With the increase in environmental pressure and rapid development of renewable energy technologies, an integrated energy system has been recognized as an effective approach to accommodate large-scale renewables and achieve environmental sustainability. While an integrated energy system significantly improves energy efficiency, the interaction between different energy systems may also bring multiple operational risks to its reliability, which necessitates an effective reliability assessment technique. In this paper, we proposed a multi-level fuzzy evaluation model based on combined empowerment for the reliability evaluation of an integrated energy system. The analytic hierarchy process method and entropy weight method were used to calculate the weight of each index in the evaluation model. Fuzzy evaluation matrix was constructed by the membership degree of a single factor, which was defined by the fuzzy comprehensive evaluation method. The multi-level fuzzy evaluation results were obtained based on single-level evaluation results. Finally, case studies were carried out based on a practical integrated energy system; we proposed 5 first-level indicators such as reliability and economy and 12 second-level indicators such as mean time of incapacity. The simulation results (85.15) showed the effectiveness and advantages of the proposed model.

Recognizing and understanding various objects in visual information is paramount for ensuring safe and efficient autonomous navigation, especially in challenging environmental conditions. However, relying solely on single-modal data to perceive information, such as visible images, can compromise the quality and reliability of the extracted information, posing potential risks to autonomous driving systems. To address this challenge, we present a novel fusion method based on mutual information theory in semantic segmentation tasks for secure and efficient autonomous vehicles. The proposed method involves two primary components, i.e., multispectral fusion representation module (FRM) and semantic segmentation module (SSM). To optimize the FRM, we establish a unified quality representation for feature fusion by incorporating an image restoration mechanism, enhancing autonomous driving systems' overall performance and adaptability in complex environments. Meanwhile, we employ mutual information maximization to capture the interimage relations among pixels from the same semantic content, which is achieved by leveraging the semantic representation learned by the SSM in a high-dimensional feature space. Experimental results and comparisons with famous fusion approaches and segmentation models on four public datasets validate our method's effectiveness, robustness, and overall superiority.

Sensor-based Human Activity Recognition (SHAR) has gained more attention due to the rapid development of the Internet of Things (IoT). The critical issue for SHAR is rescuing the performance bottleneck from expensive feature engineering. Recent works have explored combining hybrid neural networks to improve the SHAR model architecture for learning informative representation. However, existing studies have not adequately provided a hierarchical structure that can represent human activities and capture specific representations hidden beneath interrelated low-level human activity sequences. In this work, we introduce a dual pipeline with a spatio-temporal attention fusion approach, termed the ST-attention dual pipeline, to address this problem. Specifically, the ST-attention dual pipeline employs sequence learning techniques in one pipeline to capture complex dependencies within behavior data and residual learning techniques in another pipeline to extract hierarchical details, then fuse them by the ST-attention fusion mechanism generated across spatial and temporal dimensions to improve presentation capabilities. Extensive experiments on public datasets (i.e., OPPORTUNITY, PAMAP2, and USC-HAD) have shown the ST-attention dual pipeline yields compelling results, and the spatio-temporal attention mechanism also achieves superior performance over other fusion methods.

Discovering tactical knowledge aims to extract tactical data derived from battlefield signal data, which is vital in information warfare. The learning and reasoning from battlefield signal information can help commanders make effective decisions. However, traditional methods are limited in capturing sequential and global representation due to their reliance on prior knowledge or feature engineering. The current models based on deep learning focus on extracting implicit behavioral characteristics from combat process data, overlooking the embedded martial knowledge within the recognition of combat intentions. In this work, we fill the above challenge by proposing a dual fusion pipeline introducing graph representation learning into sequence learning to construct tactical behavior sequence graphs expressing implicit martial knowledge, named TBGCN. Specifically, the TBGCN utilizes graph representation learning to represent prior knowledge by building a graph to induce deep learning paradigms, and sequence learning finds the hidden representation from the target’s serialized data. Then, we employ a fusion module to merge two such representations. The significance of integrating graphs with deep learning lies in using the artificial experience of implicit graph structure guiding adaptive learning, which can improve representation ability and model generalization. Extensive experimental results demonstrate that the proposed TBGCN can effectively discover tactical knowledge and significantly outperform the traditional and deep learning methods.

Audio–visual depression recognition in real-world scenarios is often challenged by temporal sparsity and cross-modal asynchrony, where depression-related cues may appear only in short segments and may not align precisely across modalities. Under such conditions, global pooling or dense attention tends to dilute sparse discriminative evidence with redundant context, leading to unstable utterance-level representations. To address this issue, we propose an audio–visual depression recognition framework that integrates modality feature adaptation, bidirectional cross-modal interaction, and graph-based prototype abstraction. Specifically, heterogeneous audio and visual streams are first transformed into compatible representations, after which bidirectional cross-attention models content-dependent dependencies across modalities without requiring index-wise correspondence. The fused tokens are then interpreted as graph nodes and aggregated into a compact set of semantic prototypes through graph convolution and differentiable soft clustering. In addition, audio perturbation is introduced during training as a task-oriented regularisation strategy for partial acoustic evidence loss and temporal misalignment. Experiments on the LMVD dataset demonstrate clear improvements on the primary depression-recognition task, while auxiliary evaluations on MIntRec and CMU-MOSI suggest that the structured prototype representation is beneficial for other temporally sparse audio–visual recognition tasks. These results indicate that structured prototype learning is effective for preserving sparse depression-related cues, while training-time perturbation provides a complementary regularisation effect under asynchronous multimodal conditions.

Sensor-based human activity recognition (SHAR) serves as a core research direction in pervasive computing, mobile health, and related fields. Although existing deep learning methods have achieved promising progress in SHAR tasks, most optimize from a single dimension only. They struggle to simultaneously balance recognition accuracy, noise robustness, adaptation to class imbalance, and lightweight deployment requirements, leading to performance bottlenecks in real-world scenarios. To address these challenges, this paper proposes a lightweight joint optimization framework named AWARE-Net. Leveraging the lightweight TS-ResNet as a backbone encoder, the framework integrates spatiotemporal dynamic convolution feature encoding with a global loss function that fuses class-balanced loss, contrastive learning auxiliary loss, and temporal smooth regularization to achieve multi-objective joint optimization. Extensive experiments on three widely used SHAR benchmark datasets, namely OPPORTUNITY, PAMAP2, and USC-HAD, demonstrate that the proposed AWARE-Net achieves competitive performance compared with representative state-of-the-art HAR methods.