To address the susceptibility of existing MTS(multivariate time series)anomaly detection models to training set contamination and their limited ability to capture complex spatial-temporal correlations in MTS,a novel anomaly detection model based on spatial-temporal normalizing flow was proposed.This model employed the conditional normalizing flow to estimate the density of patterns in MTS,enabling robust anomaly detection even in the presence of contaminated training data.Additionally,a patched long short-term memory module was introduced to effectively learn long-term temporal dependencies within MTS,and a dynamic graph learning module based on attention mechanisms was devised to model the evolving correlations among different dimensions of MTS.Experimental results on three real-world cyber-physical system datasets demonstrate that the proposed model significantly outperforms state-of-the-art baselines in both detection accuracy and robustness.
The electromagnetic environment serves as the fundamental medium for radio wave propagation and profoundly influences the operational efficiency of global information infrastructure systems,including communications,navigation,and security.The research focused on electromagnetic environment sensing constellations developed within the commercial spaceflight sector and summarized the evolutionary development of electromagnetic environment sensing constellations through three major phases:the military electromagnetic reconnaissance-dominated era,the stage of global collaborative competition,and the phase of commercial and national co-leadership with coordinated development.Representative constellations and their functional characteristics were outlined for each phase.Current in-orbit constellations were categorized into four primary types-propagation environment sensing,broadband radio frequency signal sensing,typical operational signal sensing,and electromagnetic environment integrated sensing-and their functional characteristics,core capabilities,key technologies,and application scenarios were analyzed.Ongoing development trends are identified in commercial space-based electromagnetic environment sensing toward space-ground system integration,multi-satellite network collaboration,multimodal fusion,and real-time artificial intelligence integration.
With the continuous expansion of electromagnetic space applications and increasing operational complexity,spectrum resources show growing dynamism,diverse signal structures,and complex environments-posing serious challenges to traditional spectrum sensing and management.This paper reviewed key limitations in current electromagnetic spectrum cognition,such as static deployment,isolated knowledge,and closed-loop cognition,which restrict adaptability,autonomous decision-making,and collaborative perception.To address these challenges,the embodied intelligent cognition for electromagnetic spectrum space was proposed.This framework integrates distributed mobile embodied agents with programmable RF front-ends to enable multimodal perception and structured understanding of complex environments.It incorporated large models and agent-based mechanisms for task-driven reasoning and decision-making,using federated and continual learning to build an evolving knowledge system,granting"electromagnetic muscle memory".Finally,coordination mechanisms and key technologies across three core modules were analyzed,emphasizing embodied intelligence's role in real-time perception,proactive regulation,and continuous optimization in complex spectrum spaces,providing theoretical foundations and engineering insights for intelligent cognition and spectrum management across multi-scenario,multi-task environments.
To address the issue of insufficient recognition accuracy caused by the continuous emergence of novel modulated signals in dynamic scenarios,an incremental recognition method for modulated signals driven by features and semantics was proposed.A multi-dimensional feature representation of modulated signals was constructed.A class-incremental knowledge distillation learning mechanism was introduced into a parallel temporal convolutional network to mitigate feature drift under multi-task iteration in dynamic environments.Meanwhile,a modulated semantic map was built based on multi-dimensional features,and a nearest neighbor strategy was adopted to classify both new and existing modulated signals.Furthermore,a joint loss function was designed by integrating distance loss,Laplacian eigenvalue optimization loss,and knowledge distillation loss,which enhances intra-class compactness and inter-class separability of different modulated signals in the semantic space,thereby improving recognition accuracy.Experimental results demonstrate that the proposed method achieves an average recognition accuracy of 84.46％across multiple incremental tasks,outperforming conventional incremental recognition methods by 10％.It effectively enhances the capability of incremental recognition of signal modulation types in dynamic scenarios.
Graph combinatorial optimization problems are challenging to solve due to their NP-hard(non-deterministic polynomial-time hard)nature,and both traditional exact algorithms and existing machine learning methods have limitations.To address this,a unified solving framework based on a pre-training and fine-tuning paradigm was proposed,aiming to enhance the model's generalization capability and efficiency across various tasks.The framework reduced different problems to a unified form,constructing a consistent representation space,learning common knowledge through cross-problem pre-training,and adapting to different test distributions via multiple fine-tuning strategies.Experiments demonstrate that the proposed method achieves superior generalization performance and stable efficiency on multiple classical tasks,providing a viable path toward a general-purpose solver.
