As a critical branch of the Internet of Things (IoT) in the medicine industry, the Internet of Medical Things (IoMT) significantly improves the quality of healthcare due to its real-time monitoring and low medical cost. Benefiting from edge and cloud computing, IoMT is provided with more computing and storage resources near the terminal to meet the low-delay requirements of computation-intensive services. However, the service offloading from health monitoring units (HMUs) to edge servers generates additional energy consumption. Fortunately, artificial intelligence (AI), which has developed rapidly in recent years, has proved effective in some resource allocation applications. Taking both energy consumption and delay into account, we propose an energy-aware service offloading algorithm under an end-edge-cloud collaborative IoMT system with Asynchronous Advantage Actor-critic (A3C), named ECAC. Technically, ECAC uses the structural similarity between the natural distributed IoMT system and A3C, whose parameters are asynchronously updated. Besides, due to the typical delay-sensitivity mechanism and time-energy correction, ECAC can adjust dynamically to the diverse service types and system requirements. Finally, the effectiveness of ECAC for IoMT is proved on real data.

Visual anomaly detection plays an important role in industrial product quality inspection, but the scarcity of anomaly samples makes the training of supervised models extremely challenging. Many knowledge distillation-based unsupervised detection methods have been proposed recently; however, these anomaly detection methods mostly use similar or identical symmetric structures to build teacher–student (T–S) models, which hinders the different expression of anomalies between T–S networks. To address this issue, this article proposes an asymmetric T–S feature pyramid matching network (ATSN). This network consists of a teacher network, a student network, and a lightweight feature fusion module (FFM). The teacher network is constructed with ResNet18, and the student network comprises a U-shaped network consisting of an encoder and a decoder. In the knowledge transfer process, the encoder acts as an intermediate transmitter of information, and the decoder reconstructs the information transmitted by the encoder into multiscale features. The U-shaped design can effectively improve the accuracy of knowledge transfer, thus increasing the differences in anomalous representation via T–S feature pyramid matching. Additionally, an FFM is used to fuse the correct information from the multiscale feature representation in the encoder and the decoder, thereby compensating for the loss of key information in the decoder and further increasing the difference in anomalous representation. Experiments on the MVTec AD dataset show that our proposed method surpasses the current state-of-the-art methods.

Unsupervised anomaly detection methods are widely used in industrial anomaly detection, primarily since there is a lack of anomalous data available for training. However, these methods still struggle to effectively detect and localize anomalies due to the diverse types of anomalies and frequent variations in their sizes and shapes. To address this challenge, we propose a new flow-based framework called Two-Hierarchy Normalizing Flow (THF) for anomaly detection and localization. THF consists of two main components: a masked flow and a constant flow. In the masked flow, we improve the traditional convolutional kernel by masking partial regions of the kernel to prevent excessive minimization of negative log-likelihood. This enhancement greatly helps in effectively detecting and localizing anomalies. The constant flow incorporates dual units and a volume-preserving flow module. The dual units consist of a characterization unit and a mixture unit. The characterization unit accurately captures both local and global feature information, while the mixture unit learns neighboring feature representations. Unlike traditional flow-based methods, the volume-preserving flow module maintains the volume of the probability distribution invariable, facilitating precise detection and localization of high-scoring anomalies. Extensive experiments conducted on multiple widely used anomaly detection datasets demonstrate that THF outperforms state-of-the-art methods in both anomaly detection and localization.