Dynamic Structural Health Monitoring With Filter Net De-noising and SSDBN Model Using Vibrational Data Article Swipe
Pradeep Kumar
,
M. Beena Mol
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20855/ijav.2023.28.31978
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20855/ijav.2023.28.31978
Measurement noise is always part of the vibration data in vibration-based structural health monitoring (SHM). However, it might be challenging to regulate the state in which civil constructions are tested in the field. Moreover, strong noise from a variety of sources, make damage detection inaccurate. Additionally, the precision of the current studies will eventually begin to saturate and possibly deteriorate. To overcome the mentioned limitations, this research proposed a deep learning framework for monitoring the structural health. First, Filter Net is suggested, which integrates neural network techniques for de-noising observed vibration signals with skip connection, dropout and shuffling. The next step was to propose a smooth sparse deep boltzmann network to detect structural degradation. A sparse penalty component built on the inverse function norm was added to improve performance. In addition, a greedy algorithm is used to perform unsupervised learning, which trains the first Restricted Boltzmann Machines (RBM) using the sampling data before using the first RBM's parameters to initialize the Deep belief networks (DBNs) first layer's parameters. Then, a BP network is used in a fine-tuning method to get the final systematic parameters. As a result, the RBM provides the Smooth Sparse Deep Boltzmann Network (SSDBN) with a decent starting value and therefore ensures higher performance.
Related Topics
Concepts
Restricted Boltzmann machine
Computer science
Dropout (neural networks)
Structural health monitoring
Artificial intelligence
Deep learning
Noise (video)
Boltzmann machine
Artificial neural network
Filter (signal processing)
Deep belief network
Compressed sensing
Pattern recognition (psychology)
Algorithm
Machine learning
Computer vision
Engineering
Structural engineering
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.20855/ijav.2023.28.31978
- https://doi.org/10.20855/ijav.2023.28.31978
- OA Status
- bronze
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387229795
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387229795Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20855/ijav.2023.28.31978Digital Object Identifier
- Title
-
Dynamic Structural Health Monitoring With Filter Net De-noising and SSDBN Model Using Vibrational DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-30Full publication date if available
- Authors
-
Pradeep Kumar, M. Beena MolList of authors in order
- Landing page
-
https://doi.org/10.20855/ijav.2023.28.31978Publisher landing page
- PDF URL
-
https://doi.org/10.20855/ijav.2023.28.31978Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.20855/ijav.2023.28.31978Direct OA link when available
- Concepts
-
Restricted Boltzmann machine, Computer science, Dropout (neural networks), Structural health monitoring, Artificial intelligence, Deep learning, Noise (video), Boltzmann machine, Artificial neural network, Filter (signal processing), Deep belief network, Compressed sensing, Pattern recognition (psychology), Algorithm, Machine learning, Computer vision, Engineering, Structural engineering, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4387229795 |
|---|---|
| doi | https://doi.org/10.20855/ijav.2023.28.31978 |
| ids.doi | https://doi.org/10.20855/ijav.2023.28.31978 |
| ids.openalex | https://openalex.org/W4387229795 |
| fwci | 0.0 |
| type | article |
| title | Dynamic Structural Health Monitoring With Filter Net De-noising and SSDBN Model Using Vibrational Data |
| biblio.issue | 3 |
| biblio.volume | 28 |
| biblio.last_page | 331 |
| biblio.first_page | 323 |
| topics[0].id | https://openalex.org/T10534 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9994000196456909 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2205 |
| topics[0].subfield.display_name | Civil and Structural Engineering |
| topics[0].display_name | Structural Health Monitoring Techniques |
| topics[1].id | https://openalex.org/T11606 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9976000189781189 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2205 |
| topics[1].subfield.display_name | Civil and Structural Engineering |
| topics[1].display_name | Infrastructure Maintenance and Monitoring |
| topics[2].id | https://openalex.org/T10662 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9855999946594238 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2211 |
| topics[2].subfield.display_name | Mechanics of Materials |
| topics[2].display_name | Ultrasonics and Acoustic Wave Propagation |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C199354608 |
| concepts[0].level | 3 |
| concepts[0].score | 0.7782354354858398 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q7316287 |
| concepts[0].display_name | Restricted Boltzmann machine |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6727344989776611 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2776145597 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6301900744438171 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q25339462 |
| concepts[2].display_name | Dropout (neural networks) |
| concepts[3].id | https://openalex.org/C2776247918 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6132955551147461 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1423713 |
| concepts[3].display_name | Structural health monitoring |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5976619720458984 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C108583219 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5831546783447266 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[5].display_name | Deep learning |
| concepts[6].id | https://openalex.org/C99498987 |
| concepts[6].level | 3 |
| concepts[6].score | 0.5361717939376831 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2210247 |
| concepts[6].display_name | Noise (video) |
| concepts[7].id | https://openalex.org/C192576344 |
| concepts[7].level | 3 |
| concepts[7].score | 0.5295644402503967 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q194706 |
| concepts[7].display_name | Boltzmann machine |
| concepts[8].id | https://openalex.org/C50644808 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4673490822315216 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[8].display_name | Artificial neural network |
| concepts[9].id | https://openalex.org/C106131492 |
| concepts[9].level | 2 |
| concepts[9].score | 0.46453696489334106 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q3072260 |
| concepts[9].display_name | Filter (signal processing) |
| concepts[10].id | https://openalex.org/C97385483 |
| concepts[10].level | 3 |
| concepts[10].score | 0.46080470085144043 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q16954980 |
| concepts[10].display_name | Deep belief network |
| concepts[11].id | https://openalex.org/C124851039 |
| concepts[11].level | 2 |
| concepts[11].score | 0.41790446639060974 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2665459 |
| concepts[11].display_name | Compressed sensing |
| concepts[12].id | https://openalex.org/C153180895 |
| concepts[12].level | 2 |
| concepts[12].score | 0.40061289072036743 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[12].display_name | Pattern recognition (psychology) |
| concepts[13].id | https://openalex.org/C11413529 |
| concepts[13].level | 1 |
| concepts[13].score | 0.3735361099243164 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[13].display_name | Algorithm |
| concepts[14].id | https://openalex.org/C119857082 |
| concepts[14].level | 1 |
| concepts[14].score | 0.35632503032684326 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[14].display_name | Machine learning |
| concepts[15].id | https://openalex.org/C31972630 |
| concepts[15].level | 1 |
| concepts[15].score | 0.16471225023269653 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[15].display_name | Computer vision |
| concepts[16].id | https://openalex.org/C127413603 |
| concepts[16].level | 0 |
| concepts[16].score | 0.15059679746627808 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[16].display_name | Engineering |
| concepts[17].id | https://openalex.org/C66938386 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q633538 |
| concepts[17].display_name | Structural engineering |
| concepts[18].id | https://openalex.org/C115961682 |
| concepts[18].level | 2 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[18].display_name | Image (mathematics) |
| keywords[0].id | https://openalex.org/keywords/restricted-boltzmann-machine |
| keywords[0].score | 0.7782354354858398 |
| keywords[0].display_name | Restricted Boltzmann machine |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6727344989776611 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/dropout |
| keywords[2].score | 0.6301900744438171 |
| keywords[2].display_name | Dropout (neural networks) |
| keywords[3].id | https://openalex.org/keywords/structural-health-monitoring |
| keywords[3].score | 0.6132955551147461 |
| keywords[3].display_name | Structural health monitoring |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5976619720458984 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/deep-learning |
| keywords[5].score | 0.5831546783447266 |
| keywords[5].display_name | Deep learning |
| keywords[6].id | https://openalex.org/keywords/noise |
| keywords[6].score | 0.5361717939376831 |
| keywords[6].display_name | Noise (video) |
| keywords[7].id | https://openalex.org/keywords/boltzmann-machine |
| keywords[7].score | 0.5295644402503967 |
| keywords[7].display_name | Boltzmann machine |
| keywords[8].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[8].score | 0.4673490822315216 |
| keywords[8].display_name | Artificial neural network |
| keywords[9].id | https://openalex.org/keywords/filter |
| keywords[9].score | 0.46453696489334106 |
| keywords[9].display_name | Filter (signal processing) |
| keywords[10].id | https://openalex.org/keywords/deep-belief-network |
| keywords[10].score | 0.46080470085144043 |
| keywords[10].display_name | Deep belief network |
| keywords[11].id | https://openalex.org/keywords/compressed-sensing |
| keywords[11].score | 0.41790446639060974 |
| keywords[11].display_name | Compressed sensing |
| keywords[12].id | https://openalex.org/keywords/pattern-recognition |
| keywords[12].score | 0.40061289072036743 |
| keywords[12].display_name | Pattern recognition (psychology) |
| keywords[13].id | https://openalex.org/keywords/algorithm |
| keywords[13].score | 0.3735361099243164 |
| keywords[13].display_name | Algorithm |
| keywords[14].id | https://openalex.org/keywords/machine-learning |
| keywords[14].score | 0.35632503032684326 |
| keywords[14].display_name | Machine learning |
| keywords[15].id | https://openalex.org/keywords/computer-vision |
| keywords[15].score | 0.16471225023269653 |
| keywords[15].display_name | Computer vision |
| keywords[16].id | https://openalex.org/keywords/engineering |
| keywords[16].score | 0.15059679746627808 |
| keywords[16].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.20855/ijav.2023.28.31978 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2489774306 |
| locations[0].source.issn | 1027-5851, 2415-1408 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1027-5851 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | The International Journal of Acoustics and Vibration |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | https://doi.org/10.20855/ijav.2023.28.31978 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | The International Journal of Acoustics and Vibration |
| locations[0].landing_page_url | http://doi.org/10.20855/ijav.2023.28.31978 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5100759277 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0802-9305 |
| authorships[0].author.display_name | Pradeep Kumar |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I96797292 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Civil, Noorul Islam Center for Higher Education, Kumaracoil, KK Dist. TN, India. |
| authorships[0].institutions[0].id | https://openalex.org/I96797292 |
| authorships[0].institutions[0].ror | https://ror.org/01y2gf490 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I96797292 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Noorul Islam University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Pradeep Kumar |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Civil, Noorul Islam Center for Higher Education, Kumaracoil, KK Dist. TN, India. |
| authorships[1].author.id | https://openalex.org/A5053989600 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | M. Beena Mol |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Civil, LBS College of Engineering, Kasargod, Kerala, India. |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | M. Beenamol |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Civil, LBS College of Engineering, Kasargod, Kerala, India. |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.20855/ijav.2023.28.31978 |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Dynamic Structural Health Monitoring With Filter Net De-noising and SSDBN Model Using Vibrational Data |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10534 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9994000196456909 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2205 |
| primary_topic.subfield.display_name | Civil and Structural Engineering |
| primary_topic.display_name | Structural Health Monitoring Techniques |
| related_works | https://openalex.org/W2287713958, https://openalex.org/W3121598771, https://openalex.org/W2892911634, https://openalex.org/W3082895349, https://openalex.org/W2955124940, https://openalex.org/W870929296, https://openalex.org/W2810292802, https://openalex.org/W2567271240, https://openalex.org/W2971852391, https://openalex.org/W3123344745 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.20855/ijav.2023.28.31978 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2489774306 |
| best_oa_location.source.issn | 1027-5851, 2415-1408 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1027-5851 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | The International Journal of Acoustics and Vibration |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://doi.org/10.20855/ijav.2023.28.31978 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | The International Journal of Acoustics and Vibration |
| best_oa_location.landing_page_url | http://doi.org/10.20855/ijav.2023.28.31978 |
| primary_location.id | doi:10.20855/ijav.2023.28.31978 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2489774306 |
| primary_location.source.issn | 1027-5851, 2415-1408 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1027-5851 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | The International Journal of Acoustics and Vibration |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | https://doi.org/10.20855/ijav.2023.28.31978 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | The International Journal of Acoustics and Vibration |
| primary_location.landing_page_url | http://doi.org/10.20855/ijav.2023.28.31978 |
| publication_date | 2023-09-30 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3136972621, https://openalex.org/W3115652015, https://openalex.org/W3183660758, https://openalex.org/W3096192165, https://openalex.org/W3092593798, https://openalex.org/W3033621544, https://openalex.org/W3135518078, https://openalex.org/W3207739109, https://openalex.org/W3214721285, https://openalex.org/W3175039757, https://openalex.org/W3197025840, https://openalex.org/W3092371304, https://openalex.org/W2884162743, https://openalex.org/W2946369859, https://openalex.org/W3042327718, https://openalex.org/W3119202309, https://openalex.org/W3159939058 |
| referenced_works_count | 17 |
| abstract_inverted_index.A | 114 |
| abstract_inverted_index.a | 37, 68, 104, 131, 169, 175, 185, 198 |
| abstract_inverted_index.As | 184 |
| abstract_inverted_index.BP | 170 |
| abstract_inverted_index.In | 129 |
| abstract_inverted_index.To | 60 |
| abstract_inverted_index.be | 18 |
| abstract_inverted_index.in | 9, 24, 30, 174 |
| abstract_inverted_index.is | 2, 80, 134, 172 |
| abstract_inverted_index.it | 16 |
| abstract_inverted_index.of | 5, 39, 48 |
| abstract_inverted_index.on | 119 |
| abstract_inverted_index.to | 20, 55, 102, 110, 126, 136, 158, 178 |
| abstract_inverted_index.Net | 79 |
| abstract_inverted_index.RBM | 188 |
| abstract_inverted_index.The | 98 |
| abstract_inverted_index.and | 57, 96, 202 |
| abstract_inverted_index.are | 28 |
| abstract_inverted_index.for | 72, 87 |
| abstract_inverted_index.get | 179 |
| abstract_inverted_index.the | 6, 22, 31, 46, 49, 62, 74, 120, 142, 149, 154, 160, 180, 187, 190 |
| abstract_inverted_index.was | 101, 124 |
| abstract_inverted_index.Deep | 161, 193 |
| abstract_inverted_index.data | 8, 151 |
| abstract_inverted_index.deep | 69, 107 |
| abstract_inverted_index.from | 36 |
| abstract_inverted_index.make | 41 |
| abstract_inverted_index.next | 99 |
| abstract_inverted_index.norm | 123 |
| abstract_inverted_index.part | 4 |
| abstract_inverted_index.skip | 93 |
| abstract_inverted_index.step | 100 |
| abstract_inverted_index.this | 65 |
| abstract_inverted_index.used | 135, 173 |
| abstract_inverted_index.will | 52 |
| abstract_inverted_index.with | 92, 197 |
| abstract_inverted_index.(RBM) | 147 |
| abstract_inverted_index.RBM's | 156 |
| abstract_inverted_index.Then, | 168 |
| abstract_inverted_index.added | 125 |
| abstract_inverted_index.begin | 54 |
| abstract_inverted_index.built | 118 |
| abstract_inverted_index.civil | 26 |
| abstract_inverted_index.final | 181 |
| abstract_inverted_index.first | 143, 155, 165 |
| abstract_inverted_index.might | 17 |
| abstract_inverted_index.noise | 1, 35 |
| abstract_inverted_index.state | 23 |
| abstract_inverted_index.using | 148, 153 |
| abstract_inverted_index.value | 201 |
| abstract_inverted_index.which | 25, 82, 140 |
| abstract_inverted_index.(DBNs) | 164 |
| abstract_inverted_index.(SHM). | 14 |
| abstract_inverted_index.Filter | 78 |
| abstract_inverted_index.First, | 77 |
| abstract_inverted_index.Smooth | 191 |
| abstract_inverted_index.Sparse | 192 |
| abstract_inverted_index.always | 3 |
| abstract_inverted_index.before | 152 |
| abstract_inverted_index.belief | 162 |
| abstract_inverted_index.damage | 42 |
| abstract_inverted_index.decent | 199 |
| abstract_inverted_index.detect | 111 |
| abstract_inverted_index.field. | 32 |
| abstract_inverted_index.greedy | 132 |
| abstract_inverted_index.health | 12 |
| abstract_inverted_index.higher | 205 |
| abstract_inverted_index.method | 177 |
| abstract_inverted_index.neural | 84 |
| abstract_inverted_index.smooth | 105 |
| abstract_inverted_index.sparse | 106, 115 |
| abstract_inverted_index.strong | 34 |
| abstract_inverted_index.tested | 29 |
| abstract_inverted_index.trains | 141 |
| abstract_inverted_index.(SSDBN) | 196 |
| abstract_inverted_index.Network | 195 |
| abstract_inverted_index.current | 50 |
| abstract_inverted_index.dropout | 95 |
| abstract_inverted_index.ensures | 204 |
| abstract_inverted_index.health. | 76 |
| abstract_inverted_index.improve | 127 |
| abstract_inverted_index.inverse | 121 |
| abstract_inverted_index.layer's | 166 |
| abstract_inverted_index.network | 85, 109, 171 |
| abstract_inverted_index.penalty | 116 |
| abstract_inverted_index.perform | 137 |
| abstract_inverted_index.propose | 103 |
| abstract_inverted_index.result, | 186 |
| abstract_inverted_index.signals | 91 |
| abstract_inverted_index.studies | 51 |
| abstract_inverted_index.variety | 38 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.Machines | 146 |
| abstract_inverted_index.function | 122 |
| abstract_inverted_index.learning | 70 |
| abstract_inverted_index.networks | 163 |
| abstract_inverted_index.observed | 89 |
| abstract_inverted_index.overcome | 61 |
| abstract_inverted_index.possibly | 58 |
| abstract_inverted_index.proposed | 67 |
| abstract_inverted_index.provides | 189 |
| abstract_inverted_index.regulate | 21 |
| abstract_inverted_index.research | 66 |
| abstract_inverted_index.sampling | 150 |
| abstract_inverted_index.saturate | 56 |
| abstract_inverted_index.sources, | 40 |
| abstract_inverted_index.starting | 200 |
| abstract_inverted_index.Boltzmann | 145, 194 |
| abstract_inverted_index.Moreover, | 33 |
| abstract_inverted_index.addition, | 130 |
| abstract_inverted_index.algorithm | 133 |
| abstract_inverted_index.boltzmann | 108 |
| abstract_inverted_index.component | 117 |
| abstract_inverted_index.detection | 43 |
| abstract_inverted_index.framework | 71 |
| abstract_inverted_index.learning, | 139 |
| abstract_inverted_index.mentioned | 63 |
| abstract_inverted_index.precision | 47 |
| abstract_inverted_index.therefore | 203 |
| abstract_inverted_index.vibration | 7, 90 |
| abstract_inverted_index.Restricted | 144 |
| abstract_inverted_index.de-noising | 88 |
| abstract_inverted_index.eventually | 53 |
| abstract_inverted_index.initialize | 159 |
| abstract_inverted_index.integrates | 83 |
| abstract_inverted_index.monitoring | 13, 73 |
| abstract_inverted_index.parameters | 157 |
| abstract_inverted_index.shuffling. | 97 |
| abstract_inverted_index.structural | 11, 75, 112 |
| abstract_inverted_index.suggested, | 81 |
| abstract_inverted_index.systematic | 182 |
| abstract_inverted_index.techniques | 86 |
| abstract_inverted_index.Measurement | 0 |
| abstract_inverted_index.challenging | 19 |
| abstract_inverted_index.connection, | 94 |
| abstract_inverted_index.fine-tuning | 176 |
| abstract_inverted_index.inaccurate. | 44 |
| abstract_inverted_index.parameters. | 167, 183 |
| abstract_inverted_index.degradation. | 113 |
| abstract_inverted_index.deteriorate. | 59 |
| abstract_inverted_index.limitations, | 64 |
| abstract_inverted_index.performance. | 128, 206 |
| abstract_inverted_index.unsupervised | 138 |
| abstract_inverted_index.Additionally, | 45 |
| abstract_inverted_index.constructions | 27 |
| abstract_inverted_index.vibration-based | 10 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.16886692 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |