A Novel Black Box Process Quality Optimization Approach based on Hit Rate

Hit rate is a key performance metric in predicting process product quality in integrated industrial processes. It represents the percentage of products accepted by downstream processes within a controlled range of quality. However, optimizing hit rate is a non-convex and challenging problem. To address this issue, we propose a data-driven quasi-convex approach that combines factorial hidden Markov models, multitask elastic net, and quasi-convex optimization. Our approach converts the original non-convex problem into a set of convex feasible problems, achieving an optimal hit rate. We verify the convex optimization property and quasi-convex frontier through Monte Carlo simulations and real-world experiments in steel production. Results demonstrate that our approach outperforms classical models, improving hit rates by at least 41.11% and 31.01% on two real datasets. Furthermore, the quasi-convex frontier provides a reference explanation and visualization for the deterioration of solutions obtained by conventional models.

Clear Memory-Augmented Auto-Encoder for Surface Defect Detection

In surface defect detection, due to the extreme imbalance in the number of positive and negative samples, positive-samples-based anomaly detection methods have received more and more attention. Specifically, reconstruction-based methods are the most popular. However, exiting methods are either difficult to repair abnormal foregrounds or reconstruct clear backgrounds. Therefore, we propose a clear memory-augmented auto-encoder. At first, we propose a novel clear memory-augmented module, which combines the encoding and memory-encoding in a way of forgetting and inputting, thereby repairing abnormal foregrounds and preservation clear backgrounds. Secondly, a general artificial anomaly generation algorithm is proposed to simulate anomalies that are as realistic and feature-rich as possible. At last, we propose a novel multi scale feature residual detection method for defect segmentation, which makes the defect location more accurate. CMA-AE conducts comparative experiments using 11 state-of-the-art methods on five benchmark datasets, showing an average 18.6% average improvement in F1-measure.