Research on noise prediction methods for sound barriers based on the integration of conditional generative adversarial networks and numerical methods Article Swipe
Qian Hu
,
Zhiwei Cui
,
Hongxue Liu
,
Senhao Zhong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fphy.2025.1539545
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fphy.2025.1539545
This study proposes a novel approach utilizing Conditional Generative Adversarial Networks (CGANs) to accelerate wideband acoustic state analysis, addressing the computational challenges in traditional Boundary Element Method (BEM) approaches. Traditional BEM-based acoustic analysis requires repeated computation of frequency-dependent system matrices across multiple frequencies, leading to significant computational costs. The asymmetry and full-rank nature of the BEM coefficient matrices further increase computational demands, particularly in large-scale problems. To overcome these challenges, this paper introduces a CGAN-based modeling framework that significantly reduces computation time while maintaining high predictive accuracy. The framework demonstrates exceptional adaptability when handling datasets with varying characteristics, effectively capturing underlying patterns within the data. Numerical experiments validate the effectiveness of the proposed method, highlighting its advantages in both accuracy and computational efficiency. This CGAN-based approach provides a promising alternative for efficient wideband acoustic analysis, significantly reducing computation time while ensuring accuracy.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fphy.2025.1539545
- https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1539545/pdf
- OA Status
- gold
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408988909
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4408988909Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fphy.2025.1539545Digital Object Identifier
- Title
-
Research on noise prediction methods for sound barriers based on the integration of conditional generative adversarial networks and numerical methodsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-31Full publication date if available
- Authors
-
Qian Hu, Zhiwei Cui, Hongxue Liu, Senhao ZhongList of authors in order
- Landing page
-
https://doi.org/10.3389/fphy.2025.1539545Publisher landing page
- PDF URL
-
https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1539545/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1539545/pdfDirect OA link when available
- Concepts
-
Adversarial system, Generative grammar, Computer science, Noise (video), Generative adversarial network, Sound (geography), Artificial intelligence, Acoustics, Deep learning, Physics, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4408988909 |
|---|---|
| doi | https://doi.org/10.3389/fphy.2025.1539545 |
| ids.doi | https://doi.org/10.3389/fphy.2025.1539545 |
| ids.openalex | https://openalex.org/W4408988909 |
| fwci | 0.0 |
| type | article |
| title | Research on noise prediction methods for sound barriers based on the integration of conditional generative adversarial networks and numerical methods |
| biblio.issue | |
| biblio.volume | 13 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10822 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9972000122070312 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2204 |
| topics[0].subfield.display_name | Biomedical Engineering |
| topics[0].display_name | Acoustic Wave Phenomena Research |
| topics[1].id | https://openalex.org/T10688 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9918000102043152 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Image and Signal Denoising Methods |
| topics[2].id | https://openalex.org/T11008 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.987500011920929 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2202 |
| topics[2].subfield.display_name | Aerospace Engineering |
| topics[2].display_name | Aerodynamics and Acoustics in Jet Flows |
| is_xpac | False |
| apc_list.value | 2490 |
| apc_list.currency | USD |
| apc_list.value_usd | 2490 |
| apc_paid.value | 2490 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2490 |
| concepts[0].id | https://openalex.org/C37736160 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7856895327568054 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1801315 |
| concepts[0].display_name | Adversarial system |
| concepts[1].id | https://openalex.org/C39890363 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7564508318901062 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q36108 |
| concepts[1].display_name | Generative grammar |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5978596806526184 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C99498987 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5642842054367065 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2210247 |
| concepts[3].display_name | Noise (video) |
| concepts[4].id | https://openalex.org/C2988773926 |
| concepts[4].level | 3 |
| concepts[4].score | 0.516061007976532 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q25104379 |
| concepts[4].display_name | Generative adversarial network |
| concepts[5].id | https://openalex.org/C203718221 |
| concepts[5].level | 2 |
| concepts[5].score | 0.49205654859542847 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q491713 |
| concepts[5].display_name | Sound (geography) |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3613244891166687 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C24890656 |
| concepts[7].level | 1 |
| concepts[7].score | 0.24107056856155396 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q82811 |
| concepts[7].display_name | Acoustics |
| concepts[8].id | https://openalex.org/C108583219 |
| concepts[8].level | 2 |
| concepts[8].score | 0.14928144216537476 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[8].display_name | Deep learning |
| concepts[9].id | https://openalex.org/C121332964 |
| concepts[9].level | 0 |
| concepts[9].score | 0.055558621883392334 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[9].display_name | Physics |
| concepts[10].id | https://openalex.org/C115961682 |
| concepts[10].level | 2 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[10].display_name | Image (mathematics) |
| keywords[0].id | https://openalex.org/keywords/adversarial-system |
| keywords[0].score | 0.7856895327568054 |
| keywords[0].display_name | Adversarial system |
| keywords[1].id | https://openalex.org/keywords/generative-grammar |
| keywords[1].score | 0.7564508318901062 |
| keywords[1].display_name | Generative grammar |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5978596806526184 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/noise |
| keywords[3].score | 0.5642842054367065 |
| keywords[3].display_name | Noise (video) |
| keywords[4].id | https://openalex.org/keywords/generative-adversarial-network |
| keywords[4].score | 0.516061007976532 |
| keywords[4].display_name | Generative adversarial network |
| keywords[5].id | https://openalex.org/keywords/sound |
| keywords[5].score | 0.49205654859542847 |
| keywords[5].display_name | Sound (geography) |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.3613244891166687 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/acoustics |
| keywords[7].score | 0.24107056856155396 |
| keywords[7].display_name | Acoustics |
| keywords[8].id | https://openalex.org/keywords/deep-learning |
| keywords[8].score | 0.14928144216537476 |
| keywords[8].display_name | Deep learning |
| keywords[9].id | https://openalex.org/keywords/physics |
| keywords[9].score | 0.055558621883392334 |
| keywords[9].display_name | Physics |
| language | en |
| locations[0].id | doi:10.3389/fphy.2025.1539545 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2596760093 |
| locations[0].source.issn | 2296-424X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2296-424X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Frontiers in Physics |
| locations[0].source.host_organization | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_name | Frontiers Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_lineage_names | Frontiers Media |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1539545/pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Frontiers in Physics |
| locations[0].landing_page_url | https://doi.org/10.3389/fphy.2025.1539545 |
| locations[1].id | pmh:oai:doaj.org/article:d3a9f2a6b1ae41dca358c749f0663d9b |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Frontiers in Physics, Vol 13 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/d3a9f2a6b1ae41dca358c749f0663d9b |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5100620325 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7831-899X |
| authorships[0].author.display_name | Qian Hu |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Qian Hu |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5002719553 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8338-4270 |
| authorships[1].author.display_name | Zhiwei Cui |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ziyu Cui |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5101670165 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3323-4642 |
| authorships[2].author.display_name | Hongxue Liu |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hongxue Liu |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5111302072 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Senhao Zhong |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Senhao Zhong |
| authorships[3].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1539545/pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Research on noise prediction methods for sound barriers based on the integration of conditional generative adversarial networks and numerical methods |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10822 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9972000122070312 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2204 |
| primary_topic.subfield.display_name | Biomedical Engineering |
| primary_topic.display_name | Acoustic Wave Phenomena Research |
| related_works | https://openalex.org/W2502115930, https://openalex.org/W2482350142, https://openalex.org/W4246396837, https://openalex.org/W2888032422, https://openalex.org/W2996316059, https://openalex.org/W4377980832, https://openalex.org/W2897769091, https://openalex.org/W2845413374, https://openalex.org/W3005996785, https://openalex.org/W4235873501 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3389/fphy.2025.1539545 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2596760093 |
| best_oa_location.source.issn | 2296-424X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2296-424X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Frontiers in Physics |
| best_oa_location.source.host_organization | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_name | Frontiers Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_lineage_names | Frontiers Media |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1539545/pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Frontiers in Physics |
| best_oa_location.landing_page_url | https://doi.org/10.3389/fphy.2025.1539545 |
| primary_location.id | doi:10.3389/fphy.2025.1539545 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2596760093 |
| primary_location.source.issn | 2296-424X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2296-424X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Frontiers in Physics |
| primary_location.source.host_organization | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_name | Frontiers Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_lineage_names | Frontiers Media |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1539545/pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Frontiers in Physics |
| primary_location.landing_page_url | https://doi.org/10.3389/fphy.2025.1539545 |
| publication_date | 2025-03-31 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4400130343, https://openalex.org/W4399820711, https://openalex.org/W4300963280, https://openalex.org/W4396813727, https://openalex.org/W4229040203, https://openalex.org/W4396499234, https://openalex.org/W4406748860, https://openalex.org/W4404506016, https://openalex.org/W4283014470, https://openalex.org/W4283689884, https://openalex.org/W2961344315, https://openalex.org/W4405027716, https://openalex.org/W2152787993, https://openalex.org/W2008725251, https://openalex.org/W1500158105, https://openalex.org/W2012975368, https://openalex.org/W4382810435, https://openalex.org/W4307364051, https://openalex.org/W4311422944, https://openalex.org/W4403712584, https://openalex.org/W4406539888, https://openalex.org/W4406272443, https://openalex.org/W2132388118, https://openalex.org/W4390683274, https://openalex.org/W4404386654, https://openalex.org/W4403237407, https://openalex.org/W4220764579, https://openalex.org/W35758832, https://openalex.org/W2998847955, https://openalex.org/W1978018437, https://openalex.org/W2024288291, https://openalex.org/W3001165133, https://openalex.org/W2009977399, https://openalex.org/W2886433320, https://openalex.org/W6841717203, https://openalex.org/W2055689370, https://openalex.org/W4386136732, https://openalex.org/W6730042731, https://openalex.org/W4322775126, https://openalex.org/W4384699974, https://openalex.org/W3006405344, https://openalex.org/W2753246113, https://openalex.org/W4388883835, https://openalex.org/W4206622094, https://openalex.org/W4313594846, https://openalex.org/W4225400696, https://openalex.org/W4391719889, https://openalex.org/W4200551307, https://openalex.org/W4385489968, https://openalex.org/W4392528287, https://openalex.org/W4396216980, https://openalex.org/W3088611441, https://openalex.org/W2076450467, https://openalex.org/W2125389028, https://openalex.org/W2963238274, https://openalex.org/W4291961049 |
| referenced_works_count | 56 |
| abstract_inverted_index.a | 3, 73, 127 |
| abstract_inverted_index.To | 66 |
| abstract_inverted_index.in | 22, 63, 117 |
| abstract_inverted_index.of | 36, 53, 110 |
| abstract_inverted_index.to | 12, 44 |
| abstract_inverted_index.BEM | 55 |
| abstract_inverted_index.The | 48, 87 |
| abstract_inverted_index.and | 50, 120 |
| abstract_inverted_index.for | 130 |
| abstract_inverted_index.its | 115 |
| abstract_inverted_index.the | 19, 54, 103, 108, 111 |
| abstract_inverted_index.This | 0, 123 |
| abstract_inverted_index.both | 118 |
| abstract_inverted_index.high | 84 |
| abstract_inverted_index.that | 77 |
| abstract_inverted_index.this | 70 |
| abstract_inverted_index.time | 81, 138 |
| abstract_inverted_index.when | 92 |
| abstract_inverted_index.with | 95 |
| abstract_inverted_index.(BEM) | 27 |
| abstract_inverted_index.data. | 104 |
| abstract_inverted_index.novel | 4 |
| abstract_inverted_index.paper | 71 |
| abstract_inverted_index.state | 16 |
| abstract_inverted_index.study | 1 |
| abstract_inverted_index.these | 68 |
| abstract_inverted_index.while | 82, 139 |
| abstract_inverted_index.Method | 26 |
| abstract_inverted_index.across | 40 |
| abstract_inverted_index.costs. | 47 |
| abstract_inverted_index.nature | 52 |
| abstract_inverted_index.system | 38 |
| abstract_inverted_index.within | 102 |
| abstract_inverted_index.(CGANs) | 11 |
| abstract_inverted_index.Element | 25 |
| abstract_inverted_index.further | 58 |
| abstract_inverted_index.leading | 43 |
| abstract_inverted_index.method, | 113 |
| abstract_inverted_index.reduces | 79 |
| abstract_inverted_index.varying | 96 |
| abstract_inverted_index.Boundary | 24 |
| abstract_inverted_index.Networks | 10 |
| abstract_inverted_index.accuracy | 119 |
| abstract_inverted_index.acoustic | 15, 31, 133 |
| abstract_inverted_index.analysis | 32 |
| abstract_inverted_index.approach | 5, 125 |
| abstract_inverted_index.datasets | 94 |
| abstract_inverted_index.demands, | 61 |
| abstract_inverted_index.ensuring | 140 |
| abstract_inverted_index.handling | 93 |
| abstract_inverted_index.increase | 59 |
| abstract_inverted_index.matrices | 39, 57 |
| abstract_inverted_index.modeling | 75 |
| abstract_inverted_index.multiple | 41 |
| abstract_inverted_index.overcome | 67 |
| abstract_inverted_index.patterns | 101 |
| abstract_inverted_index.proposed | 112 |
| abstract_inverted_index.proposes | 2 |
| abstract_inverted_index.provides | 126 |
| abstract_inverted_index.reducing | 136 |
| abstract_inverted_index.repeated | 34 |
| abstract_inverted_index.requires | 33 |
| abstract_inverted_index.validate | 107 |
| abstract_inverted_index.wideband | 14, 132 |
| abstract_inverted_index.BEM-based | 30 |
| abstract_inverted_index.Numerical | 105 |
| abstract_inverted_index.accuracy. | 86, 141 |
| abstract_inverted_index.analysis, | 17, 134 |
| abstract_inverted_index.asymmetry | 49 |
| abstract_inverted_index.capturing | 99 |
| abstract_inverted_index.efficient | 131 |
| abstract_inverted_index.framework | 76, 88 |
| abstract_inverted_index.full-rank | 51 |
| abstract_inverted_index.problems. | 65 |
| abstract_inverted_index.promising | 128 |
| abstract_inverted_index.utilizing | 6 |
| abstract_inverted_index.CGAN-based | 74, 124 |
| abstract_inverted_index.Generative | 8 |
| abstract_inverted_index.accelerate | 13 |
| abstract_inverted_index.addressing | 18 |
| abstract_inverted_index.advantages | 116 |
| abstract_inverted_index.challenges | 21 |
| abstract_inverted_index.introduces | 72 |
| abstract_inverted_index.predictive | 85 |
| abstract_inverted_index.underlying | 100 |
| abstract_inverted_index.Adversarial | 9 |
| abstract_inverted_index.Conditional | 7 |
| abstract_inverted_index.Traditional | 29 |
| abstract_inverted_index.alternative | 129 |
| abstract_inverted_index.approaches. | 28 |
| abstract_inverted_index.challenges, | 69 |
| abstract_inverted_index.coefficient | 56 |
| abstract_inverted_index.computation | 35, 80, 137 |
| abstract_inverted_index.effectively | 98 |
| abstract_inverted_index.efficiency. | 122 |
| abstract_inverted_index.exceptional | 90 |
| abstract_inverted_index.experiments | 106 |
| abstract_inverted_index.large-scale | 64 |
| abstract_inverted_index.maintaining | 83 |
| abstract_inverted_index.significant | 45 |
| abstract_inverted_index.traditional | 23 |
| abstract_inverted_index.adaptability | 91 |
| abstract_inverted_index.demonstrates | 89 |
| abstract_inverted_index.frequencies, | 42 |
| abstract_inverted_index.highlighting | 114 |
| abstract_inverted_index.particularly | 62 |
| abstract_inverted_index.computational | 20, 46, 60, 121 |
| abstract_inverted_index.effectiveness | 109 |
| abstract_inverted_index.significantly | 78, 135 |
| abstract_inverted_index.characteristics, | 97 |
| abstract_inverted_index.frequency-dependent | 37 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.10504658 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |