Search Results (3,412)

With the increasing penetration of renewable energy sources and power-electronic devices, modern power systems exhibit pronounced wideband oscillation characteristics with large frequency spans, strong modal coupling, and significant time-varying behaviors. Accurate identification and classification of wideband oscillation patterns have therefore become critical challenges for ensuring the secure and stable operation of “dual-high” power systems. Existing methods based on signal processing or single-modality deep-learning models often fail to fully exploit the complementary information embedded in heterogeneous data representations, resulting in limited performance when dealing with complex oscillation patterns.To address these challenges, this paper proposes a multimodal attention-based fusion network for wideband oscillation classification. A dual-branch deep-learning architecture is developed to process Gramian Angular Difference Field images and raw time-series signals in parallel, enabling collaborative extraction of global structural features and local temporal dynamics. An improved Inception module is employed in the image branch to enhance multi-scale spatial feature representation, while a gated recurrent unit network is utilized in the time-series branch to model dynamic evolution characteristics. Furthermore, an attention-based fusion mechanism is introduced to adaptively learn the relative importance of different modalities and perform dynamic feature aggregation. Extensive experiments are conducted using a dataset constructed from mathematical models and engineering-oriented simulations. Comparative studies and ablation studies demonstrate that the proposed method significantly outperforms conventional signal-processing-based approaches and single-modality deep-learning models in terms of classification accuracy, robustness, and generalization capability. The results confirm the effectiveness of multimodal feature fusion and attention mechanisms for accurate wideband oscillation classification, providing a promising solution for advanced power system monitoring and analysis. Full article

Nonlinear loads are widely applied, making the generation mechanism of grid harmonics increasingly intricate. However, high-precision monitoring devices suffer from high deployment costs and limited coverage. This poses a major challenge to directly acquiring harmonic voltages at some nodes. To solve this problem, this paper proposes a harmonic state estimation method based on a Dual-Stream Adaptive Fusion Generative Adversarial Network (DSAF-GAN), with an innovative design in its generator architecture. A dual-path generator is developed to extract multi-scale features through heterogeneous network branches collaboratively. The ResNet-GRU path integrates convolutional residual modules with Bidirectional Gated Recurrent Units (Bi-GRUs). It effectively captures local spatial patterns and temporal dynamic characteristics of time-series data. The multi-layer perceptron (MLP) path focuses on mining global nonlinear correlations, thereby enhancing the overall feature-expressing capability. An adaptive weight fusion module (Attention Weight Net) fuses the outputs of the two paths. It dynamically allocates contribution weights, improving the model’s flexibility and generalization performance. Experimental results show that the proposed DSAF-GAN can accurately reconstruct the harmonic voltage component content rate of missing nodes. Full article

Accurately predicting molten steel carbon content plays a crucial role in improving productivity and energy efficiency during the Basic Oxygen Furnace (BOF) steelmaking process. However, current data-driven methods primarily focus on endpoint carbon content prediction, while lacking sufficient investigation into real-time curve forecasting during the blowing process, which hinders real-time closed-loop BOF control. In this article, a novel Transformer-based framework is presented for real-time carbon content prediction. The contributions include three main aspects. First, the prediction paradigm is reconstructed by converting the regression task into a sequence classification task, which demonstrates superior robustness and accuracy compared to traditional regression methods. Second, the focus is shifted from traditional endpoint-only forecasting to long-term prediction by introducing a Transformer-based model for continuous, real-time prediction of carbon content. Last, spatial–temporal feature representation is enhanced by integrating an optical flow channel with the original RGB channels, and the resulting four-channel input tensor effectively captures the dynamic characteristics of the converter mouth flame. Experimental results on an independent test dataset demonstrate favorable performance of the proposed framework in predicting carbon content trajectories. The model achieves high accuracy, reaching 84% during the critical decarburization endpoint phase where carbon content decreases from 0.0829 to 0.0440, and delivers predictions with approximately 75% of errors within ±0.05. Such performance demonstrates the practical potential for supporting intelligent BOF steelmaking. Full article

Gross Domestic Product (GDP) serves as a core indicator for measuring the sustainable economic development of countries and regions. Accurate understanding of its spatio-temporal distribution is crucial for achieving the United Nations Sustainable Development Goals (SDGs). However, current grid-based GDP data for China’s regions predominantly consists of data from specific years, making it difficult to capture fine-grained changes in economic development. To address this, this study proposes a spatial GDP framework integrating cross-scale feature extraction (CSFs) with stacked ensemble learning. Based on China’s county-level GDP statistics and multi-source auxiliary data, it first generates a density-weighted estimation layer. This is then processed through dasymetric mapping to produce China’s Annual High-Resolution Gridded GDP Dataset (CA_GDP) from 2000 to 2021. Evaluation demonstrates the framework’s superior performance in density weight estimation, achieving an R² of 0.82 against statistical data. Compared to traditional single models like Random Forests (RF), it improves R² by 13–54%, reduces mean absolute error (MAE) by 2–26%, and lowers root mean square error (RMSE) by 19–39%, with these advantages remaining stable across time series. The dasymetric mapping of the CA_GDP dataset clearly depicts the economic development patterns and urban agglomeration effects in the southeastern coastal regions, as well as the relatively lagging economic development in western areas. Compared to existing public datasets, CA_GDP offers significant advantages in reflecting the fine-grained economic spatial structure within county-level units, providing a more reliable data foundation for identifying regional economic disparities, policy formulation and evaluation, and related research. Full article

In recent years, the expansion of network scale and the diversification of attack methods pose dual challenges to intrusion detection systems in extracting effective features and addressing class imbalance. To address these issues, the Spatial–Temporal Equilibrium Graph Convolutional Network (STEGCN) is proposed. This model integrates Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU), leveraging GCN to extract high-order spatial features from network traffic data while capturing complex topological relationships and latent patterns. Meanwhile, GRU efficiently models the dynamic evolution of network traffic over time, accurately depicting temporal trends and anomaly patterns. The synergy of these two components provides a comprehensive representation of network behavior. To mitigate class imbalance in intrusion detection, the Equalization Loss v2 (EQLv2) is introduced. By dynamically adjusting gradient contributions, this function reduces the dominance of majority classes, thereby enhancing the model’s sensitivity to minority-class attacks. Experimental results demonstrate that STEGCN achieves superior detection performance on the UNSW-NB15 and CICIDS2017 datasets. Compared with traditional deep learning models, STEGCN shows significant improvements in accuracy and recall, particularly in detecting minority-class intrusions. Full article

Industrial Control Systems (ICSs), critical to infrastructure, face escalating cyber threats under Industry 4.0, yet existing intrusion detection methods are hindered by attack sample scarcity, spatiotemporal heterogeneity of industrial protocols, and resource constraints of embedded devices. This paper proposes a four-stage closed-loop intrusion detection framework for ICSs, with its core innovations integrating the following key components: First, a protocol-conditioned Conditional Generative Adversarial Network (CTGAN) is designed to synthesize realistic attack traffic by enforcing industrial protocol constraints and validating syntax through dual-path discriminators, ensuring generated traffic adheres to protocol specifications. Second, a three-tiered sliding window encoder transforms raw network flows into structured RGB images, capturing protocol syntax, device states, and temporal autocorrelation to enable multiresolution spatiotemporal analysis. Third, an Efficient Multiscale Attention Visual State Space Model (EMA-VSSM) is developed by integrating gate-enhanced state-space layers with multiscale attention mechanisms and contrastive learning, enhancing threat detection through improved long-range dependency modeling and spatial–temporal correlation capture. Finally, a lightweight EMA-VSSM student model, developed via hierarchical distillation, achieves a model compression rate of 64.8% and an inference efficiency enhancement of approximately 30% relative to the original model. Experimental results on a real-world ICS dataset demonstrate that this lightweight model attains an accuracy of 98.20% with a False Negative Rate (FNR) of 0.0316, outperforming state-of-the-art baseline methods such as XGBoost and Swin Transformer. By effectively balancing protocol compliance, multi-resolution feature extraction, and computational efficiency, this framework enables real-time deployment on resource-constrained ICS controllers. Full article

Earth Observation (EO) and Remote Sensing (RS) data are widely used in various fields, including weather, environment, and natural disaster modeling and prediction. EO and RS done through geostationary satellite constellations in fields such as these are limited to a smaller region, while sun synchronous satellite constellations have discontinuous spatial and temporal coverage. This limits the ability of EO and RS data for near-real-time weather, environment, and natural disaster applications. To address these limitations, we introduce Now Observation Assemble Horizon (NOAH), a multi-modal, sensor fusion dataset that combines Ground-Based Sensors (GBS) of weather stations with topography, vegetation (land cover, biomass, and crown cover), and fuel types data from RS data sources. NOAH is collated using publicly available data from Environment and Climate Change Canada (ECCC), Spatialized CAnadian National Forest Inventory (SCANFI) and United States Geological Survey (USGS), which are well-maintained, documented, and reliable. Applications of the NOAH dataset include, but are not limited to, expanding RS data tiles, filling in missing data, and super-resolution of existing data sources. Additionally, Generative Artificial Intelligence (GenAI) or Generative Modeling (GM) can be applied for near-real-time model-generated or synthetic estimate data for disaster modeling in remote locations. This can complement the use of existing observations by field instruments, rather than replacing them. UNet backbone with Feature-wise Linear Modulation (FiLM) injection of GBS data was used to demonstrate the initial proof-of-concept modeling in this research. This research also lists ideal characteristics for GM or GenAI datasets for RS. The code and a subset of the NOAH dataset (NOAH mini) are made open-sourced. Full article

Traffic classification remains a critical challenge in the Internet of Things (IoT), particularly for enhancing security and ensuring Quality of Service (QoS). Although deep learning methods have shown strong performance in traffic classification, learning diverse and complementary representations across heterogeneous network traffic patterns remains difficult. To address this issue, this study proposes a novel Mixture of Experts (MoE) architecture for multiclass traffic classification in IoT environments. The proposed model integrates five specialized expert networks, each targeting a distinct feature category in network traffic. Specifically, it employs a Dense Neural Network for general features, a Convolutional Neural Network (CNN) for spatial patterns, a Gated Recurrent Unit (GRU)-based model for statistical variations, a Convolutional Autoencoder (CAE) for frequency-domain representations, and a Long Short-Term Memory (LSTM) for temporal dependencies. A dynamic gating mechanism, coupled with an Attention-based Learnable Fusion (ALF) module, adaptively aggregates the experts’ outputs to produce the final classification decision. The proposed ALF-MoE model was evaluated on three public benchmark datasets, such as ISCX VPN-nonVPN, Unicauca, and UNSW-IoTraffic, achieving accuracies of 98.43%, 98.96%, and 97.93%, respectively. These results confirm its effectiveness and reliability across diverse scenarios. It also outperforms baseline methods in terms of its accuracy and the F1-score. Full article

Tea bud recognition and localization constitute a fundamental step toward enabling fine-grained tea plantation management and intelligent harvesting, offering substantial value in improving the picking quality of premium tea materials, reducing labor dependency, and accelerating the development of smart tea agriculture. However, most existing methods for detecting tea buds are built upon Artificial Neural Networks (ANNs) and rely extensively on floating-point computation, making them difficult to deploy efficiently on energy-constrained edge platforms. To address this challenge, this paper proposes an energy-efficient tea bud detection model, GAE-SpikeYOLO, which improves upon the Spiking Neural Networks (SNNs) detection framework SpikeYOLO. Firstly, Gated Attention Coding (GAC) is introduced into the input encoding stage to generate spike streams with richer spatiotemporal dynamics, strengthening shallow feature saliency while suppressing redundant background spikes. Secondly, the model incorporates the Temporal-Channel-Spatial Attention (TCSA) module into the neck network to enhance deep semantic attention on tea bud regions and effectively suppress high-level feature responses unrelated to the target. Lastly, the proposed model adopts the EIoU loss function to further improve bounding box regression accuracy. The detection capability of the model is systematically validated on a tea bud object detection dataset collected in natural tea garden environments. Experimental results show that the proposed GAE-SpikeYOLO achieves a Precision (P) of 83.0%, a Recall (R) of 72.1%, a mAP@0.5 of 81.0%, and a mAP@[0.5:0.95] of 60.4%, with an inference energy consumption of only 49.4 mJ. Compared with the original SpikeYOLO, the proposed model improves P, R, mAP@0.5, and mAP@[0.5:0.95] by 1.4%, 1.6%, 2.0%, and 3.3%, respectively, while achieving a relative reduction of 24.3% in inference energy consumption. The results indicate that GAE-SpikeYOLO provides an efficient and readily deployable solution for tea bud detection and other agricultural vision tasks in energy-limited scenarios. Full article

The Earth’s climate system exhibits nonlinear behavior driven by interactions among the atmosphere, oceans, cryosphere, land, and biosphere. These dynamics have given rise to relatively stable environments that shape the structure and function of the modern biosphere. This review is a primer for conservation practitioners and natural resource managers to develop a deep understanding of how the Earth System works. The key is to recognize that shifts in Earth System dynamics due to global climate change can destabilize the biosphere in unforeseen ways. The potential emergence of novel ecoregions must be a critical factor in adaptation planning for conservation and resource management. We review how thermodynamic constraints and global circulation dynamics determine the distribution of terrestrial and marine biomes. These dynamics stem from the Earth System functioning as a heat engine, transporting excess heat from low to high latitudes. We illustrate how biome climates are organized into climate regimes, with spatial and temporal characteristics linked to complex features of atmospheric and oceanic circulation. At centennial to millennial scales, these dynamics have created a stable envelope of natural variability in climate that has established a long-standing operating space for biota. However, this stability is becoming increasingly uncertain due to the growing positive energy imbalance in the Earth System primarily driven by anthropogenic greenhouse gas emissions. This forcing is leading to disruptive climatic change, putting the biosphere on a trajectory toward new transient states. Such global to regional climatic instability and biospheric restructuring introduce a high level of uncertainty in ecological futures, with major implications for natural resource management, biodiversity conservation strategies, and societal adaptation. We conclude by discussing frameworks for impact assessments and decision making under climate uncertainty. Full article

Traffic forecasting in large-scale urban networks must operate reliably under imperfect sensing conditions, where measurements may contain noise or missing values. Most existing spatio-temporal graph neural networks focus primarily on modeling spatial–temporal dependencies, while paying limited attention to the propagation of irrelevant or unstable information through dynamic graph structures. In this work, we propose a Dynamic Graph Information Bottleneck (DGIB) framework that enhances prediction stability by introducing task-aware representation compression into dynamic graph learning. Instead of relying solely on architectural complexity, DGIB explicitly regulates the information flow within spatio-temporal embeddings through a variational bottleneck objective. The model adaptively constructs time-evolving adjacency matrices, extracts spatial features via graph convolutions, captures temporal dependencies using recurrent modeling, and constrains the latent representation to retain only predictive content relevant to future traffic states. By jointly optimizing topology adaptation and information-theoretic regularization in an end-to-end manner, the proposed framework mitigates the amplification of noisy or redundant signals in dynamic graphs. Experiments on multiple benchmark traffic datasets demonstrate that DGIB achieves competitive forecasting accuracy while maintaining strong robustness under noisy and incomplete data scenarios. Full article

To solve the difficulty of autonomous decision-making caused by the complex driving environment and changeable weather conditions, an end-to-end autonomous decision-making method based on residual network (ResNet), convolutional block attention module (CBAM) and bidirectional long short-term memory network (BiLSTM) is proposed for intelligent vehicles. Firstly, ResNet is used to extract spatial feature information contained in driving scene images. Then, CBAM is adopted to assign weights to each network channel and dynamically focus on important spatial regions in the image. Finally, BiLSTM is constructed to process the contextual features of continuous scenes, and the autonomous decision-making of intelligent vehicles is achieved through the fusion of spatial features and temporal information. On this basis, the proposed network model is trained using a real-world driving dataset and fully tested in various scenarios. Moreover, ablation experiments are conducted to verify the contribution of each module to the overall performance. The results show that the proposed method has better accuracy and stability compared with multiple existing methods, including PilotNet, FCN-LSTM, and DBNet, and its accuracy reaches 90.16% under clear weather conditions, as well as 81.29% under nighttime and snowy weather conditions. Full article

A hybrid machine learning framework has been developed in this study to estimate Terrestrial Water Storage Anomalies (TWSA) in Morocco’s Casablanca–Settat region, which faces serious groundwater stress due to rapid urbanization, intensive agriculture, and climate variability. In this study, TWSA is used as an integrated proxy for groundwater-related storage changes, while acknowledging that it also includes contributions from soil moisture and surface water. The approach combines satellite-based observations from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) with key environmental indicators such as rainfall, evapotranspiration, and land use data to track changes in groundwater availability with improved spatial detail. After preprocessing the data through feature selection, normalization, and outlier handling, the model applies six base learners, i.e., Huber regressor, automatic relevance determination regression, kernel ridge, long short-term memory, k-nearest neighbors, and gradient boosting. Their predictions are aggregated using a random forest meta-learner to improve accuracy and stability. The ensemble achieved strong results, with a root mean square error of 0.13, a mean absolute error of 0.108, and a determination coefficient of 0.97—far better than single-model baselines—based on a temporally independent train-test split. Spatial analysis highlighted clear patterns of groundwater depletion linked to land cover and usage. These results can guide targeted aquifer recharge efforts, drought response planning, and smarter irrigation management. The model also aligns with national goals under Morocco’s water sustainability initiatives and can be adapted for use in other regions with similar environmental challenges. Full article

Accurate forecasting of citywide electric vehicle (EV) charging load is critical for alleviating station-level congestion, improving energy dispatching, and supporting the stability of intelligent transportation systems. However, large-scale EV charging networks exhibit complex and heterogeneous spatio-temporal dependencies, and existing approaches often struggle to scale with increasing station density or long forecasting horizons. To address these challenges, we develop a modular spatio-temporal prediction framework that decouples temporal sequence modeling from spatial dependency learning under an encoder–decoder paradigm. For temporal representation, we introduce a global aggregation mechanism that compresses multi-station time-series signals into a shared latent context, enabling efficient modeling of long-range interactions while mitigating the computational burden of cross-channel correlation learning. For spatial representation, we design a dynamic multi-scale attention module that integrates graph topology with data-driven neighbor selection, allowing the model to adaptively capture both localized charging dynamics and broader regional propagation patterns. In addition, a cross-step transition bridge and a gated fusion unit are incorporated to improve stability in multi-horizon forecasting. The cross-step transition bridge maps historical information to future time steps, reducing error propagation. The gated fusion unit adaptively merges the temporal and spatial features, dynamically adjusting their contributions based on the forecast horizon, ensuring effective balance between the two and enhancing prediction accuracy across multiple time steps. Extensive experiments on a real-world dataset of 18,061 charging piles in Shenzhen demonstrate that the proposed framework achieves superior performance over state-of-the-art baselines in terms of MAE, RMSE, and MAPE. Ablation and sensitivity analyses verify the effectiveness of each module, while efficiency evaluations indicate significantly reduced computational overhead compared with existing attention-based spatio-temporal models. Full article

In the information age, Internet of Things (IoT) devices are more susceptible to intrusion due to today’s complex network attack methods. Therefore, accurately detecting evolving network attacks from complex and ever-changing IoT environments has become a key research goal in the current intrusion detection field. Due to the spatial and temporal characteristics of IoT data, this paper proposes a Spatiotemporal Feature Weighted Fusion Approach Combining Gating Attention Transformation (STWGA). STWGA consists of three parts, namely spatial feature learning, the gated attention transformer, and the temporal feature learning module. It integrates improved convolutional neural networks (CNN), batch normalization, and Bidirectional Long Short-Term Memory Network (Bi-LSTM) to fully learn the deep spatial and temporal features of the data, achieving the goal of global deep spatiotemporal feature extraction. The gated attention transformer introduces an attention mechanism. In addition, an additional control mechanism is introduced in the self-attention module to more effectively improve detection accuracy. Finally, the experimental results show that STWGA has better spatiotemporal feature extraction ability and can effectively improve the intrusion detection effect of anomalies. Full article