Power batteries are the core of electric vehicles, but minor faults can easily cause accidents; therefore, fault diagnosis of the batteries is very important. In order to improve the practicality of battery fault diagnosis methods, a fault diagnosis method for lithium-ion batteries in electric vehicles based on multi-method fusion of big data is proposed. Firstly, the anomalies are removed and early fault analysis is performed by t-distribution random neighborhood embedding (t-Sne) and wavelet transform denoising. Then, different features of the vehicle that have a large influence on the battery fault are identified by factor analysis, and the faulty features are extracted by a two-way long and short-term memory network method with convolutional neural network. Finally a self-learning Bayesian network is used to diagnose the battery fault. The results show that the method can improve the accuracy of fault diagnosis by about 12% when verified with data from different vehicles, and after comparing with other methods, the method not only has higher fault diagnosis accuracy, but also reduces the response time of fault diagnosis, and shows superiority compared to graded faults, which is more in line with the practical application of engineering.

Real-time and accurate hand gesture detection is essential for safe and intuitive Human-Robot Interaction (HRI), enabling robots to interpret non-verbal cues and respond appropriately in dynamic environments. This research evaluates the effectiveness of YOLOv8n through YOLOv13n models in recognizing static hand gestures from the TSL detection dataset, which includes 5469 grayscale images across 31 gesture classes. The models underwent training with uniform data augmentation protocols and were assessed using object detection metrics including precision, recall, and mean average precision computed at an IoU threshold of 0.50 as well as over the interval from 0.50 to 0.95. The evaluation of computational efficiency involved metrics such as how fast the model infers, its frame rate, size and the total training duration. YOLOv9t exhibited the most robust detection accuracy across all evaluated metrics, achieving the highest mean mAP at 0.50 (0.990), mAP at 0.50 to 0.95 (0.876), precision (0.975), and recall (0.966). In contrast, YOLOv10n achieved the lowest inference latency (0.7 ms). These findings highlight the trade-off between accuracy and efficiency in gesture detection and show that YOLOv9t and YOLOv10n represent strong choices for accuracy and latency-critical applications, respectively.