Multi-view information fusion can provide more accurate, complete and reliable data descriptions for monitoring objects, effectively improve the limitations and unreliability of single-view data. Existing multi-view information fusion based on deep learning mostly focuses on the feature level and decision level, with large information loss, and does not distinguish the view weight in the fusion process. To this end, a multi-view data level information fusion model CAM_MCFCNN with view weight was proposed based on a channel attention mechanism and convolutional neural network. The model used the channel characteristics to implement multi-view information fusion at the data level stage, which made the fusion position and mode more natural and reduced the loss of information. A multi-channel fusion convolutional neural network was used for feature learning. In addition, the channel attention mechanism was used to learn the view weight, so that the algorithm could pay more attention to the views that contribute more to the fault identification task during the training process, and more reasonably integrate the information of different views. The proposed method was verified by the data of the planetary gearbox experimental platform. The multi-view data and single-view data were used as the input of the CAM_MCFCNN model and single-channel CNN model respectively for comparison. The average accuracy of CAM_MCFCNN on three constant-speed datasets reached 99.95%, 99.87% and 99.92%, which was an improvement of 0.95%, 2.25%, and 0.04%, compared with the single view with the highest diagnostic accuracy, respectively. When facing limited samples, CAM_MCFCNN had similar performance. Finally, compared with different multi-view information fusion algorithms, CAM_MCFCNN showed better stability and higher accuracy. The experimental results showed that the proposed method had better performance, higher diagnostic accuracy and was more reliable, compared with other methods.

River water quality prediction is a crucial aspect of water environment management and ecological conservation, holding significant importance for ensuring the sustainable utilization of water resources. As a key indicator for assessing river self-purification capacity and aquatic ecosystem health, the accurate prediction of dissolved oxygen (DO) is particularly vital for water quality early warning. To address the challenges that single deep learning models face in collaboratively modeling long- and short-term dependencies, and that most hybrid methods fail to adequately consider the characteristic differences in various components within a time series, this paper proposes an STL-TCN-LSTM model for predicting DO concentration in river water. The proposed model first employs seasonal-trend decomposition using Loess (STL) to decompose the original time series into three components: trend, seasonality, and residual, aiming to separate features at different time scales. Then, three parallel Temporal Convolutional Networks (TCNs) are utilized to extract temporal features from each component and reconstruct the sequence. Finally, the reconstructed results are fed into a Long Short-Term Memory (LSTM) network to further model their dynamic temporal dependencies, thereby enhancing prediction accuracy. The performance of the proposed model is validated on three river water quality datasets from different river basins with varying sampling frequencies. The experimental results on the three river datasets show that the STL-TCN-LSTM model consistently outperforms all baseline models, including LSTM, TCN, BiLSTM, GRU, CNN-LSTM, and XGBoost. Specifically, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are reduced by an average of 14.47%, 14.51%, and 14.27%, respectively, while the coefficient of determination (R²) improves by an average of 0.79%. The Wilcoxon signed-rank test confirms that all performance improvements are statistically significant (p < 0.05). These results demonstrate that the proposed model achieves higher prediction accuracy and exhibits stronger generalization capability in DO forecasting, thereby offering a reliable tool for water quality early warning and aquatic environmental management.

Infrared images are often degraded by complex noise due to hardware and environmental factors, posing challenges for subsequent processing and target detection. To overcome the shortcomings of existing denoising methods in balancing noise removal and detail preservation, this paper proposes a Wavelet Transform Enhanced Infrared Denoising Model (WTEIDM). Firstly, a Wavelet Transform Self-Attention (WTSA) is designed, which combines the frequency-domain decomposition ability of the discrete wavelet transform (DWT) with the dynamic weighting mechanism of self-attention to achieve effective separation of noise and detail. Secondly, a Multi-Scale Gated Linear Unit (MSGLU) is devised to improve the ability to capture detail information and dynamically control features through dual-branch multi-scale depth-wise convolution and gating strategy. Finally, a Parallel Hybrid Attention Module (PHAM) is proposed to enhance cross-dimensional feature fusion effect through the parallel cross-interaction of spatial and channel attention. Extensive experiments are conducted on five infrared datasets under different noise levels (σ = 15, 25, and 50). The results demonstrate that the proposed WTEIDM outperforms several state-of-the-art denoising algorithms on both PSNR and SSIM metrics, confirming its superior generalization capability and robustness.

Infrared small targets in complex backgrounds suffer from small size, sparse features, and strong noise, leading to a pronounced decline in detection accuracy. To address this problem, we propose PDN-YOLOv11 (PPA_DSCU_NWD-YOLOv11), an improved detection algorithm based on the YOLOv11n model. First, a Parallelized Patch-Aware Attention (PPA) module is introduced, which employs a multi-branch feature extraction strategy to capture feature information at different scales and hierarchies, thereby ensuring the effective retention of critical information after multiple downsampling operations. Then, a Dual-path Spatial-channel Co-reconstruction Unit (DSCU) is constructed. It integrates residual-enhanced Spatial Reconstruction Units (SRU-R) and Channel Reconstruction Units (CRU-R) to reduce feature redundancy and enhance the model's feature representation capacity. Furthermore, the detection head is optimized by adding a P2 layer to strengthen the capture of fine details for small targets, while the P5 layer is removed to suppress excessive attention to large-scale backgrounds, thereby improving both detection accuracy and efficiency. Finally, the Normalized Wasserstein Distance (NWD) loss function is adopted to mitigate localization biases caused by the small target size. Experimental results demonstrate that compared to the original YOLOv11 model, our PDN-YOLOv11 achieves increases of 1.2% in mAP@0.5 and 6.3% in mAP@0.5:0.95 on the NUDT-SIRST dataset, while simultaneously reducing the number of model parameters by 22.9%. This indicates a favorable balance between detection accuracy and inference efficiency. Furthermore, in the generalization experiments, the proposed model achieves increases in mAP@0.5 of 1.1% and 1.6% on the IRSTD-1K and NUAA-SIRST datasets, respectively, compared to the YOLOv11 model, reaching 94.1% and 84.7%.