Convolutional Neural Networks (CNNs) have emerged as the dominant algorithms for object recognition in optical images. However, due to the complex imaging mechanism of Synthetic Aperture Radar (SAR), labeled data is scarce and annotation costs are high, making it difficult to meet the demand of CNNs for large-scale, high-quality training data. Therefore, leveraging simulated or optical images for model training, and employing Unsupervised Domain Adaptation (UDA) techniques to bridge the domain gap between simulated or optical images and real SAR images, has become a viable solution. Nevertheless, existing UDA methods typically assume that all features from the source domain are transferable, overlooking the fact that domain-specific features may induce negative transfer. Meanwhile, noise inherent in pseudo-labels can also degrade the efficacy of domain alignment. To address these challenges, this paper proposes a domain adaptation framework that integrates feature disentanglement with label noise suppression. The framework employs a feature disentanglement module to decompose sample representations into transferable domain-invariant features and domain-specific features. By aligning only the domain-invariant features, the risk of negative transfer is effectively mitigated. Furthermore, a Weighted Generalized Cross Entropy (WGCE) loss function is designed to suppress noise interference arising during the iterative pseudo-labeling process. Experimental results on cross-domain SAR target recognition tasks demonstrate that the proposed method significantly enhances both recognition accuracy and the robustness of domain adaptation.

The microservices architecture has been proposed to overcome the drawbacks of the traditional monolithic architecture. Scalability is one of the most attractive features of microservices. Scaling in the microservices architecture requires the scaling of specified services only, rather than the entire application. Scaling services can be achieved by deploying the same service multiple times on different physical machines. However, problems with load balancing may arise. Most existing solutions of microservices load balancing focus on individual tasks and ignore dependencies between these tasks. In the present paper, we propose TCLBM, a task chain-based load balancing algorithm for microservices. When an Application Programming Interface (API) request is received, TCLBM chooses target services for all tasks of this API call and achieves load balancing by evaluating the system resource usage of each service instance. TCLBM reduces the API response time by reducing data transmissions between physical machines. We use three heuristic algorithms, namely, Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Genetic Algorithm (GA), to implement TCLBM, and comparison results reveal that GA performs best. Our findings show that TCLBM achieves load balancing among service instances and reduces API response times by up to 10% compared with existing methods.