Skin cancer has a high mortality rate, and early detection can greatly reduce patient mortality. Convolutional neural network (CNN) has been widely applied in the field of computer‐aided diagnosis. To improve the ability of convolutional neural networks to accurately classify skin lesions, we propose a multiscale feature fusion model for skin lesion classification. We use a two‐stream network, which are a densely connected network (DenseNet‐121) and improved visual geometry group network (VGG‐16). In the feature fusion module, we construct multireceptive fields to obtain multiscale pathological information and use generalized mean pooling (GeM pooling) to reduce the spatial dimensionality of lesion features. Finally, we built and tested a system with the developed skin lesion classification model. The experiments were performed on the dataset ISIC2018, which can achieve a good classification performance with a test accuracy of 91.24% and macroaverages of 95%.

Unmanned aerial vehicles (UAV), due to their flexibility and extensive coverage, have gradually become essential for substation inspections. Wireless mesh networks (WMN) provide a scalable and resilient network environment for UAVs, where each node can serve as either an access point or a relay point, thereby enhancing the network’s fault tolerance and overall resilience. However, the UAV-WMN combined system is complex and dynamic, facing the challenge of dynamically adjusting node transmission power to minimize end-to-end (E2E) delay while ensuring channel utilization efficiency. Real-time topology changes, high-dimensional state spaces, and large solution spaces make it difficult for traditional algorithms to guarantee convergence and stability. Generic reinforcement learning (RL) methods also struggle with stable convergence. This paper introduces a new Lyapunov function-based proof to address these issues and provide a stable condition for dynamic control strategies. Then, we developed a specialized neural network power controller and combined it with the MATD3 algorithm, effectively enhancing the system’s convergence and E2E performance. Simulation experiments validate the effectiveness of this method and demonstrate its superior performance in complex scenarios compared to other algorithms.

Person re-identification (ReID) is a crucial task in identifying pedestrians of interest across multiple surveillance camera views. ReID methods in recent years have shown that using global features or part features of the pedestrian is extremely effective, but many models do not have further design models to make more reasonable use of global and part features. A new model is proposed to use global features more rationally and extract more fine-grained part features. Specifically, our model captures global features by using a multi-scale attention global feature extraction module, and we design a new context-based adaptive part feature extraction module to consider continuity between different body parts of pedestrians. In addition, we have added additional enhancement modules to the model to enhance its performance. Experiments show that our model achieves competitive results on the Market1501, Dukemtmc-ReID, and MSMT17 datasets. The ablation experiments demonstrate the effectiveness of each module of our model. The code of our model is available at: https://github.com/davidtqw/Person-Re-Identification.

The adoption of renewable energy sources (RESs) has reshaped power systems, causing voltage violations and power imbalances. Electric vehicles (EVs) not only add to this burden but also offer potential active and reactive power support. Distribution system operators (DSOs) need effective distributed control to manage uncertainties and address privacy concerns. Existing research has mainly relied on model-based methods to solve active and reactive power control problems. However, these studies often neglect the privacy issues of EV owners and lack effective incentives to encourage users to participate in power regulation. This article proposes a supply-side and demand-side cooperative control framework based on a multiagent twin-delayed deep deterministic (MATD3) policy gradient. This framework uses graph convolutional networks (GCNs) as the state encoder and combines a regional–global joint supervision strategy to construct the safety control policy, while the federated-EVs control scheme is employed to protect user privacy and enhance user satisfaction. Simulation results show that the proposed algorithm demonstrates superior performance and advantages in peak voltage control, reducing peak power loss, meeting user demands, and improving user benefits.

The purpose of person re-identification (ReID) is to retrieve a person of interest from a set of images taken by multiple cameras. In the person ReID task, the use of global-local features and attention mechanisms to construct robust pedestrian features has been shown to be effective. However, in these methods, the models only focus on extracting pedestrian features with strong discrimination while ignoring the potential features, which are equally valuable and can play an important role in person ReID tasks. To extract these potential features, which are hidden by salient features, we propose a person ReID network based on weight-driven saliency hierarchical utilization. Three improvements are exploited to extract more comprehensive and diverse information of pedestrians. First, we use the non-local module to enhance the feature extraction ability of the model. To mine potential features, we use a saliency enhancement and suppression operation in the non-local module. Second, we employ a new multi-stage global feature fusion module to increase the diversity of features. Third, we use the multi-branch attention module to extract more fine-grained part features to improve the model performance. Extensive experiments show that our model achieves excellent performance on the Market-1501, duke multi-tracking multi-camera re-identification, and multi-scene multi-time person ReID datasets.