In actual scenarios, industrial and cloud computing platforms usually need to monitor equipment and traffic anomalies through multivariable time series data. However, the existing anomaly detection methods can not capture the long-distance temporal correlations of data and the potential relationships between features simultaneously, and only have high detection accuracy for specific time sequence anomaly detection scenarios without good generalization ability. This paper proposes a time-series anomaly-detection framework for multiple scenarios, Anomaly-PTG (anomaly parallel transformer GRU), given the above limitations. The model uses the parallel transformer GRU as the information extraction module of the model to learn the long-distance correlation between timestamps and the global feature relationship of multivariate time series, which enhances the ability to extract hidden information from time series data. After extracting the information, the model learns the sequential representation of the data, conducts the sequential modeling, and transmits the data to the full connection layer for prediction. At the same time, it also uses the autoencoder to learn the potential representation of the data and reconstruct the data. The two are optimally combined to form an anomaly detection module of the model. The module combines timestamp prediction with time series data reconstruction, improving the detection rate of rare anomalies and detection accuracy. By using three public datasets of physical devices and one dataset of network traffic intrusion detection, the model’s effectiveness was verified, and the model’s generalization ability and strong robustness were demonstrated. Compared with the most advanced method, the average F1 value of the Anomaly-PTG model on four datasets was increased by 2.2%, and the F1 value on each dataset was over 94%.

Iris segmentation is critical for iris recognition. In this study, the authors present a circle‐based iris segmentation method for non‐ideally captured iris by employing anisotropic diffusion. Our proposal consists of two component steps by which interior and exterior boundaries are localised, respectively. To save computational load, Laplace pyramid (LP) framework is incorporated into both steps. During the first step, when iris has been decomposed into the coarse level by LP, reflections will be removed by anisotropic diffusion and morphologic operations. In the second step, the authors present the innovated curve evolution to detect exterior boundary. Moreover, order statistical filters are employed to enhance the contrast of iris and sclera. Experimental results depict a high correct ratio of segmentation that is more than 96.90% thereby validating the proposed approach.

Detecting product surface defects is an important issue in industrial scenarios. In the actual scene, the shooting angle and the distance between the industrial camera and the shooting object often vary, which results in a large variation in the scale and angle. In addition, high-speed cameras are prone to motion blur, which further deteriorates the defect detection results. In order to solve the above problems, this study proposes a surface defect detection model for industrial products based on attention enhancement. The network takes advantage of the lower-level and higher-resolution feature map from the backbone to improve Path Aggregation Network (PANet) in object detection. This study makes full use of multihead self-attention (MHSA), an independent attention block for enhancing the backbone network, which has made considerable progress for practical application in industry and further improvement of the surface defect detection. Moreover, some tricks have been adopted that can improve the detection performance, such as data augmentation, grayscale filling, and channel conversion of input images. Experiments in this study on internal datasets and four public datasets demonstrate that our model has achieved good performance in industrial scenarios. On the internal dataset, the mAP@.5 result of our model is 98.52%. In the RSDDs dataset, the model in this study achieves 86.74%. In the BSData dataset, the model reaches 82.00%. Meanwhile, it achieves 81.09% and 74.67% on the NRSD-MN and NEU-DET datasets, respectively. This study has demonstrated the effectiveness and certain generalization ability of the model from internal datasets and public datasets.