Search Results (422)

Unmanned aerial vehicles (UAVs) operating in safety-critical missions require effective anomaly detection methods to identify propulsion-system faults before they cause catastrophic failures. However, current vibration-based diagnostic models typically rely on datasets representing only discrete, isolated fault states, and do not capture the continuous structural degradation that occurs during real flight operations. To address this gap, this study proposes a severity-ordered vibration data augmentation framework for anomaly detection in rotary-wing UAV propulsion systems. Controlled experiments were conducted under healthy, tape-induced imbalance, scratch, and cut propeller conditions using stepped throttle excitation from 10% to 100% in 10% increments, with 40 s per level. A severity-ordered arrangement strategy based on throttle level and a robust peak-to-peak severity metric generated approximately 7.5 h of augmented vibration data per axis, representing a continuous degradation trajectory. Three-axis continuous wavelet transform (CWT) scalograms of size $48\times 96\times 3$ were used to train an unsupervised anomaly detection framework. Comparative experiments with Isolation Forest, One-Class SVM, and LSTM–AE demonstrated that the proposed Convolutional Neural Network (CNN)–Bidirectional Gated Recurrent Unit (BiGRU)–State-Space Model (SSM)–Autoencoder (AE) architecture achieved the best performance, reaching 0.9959 precision, 0.4428 recall, 0.6131 F1-score, and 0.9284 Area Under the Receiver Operating Characteristic Curve (AUROC). The ablation study further showed that incorporating temporal modeling and state-space dynamics improves detection robustness compared with CNN–AE and CNN–BiGRU–AE baselines. These results show that combining severity-ordered augmentation with deep temporal learning improves progressive propulsion anomaly detection in UAV vibration monitoring. This work introduces a methodology that connects rotor dynamics principles with deep learning, providing a continuous degradation manifold that improves early-stage detection and condition monitoring of UAV propulsion systems. Full article

The rapid growth of the elderly population worldwide demands reliable activity recognition technologies to support independent living and continuous health supervision. However, conventional wearable sensor-based human activity recognition (HAR) techniques often fail to capture the complex temporal behaviour and subtle motion patterns characteristic of the elderly. To address these limitations, this study introduces a hybrid deep residual architecture—CNN-CBAM-BiGRU—that integrates convolutional neural networks (CNNs), the convolutional block attention module (CBAM), and bidirectional gated recurrent units (BiGRUs) to improve activity recognition using inertial measurement unit (IMU) data. In the proposed CNN-CBAM-BiGRU framework, CNN layers automatically derive representative features from raw sensor signals, CBAM applies adaptive channel and spatial attention to highlight informative patterns, and BiGRU captures long-range temporal relationships within activity sequences. The approach was evaluated on three benchmark datasets designed for elderly populations—HAR70+, HARTH, and SisFall—covering daily activities and fall events. The proposed model consistently outperforms existing methods across all datasets, achieving accuracies exceeding 96%, F1-scores above 93%, and a fall detection recall of 93.74%, confirming its robustness and suitability for safety-critical monitoring applications. Class-level evaluation indicates excellent recognition of static postures and consistent performance for dynamic actions. Convergence analysis further confirms efficient learning with limited overfitting across datasets. The proposed framework thus provides a robust and accurate solution for wearable-based elderly activity recognition, with strong potential for deployment in fall detection, health monitoring, and ambient assisted living systems. Full article

Accurate forecasting of fatal construction accidents is critical for proactive safety management; however, accident time series exhibit strong non-stationarity, nonlinear dynamics, and multi-scale temporal patterns that challenge conventional models. This study proposes a hybrid STL–BiGRU framework that integrates Seasonal–Trend decomposition using Loess (STL) with a Bidirectional Gated Recurrent Unit (BiGRU) network to deliver robust and interpretable forecasts tailored to construction safety needs. STL first decomposes the original monthly accident series (January 2012–December 2024, OSHA) into trend, seasonal, and residual components, reducing structural complexity and mitigating non-stationarity. Independent BiGRU models are then trained on each component to capture bidirectional temporal dependencies, and final forecasts are reconstructed through component aggregation. Comparative experiments against Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), and their STL-enhanced variants demonstrate that the proposed STL–BiGRU model achieves superior performance across both short-term and medium-term horizons. The model achieves the lowest error levels, with a short-term Root Mean Squared Error (RMSE) of 6.8522 and a medium-term RMSE of 7.0568, and shows consistent improvements in Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results indicate that multi-scale decomposition combined with bidirectional deep learning provides a practical, forward-looking tool. It helps regulators and contractors anticipate high-risk periods, optimize resource allocation, and reduce fatal accidents through targeted preventive measures. Full article

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article

Against the backdrop of the global energy transition toward deep electrification, the natural gas industry faces challenges, including increased load forecasting uncertainty and frequent extreme weather impacts. To enhance natural gas load forecasting accuracy and support system resilience planning, this study constructs a forecasting model based on quadratic decomposition and hybrid deep learning, incorporating an electricity substitution coefficient to characterize the coupling substitution effect between electricity and natural gas. Under the basic scenario, the VMD-WPD-TCN-BiGRU model is proposed. It employs variational mode decomposition and wavelet packet denoising for secondary signal denoising, combined with a time-series convolutional network and bidirectional gated recurrent unit to extract temporal features. Experiments demonstrate that, compared to mainstream methods such as CNN, BiLSTM, SVM, and XGBoost, this model achieves statistically significant reductions in MSE (11.11–96.21%), MAE (0.89–76.50%), and MAPE (4.10–67.94%), significantly improving forecasting accuracy. In the deep electrification scenario, the introduction of the electricity substitution coefficient further optimizes peak load forecasting for system pressure days under extreme low temperatures, elevating the overall R² to 0.9905 in the deep electrification scenario. Research indicates that the proposed model not only effectively improves the accuracy of short-term natural gas load forecasting but also provides quantitative support for enterprises to plan peak-shaving facilities, optimize pipeline networks, and respond to extreme weather emergencies in data silo environments. This contributes to strengthening the adaptability and long-term resilience of natural gas systems during the energy transition, thereby supporting the sustainable development of energy infrastructure. Full article

Precise assessment of battery state of health (SoH) is vital for certifying consistent performance in order to enable maintenance of energy storage system. This work compares different deep learning methods to learn and predict the complex and nonlinear dynamics of battery. The models are developed and tested for predicting SoH using sequential degradation data from batteries. The effectiveness of these models is assessed using matrices such as RMSE, MAE and R², along with qualitative analysis. The experiment results show that the BiLSTM model performs better than the others. It has the lowest RMSE (0.90), the lowest MAE (0.72), and the highest R² (0.99), which highlights its enhanced ability to capture long-term temporal dependencies. The proposed models are validated using NASA lithium-ion battery aging dataset (B0005), which is widely used as a benchmark for battery health predictions studies. Overall, the findings indicate that bidirectional network architecture significantly improves the accuracy and consistency of SoH predictions when compared to unidirectional models. Full article

Classroom humor is an important instructional strategy that enhances teaching effectiveness and improves student engagement. However, its automatic detection remains challenging due to the strong contextual dependency and implicit semantic shifts that characterize humorous expressions in teaching discourse. Conventional pretrained language models capture global semantics but often fail to focus on the subtle humor anchors that trigger incongruity. To address this issue, we propose T-HumorAGSA, a cognitive-inspired classroom teacher humor language detection model. The model employs BERT for contextualized semantic encoding, followed by a Gated Anchor-Guided Self-Attention (AGSA) mechanism that adaptively amplifies anchor-related features responsible for humor generation. A bidirectional gated recurrent unit (BiGRU) layer is further integrated to model long-range temporal dependencies within teaching utterances. T-HumorAGSA is evaluated on five datasets, including SemEval 2021 Task 7-1a, ColBERT, CCL2018, CCL2019 and the self-constructed teacher humor language dataset (T-Humor), demonstrating consistently strong performance. For instance, it achieves 0.9874 F1 on ColBERT and 0.9508 F1 on SemEval 2021 Task 7-1a, both outperforming the best baseline models. On the T-Humor dataset, the model attains a high F1 score of 0.9895, validating its capacity to detect subtle humorous cues in instructional discourse. The results demonstrate that the proposed model delivers excellent performance in classroom humor detection. Full article

Network Intrusion Detection Systems (NIDS) play a crucial role in protecting network environments against cyberattacks. However, traditional NIDS rely heavily on predefined attack signatures, which limits their ability to detect zero-day attacks. Although machine learning-based intrusion detection techniques have been widely adopted in Network Intrusion Prevention Systems (NIPS), publicly available network traffic datasets often suffer from severe class imbalance, leading to biased learning and degraded detection performance. To address this issue, this study proposes data augmentation framework based on a 3D-GAN (Three-Discriminator Generative Adversarial Network). The proposed architecture integrates an autoencoder, a CNN (Convolutional Neural Network), and an LSTM (Long Short-Term Memory) network as parallel discriminators to capture the statistical, spatial, and temporal characteristics of network traffic. By jointly optimizing multiple discriminator losses, the framework enhances training stability and generates high-quality synthetic samples. Experiments were conducted on the CIC-UNSW-NB15 dataset using Random Forest-, XGBoost (eXtreme Gradient Boosting)-, and BiGRU (Bidirectional Gated Recurrent Unit)-based classifiers. Two augmented datasets were constructed to address class imbalance, containing approximately 100,000 and 350,000 samples, respectively. Among them, Dataset 2, augmented using the proposed 3D-GAN, demonstrated the most significant performance improvement. Compared to the original imbalanced dataset, the XGBoost classifier trained on Dataset 2 achieved approximately a 4% increase in both accuracy and F1-score, while reducing the false positive rate and false negative rate by approximately 3.5%. Furthermore, the optimal configuration attained an F1-score of 0.9816, indicating superior capability in modeling complex network traffic patterns. Overall, this study highlights the potential of GAN-based data augmentation for alleviating class imbalance and improving the robustness and generalization of intrusion detection systems. Full article

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F₁ score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F₁ scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively. Full article

Tropical cyclones are among the most destructive meteorological systems on Earth. Accurate track forecasting of tropical cyclones remains a core challenge in atmospheric science, and it is of great significance for disaster prevention and mitigation. This study targets the critical limitations of existing tropical cyclone track forecasting models: the insufficient ability to extract non-linear spatiotemporal features from 3D atmospheric circulation fields and the long-standing bottlenecks in multi-source heterogeneous meteorological data fusion. To address these issues, we propose a Dual-Stream Gated Fusion Network (DGF-Net), a high-precision track forecasting method tailored to the Northwest Pacific basin. The proposed framework takes the Best Track dataset and ERA5 Reanalysis Dataset as primary inputs: a Bidirectional Gated Recurrent Unit (Bi-GRU) is adopted to capture the temporal evolution characteristics of 2D tropical cyclone trajectory sequences, and a SpatioTemporal Convolutional Gated Recurrent Unit (STConvGRU) is used to extract complex non-linear features from 3D atmospheric environmental fields. Then, a multimodal fusion module integrating gating and attention mechanism is constructed to achieve deep fusion of cross-dimensional features, which effectively mines the intrinsic physical correlations between tropical cyclone track evolution and environmental driving factors. Comparative experiments based on historical observational datasets of the Northwest Pacific show that DGF-Net achieves superior forecasting performance, with the 6 h, 12 h, and 24 h Great Circle Distance (GCD) errors of 35.62 km, 43.53 km, and 135.49 km, respectively. The results significantly outperform mainstream baseline models, which validates the effectiveness of DGF-Net in feature extraction and multimodal fusion and provides solid technical support for tropical cyclone disaster prevention and operational decision-making. Full article

Precise forecasting and physical elucidation of seepage behavior are crucial for maintaining the operational safety of concrete dams. Nonetheless, current monitoring methodologies frequently fail to adequately encompass nonlinear temporal relationships in seepage processes and exhibit a deficiency in straightforward interpretability. This paper provides an explainable monitoring approach that combines an alpha-evolution Bidirectional Gated Recurrent Unit (AE-BiGRU) with Shapley Additive Explanations (SHAP)-based interpretability analysis to solve these shortcomings. An AE-BiGRU prediction model is first developed, in which the BiGRU architecture exploits bidirectional temporal dependencies to enhance prediction accuracy and robustness. The alpha-evolution algorithm is then employed to optimize key hyperparameters of the neural network, thereby further improving model performance. Subsequently, SHAP interpretability analysis is applied to quantify the contribution of individual input variables and to elucidate the physical drivers of seepage variation. Validation utilizing long-term seepage monitoring data from a roller-compacted concrete (RCC) gravity dam indicates that the proposed AE-BiGRU model substantially surpasses benchmark models, including LSTM and traditional GRU variations. Furthermore, SHAP interpretability analysis reveals the predominant influences of reservoir water level fluctuations and cumulative temporal factors on seepage evolution patterns. The suggested approach attains high-precision seepage prediction while ensuring physically meaningful interpretability, thus providing a dependable foundation for safety evaluation and intelligent monitoring of concrete dams. Full article

This paper focuses on enhancing the accuracy and efficiency of bus travel speed prediction by improving the optimization process for deep learning model parameters. Existing intelligent optimization algorithms often suffer from slow convergence and substantial errors when tuning parameters for such predictive tasks. To mitigate these shortcomings, this study presents a new predictive framework that synergizes an Improved Atom Search Optimization (IASO) algorithm with a Bidirectional Gated Recurrent Unit (BiGRU) network. The EASO algorithm is developed through three principal modifications: (1) population initialization using a Logistic-Tent composite chaotic map to enhance diversity and initial quality; (2) incorporation of a hybrid operator merging refraction opposition-based learning and Cauchy mutation to broaden the search around promising solutions and alleviate issues of local stagnation and early convergence; and (3) implementation of an adaptive variable spiral search to recalibrate the position update rule, thereby improving the trade-off between extensive exploration and intensive exploitation. Based on the analysis of bus travel speed determinants, the IASO algorithm is applied to optimize the hyperparameters of the BiGRU network, culminating in the proposed IASO-BiGRU predictive model. Validation tests indicate that the devised IASO algorithm shows improved performance in certain aspects compared to several contemporary intelligent optimization techniques in terms of solution accuracy and convergence efficiency. Under the specific experimental conditions of this study, the IASO-BiGRU model achieves MAE, RMSE, and MAPE values of 1.62, 1.80, and 6.70%, respectively, corresponding to an improvement of 1.91–7.56% compared to the baseline models tested. These findings offer valuable data support and a decision-making foundation for bus operation scheduling and passenger travel planning. Full article

Acoustic material classification under non-line-of-sight (NLOS) conditions—where direct sound paths are obstructed—is a challenging task due to echo attenuation, complex reflections, and noise effects. This study aims to improve NLOS material recognition by introducing a novel wavelet–acoustic hybrid feature fusion method integrated with deep recurrent neural network architectures. Echo signals from nine different materials were collected using the newly developed ANLOS-R (Acoustic Non-Line-of-Sight Recognition) dataset, which was specifically designed to simulate realistic NLOS propagation environments. From these recordings, time-domain acoustic features and multi-scale wavelet-based energy and entropy statistics were extracted using ten wavelet families. The resulting 70-dimensional hybrid feature set was used to train several deep learning architectures, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network–LSTM (CNN–LSTM). Among these, the CNN–LSTM achieved the highest balanced accuracy and macro-F1 score of 0.99, showing strong generalization and convergence performance. SHapley Additive exPlanations (SHAP) analysis indicated that Mel-Frequency Cepstral Coefficients (MFCCs) and wavelet entropy–energy features play complementary roles in material discrimination. The proposed approach provides a robust and interpretable framework for real-time NLOS acoustic sensing, bridging data-driven deep learning with the physical understanding of acoustic material behavior. Full article

A productivity simulation for hydraulically fractured wells with complex fracture geometry involves a heavy computational burden and is therefore not suitable for engineering-scale fracture-optimization designs and production-analysis applications. This paper develops a productivity-prediction surrogate model based on a deep convolution–bidirectional gated recurrent unit temporal network (DC-BiGRU) framework where a deep convolutional neural network is used to extract features from fracture images, while a BiGRU model was designed to fully capture valuable information from the production sequence. Some additional inputs, e.g., cluster spacing and stage spacing, that account for different fracture-placement designs in horizontal wells were also considered. A large number of shale-gas production data samples at different times were generated using a fractured-horizontal-well productivity simulator under diverse hydraulic-fracture geometries and bottom-hole flowing pressures. The surrogate model had relative errors below 10% with an average error of about 6%. Compared to high-fidelity capacity prediction simulators, the computational efficiency of the deep learning surrogate models was improved by two to three orders of magnitude. The runtime of the high-fidelity numerical simulator was about 20 min, while the surrogate model, which was run on an NVIDIA Tesla P100 GPU (NVIDIA, Santa Clara, CA, USA), took less than 1 s, which is almost negligible. The proposed surrogate model resolved the low efficiency of the productivity simulation for complex-fracture hydraulic fracturing wells in unconventional reservoirs, enabling rapid dynamic forecasting of fractured-well productivity. Full article

With the global energy structure transitioning toward low-carbon and sustainable development, improving the stability and predictability of renewable energy generation has become a key challenge for achieving carbon neutrality goals. However, photovoltaic power output exhibits significant variability and uncertainty, and accurate power forecasting is of great significance for optimizing grid dispatch, improving renewable energy integration capacity, and reducing system reserve requirements. Therefore, this paper proposes a multi-stage prediction model that integrates Ensemble Empirical Mode Decomposition (EEMD), Improved Whale Optimization Algorithm-based Variational Mode Decomposition (IWOA-VMD), and an Improved Sparrow Search Algorithm (ISSA)-optimized Bidirectional Gated Recurrent Unit (BiGRU) network. Specifically, EEMD is first used to decompose the photovoltaic power sequence to extract Intrinsic Mode Functions (IMFs); then, the residual IMF is further decomposed using IWOA-optimized VMD to enhance low-frequency modeling capability; next, ISSA adaptively optimizes the hidden layer dimensions and learning rate of the BiGRU; Finally, each component is predicted individually, and the overall power sequence is reconstructed. Experimental results based on publicly available real photovoltaic data demonstrate that the proposed model outperforms BiGRU and several hybrid models in terms of MAE and RMSE. The research findings contribute to improving the accuracy of photovoltaic power forecasting, thereby providing technical support for the low-carbon transition and sustainable development of energy systems. Full article