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Search Results (2,436)

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18 pages, 6195 KB  
Article
Hybrid Wind Power Forecasting for Turbine Clusters: Integrating Spatiotemporal WGANs with Extreme Missing-Data Resilience
by Hongsheng Su, Yuwei Du, Yulong Che, Dan Li and Wenyao Su
Sustainability 2025, 17(20), 9200; https://doi.org/10.3390/su17209200 (registering DOI) - 17 Oct 2025
Abstract
The global pursuit of sustainable development amplifies renewable energy’s strategic importance, positioning wind power as a vital modern grid component. Accurate wind forecasting is essential to counter inherent volatility, enabling robust grid operations, security protocols, and optimization strategies. Such predictive precision directly governs [...] Read more.
The global pursuit of sustainable development amplifies renewable energy’s strategic importance, positioning wind power as a vital modern grid component. Accurate wind forecasting is essential to counter inherent volatility, enabling robust grid operations, security protocols, and optimization strategies. Such predictive precision directly governs wind energy systems’ stability and sustainability. This research introduces a novel spatio-temporal hybrid model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and graph convolutional networks (GCN) to extract temporal patterns and meteorological dynamics (wind speed, direction, temperature) across 134 wind turbines. Building upon conventional methods, our architecture captures turbine spatio-temporal correlations while assimilating multivariate meteorological characteristics. Addressing data integrity compromises from equipment failures and extreme weather-which undermine data-driven models-we implement Wasserstein GAN (WGAN) for generative missing-value interpolation. Validation across severe data loss scenarios (30–90% missing values) demonstrates the model’s enhanced predictive capacity. Rigorous benchmarking confirms significant accuracy improvements and reduced forecasting errors. Full article
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36 pages, 3174 KB  
Review
A Bibliometric-Systematic Literature Review (B-SLR) of Machine Learning-Based Water Quality Prediction: Trends, Gaps, and Future Directions
by Jeimmy Adriana Muñoz-Alegría, Jorge Núñez, Ricardo Oyarzún, Cristian Alfredo Chávez, José Luis Arumí and Lien Rodríguez-López
Water 2025, 17(20), 2994; https://doi.org/10.3390/w17202994 (registering DOI) - 17 Oct 2025
Abstract
Predicting the quality of freshwater, both surface and groundwater, is essential for the sustainable management of water resources. This study collected 1822 articles from the Scopus database (2000–2024) and filtered them using Topic Modeling to create the study corpus. The B-SLR analysis identified [...] Read more.
Predicting the quality of freshwater, both surface and groundwater, is essential for the sustainable management of water resources. This study collected 1822 articles from the Scopus database (2000–2024) and filtered them using Topic Modeling to create the study corpus. The B-SLR analysis identified exponential growth in scientific publications since 2020, indicating that this field has reached a stage of maturity. The results showed that the predominant techniques for predicting water quality, both for surface and groundwater, fall into three main categories: (i) ensemble models, with Bagging and Boosting representing 43.07% and 25.91%, respectively, particularly random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGB), along with their optimized variants; (ii) deep neural networks such as long short-term memory (LSTM) and convolutional neural network (CNN), which excel at modeling complex temporal dynamics; and (iii) traditional algorithms like artificial neural network (ANN), support vector machines (SVMs), and decision tree (DT), which remain widely used. Current trends point towards the use of hybrid and explainable architectures, with increased application of interpretability techniques. Emerging approaches such as Generative Adversarial Network (GAN) and Group Method of Data Handling (GMDH) for data-scarce contexts, Transfer Learning for knowledge reuse, and Transformer architectures that outperform LSTM in time series prediction tasks were also identified. Furthermore, the most studied water bodies (e.g., rivers, aquifers) and the most commonly used water quality indicators (e.g., WQI, EWQI, dissolved oxygen, nitrates) were identified. The B-SLR and Topic Modeling methodology provided a more robust, reproducible, and comprehensive overview of AI/ML/DL models for freshwater quality prediction, facilitating the identification of thematic patterns and research opportunities. Full article
(This article belongs to the Special Issue Machine Learning Applications in the Water Domain)
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27 pages, 3065 KB  
Article
Chinese Financial News Analysis for Sentiment and Stock Prediction: A Comparative Framework with Language Models
by Hsiu-Min Chuang, Hsiang-Chih He and Ming-Che Hu
Big Data Cogn. Comput. 2025, 9(10), 263; https://doi.org/10.3390/bdcc9100263 - 16 Oct 2025
Abstract
Financial news has a significant impact on investor sentiment and short-term stock price trends. While many studies have applied natural language processing (NLP) techniques to financial forecasting, most have focused on single tasks or English corpora, with limited research in non-English language contexts [...] Read more.
Financial news has a significant impact on investor sentiment and short-term stock price trends. While many studies have applied natural language processing (NLP) techniques to financial forecasting, most have focused on single tasks or English corpora, with limited research in non-English language contexts such as Taiwan. This study develops a joint framework to perform sentiment classification and short-term stock price prediction using Chinese financial news from Taiwan’s top 50 listed companies. Five types of word embeddings—one-hot, TF-IDF, CBOW, skip-gram, and BERT—are systematically compared across 17 traditional, deep, and Transformer models, as well as a large language model (LLaMA3) fully fine-tuned on the Chinese financial texts. To ensure annotation quality, sentiment labels were manually assigned by annotators with finance backgrounds and validated through a double-checking process. Experimental results show that a CNN using skip-gram embeddings achieves the strongest performance among deep learning models, while LLaMA3 yields the highest overall F1-score for sentiment classification. For regression, LSTM consistently provides the most reliable predictive power across different volatility groups, with Bayesian Linear Regression remaining competitive for low-volatility firms. LLaMA3 is the only Transformer-based model to achieve a positive R2 under high-volatility conditions. Furthermore, forecasting accuracy is higher for the five-day horizon than for the fifteen-day horizon, underscoring the increasing difficulty of medium-term forecasting. These findings confirm that financial news provides valuable predictive signals for emerging markets and that short-term sentiment-informed forecasts enhance real-time investment decisions. Full article
(This article belongs to the Special Issue Natural Language Processing Applications in Big Data)
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16 pages, 5302 KB  
Article
A Parallel Network for Continuous Motion Estimation of Finger Joint Angles with Surface Electromyographic Signals
by Chuang Lin and Shengshuo Zhou
Appl. Sci. 2025, 15(20), 11078; https://doi.org/10.3390/app152011078 - 16 Oct 2025
Abstract
The implementation of surface electromyographic (sEMG) signals in the interaction between human beings and machines is an important line of research. In the system of human–machine interaction, continuous-motion-estimation-based control plays an important role because it is more natural and intuitive than pattern recognition-based [...] Read more.
The implementation of surface electromyographic (sEMG) signals in the interaction between human beings and machines is an important line of research. In the system of human–machine interaction, continuous-motion-estimation-based control plays an important role because it is more natural and intuitive than pattern recognition-based control. In this paper, we propose a parallel network consisting of a CNN with a multi-head attention mechanism and a BiLSTM (bidirectional long short-term memory) network to improve the accuracy of continuous motion estimation. The proposed network is evaluated in the Ninapro dataset. Six finger movements of 10 subjects were tested in the Ninapro DB2 dataset to evaluate the performance of the neural network and calculate the PCC (Pearson Correlation Coefficient) between the predicted joint angle sequence and the actual joint angle sequence. The experimental results show that the average accuracy (PCC) of the proposed network reaches 0.87 ± 0.02, which is significantly better than that of the BiLSTM network (0.79 ± 0.04, p < 0.05), CNN-Attention (0.80 ± 0.01, p < 0.05), CNN (0.70 ± 0.03, p < 0.05), CNN-BiLSTM (0.83 ± 0.02, p < 0.05), and TCN (0.76 ± 0.05, p < 0.05). It is worth noting that in this work, we extract multiple features from the raw sEMG signals and fuse them. We found that better continuous estimation accuracy can be achieved using multi-feature sEMG data. The model proposed in this paper skillfully integrates the convolutional neural network, multi-head attention mechanism, and bidirectional long short-term memory network, and its performance has good stability and accuracy. The model realizes more natural and accurate human–computer interaction. Full article
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24 pages, 12281 KB  
Article
Prediction Method of Available Nitrogen in Red Soil Based on BWO-CNN-LSTM
by Yun Deng, Yuchen Cao and Chang Liu
Appl. Sci. 2025, 15(20), 11077; https://doi.org/10.3390/app152011077 - 16 Oct 2025
Abstract
Accurate assessment of forest soil nitrogen from hyperspectral spectra is critical for precision fertilization, yet conventional preprocessing and baseline CNNs constrain predictive accuracy. We introduce streamlined spectral preprocessing and an optimized CNN–LSTM framework and evaluate it on Guangxi forest soils against competitive models [...] Read more.
Accurate assessment of forest soil nitrogen from hyperspectral spectra is critical for precision fertilization, yet conventional preprocessing and baseline CNNs constrain predictive accuracy. We introduce streamlined spectral preprocessing and an optimized CNN–LSTM framework and evaluate it on Guangxi forest soils against competitive models using standard validation metrics. Results: The proposed approach outperformed comparative models (CNN, LSTM, and BiLSTM), achieving a validation set R2 of 0.889 and RMSE of 16.5722, representing improvements of 6.79–10.37% in R2 and 18.60–24.44% in RMSE over baseline methods. The method delivers accurate, scalable nitrogen estimation from spectra, supporting timely fertilization decisions and sustainable soil management. Full article
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28 pages, 7034 KB  
Article
Water Quality Prediction Model Based on Temporal Attentive Bidirectional Gated Recurrent Unit Model
by Hongyu Yang, Lei Guo and Qingqing Tian
Sustainability 2025, 17(20), 9155; https://doi.org/10.3390/su17209155 (registering DOI) - 16 Oct 2025
Abstract
Water pollution has caused serious consequences for human health and aquatic systems. Therefore, analyzing and predicting water quality is of great significance for the early prevention and control of water pollution. Aiming at the shortcomings of the Gated Recurrent Unit (GRU) water quality [...] Read more.
Water pollution has caused serious consequences for human health and aquatic systems. Therefore, analyzing and predicting water quality is of great significance for the early prevention and control of water pollution. Aiming at the shortcomings of the Gated Recurrent Unit (GRU) water quality prediction model, such as the low utilization rate of early information and poor deep feature extraction ability of the hidden state mechanism, this study combines the temporal attention (TA) mechanism with the bidirectional superimposed neural network. A time-focused bidirectional gated recurrent unit (TA-Bi-GRU) model is proposed. Taking the actual water quality data of the water source reservoir in Xiduan Village as the research object, this model was used to predict four core water quality indicators, namely pH, ammonia nitrogen (NH3N), total nitrogen (TN), and dissolved oxygen (DOX). Predictions are made within multiple time ranges, with prediction periods of 7 days, 10 days, 15 days, and 30 days. In the long-term prediction of the TA-Bi-GRU model, its average R2 was 0.858 (7 days), 0.772 (10 days), 0.684 (15 days), and 0.553 (30 days), and the corresponding average MAE and MSE were both lower than those of the comparison models. The experimental results show that the TA-Bi-GRU model has higher prediction accuracy and stronger generalization ability compared with the existing GRU, bidirectional GRU (Bi-GRU), Time-focused Gated Recurrent Unit (TA-GRU), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Deep Temporal Convolutional Networks-Long Short-Term Memory (DeepTCN-LSTM) models. Full article
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31 pages, 8374 KB  
Article
Distributed Photovoltaic Short-Term Power Forecasting Based on Seasonal Causal Correlation Analysis
by Zhong Wang, Mao Yang, Jianfeng Che, Wei Xu, Wei He and Kang Wu
Appl. Sci. 2025, 15(20), 11063; https://doi.org/10.3390/app152011063 - 15 Oct 2025
Abstract
In recent years, with the development of distributed photovoltaic (PV) systems, their impact on power grids has become increasingly significant. However, the complexity of meteorological variations makes the prediction of distributed PV power challenging and often ineffective. This study proposes a short-term power [...] Read more.
In recent years, with the development of distributed photovoltaic (PV) systems, their impact on power grids has become increasingly significant. However, the complexity of meteorological variations makes the prediction of distributed PV power challenging and often ineffective. This study proposes a short-term power forecasting method for distributed photovoltaics that can identify seasonal characteristics matching weather types, enabling a deeper analysis of complex meteorological changes. First, historical power data is decomposed seasonally using the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Next, each component is reconstructed based on a characteristic similarity approach, and a two-stage feature selection process is applied to identify the most relevant features for reconstruction, addressing the issue of nonlinear variable selection. A CNN-LSTM-KAN model with multi-dimensional spatial representation is then proposed to model different weather types obtained by the K-shape clustering method, enabling the segmentation of weather processes. Finally, the proposed method is applied to a case study of distributed PV users in a certain province for short-term power prediction. The results indicate that, compared to traditional methods, the average RMSE decreases by 8.93%, the average MAE decreases by 4.82%, and the R2 increases by 9.17%, demonstrating the effectiveness of the proposed method. Full article
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26 pages, 3454 KB  
Article
Hybrid Deep Learning Approaches for Accurate Electricity Price Forecasting: A Day-Ahead US Energy Market Analysis with Renewable Energy
by Md. Saifur Rahman and Hassan Reza
Mach. Learn. Knowl. Extr. 2025, 7(4), 120; https://doi.org/10.3390/make7040120 - 15 Oct 2025
Abstract
Forecasting day-ahead electricity prices is a crucial research area. Both wholesale and retail sectors highly value improved forecast accuracy. Renewable energy sources have grown more influential and effective in the US power market. However, current forecasting models have shortcomings, including inadequate consideration of [...] Read more.
Forecasting day-ahead electricity prices is a crucial research area. Both wholesale and retail sectors highly value improved forecast accuracy. Renewable energy sources have grown more influential and effective in the US power market. However, current forecasting models have shortcomings, including inadequate consideration of renewable energy impacts and insufficient feature selection. Many studies lack reproducibility, clear presentation of input features, and proper integration of renewable resources. This study addresses these gaps by incorporating a comprehensive set of input features, while these features are engineered to capture complex market dynamics. The model’s unique aspect is its inclusion of renewable-related inputs, such as temperature data for solar energy effects and wind speed for wind energy impacts on US electricity prices. The research also employs data preprocessing techniques like windowing, cleaning, normalization, and feature engineering to enhance input data quality and relevance. We developed four advanced hybrid deep learning models to improve electricity price prediction accuracy and reliability. Our approach combines variational mode decomposition (VMD) with four deep learning (DL) architectures: dense neural networks (DNNs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and bidirectional LSTM (BiLSTM) networks. This integration aims to capture complex patterns and time-dependent relationships in electricity price data. Among these, the VMD-BiLSTM model consistently outperformed the others across all window implementations. Using 24 input features, this model achieved a remarkably low mean absolute error of 0.2733 when forecasting prices in the MISO market. Our research advances electricity price forecasting, particularly for the US energy market. These hybrid deep neural network models provide valuable tools and insights for market participants, energy traders, and policymakers. Full article
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16 pages, 10961 KB  
Article
Exploratory Proof-of-Concept: Predicting the Outcome of Tennis Serves Using Motion Capture and Deep Learning
by Gustav Durlind, Uriel Martinez-Hernandez and Tareq Assaf
Mach. Learn. Knowl. Extr. 2025, 7(4), 118; https://doi.org/10.3390/make7040118 - 14 Oct 2025
Abstract
Tennis serves heavily impact match outcomes, yet analysis by coaches is limited by human vision. The design of an automated tennis serve analysis system could facilitate enhanced performance analysis. As serve location and serve success are directly correlated, predicting the outcome of a [...] Read more.
Tennis serves heavily impact match outcomes, yet analysis by coaches is limited by human vision. The design of an automated tennis serve analysis system could facilitate enhanced performance analysis. As serve location and serve success are directly correlated, predicting the outcome of a serve could provide vital information for performance analysis. This article proposes a tennis serve analysis system powered by Machine Learning, which classifies the outcome of serves as “in”, “out” or “net”, and predicts the coordinate outcome of successful serves. Additionally, this work details the collection of three-dimensional spatio-temporal data on tennis serves, using marker-based optoelectronic motion capture. The classification uses a Stacked Bidirectional Long Short-Term Memory architecture, whilst a 3D Convolutional Neural Network architecture is harnessed for serve coordinate prediction. The proposed method achieves 89% accuracy for tennis serve classification, outperforming the current state-of-the-art whilst performing finer-grain classification. The results achieve an accuracy of 63% in predicting the serve coordinates, with a mean absolute error of 0.59 and a root mean squared error of 0.68, exceeding the current state-of-the-art with a new method. The system contributes towards the long-term goal of designing a non-invasive tennis serve analysis system that functions in training and match conditions. Full article
41 pages, 12462 KB  
Article
Real-Time Efficient Approximation of Nonlinear Fractional-Order PDE Systems via Selective Heterogeneous Ensemble Learning
by Biao Ma and Shimin Dong
Fractal Fract. 2025, 9(10), 660; https://doi.org/10.3390/fractalfract9100660 - 13 Oct 2025
Viewed by 91
Abstract
Rod-pumping systems represent complex nonlinear systems. Traditional soft-sensing methods used for efficiency prediction in such systems typically rely on complicated fractional-order partial differential equations, severely limiting the real-time capability of efficiency estimation. To address this limitation, we propose an approximate efficiency prediction model [...] Read more.
Rod-pumping systems represent complex nonlinear systems. Traditional soft-sensing methods used for efficiency prediction in such systems typically rely on complicated fractional-order partial differential equations, severely limiting the real-time capability of efficiency estimation. To address this limitation, we propose an approximate efficiency prediction model for nonlinear fractional-order differential systems based on selective heterogeneous ensemble learning. This method integrates electrical power time-series data with fundamental operational parameters to enhance real-time predictive capability. Initially, we extract critical parameters influencing system efficiency using statistical principles. These primary influencing factors are identified through Pearson correlation coefficients and validated using p-value significance analysis. Subsequently, we introduce three foundational approximate system efficiency models: Convolutional Neural Network-Echo State Network-Bidirectional Long Short-Term Memory (CNN-ESN-BiLSTM), Bidirectional Long Short-Term Memory-Bidirectional Gated Recurrent Unit-Transformer (BiLSTM-BiGRU-Transformer), and Convolutional Neural Network-Echo State Network-Bidirectional Gated Recurrent Unit (CNN-ESN-BiGRU). Finally, to balance diversity among basic approximation models and predictive accuracy, we develop a selective heterogeneous ensemble-based approximate efficiency model for nonlinear fractional-order differential systems. Experimental validation utilizing actual oil-well parameters demonstrates that the proposed approach effectively and accurately predicts the efficiency of rod-pumping systems. Full article
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22 pages, 3964 KB  
Article
MultiScaleSleepNet: A Hybrid CNN–BiLSTM–Transformer Architecture with Multi-Scale Feature Representation for Single-Channel EEG Sleep Stage Classification
by Cenyu Liu, Qinglin Guan, Wei Zhang, Liyang Sun, Mengyi Wang, Xue Dong and Shuogui Xu
Sensors 2025, 25(20), 6328; https://doi.org/10.3390/s25206328 - 13 Oct 2025
Viewed by 450
Abstract
Accurate automatic sleep stage classification from single-channel EEG remains challenging due to the need for effective extraction of multiscale neurophysiological features and modeling of long-range temporal dependencies. This study aims to address these limitations by developing an efficient and compact deep learning architecture [...] Read more.
Accurate automatic sleep stage classification from single-channel EEG remains challenging due to the need for effective extraction of multiscale neurophysiological features and modeling of long-range temporal dependencies. This study aims to address these limitations by developing an efficient and compact deep learning architecture tailored for wearable and edge device applications. We propose MultiScaleSleepNet, a hybrid convolutional neural network–bidirectional long short-term memory–transformer architecture that extracts multiscale temporal and spectral features through parallel convolutional branches, followed by sequential modeling using a BiLSTM memory network and transformer-based attention mechanisms. The model obtained an accuracy, macro-averaged F1 score, and kappa coefficient of 88.6%, 0.833, and 0.84 on the Sleep-EDF dataset; 85.6%, 0.811, and 0.80 on the Sleep-EDF Expanded dataset; and 84.6%, 0.745, and 0.79 on the SHHS dataset. Ablation studies indicate that attention mechanisms and spectral fusion consistently improve performance, with the most notable gains observed for stages N1, N3, and rapid eye movement. MultiScaleSleepNet demonstrates competitive performance across multiple benchmark datasets while maintaining a compact size of 1.9 million parameters, suggesting robustness to variations in dataset size and class distribution. The study supports the feasibility of real-time, accurate sleep staging from single-channel EEG using parameter-efficient deep models suitable for portable systems. Full article
(This article belongs to the Special Issue AI on Biomedical Signal Sensing and Processing for Health Monitoring)
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23 pages, 7368 KB  
Article
Construction and Comparative Analysis of a Water Quality Simulation and Prediction Model for Plain River Networks
by Yue Lan, Cundong Xu, Lianying Ding, Mingyan Wang, Zihao Ren and Zhihang Wang
Water 2025, 17(20), 2948; https://doi.org/10.3390/w17202948 - 13 Oct 2025
Viewed by 105
Abstract
In plain river networks, a sluggish flow due to the flat terrain and hydraulic structures significantly reduces water’s capacity for self-purification, leading to persistent water pollution that threatens aquatic ecosystems and human health. Despite being critical, effective water quality prediction proves challenging in [...] Read more.
In plain river networks, a sluggish flow due to the flat terrain and hydraulic structures significantly reduces water’s capacity for self-purification, leading to persistent water pollution that threatens aquatic ecosystems and human health. Despite being critical, effective water quality prediction proves challenging in such regions, with current models lacking either physical interpretability or temporal accuracy. To address this gap, both a process-based model (MIKE 21) and a deep learning model (CNN-LSTM-Attention) were developed in this study to predict key water quality indicators—dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP)—in a typical river network area in Jiaxing, China. This site was selected for its representative complexity and acute pollution challenges. The MIKE 21 model demonstrated strong performance, with R2 values above 0.88 for all indicators, offering high spatial resolution and mechanistic insight. The CNN-LSTM-Attention model excelled in capturing temporal dynamics, achieving an R2 of 0.9934 for DO. The results indicate the complementary nature of these two approaches: while MIKE 21 supports scenario-based planning, the deep learning model enables highly accurate real-time forecasting. The findings are transferable to similar river network systems, providing a robust reference for selecting modeling frameworks in the design of water pollution control strategies. Full article
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25 pages, 18664 KB  
Article
Study on Lower Limb Motion Intention Recognition Based on PO-SVMD-ResNet-GRU
by Wei Li, Mingsen Wang, Daxue Sun, Zhuoda Jia and Zhengwei Yue
Processes 2025, 13(10), 3252; https://doi.org/10.3390/pr13103252 - 13 Oct 2025
Viewed by 156
Abstract
This study aims to enhance the accuracy of human lower limb motion intention recognition based on surface electromyography (sEMG) signals and proposes a signal denoising method based on Sequential Variational Mode Decomposition (SVMD) optimized by the Parrot Optimization (PO) algorithm and a joint [...] Read more.
This study aims to enhance the accuracy of human lower limb motion intention recognition based on surface electromyography (sEMG) signals and proposes a signal denoising method based on Sequential Variational Mode Decomposition (SVMD) optimized by the Parrot Optimization (PO) algorithm and a joint motion angle prediction model combining Residual Network (ResNet) with Gated Recurrent Unit (GRU) for the two aspects of signal processing and predictive modeling, respectively. First, for the two motion conditions of level walking and stair climbing, sEMG signals from the rectus femoris, vastus lateralis, semitendinosus, and biceps femoris, as well as the motion angles of the hip and knee joints, were simultaneously collected from five healthy subjects, yielding a total of 400 gait cycle data points. The sEMG signals were denoised using the method combining PO-SVMD with wavelet thresholding. Compared with denoising methods such as Empirical Mode Decomposition, Partial Ensemble Empirical Mode Decomposition, Independent Component Analysis, and wavelet thresholding alone, the signal-to-noise ratio (SNR) of the proposed method was increased to a maximum of 23.42 dB. Then, the gait cycle information was divided into training and testing sets at a 4:1 ratio, and five models—ResNet-GRU, Transformer-LSTM, CNN-GRU, ResNet, and GRU—were trained and tested individually using the processed sEMG signals as input and the hip and knee joint movement angles as output. Finally, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were used as evaluation metrics for the test results. The results show that for both motion conditions, the evaluation metrics of the ResNet-GRU model in the test results are superior to those of the other four models. The optimal evaluation metrics for level walking are 2.512 ± 0.415°, 1.863 ± 0.265°, and 0.979 ± 0.007, respectively, while the optimal evaluation metrics for stair climbing are 2.475 ± 0.442°, 2.012 ± 0.336°, and 0.98 ± 0.009, respectively. The method proposed in this study achieves improvements in both signal processing and predictive modeling, providing a new method for research on lower limb motion intention recognition. Full article
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26 pages, 2445 KB  
Article
Image-Based Deep Learning Approach for Drilling Kick Risk Prediction
by Wei Liu, Yuansen Wei, Jiasheng Fu, Qihao Li, Yi Zou, Tao Pan and Zhaopeng Zhu
Processes 2025, 13(10), 3251; https://doi.org/10.3390/pr13103251 - 13 Oct 2025
Viewed by 171
Abstract
As oil and gas exploration and development advance into deep and ultra-deep areas, kick accidents are becoming more frequent during drilling operations, posing a serious threat to construction safety. Traditional kick monitoring methods are limited in their multivariate coupling modeling. These models rely [...] Read more.
As oil and gas exploration and development advance into deep and ultra-deep areas, kick accidents are becoming more frequent during drilling operations, posing a serious threat to construction safety. Traditional kick monitoring methods are limited in their multivariate coupling modeling. These models rely too heavily on single-feature weights, making them prone to misjudgment. Therefore, this paper proposes a drilling kick risk prediction method based on image modality. First, a sliding window mechanism is used to slice key drilling parameters in time series to extract multivariate data for continuous time periods. Second, data processing is performed to construct joint logging curve image samples. Then, classical CNN models such as VGG16 and ResNet are used to train and classify image samples; finally, the performance of the model on a number of indicators is evaluated and compared with different CNN and temporal neural network models. Finally, the model’s performance is evaluated across multiple metrics and compared with CNN and time series neural network models of different structures. Experimental results show that the image-based VGG16 model outperforms typical convolutional neural network models such as AlexNet, ResNet, and EfficientNet in overall performance, and significantly outperforms LSTM and GRU time series models in classification accuracy and comprehensive discriminative power. Compared to LSTM, the recall rate increased by 23.8% and the precision increased by 5.8%, demonstrating that its convolutional structure possesses stronger perception and discriminative capabilities in extracting local spatiotemporal features and recognizing patterns, enabling more accurate identification of kick risks. Furthermore, the pre-trained VGG16 model achieved an 8.69% improvement in accuracy compared to the custom VGG16 model, fully demonstrating the effectiveness and generalization advantages of transfer learning in small-sample engineering problems and providing feasibility support for model deployment and engineering applications. Full article
(This article belongs to the Section Energy Systems)
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18 pages, 2022 KB  
Article
Research on the Spatiotemporal Effects of Water Temperature in the Construction of Cascade Dams on the Yangtze River Main Stream Based on Optimized CNN-LSTM Attention Model
by Shanghong Zhang, Hao Wang, Ruicheng Zhang, Hua Zhang and Yang Zhou
Sustainability 2025, 17(20), 9046; https://doi.org/10.3390/su17209046 (registering DOI) - 13 Oct 2025
Viewed by 94
Abstract
Hydrothermal conditions are a key indicator influencing the evolution of aquatic ecosystems, closely affecting the physical, chemical, and biological properties of water bodies. The construction of cascaded dams on the main stem of the Yangtze River has altered the natural water temperature regime, [...] Read more.
Hydrothermal conditions are a key indicator influencing the evolution of aquatic ecosystems, closely affecting the physical, chemical, and biological properties of water bodies. The construction of cascaded dams on the main stem of the Yangtze River has altered the natural water temperature regime, impacting the hydrothermal status of the water. Utilizing multi-source remote sensing data from Google Earth Engine to invert river surface water temperatures, a parameter-optimized CNN-LSTM-Attention hybrid interpretable water temperature prediction model was constructed. The model demonstrated credible accuracy. Based on the inversion results, the study revealed the spatiotemporal evolution characteristics of water temperature in the main stem of the Yangtze River before and after cascaded dam construction in the lower Jinsha River region and the Three Gorges Reservoir area. The results found that after the construction of the Three Gorges Dam, the annual average water temperature increased significantly by 0.813 °C. The “cold water stagnation effect” induced by cascaded development caused the water temperature amplitude to increase from 8.96 °C to 10.6 °C. Furthermore, the regulating effect of tributary confluence exhibited significant differences. For instance, colder tributaries like the Yalong River reduced the main stem water temperature, while warmer tributaries like the Jialing River, conversely, increased the main stem temperature. The construction of cascaded dams led to distinct variation characteristics in the areas downstream of the dams within the reservoir regions, where tributary inflows caused corresponding changes in the main stem water temperature. This study elucidates the long-term spatiotemporal variation characteristics of water temperature in the main stem of the Yangtze River. The model prediction results can assist in constructing an early warning indicator system for water temperature changes, providing reliable data support for promoting water environment sustainability and ecological civilization construction in the river basin. Full article
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