Fiber optic gyro belongs to highly reliable and long-life components, which cannot be realized by traditional reliability assessment methods due to the difficulty of obtaining failure data; the Wiener process model can better model the degradation process of the device, thus realizing the reliability assessment based on the performance degradation. However, the performance degradation of fiber optic gyro exhibits nonlinear characteristics, and there is significant variability in degradation patterns among individual units within the same batch. Traditional Wiener process modeling fails to account for these two critical features. In this paper, a reliability assessment method based on the nonlinear random effect Wiener process is proposed. The nonlinear relationships are first transformed into linear forms through time-scale transformation, while the drift coefficients of the Wiener process are randomized to construct a more comprehensive stochastic degradation model. Subsequently, the Gibbs sampling method is introduced to achieve precise parameter estimation and model resolution. The proposed methodology is then applied to zero-bias performance degradation data from fiber optic gyros for reliability evaluation, generating corresponding reliability curves. The experiments show that the Akaike Information Criterion (AIC) value of the model in this paper is significantly reduced by 28.7% compared with the traditional method, indicating that the model achieves a better balance between complexity and goodness-of-fit. Therefore, the developed methodology provides a more accurate representation of the nonlinear degradation characteristics in fiber optic gyro, thereby significantly enhancing the credibility of the assessment outcomes.

Aoday, the Internet of Things (IoT) is changing fields by allowing interconnected devices to collect, share, and process data. As for traditional IoT networks that depend on centralized cloud computing, they come with high latency, redundant bandwidth consumption and energy inefficiency. This paper examines edge computing and identifies it as an enabling solution to these challenges. This facilitates real-time analytics of larger groups of data from smaller inputs and is the key characteristic of the edge computing model, by processing data closer to the source; edge computing minimizes latency, optimizes bandwidth usage, and enhances scalability. It examines architectural designs, optimization techniques, and practical applications of edge computing. The empirical evidence also shows that edge computing achieves up to 80% latency reduction, compared to the cloud, a bandwidth saving due to the fact that edge computing could process data at the source (thereby reducing data transfer to the cloud), and that edge computing could reduce overhead energy consumption by approximately 90% compared to cloud computing. The solutions proposed include hierarchical architectures, dynamic resource allocation, and integration with the blockchain, tackling challenges such as scalability, security, and energy efficiency. This work concludes that edge computing is a major breakthrough in iot networks and an enabling technology for real-time, efficient and sustainable applications.

Face mask wearing detection is an important application scenario in current technology. This study proposes a method based on the YOLOv5 object detection algorithm to address this issue. Traditional methods face challenges such as the diversity of mask-wearing postures and variations in lighting conditions, which affect their performance. To tackle these challenges, this research presents a new approach that combines the YOLOv5 object detection algorithm with an improved ResNet network architecture. By integrating the detection capabilities of YOLOv5 with the enhanced ResNet network, the method can more accurately detect masks and their wearing status, effectively capturing mask features in images, thereby significantly improving recognition accuracy and stability. The use of a custom mask dataset enables the model to better adapt to diverse lighting and posture conditions. Using deep learning frameworks like PyTorch for inference tools has significantly improved inference speed on GPUs. Experimental results show that after 200 training epochs, the proposed method achieved an accuracy exceeding 85% in face mask wearing detection tasks, with detection accuracy surpassing 98% on certain test datasets. Furthermore, the mean average precision (mAP) reached 97.5%, demonstrating the model's robustness under complex backgrounds and diverse populations. Finally, this paper discusses potential future development directions in the field of face mask wearing detection, including further enhancing the model's adaptability to varying environmental conditions and its application in real-time detection systems.

In view of the challenges posed by traditional methods in accurately identifying the damage types of film images, this paper proposes an improved approach by leveraging transfer learning based on ResNet50, incorporating the channel attention mechanism from the Convolutional Block Attention Module (CBAM). Additionally, the AlphaDropout model is integrated with the SeLU activation function to enhance stability and performance, and the resultant model is named CBAM-ResNet50. The film dataset employed in this study comprises four types of damage: cracks, dewetting, particles, and scratches. Extensive training and testing of the CBAM-ResNet50 model on this dataset demonstrate significant improvements in classification accuracy. Compared to the original ResNet50 model, the proposed method achieves a remarkable 25.57% improvement in accuracy, reaching 90.58%. This work highlights the potential of combining attention mechanisms and advanced activation strategies to address complex image classification tasks. Furthermore, the approach paves the way for practical applications in quality inspection and automated defect detection in industrial processes.