Yourong Chen

Accurate localization of human acupotints remains a fundamental challenge in the development of massage robot systems. While deep learning algorithms leveraging the classical Transformer architecture have improved recognition accuracy, a notable gap still exists between current performance and the robustness needed to handle complex lighting conditions and individual variability. To fill this gap, this paper focuses on the scenarios involving low-light conditions and subjects wearing close-fitting clothing. To address this issue, the paper proposes an acupoint localization algorithm tailored to the scenario by employing multimodal data fusion techniques that intergrate thermal imaging with depth map information. The algorithm is based on YOLOv8 and has been architecturally improved, referred to as YOLOv8-TCC. Specifically, a dual-branch parallel backbone network architecture is designed, incorporating the Channel Prior Convolutional Attention (CPCA) module to enhance multimodal feature representation. Next, a Cross-Modal Feature Fusion (CTF) module is introduced to facilitate information integration, while a dynamic template alignment approach is proposed to accommodate variations in body shape and posture. Experimental results show that the YOLOv8-TCC method achieves higher accuracy compared to the traditional single-modal model under varying lighting conditions. With the introduction of the CTF and CPCA modules, the average precision at Intersection over Union (AP@0.75) increased by 2.8% and 0.8%, respectively. Moreover, the success rate under the clinically meaningful 0.5 cun constraint (SR@0.5 cun) reached 95%. It is worth mentioning that our method remains effective when subjects wear light and close-fitting clothing. The proposed method exhibits strong generalizability and scalability, offering an accurate and efficient solution for practical applications.

To solve issues of frequent network topology changes to slow consensus speed and low security in consensus algorithms within heterogeneous dynamic Internet of Things (IoT) systems, we propose a fast consensus algorithm of large-scale heterogeneous dynamic IoT nodes for directed acyclic graph (DAG)-based blockchain (LSHD_DAG). First, an adaptive regional division method for heterogeneous dynamic nodes is proposed, which monitors nodes’ state and counts within each region, adjusting the boundaries of adjacent regions. Second, an event-triggered main–sub DAG chain design is proposed. When transaction information pertains to a single region, the transaction is processed as a local transaction to construct a regional DAG sub chain. If transaction information involves multiple regions, it is organized into a multiregional transaction set and incorporated into the global DAG main chain. Next, a node grade identification mechanism that balances state and reputation factors is proposed. This mechanism adopts the level of participation (LOF) algorithm to classify or dynamically update node grades by considering node state and reputation evaluation results, assigning them identity permissions. Finally, a weighted voting consensus based on the main–sub DAG chain is introduced. Nearby honest nodes with stable movement are selected as consensus nodes, which perform multiregional weighted voting consensus on transactions and transaction sets. This process concludes with two-way pegging with multisignature on the main–sub DAG, ensuring secure and rapid consensus for data in heterogeneous and dynamic IoT environments. The experimental results show that no matter how the number of nodes changes, LSHD_DAG can improve transaction throughput and reduce latency and communication overhead, outperforming the credit-delegated byzantine fault tolerance (CDBFT), DAG double (DAG-D), and Avalanche.