CAMGnet: A self-learning classification model for univariate continuous time series signals via dynamic fusion of multi-dimensional cross-domain features

IF 6.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rui Zhang, Zheqi Rong, Zehua Dong
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引用次数: 0

Abstract

Improvements in classifying continuous, univariate time series signals with low value density have been hindered by insufficient feature detail, inadequate modeling of cross-domain dynamics, and inefficient parameter optimization. To address this, we propose a self-learning Cross-domain Adaptive Multi-dimensional Fusion Graph Neural Network (CAMGnet). We construct a Temporal Synergetic Pyramid (TSP) module to hierarchically extract time domain features from short-term to long-term trends. We develop an Entropy-adaptive Graph Construction (EAGC) mechanism to model cross-domain feature correlations. EAGC dynamically infers implicit feature-space/graph-topology relationships using self-adaptive adjacency matrices, minimizing reliance on prior knowledge and enabling autonomous, data-driven discovery of cross-domain interactions. Graph Convolutional Network–Graph Isomorphism Network(GCN-GIN) based hybrid encoding facilitates deep collaborative optimization between feature spaces and graph topologies. We also develop a Competitive Cross-attention (CCA) fusion mechanism to perform competitive multi-modal feature selection, enabling temporal/multi-domain graphs to capture cross-modal dependencies. Furthermore, we propose an Adaptive Perturbation Dynamic Escape Exploration–Exploitation Co-evolutionary Pool Strategy (AEP-IVYA) for improving the Ivy Optimization Algorithm.Adaptive perturbation balances exploration-exploitation by dynamically adjusting perturbation parameters. The dynamic escape strategy introduces a cross-dimensional transition mechanism to overcome local optima. The co-evolutionary pool uses a dual-path architecture to optimize global diversity and convergence. Evaluated on weld seam defect diagnosis and UCR datasets, AEP-IVYA improved hyperparameter configuration reliability. The self-optimized CAMGnet achieved 98.7 % accuracy in weld defect classification, surpassing traditional methods by 8.1 percentage points. On 25 UCR datasets, CAMGnet achieved 13 optimal and 6 suboptimal results, with a Wilcoxon-test average rank of 2.0, demonstrating significant generalization and domain applicability advantages over mainstream models.
CAMGnet:一种基于多维跨域特征动态融合的单变量连续时间序列信号自学习分类模型
由于特征细节不足、跨域动力学建模不足以及参数优化效率低下,对低值密度的连续单变量时间序列信号进行分类的改进受到了阻碍。为了解决这个问题,我们提出了一种自学习跨域自适应多维融合图神经网络(CAMGnet)。我们构建了一个时间协同金字塔(TSP)模块来分层提取从短期到长期趋势的时域特征。我们开发了一种熵自适应图构建(EAGC)机制来建模跨域特征关联。EAGC使用自适应邻接矩阵动态推断隐式特征空间/图拓扑关系,最大限度地减少对先验知识的依赖,并实现自主的、数据驱动的跨域交互发现。基于图卷积网络-图同构网络(GCN-GIN)的混合编码促进了特征空间和图拓扑之间的深度协同优化。我们还开发了一个竞争性交叉注意(CCA)融合机制来执行竞争性多模态特征选择,使时间/多域图能够捕获跨模态依赖关系。在此基础上,提出了一种自适应摄动动态逃逸探索-开发协同进化池策略(AEP-IVYA)来改进Ivy优化算法。自适应摄动通过动态调整摄动参数来平衡勘探和开采。动态逃逸策略引入了跨维转移机制来克服局部最优。协同进化池采用双路径架构,优化全局多样性和收敛性。通过对焊缝缺陷诊断和UCR数据集的评估,AEP-IVYA提高了超参数配置的可靠性。自优化CAMGnet对焊缝缺陷的分类准确率达到98.7 %,比传统方法提高8.1个百分点。在25个UCR数据集上,CAMGnet获得了13个最优结果和6个次优结果,Wilcoxon-test平均秩为2.0,与主流模型相比具有显著的泛化和领域适用性优势。
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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