The spatial information of Electroencephalography (EEG) is essential for emotion recognition model to learn discriminative feature. The convolutional networks and recurrent networks are the conventional choices to learn the complex spatial dependencies through a number of electrodes and brain regions. However, these models have difficulty in capturing long-range dependencies due to the operations of local feature learning. To enhance EEG spatial dependencies capturing and improve the accuracy of emotion recognition, we propose a transformer- based model to hierarchically learn the discriminative spatial information from electrode level to brain-region-level. In the electrode-level spatial learning, the transformer encoders are adopted to integrate information within different brain regions. Next, in view of the different roles of brain regions in the emotion recognition, the self-attention within the transformer could emphasize the contributive brain regions. Hence, in the brain-region-level spatial learning, a transformer encoder is utilized to capture the spatial dependencies among the brain regions. Finally, to validate the effectiveness of the proposed model, the subject-independent experiments are conducted on the DEAP and MAHNOB-HCI database. The experimental results demonstrate that the proposed model achieves outstanding performance in emotion recognition with arousal and valence level. Moreover, the visualization of self-attention indicates that the proposed model could emphasize the discriminative spatial information from pre-frontal lobe, frontal lobe, temporal lobe and parietal lobe.

Insulator defect detection is of great significance to ensure the normal operation of power transmission and distribution networks. In response to the problems of low speed, low accuracy, and difficulty in deploying to embedded terminals in existing insulator defect detection, this paper proposes a lightweight insulator defect detection model based on an improved YOLOv5s, named LiteYOLO-ID. Firstly, to significantly reduce the model parameters while maintaining detection accuracy, we design a new lightweight convolution module called EGC (ECA-GhostNet-C2f). Secondly, based on the EGC module, we construct the EGC-CSPGhostNet backbone network, which optimizes the feature extraction process and achieves model compression. Additionally, we design a lightweight neck network, EGC-PANet, to further reduce the parameter count and achieve efficient feature fusion. Experimental results show that on the IDID-Plus dataset, compared to the original YOLOv5s model, not only does LiteYOLO-ID reduce the model parameters by 47.13%, but it also improves the mAP(0.5) by 1%. Furthermore, the generalization of the model is validated on the Pascal VOC dataset and the SFID dataset. Importantly, after TensorRT optimization, the inference speed of the LiteYOLO-ID algorithm on the Jetson TX2 NX reaches 20.2 FPS, meeting the real-time detection requirements of insulator defects. Our code, weight models, and datasets can be obtained at the following URL: https://github.com/LuYang-2023/Insulator-defect-detection.

The temporal and spatial information of electroencephalogram (EEG) are essential for the emotion recognition model to learn the discriminative features. Hence, we propose a novel hybrid spatial-temporal feature fusion neural network (STFFNN) to extract the discriminative features and integrate complementary information. The generated power topographic maps, which capture dependencies among the electrodes, are fed to convolutional neural network (CNN) for spatial feature learning. Furthermore, instance normalizations (INs) and batch normalizations (BNs) within the CNN are appropriately combined to alleviate the individual difference and preserve the domain-invariant information. Meanwhile, a feedforward network is adopted for temporal feature learning. Due to the high dimensionality of EEG features, we propose a grid-search-based configurational optimization method to robustly reduce the dimensionality. Finally, inspired by the multimodal fusion strategies that leverage the complementarity of data to obtain more robust predictions, we utilize a bidirectional long short-term memory (Bi-LSTM) network for temporal and spatial feature fusion. To validate the effectiveness of the proposed method, the tenfold cross-validation experiments and subject-dependent experiments are both conducted on the DEAP database. The experimental results demonstrate that the proposed method achieves outstanding performance in emotion recognition with arousal and valence level.