Abstract
Via cross-correlation algorithms or synchronized acquisition of signals, the alignment of heterogeneous data with unknown semantic time shifts and intermittent semantic variations cannot be solved. The shift is caused by different data acquisition principles of sensors, different response discrimination principles using heterogeneous data, etc. Here, we report an unsupervised alignment architecture with a supervised learning model as the kernel to overcome the limitations of brain cognition, perception, and storage in aligning complex heterogeneous data. A set of data with a time shift is input into the kernel model of the architecture to predict the semantic labels, features or continuous values corresponding to another set of data. The time shift corresponding to the maximum testing accuracy or the minimum mean squared error is the alignment parameter for the two heterogeneous datasets. This architecture is expected to serve as a preprocessing step for semantic mining of signals and for information fusion.
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Data availability
The authors declare that the main data supporting the findings of this study are available within the article and its Supplementary Information files. Source Data are provided with this paper. All other relevant data are available from the corresponding author upon request. The datasets used for data alignment, as well as training and testing of the arc detection models have been deposited in the public repository (https://www.scidb.cn/en/s/iMnaii). Source data are provided with this paper.
Code availability
The synchronize triggering software and code for data generation, data processing, data alignment, and obtaining arc detection models have been deposited in the public repository53.
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Acknowledgements
This work is funded by the National Natural Science Foundation of China No.92266206 (Z.M.), No.52525510 (Z.M.), and No.52550005 (Z.M.), the National Key R&D Program of China No.2023YFF0716800 (Z.M.) and Jilin Province Science and Technology Development Plan No.20240302065GX (Z.M.) and No.20250101004JJ (Z.M.).
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C.L. and Z.M. conceived the research and designed the experiments. C.L. carried out the experiments, edited the code and analyzed the data. Y.Z. prepared the specimens. Z.Y.(Zaizheng Yang), J.L. Z.Y.(Zheng Yang) and J.X. checked the code. C.L. wrote and revised the manuscript with input from the other authors. S.N., Z.W. assisted C.L. in revising the manuscript, with input from the other authors. Z.M. examined and polished the manuscript. Z.M., H.Z. and L.R. supervised the research. All authors contributed to the interpretation and drafting of the paper.
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Li, C., Ma, Z., Zeng, Y. et al. Machine learning-driven alignment architecture of heterogeneous data with transient varying semantics. Nat Commun (2026). https://doi.org/10.1038/s41467-026-72377-w
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DOI: https://doi.org/10.1038/s41467-026-72377-w
