Search Results (24,590)

The precise prediction of the remaining useful life (RUL) of lithium-ion batteries is of great significance for improving energy management efficiency and extending battery lifespan, and it is widely applied in the fields of new energy and electric vehicles. However, accurate RUL prediction still faces significant challenges. Although various methods based on deep learning have been proposed, the performance of their neural networks is strongly correlated with the hyperparameters. To overcome this limitation, this study proposes an innovative approach that combines the Alpha evolutionary (AE) algorithm with a deep learning model. Specifically, this hybrid deep learning architecture consists of convolutional neural network (CNN), time convolutional network (TCN), bidirectional long short-term memory (BiLSTM) and multi-scale attention mechanism, which extracts the spatial features, long-term temporal dependencies, and key degradation information of battery data, respectively. To optimize the model performance, the AE algorithm is introduced to automatically optimize the hyperparameters of the hybrid model, including the number and size of convolutional kernels in CNN, the dilation rate in TCN, the number of units in BiLSTM, and the parameters of the fusion layer in the attention mechanism. Experimental results demonstrate that our method significantly enhances prediction accuracy and model robustness compared to conventional deep learning techniques. This approach not only improves the accuracy and robustness of battery RUL prediction but also provides new ideas for solving the parameter tuning problem of neural networks. Full article

Turbidity is a crucial and reliable indicator that is extensively utilized in water quality monitoring through remote sensing technology. The development of accurate and applicable models for turbidity estimation is essential. While many existing studies rely on uniform models based on statistical regression or traditional machine learning techniques, the application of deep learning models for turbidity estimation remains limited. This study proposed deep learning models for turbidity estimation based on optical classification of inland waters using Sentinel-2 data. Specifically, the fuzzy c-means (FCM) clustering method was employed to classify optical water types (OWTs) based on their spectral reflectance characteristics. A weighted sum of the turbidity prediction results was generated by the OWT-based convolutional neural network-random forest (CNN-RF) model, with weights derived from the FCM membership degrees. Turbidity for four typical waters was mapped by the proposed method using Sentinel-2 images. The FCM method efficiently classified waters into three OWTs. The OWT-based weighted CNN-RF model demonstrated strong robustness and generalization performance, achieving a high prediction accuracy (R² = 0.900, RMSE = 11.698 NTU). The turbidity maps preserved the spatial continuity of the turbidity distribution and accurately reflected water quality conditions. These findings facilitate the application of deep learning models based on optical classification in turbidity estimation and enhance the capabilities of remote sensing for water quality monitoring. Full article

To improve the accuracy and stability of anomaly detection in lithium-ion batteries for electric bicycles, in this study, we propose a hybrid deep learning model that integrates a convolutional neural network (CNN), long short-term memory (LSTM) network, and attention mechanism to extract local temporal features, capture long-term dependencies, and adaptively focus on key time segments around anomaly occurrences, respectively, thereby achieving a balance between local and global feature modeling. In terms of data preprocessing, separate feature sets are constructed for charging and discharging conditions, and sliding windows combined with min–max normalization are applied to generate model inputs. The model was trained and validated on large-scale real-world battery operation data. The experimental results demonstrate that the proposed method achieves high detection accuracy and robustness in terms of reconstruction error distribution, alarm rate stability, and Top-K anomaly consistency. The method can effectively identify various types of abnormal operating conditions in unlabeled datasets based on unsupervised learning. This study provides a transferable deep learning solution for enhancing the safety monitoring of electric bicycle batteries. Full article

Manufacturing workshops operate in dynamic and complex environments, where multiple orders are processed simultaneously through interdependent stages. This complexity makes it challenging to accurately predict the remaining completion time of ongoing orders. To address this issue, this paper proposes a data-driven prediction approach that analyzes key features extracted from multi-source manufacturing data. The method involves collecting heterogeneous production data, constructing a comprehensive feature dataset, and applying feature analysis to identify critical influencing factors. Furthermore, a deep learning optimization model based on a Convolutional Neural Network (CNN)–Bidirectional Long Short-Term Memory (BiLSTM)–Attention architecture is designed to handle the temporal and structural complexity of workshop data. The model integrates spatial feature extraction, temporal sequence modeling, and adaptive attention-based refinement to improve prediction accuracy. This unified framework enables the model to learn hierarchical representations, focus on salient temporal features, and deliver accurate and robust predictions. The proposed deep learning predictive model is validated on real production data collected from a discrete manufacturing workshop equipped with typical machines. Comparative experiments with other predictive models demonstrate that the CNN–BiLSTM–Attention model outperforms existing approaches in both accuracy and stability for predicting order remaining completion time, offering strong potential for deployment in intelligent production systems. Full article

To prevent or mitigate the negative impact of fires, spatial prediction maps of wildfires are created to identify susceptible locations and key factors that influence the occurrence of fires. This study uses artificial intelligence models, specifically machine learning (XGBoost) and deep learning (Kolmogorov-Arnold networks—KANs, and deep neural network—DNN), with data obtained from multi-sensor satellite imagery (MODIS, VIIRS, Sentinel-2, Landsat 8/9) for spatial modeling wildfires in Serbia (88,361 km²). Based on geographic information systems (GIS) and 199,598 wildfire samples, 16 quantitative variables (geomorphological, climatological, hydrological, vegetational, and anthropogenic) are presented, together with 3 synthesis maps and an integrated susceptibility map of the 3 applied models. The results show a varying percentage of Serbia’s very high vulnerability to wildfires (XGBoost = 11.5%; KAN = 14.8%; DNN = 15.2%; Ensemble = 12.7%). Among the applied models, the DNN achieved the highest predictive performance (Accuracy = 83.4%, ROC-AUC = 92.3%), followed by XGBoost and KANs, both of which also demonstrated strong predictive accuracy (ROC-AUC > 90%). These results confirm the robustness of deep and machine learning approaches for wildfire susceptibility mapping in Serbia. SHAP analysis determined that the most influential factors are elevation, air temperature, and humidity regime (precipitation, aridity, and series of consecutive dry/wet days). Full article

This paper redefines point cloud density by utilizing statistical skewness derived from the geometric relationships between points and their local centroids. By comparing with a symmetric uniform reference model, this method can efficiently describe distribution patterns and detect non-uniform regions. Furthermore, a deep learning model trained on these skewness features achieves 85.96% accuracy in automated boundary extraction, significantly reducing omission errors compared to conventional density-based methods. The proposed framework offers an effective solution for automated point cloud segmentation and modeling. Full article

Background/Objectives: Urinary system stone disease is an important health problem both clinically and economically due to its high recurrence rates. In this study, an innovative hybrid approach based on deep learning is proposed to predict the recurrence risk of stone disease. Methods: Patient data were divided into three subsets: anthropometric measurements (Part A), derived body composition indices (Part B), and other clinical and demographic information (Part C). Each data subset was processed with autoencoder models, and low-dimensional, meaningful features were extracted. The obtained features were combined, and the classification process was performed using four different machine learning algorithms: Extreme Gradient Boosting (XGBoost), Cubic Support Vector Machines (Cubic SVM), k-Nearest Neighbor algorithm (KNN), and Decision Tree (DT). Results: According to the experimental results, the highest classification performance was obtained with the XGBoost algorithm. The suggested approach adds to the literature by offering a novel solution that makes early risk calculation for stone disease recurrence easier. It also shows how well structural feature engineering and deep representation can be integrated in clinical prediction issues. Conclusions: Prediction of the stone recurrence risk in advance is of great importance both in terms of improving the quality of life of patients and reducing the unnecessary diagnostic evaluations along with lowering treatment costs. Full article

The tire-road friction coefficient (TRFC) is a critical parameter that significantly influences vehicle safety, handling stability, and driving comfort. Existing estimation methods based on vehicle dynamics suffer from a substantial decline in accuracy under conditions with insufficient excitation, while vision-based approaches are often limited by the generalization ability of their datasets, making them less effective in complex and variable real-driving environments. To address these challenges, this paper proposes a novel, low-cost fusion method for TRFC estimation that integrates weather conditions and road image data. The proposed approach begins by employing semantic segmentation to partition the input images into distinct regions—sky and road. The segmented images will be fed into the road recognition network and the weather recognition network for road type and weather classification. Furthermore, a fusion decision tree incorporating an uncertainty modeling mechanism is introduced to dynamically integrate these multi-source features, thereby enhancing the robustness of the estimation. Experimental results demonstrate that the proposed method maintains stable and reliable estimation performance even on unseen road surfaces, outperforming single-modality methods significantly. This indicates its high practical value and promising potential for broad application. Full article

Shimer documents that the search-and-matching model driven by productivity shocks explains only a small share of the observed volatility of unemployment and vacancies, which is known as the Shimer puzzle. We revisit this evidence by replacing the representative firm’s optimization with a deep reinforcement learning (DRL) agent that learns its vacancy-posting policy through interaction in a Diamond–Mortensen–Pissarides (DMP) model. Comparing the learning economy with a conventional log-linearized DSGE solution under the same parameters, we find that while both frameworks preserve a downward-sloping Beveridge curve, learning-based economy produces much higher volatility in key labor market variables and returns to a steady state more slowly after shocks. These results point to bounded rationality and endogenous learning as mechanisms for labor market fluctuations and suggest that reinforcement learning can serve as a useful complement to standard macroeconomic analysis. Full article

Elucidating drug response mechanisms in human melanoma is crucial for improving treatment outcomes. Moreover, the existing tools intended to provide deeper insight into melanoma drug resistance have notable limitations. Therefore, we propose a deep learning (DL)-based approach that works as follows. First, we processed two single-cell datasets related to human melanoma from the GEO (GSE108383_A375 and GSE108383_451Lu) database and trained a fully connected neural network with five adapted methods (L1-Regularization, DeepLIFT, SHAP, IG, and LRP). We then identified 100 genes by ranking all genes from the highest to the lowest based on the sum of absolute values for corresponding weights across all neurons in the first hidden layer. From a biological perspective, compared to existing bioinformatics tools, the presented DL-based methods identified a higher number of expressed genes in four well-established melanoma cell lines: MALME-3M, MDA-MB435, SK-MEL-28, and SK-MEL-5. Furthermore, we identified FDA-approved melanoma drugs (e.g., Vemurafenib and Dabrafenib), critical genes such as ARAF, SOX10, DCT, and AXL, and key TFs including MITF and TFAP2A. From a classification perspective, we utilized five-fold cross-validation and provided gene sets using all the abovementioned methods to three randomly selected machine learning algorithms, namely, support vector machines, random forests, and neural networks with different hyperparameters. The results demonstrate that the integrated gradients (IG) method adapted in our DL approach contributed to 2.2% and 0.5% overall performance improvements over the best-performing baselines when using A375 and 451Lu cell line datasets. Additional comparison against no gene selection demonstrated that IG is the only method to generate statistically significant results, with 14.4% and 11.7% overall performance improvements. Full article

Leveraging ubiquitous wireless signals for environmental sensing provides a highly promising pathway toward constructing low-cost and high-density flood monitoring systems. However, in real-world flood scenarios, the wireless channel is simultaneously affected by rainfall-induced signal attenuation and complex multipath effects caused by surface runoff (water accumulation). These two physical phenomena become intertwined in the received signals, resulting in severe feature ambiguity. This not only greatly limits the accuracy of environmental sensing but also hinders communication systems from performing effective channel compensation. How to disentangle these combined effects from a single wireless link represents a fundamental scientific challenge for achieving high-precision wireless environmental sensing and ensuring communication reliability under harsh conditions. To address this challenge, we propose a novel signal processing framework that aims to effectively decouple the effects of rainfall and surface runoff from Channel State Information (CSI) collected using commercial Wi-Fi devices. The core idea of our method lies in first constructing a two-dimensional CSI spatiotemporal spectrogram from continuously captured multicarrier CSI data. This spectrogram enables high-resolution visualization of the unique “fingerprints” of different physical effects—rainfall manifests as smooth background attenuation, whereas surface runoff appears as sparse high-frequency textures. Building upon this representation, we design and implement a Dual-Decoder Convolutional Autoencoder deep learning model. The model employs a shared encoder to learn the mixed CSI features, while two distinct decoder branches are responsible for reconstructing the global background component attributed to rainfall and the local texture component associated with surface runoff, respectively. Based on the decoupled signal components, we achieve simultaneous and highly accurate estimation of rainfall intensity (mean absolute error below 1.5 mm/h) and surface water accumulation (detection accuracy of 98%). Furthermore, when the decoupled and refined channel estimates are applied to a communication receiver for channel equalization, the Bit Error Rate (BER) is reduced by more than one order of magnitude compared to conventional equalization methods. Full article

Multi-UAV area coverage path planning is a challenging and important task in the field of multi-robots. To achieve efficient and complete coverage in grid-based environments with obstacles and complex boundaries, a multi-UAV area coverage path planning method based on an improved Deep Q-Network (DQN) is proposed in this paper. In the proposed method, a map preprocessing technique based on Depth-First Search (DFS) is introduced to automatically detect and remove unreachable areas. Subsequently, to achieve a reasonable task allocation, the Divide Areas based on Robots’ initial Positions (DARP) algorithm is utilized. In the path planning stage, an enhanced Dueling DQN reinforcement learning architecture is employed by introducing action encoding and prioritized experience replay mechanisms, which improves both training efficiency and policy quality. Moreover, a reward function specifically designed for complete coverage tasks is proposed, effectively reducing redundant visits and mitigating path degradation. Extensive experiments conducted on several benchmark maps show that the proposed method outperforms traditional DQN, Boustrophedon path planning, and Spanning Tree Coverage (STC) methods in terms of coverage rate, redundancy rate, and path length. Full article

Ensuring global food security requires accurate and robust solutions for crop health monitoring, weed detection, and large-scale land-cover classification. To this end, we propose AgroVisionNet, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction, Vision Transformers (ViTs) for capturing long-range global dependencies, and Graph Neural Networks (GNNs) for modeling spatial relationships between image regions. The framework was evaluated on five diverse benchmark datasets—PlantVillage (leaf-level disease detection), Agriculture-Vision (field-scale anomaly segmentation), BigEarthNet (satellite-based land-cover classification), UAV Crop and Weed (weed segmentation), and EuroSAT (multi-class land-cover recognition). Across these datasets, AgroVisionNet consistently outperformed strong baselines including ResNet-50, EfficientNet-B0, ViT, and Mask R-CNN. For example, it achieved 97.8% accuracy and 95.6% IoU on PlantVillage, 94.5% accuracy on Agriculture-Vision, 92.3% accuracy on BigEarthNet, 91.5% accuracy on UAV Crop and Weed, and 96.4% accuracy on EuroSAT. These results demonstrate the framework’s robustness across tasks ranging from fine-grained disease detection to large-scale anomaly mapping. The proposed hybrid approach addresses persistent challenges in agricultural imaging, including class imbalance, image quality variability, and the need for multi-scale feature integration. By combining complementary architectural strengths, AgroVisionNet establishes a new benchmark for deep learning applications in precision agriculture. Full article

Gravitational-wave (GW) detection has become a significant area of research following the first successful observation by the Laser Interferometer Gravitational-Wave Observatory (LIGO). The detection of signals emerging from binary black hole (BBH) mergers have challenges due to the presence of non-Gaussian and non-stationary noise in observational data. Using traditional matched filtering techniques to detect BBH merging are computationally expensive and may not generalize well to unexpected GW events. As a result, deep learning-based methods have emerged as powerful alternatives for robust GW signal detection. In this study, we propose a novel Transformer-based architecture that introduces energy-aware modulation into the attention mechanism through dual-energy attention masks. In the proposed framework, Q-transform and discrete wavelet transform (DWT) are employed to extract time–frequency energy representations from gravitational-wave signals which are fused into energy masks that dynamically guide the Transformer encoder. In parallel, the raw one-dimensional signal is used directly as input and segmented into temporal patches, which enables the model to leverage both learned representations and physically grounded priors. This proposed architecture allows the model to focus on energy-rich and informative regions of the signal in order to enhance the robustness of the model under realistic noise conditions. Experimental results on BBH datasets embedded in real LIGO noise show that EDAT-BBH outperforms CNN-based and standard Transformer-based approaches, achieving an accuracy of 0.9953, a recall of 0.9950, an F1-score of 0.9953, and an AUC of 0.9999. These findings demonstrate the effectiveness of energy-modulated attention in improving both the interpretability and performance of deep learning models for gravitational-wave signal classification. Full article

Reliable spatio-temporal rainfall prediction is a key element in disaster mitigation and water resource management in dynamic tropical regions such as the Bengawan Solo River Watershed. However, high climate variability and data limitations often pose significant challenges to the accuracy of conventional prediction models. This study introduces an innovative approach by applying ensemble stacking, which combines machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Light Gradient-Boosting Machine (LGBM) and deep learning models like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Networks (TCN), Convolutional Neural Network (CNN), and Transformer architecture based on monthly Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data (1981–2024). The novelty of this research lies in the systematic exploration of various model combination scenarios—both classical and deep learning and the evaluation of their performance in projecting rainfall for 2025–2030. All base models were trained on the 1981–2019 period and validated with data from the 2020–2024 period, while ensemble stacking was developed using a linear regression meta-learner. The results show that the optimal ensemble scenario reduces the MAE to 53.735 mm, the RMSE to 69.242 mm, and increases the R² to 0.795826—better than all individual models. Spatial and temporal analyses also indicate consistent model performance at most locations and times. Annual rainfall projections for 2025–2030 were then interpolated using IDW to generate a spatio-temporal rainfall distribution map. The improved accuracy provides a strong scientific basis for disaster preparedness, flood and drought management, and sustainable water planning in the Bengawan Solo River Watershed. Beyond this case, the approach demonstrates significant transferability to other climate-sensitive and data-scarce regions. Full article