1D convolutional neural networks and applications: A survey
<p dir="ltr">During the last decade, Convolutional Neural Networks (CNNs) have become the <i>de facto</i> standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsa...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2021
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إضافة وسم
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| _version_ | 1864513559089643520 |
|---|---|
| author | Serkan Kiranyaz (3762058) |
| author2 | Onur Avci (14246801) Osama Abdeljaber (14246798) Turker Ince (14150610) Moncef Gabbouj (2276533) Daniel J. Inman (5509598) |
| author2_role | author author author author author |
| author_facet | Serkan Kiranyaz (3762058) Onur Avci (14246801) Osama Abdeljaber (14246798) Turker Ince (14150610) Moncef Gabbouj (2276533) Daniel J. Inman (5509598) |
| author_role | author |
| dc.creator.none.fl_str_mv | Serkan Kiranyaz (3762058) Onur Avci (14246801) Osama Abdeljaber (14246798) Turker Ince (14150610) Moncef Gabbouj (2276533) Daniel J. Inman (5509598) |
| dc.date.none.fl_str_mv | 2021-04-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.ymssp.2020.107398 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/1D_convolutional_neural_networks_and_applications_A_survey/24225616 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Communications engineering Information and computing sciences Computer vision and multimedia computation Machine learning Artificial Neural Networks Machine learning Deep learning Convolutional neural networks Structural health monitoring Condition monitoring Arrhythmia detection and identification Fault detection Structural damage detection |
| dc.title.none.fl_str_mv | 1D convolutional neural networks and applications: A survey |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">During the last decade, Convolutional Neural Networks (CNNs) have become the <i>de facto</i> standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and electrical motor fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publicly shared in a dedicated website. While there has not been a paper on the review of 1D CNNs and its applications in the literature, this paper fulfills this gap.</p><h2>Other Information</h2><p dir="ltr">Published in: Mechanical Systems and Signal Processing<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.ymssp.2020.107398" target="_blank">https://dx.doi.org/10.1016/j.ymssp.2020.107398</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_b836f2278e40aab4814157fdfdb6ce1c |
| identifier_str_mv | 10.1016/j.ymssp.2020.107398 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24225616 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | 1D convolutional neural networks and applications: A surveySerkan Kiranyaz (3762058)Onur Avci (14246801)Osama Abdeljaber (14246798)Turker Ince (14150610)Moncef Gabbouj (2276533)Daniel J. Inman (5509598)EngineeringCommunications engineeringInformation and computing sciencesComputer vision and multimedia computationMachine learningArtificial Neural NetworksMachine learningDeep learningConvolutional neural networksStructural health monitoringCondition monitoringArrhythmia detection and identificationFault detectionStructural damage detection<p dir="ltr">During the last decade, Convolutional Neural Networks (CNNs) have become the <i>de facto</i> standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and electrical motor fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publicly shared in a dedicated website. While there has not been a paper on the review of 1D CNNs and its applications in the literature, this paper fulfills this gap.</p><h2>Other Information</h2><p dir="ltr">Published in: Mechanical Systems and Signal Processing<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.ymssp.2020.107398" target="_blank">https://dx.doi.org/10.1016/j.ymssp.2020.107398</a></p>2021-04-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.ymssp.2020.107398https://figshare.com/articles/journal_contribution/1D_convolutional_neural_networks_and_applications_A_survey/24225616CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/242256162021-04-01T00:00:00Z |
| spellingShingle | 1D convolutional neural networks and applications: A survey Serkan Kiranyaz (3762058) Engineering Communications engineering Information and computing sciences Computer vision and multimedia computation Machine learning Artificial Neural Networks Machine learning Deep learning Convolutional neural networks Structural health monitoring Condition monitoring Arrhythmia detection and identification Fault detection Structural damage detection |
| status_str | publishedVersion |
| title | 1D convolutional neural networks and applications: A survey |
| title_full | 1D convolutional neural networks and applications: A survey |
| title_fullStr | 1D convolutional neural networks and applications: A survey |
| title_full_unstemmed | 1D convolutional neural networks and applications: A survey |
| title_short | 1D convolutional neural networks and applications: A survey |
| title_sort | 1D convolutional neural networks and applications: A survey |
| topic | Engineering Communications engineering Information and computing sciences Computer vision and multimedia computation Machine learning Artificial Neural Networks Machine learning Deep learning Convolutional neural networks Structural health monitoring Condition monitoring Arrhythmia detection and identification Fault detection Structural damage detection |