Emotion recognition based on EEG signals has been a challenging task. The acquisition of EEG signals is complex, time-consuming, and has a high overhead. Artificial Intelligence Generated Content technology has been developing rapidly in image and sound generation, but effective generative models for EEG signal generation have rarely appeared. To address the problem, we propose a weighted co-training framework for emotion recognition using a frequency-spatial diffusion transformer. We propose the EEG signal generation model by utilizing frequency-spatial correlation. In the first step, we use forward diffusion to add noise to the real samples and then use the proposed model to denoise and restore the real EEG signals. It ensures that the trained generative model can generate real EEG signals. Then, we use the denoising process of the proposed model to generate a large amount of data and then use the pseudo-data weighting module to evaluate the generated samples further. Finally, the actual samples and weighted pseudo-data are used to train the classifier for better generalization jointly. We conducted comprehensive experiments on three EEG emotion recognition benchmark datasets. The experimental results show that our method improves by 0.96%-1.91% compared to state-of-the-art methods. In addition, quantitative and qualitative analysis proves the effectiveness of our proposed method.

Assisted driving and unmanned driving have been areas of focus for both industry and academia. Front-vehicle detection technology, a key component of both types of driving, has also attracted great interest from researchers. In this paper, to achieve front-vehicle detection in unmanned or assisted driving, a vision-based, efficient, and fast front-vehicle detection method based on the spatial and temporal characteristics of the front vehicle is proposed. First, a method to extract the motion vector of the front vehicle is put forward based on Oriented FAST and Rotated BRIEF (ORB) and the spatial position constraint. Then, by analyzing the differences between the motion vectors of the vehicle and those of the background, feature points of the vehicle are extracted. Finally, a feature-point clustering method based on a combination of temporal and spatial characteristics are applied to realize front-vehicle detection. The effectiveness of the proposed algorithm is verified using a large number of videos.

A two-branch convolutional neural network (CNN) architecture for feature extraction in person re-identification (re-ID) based on video surveillance is proposed. Highly discriminative person features are obtained by extracting both global and local features. Moreover, an adaptive triplet loss function based on the original triplet loss function is proposed and is used in the network training process, resulting in a significantly improved learning efficiency. The experimental results on open datasets demonstrate the effectiveness of the proposed method.

As a kind of frequent bad weather, Agglomerate fog is a serious danger to people's safe driving, especially on the highway. Therefore, the research on the detection of fog is of great practical significance to ensure the safety of pedestrians. This paper proposes a shallow convolutional neural network for agglomerate fog detection in images, including the framework of the network and the detailed design of each component. Firstly, the image is divided into several sub-images; and then a shallow convolutional neural network is constructed and employed to identify the existence of fog for each of the sub-area images; lastly, the decision results of each sub-area images were integrated to determine whether the whole image contained agglomerate fog. A large quantity of simulation data and real data were used to test the performance of the proposed method, the experimental results show that the presented method can achieve more than 90% detection accuracy, which demonstrated that the advantage of the proposed method comparing with several existed methods.

Low-altitude drones, which are unimpeded by traffic congestion or urban terrain, have become a critical asset in emergency rescue missions. To address the current lack of emergency rescue data, UAV aerial videos were collected to create an experimental dataset for action classification and localization annotation. A total of 5082 keyframes were labeled with 1–5 targets each, and 14,412 instances of data were prepared (including flight altitude and camera angles) for action classification and position annotation. To mitigate the challenges posed by high-resolution drone footage with excessive redundant information, we propose the SlowFast-Traffic (SF-T) framework, a spatio-temporal sequence-based algorithm for recognizing traffic accident rescue actions. For more efficient extraction of target–background correlation features, we introduce the Actor-Centric Relation Network (ACRN) module, which employs temporal max pooling to enhance the time-dimensional features of static backgrounds, significantly reducing redundancy-induced interference. Additionally, smaller ROI feature map outputs are adopted to boost computational speed. To tackle class imbalance in incident samples, we integrate a Class-Balanced Focal Loss (CB-Focal Loss) function, effectively resolving rare-action recognition in specific rescue scenarios. We replace the original Faster R-CNN with YOLOX-s to improve the target detection rate. On our proposed dataset, the SF-T model achieves a mean average precision (mAP) of 83.9%, which is 8.5% higher than that of the standard SlowFast architecture while maintaining a processing speed of 34.9 tasks/s. Both accuracy-related metrics and computational efficiency are substantially improved. The proposed method demonstrates strong robustness and real-time analysis capabilities for modern traffic rescue action recognition.

Shale-gas sweet-spot evaluation as a critical part of shale-gas exploration and development has always been the focus of experts and scholars in the unconventional oil and gas field. After comprehensively considering geological, engineering, and economic factors affecting the evaluation of shale-gas sweet spots, a dynamic uncertainty causality graph (DUCG) is applied for the first time to shale-gas sweet-spot evaluation. A graphical modeling scheme is presented to reduce the difficulty in model construction. The evaluation model is based on expert knowledge and does not depend on data. Through rigorous and efficient reasoning, it guarantees exact and efficient diagnostic reasoning in the case of incomplete information. Multiple conditional events and weighted graphs are proposed for specific problems in shale-gas sweet-spot evaluation, which is an extension of the DUCG that defines only one conditional event for different weighted function events and relies only on the experience of a single expert. These solutions make the reasoning process and results more objective, credible, and interpretable. The model is verified with both complete data and incomplete data. The results show that compared with other methods, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising auxiliary tool for shale-gas sweet spot evaluation.