Although autonomous driving technology has advanced rapidly in recent years, the intrinsic complexities of adverse weather conditions pose significant challenges to achieving high object detection accuracy, despite extensive research on perception systems in autonomous vehicles. Accordingly, this study proposes a YOLO-based network that integrates the DHNet residual network for image dehazing with wavelet-based channel attention modules, specifically tailored for target detection in autonomous driving under adverse weather conditions. The proposed network introduces a novel channel-value attention mechanism and sequentially applies wavelet-based soft-thresholding denoising techniques, thereby improving its sensitivity to discriminative channel information. We further introduce the first DHNet Dehazing Attention Module, which sequentially combines the DHNet residual network with the MixDehazeNet hybrid structural dehazing network. This integration synergistically improves the network’s image dehazing performance. To address dataset class imbalance, this study incorporates Adaptive Threshold Focal Loss (ATFL), which markedly improves training efficiency and model robustness. This optimization enhances the model’s generalization ability for target detection and classification tasks under challenging weather conditions. Experimental evaluations, including ablation studies and comparative analyses, demonstrate that the proposed method achieves substantial improvements in accuracy across three benchmark datasets.

With the rapid development of social media, short-text data have become increasingly important in fields such as public opinion monitoring, user feedback analysis, and intelligent recommendation systems. However, existing short-text sentiment analysis models often suffer from limited cross-domain adaptability and poor generalization performance. To address these challenges, this study proposes a novel short-text sentiment classification model based on the Bidirectional Encoder Representations from Transformers (BERTs) and a dual-stream Transformer gated attention mechanism. This model first employs Bidirectional Encoder Representations from Transformers (BERTs) and the Chinese Robustly Optimized BERT Pretraining Approach (Chinese-RoBERTa) to achieve data augmentation and multilevel semantic mining, thereby expanding the training corpus and enhancing minority class coverage. Second, a dual-stream Transformer gated attention mechanism was developed to dynamically adjust feature fusion weights, enhancing adaptability to heterogeneous texts. Finally, the model integrates a Bidirectional Gated Recurrent Unit (BiGRU) with Multi-Head Self-Attention (MHSA) to strengthen sequence information modeling and global context capture, enabling the precise identification of key sentiment dependencies. The model’s superior performance in handling data imbalance and complex textual sentiment logic scenarios is demonstrated by the experimental results, achieving significant improvements in accuracy and F1 score. The F1 score reached 92.4%, representing an average increase of 8.7% over the baseline models. This provides an effective solution for enhancing the performance and expanding the application scenarios of short-text sentiment analysis models.

Complex Event Process (CEP) is very essential in Semantic Web of Things (SWoT) that deploy a large number of sensor devices, like smart traffic and smart city. CEP mainly solves heterogenous problems of stream data processing, where streaming data is connected to internet by a mass of wireless sensor devices. The core work of CEP is rule updating. Existing researches of rule updating are designed for static environments, and it is quite laborious to transplant those rules for dynamic environments. To enhance the portability of event rules, a method of automatic rule updating based on machine learning is proposed to learn the rules of a dynamic environment. Experimental results reveal that the proposed methods are effective and efficient.

Accurate probabilistic load forecasting is essential for secure power system operation and efficient energy management, particularly under increasing renewable integration and demand-side complexity. However, traditional forecasting methods often struggle with issues such as non-linearity, non-stationarity, feature redundancy, and quantile crossing, which hinder reliable uncertainty quantification. To overcome these challenges, this study proposes a hybrid probabilistic load forecasting framework that integrates empirical mode decomposition (EMD), LASSO-based feature selection, and a monotone composite quantile regression neural network (MCQRNN) enhanced with kernel density estimation (KDE). First, EMD decomposes the raw load series into intrinsic mode functions and a trend component to mitigate non-stationarity. Then, LASSO selects the most informative features from both the decomposed components and the original time series, effectively reducing dimensionality and multicollinearity. Subsequently, the proposed MCQRNN model generates multiple quantiles under monotonicity constraints, eliminating quantile crossing and improving multi-quantile coherence through a composite loss function. Finally, Gaussian kernel density estimation reconstructs a continuous probability density function from the predicted quantiles, enabling full distributional forecasting. The framework is evaluated on two public datasets—GEFCom2014 and ISO New England—using point, interval, and density evaluation metrics. Experimental results demonstrate that the proposed EMD–LASSO–MCQRNN–KDE model outperforms benchmark approaches in both point and probabilistic forecasting, providing a robust and interpretable solution for uncertainty-aware grid operation and energy planning.

Subgraph matching on a large graph has become a popular research topic in the field of graph analysis, which has a wide range of applications including question answering and community detection. However, traditional edge-cutting strategy destroys the structure of indivisible knowledge in a large RDF graph. On the premise of load-balancing on subgraph division, a dominance-partitioned strategy is proposed to divide a large RDF graph without compromising the knowledge structure. Firstly, a dominance-connected pattern graph is extracted from a pattern graph to construct a dominance-partitioned pattern hypergraph, which divides a pattern graph as multiple fish-shaped pattern subgraphs. Secondly, a dominance-driven spectrum clustering strategy is used to gather the pattern subgraphs into multiple clusters. Thirdly, the dominance-partitioned subgraph matching algorithm is designed to conduct all isomorphic subgraphs on a cluster-partitioned RDF graph. Finally, experimental evaluation verifies that our strategy has higher time-efficiency of complex queries, and it has a better scalability on multiple machines and different data scales.