Signal Separation Based on Knowledge Representation
The separation of mixed signals typically requires appropriate prior assumptions, while traditional signal separation methods struggle to describe the differences in separation targets with significant features. This paper proposes a signal separation framework based on knowledge representation, whe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/3319 |
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| _version_ | 1849343385760956416 |
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| author | Cai Lu Xuyang Zou Jingjing Zong |
| author_facet | Cai Lu Xuyang Zou Jingjing Zong |
| author_sort | Cai Lu |
| collection | DOAJ |
| description | The separation of mixed signals typically requires appropriate prior assumptions, while traditional signal separation methods struggle to describe the differences in separation targets with significant features. This paper proposes a signal separation framework based on knowledge representation, where separation targets are represented with knowledge, guiding the branches of autoencoders for signal separation. Firstly, under the proposed knowledge representation framework, corresponding knowledge representations are obtained based on observed mixed signals. Secondly, the number of branches of the autoencoder is determined based on the number of separation target signals. Then, utilizing the results of knowledge representation, a branch autoencoder network is constructed, with branches guided by knowledge to achieve the separation of target sub-signals. Finally, a self-encoding network architecture is constructed with a combination of observation signal reconstruction error and knowledge-guided error constraints. Through numerical simulations on a layered velocity model, the Marmousi-II geological model, and the MNIST dataset, the proposed method is validated by comparing the numerical energy differences between predictions and ground truths, demonstrating its effectiveness under both limited and ample data conditions. |
| format | Article |
| id | doaj-art-3d53b87d50524c90b09a02f85b918310 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3d53b87d50524c90b09a02f85b9183102025-08-20T03:43:01ZengMDPI AGApplied Sciences2076-34172025-03-01156331910.3390/app15063319Signal Separation Based on Knowledge RepresentationCai Lu0Xuyang Zou1Jingjing Zong2School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe separation of mixed signals typically requires appropriate prior assumptions, while traditional signal separation methods struggle to describe the differences in separation targets with significant features. This paper proposes a signal separation framework based on knowledge representation, where separation targets are represented with knowledge, guiding the branches of autoencoders for signal separation. Firstly, under the proposed knowledge representation framework, corresponding knowledge representations are obtained based on observed mixed signals. Secondly, the number of branches of the autoencoder is determined based on the number of separation target signals. Then, utilizing the results of knowledge representation, a branch autoencoder network is constructed, with branches guided by knowledge to achieve the separation of target sub-signals. Finally, a self-encoding network architecture is constructed with a combination of observation signal reconstruction error and knowledge-guided error constraints. Through numerical simulations on a layered velocity model, the Marmousi-II geological model, and the MNIST dataset, the proposed method is validated by comparing the numerical energy differences between predictions and ground truths, demonstrating its effectiveness under both limited and ample data conditions.https://www.mdpi.com/2076-3417/15/6/3319signal separationknowledge representationdeep learning |
| spellingShingle | Cai Lu Xuyang Zou Jingjing Zong Signal Separation Based on Knowledge Representation Applied Sciences signal separation knowledge representation deep learning |
| title | Signal Separation Based on Knowledge Representation |
| title_full | Signal Separation Based on Knowledge Representation |
| title_fullStr | Signal Separation Based on Knowledge Representation |
| title_full_unstemmed | Signal Separation Based on Knowledge Representation |
| title_short | Signal Separation Based on Knowledge Representation |
| title_sort | signal separation based on knowledge representation |
| topic | signal separation knowledge representation deep learning |
| url | https://www.mdpi.com/2076-3417/15/6/3319 |
| work_keys_str_mv | AT cailu signalseparationbasedonknowledgerepresentation AT xuyangzou signalseparationbasedonknowledgerepresentation AT jingjingzong signalseparationbasedonknowledgerepresentation |