Hangqi Zhang

Hybrid clouds allow service providers to combine secure private resources with elastic public resources, but most schedulers still have three practical limitations: they commonly model task security as a binary constraint, focus on independent tasks rather than dependent workflows, and treat deadlines as hard feasibility constraints rather than service-level objectives with economic penalties. This paper studies multi-objective workflow scheduling in hybrid clouds under multi-level security and soft-deadline constraints. We formulate a multi-objective scheduling problem that captures workflow precedence, heterogeneous private servers and public virtual machines, security-level matching, inter-cloud data transfer, revenue, execution costs, and deadline-violation penalties. We then propose MSWSS, a Multi-level Security-aware Workflow Scheduling with Soft Deadlines algorithm. MSWSS first ranks workflow tasks by deadline pressure, upward rank, security level, and penalty sensitivity; it then filters resources by security clearance; it next selects resources using a profit-aware multi-objective score; and finally it performs local migration and compaction to reduce execution cost and deadline loss. Across 100 independent repetitions at each tested synthetic setting, MSWSS consistently achieves the lowest mean execution cost relative to the three adapted policies, while satisfaction, profit, and deadline violation show setting-dependent trade-offs. In a separate paired default-setting comparison, HEFT obtains slightly better service metrics while MSWSS reduces mean cost by 34.73%. A targeted high-contention ablation further shows that refinement reduces mean cost by 14.12% while decreasing satisfaction and profit and increasing the violation ratio, confirming a cost–service trade-off rather than universal dominance.

Industrial visual inspection plays a crucial role in intelligent manufacturing. However, existing anomaly-detection methods based on unsupervised learning paradigms often struggle with issues such as overlooking minor defects and blurring component edges in confidence maps. To address these challenges, this paper proposes an industrial anomaly-detection method based on component-level feature enhancement. This method introduces a component-level feature-enhancement module, which optimizes feature matching by calculating the structural similarity between global coarse-grained confidence features and local fine-grained confidence features, thereby generating enhanced feature maps to improve the model’s detection accuracy for minor defects and local anomalies. Additionally, we propose a region-segmentation method based on multi-layer piecewise thresholds, which effectively distinguishes between foreground and background in confidence maps, circumvents background interference and ensures the integrity of structural information of foreground components. Experimental results demonstrate that the proposed method surpasses comparative methods in both logical and structural defect detection tasks, showing significant advantages, especially in fine-grained anomaly detection, with stronger robustness and accuracy.

In modern industrial production, unsupervised anomaly detection methods have gained significant attention due to their ability to address the challenge posed by the scarcity of labeled anomaly samples. Among them, unsupervised anomaly detection methods based on reverse distillation (RD) have become a mainstream choice, which has attracted extensive research due to their excellent anomaly detection performance. However, there is a problem of “feature leakage” in the RD model, which may lead to non-anomalous regions being incorrectly identified as defects. To solve this problem, we propose a Normal Feature-Enhanced Reverse teacher–student Distillation (NFERD) method. Specifically, we designed and incorporated a normal feature bank (NFB) module into the basic RD network. This module stores normal features extracted by the teacher model, assisting the student model in learning normal features more efficiently, thereby addressing the problem of “feature leakage”. In addition, to effectively fuse the feature maps extracted by the student model with the feature maps in NFBs, we designed a Hybrid Attention Fusion Module (HAFM), which ensures the preservation of key information during the feature fusion process by the parallel processing of spatial and channel attention mechanisms. Through experimental verification on two publicly available datasets, i.e., MVTec and KSDD, our method outperformed the existing mainstream methods in both image-level and pixel-level anomaly detection. Specifically, we achieved an average I-AUROC score of 99.32% on MVTec and a 98.75% P-AUROC on the KSDD, showing clearer segmentation results, especially in complex scenarios. Furthermore, our method surpassed the second-best method by over 1.4% PRO on MVTec, demonstrating its effectiveness.