate that compared to the baseline YOLOv11 model, our method improves detection speed by 13.5%, reduces the number of parameters by 34.6%, and decreases computational load (FLOPs) by 39.7%, while the average precision (mAP) is only marginally reduced by 0.1%. These advancements make the algorithm particularly suitable for deployment on resource-constrained edge devices of inspection robots, providing reliable technical support for intelligent forestry management. Full article

Accurately identifying the load status of a ball mill is critical for optimizing grinding efficiency and ensuring operational stability. However, the one-dimensional vibration signals collected from ball mills exhibit strong non-stationarity and a high degree of entanglement between multi-scale local transient features and long-range temporal evolution patterns. To address this, rather than relying on a purely black-box approach, this paper introduces a novel Deep Multi-scale Spatial–Temporal Feature Decoupling Network (DMSTFD-Net) guided by a clear feature decoupling philosophy to enhance model interpretability. The core of DMSTFD-Net lies in its hierarchical collaborative feature refinement mechanism. It first utilizes a one-dimensional residual network (ResNet) to adaptively capture and preliminarily decouple multi-scale spatial characteristics from the raw signal. Subsequently, the extracted high-level feature sequences are fed into a bidirectional gated recurrent unit (Bi-GRU) to decouple high-order temporal dynamic patterns. Experiments on a multi-condition dataset demonstrate that the proposed network achieves a state-of-the-art accuracy of 97.65%. Furthermore, dedicated cross-condition experiments and t-SNE visualizations validate the framework’s effectiveness. The results confirm that DMSTFD-Net provides a powerful, robust, and more interpretable solution for ball mill load identification. Full article

This study explores the potential use of Polyethylene Terephthalate (PET) plastic bottles as void makers in short reinforced concrete columns under pure axial compression. Such a scheme promotes sustainability by decreasing the consumption of concrete and reducing the pollution that comes with the disposal of PET bottles. The experimental component of this study consisted of testing 16 reinforced concrete columns divided into two groups, based on the cross-section dimensions. One group contained eight columns of a length of 900 mm with a net cross-sectional area of about 40,000 mm², while the second group contained eight columns of a length of 1100 mm with a net cross-sectional area of about 62,500 mm². The diameter of the void within the small cross-section group was 100 mm and within the large cross-section group was 265 mm. The experimental program includes pairs of solid and corresponding void specimens with consideration of the size of the longitudinal steel reinforcement, lateral tie spacing, and concrete compressive strength. The tests are conducted using a universal test machine under displacement-controlled loading conditions with the help of strain gauges and Linear Variable differential transformers (LVDTs). The analysis of the test results showed that the columns that were embedded with a small void that occupied about 30% of the core area exhibited reductions of 9% in the ultimate capacity, 14% in initial stiffness, 20% in ductility, and 1% in residual strength. On the other hand, the columns that contained a large void occupying about 60% of the core area demonstrated reductions of 24% in the ultimate capacity, 34% in initial stiffness, and 26% in ductility, although the residual strength was slightly increased by 5%. The reason for the deficiency in the structural response in the latter case is because the void occupied a significant fraction of the concrete core. The theoretical part of this study showed that the ACI 318 code provisions can reasonably predict the uniaxial compressive strength of columns embedded with PET bottles if the void does not occupy more than 30% of the concrete core. This study confirmed that short columns embedded with relatively small voids made from PET bottles and subjected to pure axial compression create a balance between sustainability benefits and a structural performance tradeoff. Full article

Convolutional neural networks (CNNs) have achieved remarkable success in computer vision tasks, driving the rapid development of hardware accelerators. However, memory efficiency remains a key challenge, as conventional accelerators adopt layer-by-layer processing, leading to frequent external memory accesses (EMAs) of intermediate feature data, which increase energy consumption and latency. While layer fusion has been proposed to enhance inter-layer feature reuse, existing approaches typically rely on fixed data management tailored to specific architectures, introducing on-chip memory overhead and requiring trade-offs with EMAs. Moreover, prevalent residual connections further weaken fusion benefits due to diverse data reuse distances. To address these challenges, we propose layer-centric tile fusion, which integrates residual data loading with feature merging by leveraging receptive field relationships among feature tiles. A reuse distance-aware caching strategy is introduced to support flexible storage for various data types. We also develop a modeling framework to analyze the trade-off between on-chip memory usage and EMA-induced energy-delay product (EDP). Experimental results demonstrate that our method achieves 5.04–43.44% EDP reduction and 20.28–58.33% memory usage reduction compared to state-of-the-art designs on ResNet-18 and SRGAN. Full article