Search Results (698)

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

Air pollution forecasting is particularly challenging in basins with frequent winter seasons and temperature inversions. In this study, we developed and rigorously evaluated deep learning models to forecast PM₁₀ and the Air Quality Index (AQI) in Igdır, Türkiye, using a five-year, hourly dataset (2020–2024) from the Igdır/Central station (PM₁₀, NO₂, O₃, SO₂; meteorology: pressure, temperature, wind speed, relative humidity, precipitation, cloud cover). Using linear interpolation and Z-score normalization, sine/cosine features (hour, month) were used to encode temporal periodicity, and a 72-h lookback → 24-h look-ahead design was employed. LSTM, GRU, BiLSTM, and CNN-LSTM models were compared under a three-stage ablation (meteorology only; +cyclic encoders; +lagged targets), and their hyperparameters were tuned via Bayesian optimization. The deep learning results were further contextualized against a Multiple Linear Regression (MLR) baseline serving as a snapshot persistence model to evaluate the specific advantage of LSTM’s temporal memory in short-horizon forecasting. Multi-output forecasting is central to the proposed design, featuring a multi-task learning (MTL) framework based on a single shared temporal encoder with two task-specific regression heads that simultaneously predict PM₁₀ and AQI. Compared with separate single-task models, the multi-output setup exploits cross-target covariance (AQI’s dependence on pollutant loads under meteorology), improves data efficiency and generalization through shared representations, and promotes coherent, horizon-stable forecasts across targets, which is particularly valuable when winter stagnation regimes couple PM₁₀ and AQI dynamics. Moreover, this study introduces a structured ablation design to explicitly evaluate the added value of multi-output forecasting under inversion-dominated basin conditions. The results show stepwise gains from cyclic encoders and, most strongly, from lagged target histories. Under the optimized 24-h setting, LSTM performs best (R²_{PM₁₀} = 0.7989, RMSE = 48.74 µg/m³; R²_{AQI} = 0.6626, RMSE = 37.81), marginally surpassing GRU and clearly outperforming BiLSTM and CNN-LSTM. Horizon sensitivity confirms the benefit of nowcasting: when retrained for shorter horizons, LSTM attains R² = 0.9991 for PM₁₀ (MAE = 2.44; RMSE = 3.30 µg/m³) and 0.9535 for AQI (MAE = 4.87; RMSE = 14.03) at 1 h, and R² = 0.9792 (PM₁₀; MAE = 9.70; RMSE = 15.67) and 0.8849 (AQI; MAE = 11.19; RMSE = 22.08) at 6 h. Residual diagnostics reveal heteroskedastic, regime-dependent errors peaking near 0 °C and low winds, as well as a conservative bias that underpredicts extremes. Collectively, the findings show that multi-output, temporally aware deep models enable accurate operational forecasting in Igdır. The proposed framework provides real-time air quality alerts and daily planning, providing decision support for sustainable air quality management, public health protection, and evidence-based urban policy and is transferable to similar continental basin environments. Full article

This paper explores the feasibility of early-season crop classification based on Sentinel-2-time series using the TimeSen2Crop dataset (≈1 million pixels, 16 crops). The aim of the study was to evaluate the spectral-phenological separability of crops during the season and compare the performance of classical tabular algorithms, deep sequence models, and a seasonally oriented hybrid stacking scheme. Based on multispectral observations, a feature set was formed from 9 optical channels and 13 vegetation indices for 30 dates. F-criteria were calculated, confirming a sharp increase in interclass separability during the active vegetative growth phase and substantiating three time series truncation scenarios (early, early + mid-season, and full season). Random Forest (macro-F1: 0.46/0.74/0.75) was used as the base tabular model. LSTM, BiLSTM, GRU, 1D-CNN, and Transformer were trained in parallel, with Transformer showing the best results among the deep architectures (0.42/0.68/0.78). The main contribution of the work is a hybrid multi-layer stacking scheme combining heterogeneous base algorithms and OOF meta-features, which provides the highest quality (0.51/0.83/0.86) in all scenarios. The obtained results confirm the effectiveness of phenology-oriented selection of time windows, informative indices, and hybrid ensemble learning for improving the accuracy of early-season crop monitoring. Full article

Against the backdrop of smart cities and digital cultural tourism, the accurate prediction of urban passenger flow is of great significance for public security management and resource allocation. However, existing studies mostly rely on single data sources or only perform a simple concatenation of multi-source features, lacking systematic indicator system design. Meanwhile, weekly or monthly data are commonly used with coarse temporal granularity, making it difficult to capture short-term fluctuations and lag effects. To overcome these limitations, this paper collects the daily passenger flow data of Hangzhou from 15 March 2024 to 15 March 2025; integrates multi-dimensional factors such as keyword search trends across platforms, holidays and major events, and online public opinion; and constructs three daily characteristic indicators: online search index, humanistic–meteorological index, and textual sentiment index. The data denoising, dimensionality reduction, and sentiment quantification are realized through methods including SSA, PCA, and SnowNLP. On this basis, a hybrid CNN-GRU model integrated with the attention mechanism is proposed. An improved artificial bee colony (ABC) algorithm is adopted for global hyperparameter optimization, and a weighted hybrid loss function (JQHL) is introduced to enhance the model’s adaptability to extreme values. The results show that the ABC-CNN-GRU-Attention model, incorporating multi-dimensional indicators, outperforms traditional methods on evaluation metrics, including MAE, RMSE, MAPE, R², and RPD, demonstrating a higher prediction accuracy and robustness. Full article

Reliable sensor fault detection is critical for the safe and efficient operation of complex industrial systems, such as thermal power plants. However, traditional data-driven methods and standard deep learning models often struggle to detect incipient gradual drift faults under severe environmental noise, primarily because they ignore the inherent physical correlations among multivariate sensor signals. To address this challenge, this paper proposes a novel Physics-Coupled Deep Long Short-Term Memory Autoencoder (PC-Deep-LSTM-AE). Specifically, we integrate a deep LSTM architecture with an explicit non-linear information compression bottleneck and layer normalization to enhance robust feature extraction in high-noise environments. Furthermore, we innovatively introduce a Physics-Coupling Loss (PCC Loss) that jointly optimizes the mean squared reconstruction error and the Pearson correlation coefficient, forcing the model to strictly preserve the dynamic physical relationships among multivariable signals. Extensive experiments were conducted on a real-world thermal power plant dataset with severe noise injection. The results demonstrate that the proposed PC-Deep-LSTM-AE achieves an outstanding F1-score of over 0.98, significantly outperforming mainstream baseline models, including Vanilla LSTM-AE, GRU-AE, Bi-LSTM-AE, and CNN-AE. The proposed method exhibits exceptional robustness and high interpretability for root-cause analysis, highlighting its immense potential for real-world industrial deployment. Full article

With the continuous development of the petroleum industry, bottomhole pressure prediction technology, which exerts a significant impact on oil production and recovery, has become a key research direction in the current oil and gas field. To enhance the accuracy and robustness of bottomhole pressure prediction under transient and variable operating conditions, a method based on data augmentation strategies and hyperparameter optimization was proposed in this paper. Addressing challenges such as limited data volume and significant disturbances in actual oilfield production, a data augmentation strategy incorporating noise perturbation and sliding windows was introduced to expand training samples and improve model generalization. In terms of model architecture, a deep network integrating CNN, BiGRU, and Multi-Head Attention mechanisms was proposed in this paper, which is referred to as the CNN-BiGRU-Multi-Head Attention model. By introducing Bayesian optimization for automatic hyperparameter search, the performance of the temporal model was further enhanced, achieving efficient extraction and dynamic focusing of wellbore pressure temporal features. Prediction results demonstrated that the proposed method outperforms existing mainstream forecasting models in metrics such as Mean Absolute Error (MAE) and Coefficient of Determination (R²), with R² reaching 0.9831, which confirms its strong generalization capability and engineering applicability. Practical guidance for intelligent oilfield production management and bottomhole pressure forecasting, along with a novel prediction method, is provided by this study, which holds significant importance for extending well life and stabilizing hydrocarbon production. Full article

Short-term load forecasting plays a pivotal role in modern power system operations yet it remains challenging due to the complex spatiotemporal dependencies in load data. This paper proposes a dual-head attention residual network (DARNet) that significantly advances STLF through three key innovations: (1) a hybrid encoder combining 1D-CNN and GRU architectures to simultaneously capture the local load patterns and long-term temporal dependencies, achieving a 28% better locality awareness than that of conventional approaches; (2) a novel dual-head attention mechanism that dynamically models both the inter-temporal relationships and cross-variable dependencies, reducing the feature engineering requirements; and (3) an autocorrelation-adjusted recursive forecasting framework that cuts the multi-step prediction error accumulation by 33% compared to that with standard seq2seq models. Extensive experiments on real-world datasets from three Chinese cities demonstrate DARNet’s superior performance, outperforming six state-of-the-art benchmarks by 21–35% across all of the evaluation metrics (MAPE, SMAPE, MAE, and RRSE) while maintaining robust generalization across different geographical regions and prediction horizons. Full article

Accurate stress measurement is essential to evaluating structural integrity and plays a pivotal role in the health monitoring and predicting the service life of steel infrastructures. This study proposes a deep learning approach for stress prediction based on longitudinal critically refracted (LCR) ultrasonic waves. The model integrates gated recurrent units (GRU), attention mechanisms, and one-dimensional convolutional neural networks (1D-CNN), enabling direct stress prediction from raw ultrasonic signals without the need for manual feature extraction or explicit physical modeling. To validate the approach, LCR signals were acquired using a custom-built piezoelectric ultrasonic system from 20# steel specimens subjected to uniaxial stresses ranging from 0 to 200 MPa. A dataset comprising 4200 samples was augmented to enhance training efficiency. The proposed model achieved a mean absolute error of 1.94 MPa. Generalization tests demonstrated high accuracy across diverse stress levels, with average errors below 3 MPa, highlighting the model’s robustness. This research presents an accurate, intelligent, and calibration-free ultrasonic method for stress evaluation, providing practical support for stress evaluation in steel structures under actual operating conditions. Full article

Accurate estimation of battery state of health (SoH) is an important aspect for improving the reliability, safety, and operating efficiency of an energy storage system. This study presents a unified deep learning pipeline for prediction, latent feature extraction, and anomaly detection. A convolution neutral network autoencoder is used to learn compact latent features from a dataset (NASA battery datasets, i.e., B0005, B0006, B0007, and B0018). These features serve as inputs to random forest and linear regression models, which are further compared with the CNN and GRU. The system is evaluated using leave-one-group-out cross-validation to ensure robustness across different batteries. Latent space quality is studied using PSA, t-SNE, and UMAP analyses. Furthermore, clustering performance is measured using the Silhouette Score, and anomalies are detected using reconstruction error and the Isolation Forest technique. The obtained results show that the AE+RF model achieves the best performance, with a 0.0285 root mean square value (RMSE) and a 0.0109 mean absolute error (MAE), with a high 0.96 coefficient of determination (R²). It is evident that AE+RF shows high prediction accuracy and model reliability. The results show that latent features improve prediction accuracy, helping to clearly separate normal and abnormal patterns, providing a robust and accurate approach to battery SoH estimation that is suitable for battery management system applications. Full article

Early and accurate detection of skin cancer is critical for reducing mortality rates, particularly for malignant melanoma. Automated analysis of dermoscopic images has gained significant attention due to its potential to support clinical diagnosis and overcome the limitations of manual inspection. Motivated by challenges such as image noise, low contrast, lesion variability, and redundant feature representation, this study proposes an optimal deep hybrid framework for skin lesion detection and classification. The objective of this work is to design a robust and efficient system that integrates advanced preprocessing, precise segmentation, optimal feature selection, and accurate classification. Initially, contrast enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE) and noise reduction using Wiener filtering are applied to improve image quality. Lesion regions are then segmented using a Selective Kernel U-Net (SK-UNet), which adaptively captures multi-scale spatial information. Subsequently, discriminative color, texture, and shape features are extracted and optimized using the Fossa Optimization Algorithm (FOA) to eliminate redundancy. A hybrid one-dimensional Convolutional Neural Network–Gated Recurrent Unit (1D-CNN–GRU) classifier is employed for final classification, learning both spatial and sequential feature patterns. Experimental evaluation on the ISIC and DermMNIST datasets demonstrates that the proposed framework achieves classification accuracies of 97.6% and 95.6%, respectively, outperforming several existing methods. The results confirm that the proposed hybrid framework provides reliable, accurate, and scalable skin cancer diagnosis, highlighting its potential for assisting clinical decision-making and early detection. Full article

Pavement rutting is caused by grooves formed by vehicle traffic, affecting driving comfort, safety, and service life. Rutting detection methods have evolved from manual and automated approaches to intelligent detection for smart cities and maintenance. However, lightweight intelligent detection still faces challenges such as insufficient accuracy and technical complexity, and a mature system has yet to be established. This study aims to develop a portable intelligent terminal for pavement rut detection, which can address the challenges associated with traditional pavement rut detection while providing accuracy and reliability. In this study, rutting detection experiments were performed on a full-scale accelerated loading track to collect data on vibration acceleration, angular velocity, and attitude angles. Comparative experiments were carried out between traditional and lightweight detection methods. Subsequently, GRU-CNN, LSTM–Transformer, GRU, and LSTM models were developed to analyze and compare their performance in predicting rutting depth. The results show that the terminal operates stably, offering convenient usability and reliable data acquisition. Furthermore, vehicle angular velocity and roll angle emerge as critical indicators reflecting rutting impacts on driving states and prove suitable for pavement rut depth detection. The proposed GRU-CNN model achieves superior accuracy and overall performance relative to widely used models. Under synchronous detection conditions, the lightweight method yields a mean absolute error (MAE) of 1.22 mm, achieving performance improvements of 17.32%, 8.74%, and 10.08% over the LSTM–Transformer, GRU, and LSTM models, respectively. Additionally, the method yields a mean absolute percentage error of approximately 10.6%, representing error reductions of 15.87%, 19.08%, and 23.74% compared to the aforementioned baseline models, which meets application requirements. Innovation lies in the development of a lightweight intelligent terminal and GRU-CNN hybrid model that integrates vehicle dynamic parameters for large-scale pavement rutting detection. This study presents a lightweight, real-time pavement rutting detection method based on vehicle operation data for the construction and maintenance of smart cities and intelligent transportation infrastructure, combining the features of high cost effectiveness, high accuracy, and ease of large-scale application. Full article

The crude oil market has obvious financial entropy, and there are characteristics such as continuous uncertainty, multi-scale fluctuations and nonlinear state transitions. These characteristics bring challenges to the traditional prediction method. In this context, in order to improve the accuracy of energy financial market prediction, this study proposes an artificial intelligence-driven hybrid prediction framework, ALA-VMD-CASA. This framework is divided into three stages. First, with the goal of minimizing envelope entropy, ALA is introduced to adaptively optimize the hyperparameters of VMD, so as to generate informative sub-modes with reduced entropy. Next, the parallel prediction of each sub-mode is carried out by using the score attention mechanism based on the CNN autoencoder, and its linear time complexity can capture volatility clustering and sudden price fluctuations. Finally, the final price prediction is generated through the aggregation component. The empirical experiment of Brent crude oil spot prices from 2010 to 2025 shows that the ALA-VMD-CASA framework is superior to benchmark models such as ARIMA, RW, RWWD, LSTM, GRU, Transformer and Informer. Compared with the best standalone model, the proposed framework reduces the mean square error by more than 63% and obtains a perfect win rate in expanding-window evaluations. These results prove that the proposed framework is effective and robust for modeling financial entropy and improving energy price forecasting. 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

Remaining Useful Life (RUL) prediction is a key component of prognostics and health management for modern industrial systems. While deep learning methods have significantly improved prediction accuracy, many existing approaches rely on large neural networks that are difficult to deploy on resource-constrained edge devices. This study presents a deployment-oriented evaluation of compact neural networks for RUL prediction using the NASA C-MAPSS turbofan engine benchmark. Two lightweight hybrid architectures, CNN–GRU and CNN–TCN, were developed with approximately 28k–32k parameters to represent realistic models for CPU-based edge inference. A systematic experimental analysis was conducted across all four C-MAPSS subsets (FD001–FD004), which represent increasing levels of operational and fault complexity. In addition to baseline performance, two post-training compression techniques (i.e., global unstructured magnitude pruning and dynamic INT8 quantization) were evaluated. To assess real deployment behavior, inference latency was measured on both a high-performance Intel x86 workstation and a resource-constrained ARM platform. Results show that CNN–GRU generally achieves higher predictive accuracy, whereas CNN–TCN provides more consistent and lower inference latency due to its convolution-only temporal modeling. Unstructured pruning can yield modest improvements in prediction accuracy, suggesting a regularization effect, but it does not reliably reduce model size or latency on standard CPUs due to the overhead associated with pruning masks. Dynamic quantization substantially reduces model size (particularly for CNN–GRU) while preserving predictive accuracy; however, it increases runtime latency because of additional quantization and dequantization operations. These findings demonstrate that compression techniques commonly used for large models do not necessarily translate into deployment benefits for already compact RUL architectures and highlight the importance of hardware-aware evaluation when designing edge prognostics systems. Full article

Accurate forecasting of mining-induced surface deformation is critical for coal-mine safety assessment and hazard mitigation. InSAR deformation time series are often short, temporally sparse, and strongly nonlinear. These characteristics can make purely data-driven predictors unreliable in small-sample settings. To address this issue, we propose a generation–prediction–interpretation framework that combines generative augmentation with physics-informed forecasting. We first develop a TCN-TimeGAN model to synthesize high-fidelity deformation sequences and expand the training set. Recurrent modules in the generator and discriminator are replaced with causal TCN residual blocks, and a temporal self-attention layer is further stacked on top of the TCN backbone to adaptively reweight informative time steps. We then construct a physics-informed Kolmogorov–Arnold Network, termed PI-KAN. Subsidence-consistency and smoothness priors are embedded in the learning objective to promote physically plausible predictions while retaining spline-based interpretability. Experiments on SBAS-InSAR deformation series from the Guqiao coal mine show that the framework achieves an RMSE of 0.825 mm and an R² of 0.968. It outperforms TGAN-KAN, CNN-BiGRU, and BiGRU under the same evaluation protocol. Visualizations of the learned spline-based edge functions further reveal stronger nonlinear responses for lagged inputs closer to the forecast horizon, providing interpretable evidence of short-term temporal sensitivity under sparse observations. Full article