Learning the Dynamics of Future Marine Microgrids Using Temporal Convolutional Neural Network Article Swipe
Xiaoyu Ge
,
Ali Hosseinipour
,
Saskia A. Putri
,
Faegheh Moazeni
,
Javad Khazaei
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.03850
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.03850
Medium-voltage direct-current (MVDC) ship-board microgrids (SMGs) are the state-of-the-art architecture for onboard power distribution in navy. These systems are considered to be highly dynamic due to high penetration of power electronic converters and volatile load patterns such as pulsed-power load (PPL) and propulsion motors demand variation. Obtaining the dynamic model of an MVDC SMG is a challenging task due to the confidentiality of system components models and uncertainty in the dynamic models through time. In this paper, a dynamic identification framework based on a temporal convolutional neural network (TCN) is developed to learn the system dynamics from measurement data. Different kinds of testing scenarios are implemented, and the testing results show that this approach achieves an exceptional performance and high generalization ability, thus holding substantial promise for development of advanced data-driven control strategies and stability prediction of the system.
Related Topics
Concepts
Computer science
Electric power system
Convolutional neural network
Control engineering
Converters
Propulsion
Generalization
System dynamics
Power (physics)
Artificial intelligence
Engineering
Voltage
Electrical engineering
Aerospace engineering
Quantum mechanics
Mathematics
Mathematical analysis
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.03850
- https://arxiv.org/pdf/2312.03850
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389500818
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4389500818Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2312.03850Digital Object Identifier
- Title
-
Learning the Dynamics of Future Marine Microgrids Using Temporal Convolutional Neural NetworkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-06Full publication date if available
- Authors
-
Xiaoyu Ge, Ali Hosseinipour, Saskia A. Putri, Faegheh Moazeni, Javad KhazaeiList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.03850Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.03850Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.03850Direct OA link when available
- Concepts
-
Computer science, Electric power system, Convolutional neural network, Control engineering, Converters, Propulsion, Generalization, System dynamics, Power (physics), Artificial intelligence, Engineering, Voltage, Electrical engineering, Aerospace engineering, Quantum mechanics, Mathematics, Mathematical analysis, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4389500818 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2312.03850 |
| ids.doi | https://doi.org/10.48550/arxiv.2312.03850 |
| ids.openalex | https://openalex.org/W4389500818 |
| fwci | |
| type | preprint |
| title | Learning the Dynamics of Future Marine Microgrids Using Temporal Convolutional Neural Network |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12126 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9793999791145325 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2305 |
| topics[0].subfield.display_name | Environmental Engineering |
| topics[0].display_name | Maritime Transport Emissions and Efficiency |
| topics[1].id | https://openalex.org/T10220 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9460999965667725 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2207 |
| topics[1].subfield.display_name | Control and Systems Engineering |
| topics[1].display_name | Machine Fault Diagnosis Techniques |
| topics[2].id | https://openalex.org/T11052 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9398000240325928 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2208 |
| topics[2].subfield.display_name | Electrical and Electronic Engineering |
| topics[2].display_name | Energy Load and Power Forecasting |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.597179651260376 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C89227174 |
| concepts[1].level | 3 |
| concepts[1].score | 0.5178983211517334 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2388981 |
| concepts[1].display_name | Electric power system |
| concepts[2].id | https://openalex.org/C81363708 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5130347013473511 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[2].display_name | Convolutional neural network |
| concepts[3].id | https://openalex.org/C133731056 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4658893644809723 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q4917288 |
| concepts[3].display_name | Control engineering |
| concepts[4].id | https://openalex.org/C2778422915 |
| concepts[4].level | 3 |
| concepts[4].score | 0.4640798568725586 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q10302051 |
| concepts[4].display_name | Converters |
| concepts[5].id | https://openalex.org/C1034443 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4600616991519928 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2583685 |
| concepts[5].display_name | Propulsion |
| concepts[6].id | https://openalex.org/C177148314 |
| concepts[6].level | 2 |
| concepts[6].score | 0.45725032687187195 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q170084 |
| concepts[6].display_name | Generalization |
| concepts[7].id | https://openalex.org/C77405623 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4164232611656189 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q598451 |
| concepts[7].display_name | System dynamics |
| concepts[8].id | https://openalex.org/C163258240 |
| concepts[8].level | 2 |
| concepts[8].score | 0.3403158187866211 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q25342 |
| concepts[8].display_name | Power (physics) |
| concepts[9].id | https://openalex.org/C154945302 |
| concepts[9].level | 1 |
| concepts[9].score | 0.31253695487976074 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[9].display_name | Artificial intelligence |
| concepts[10].id | https://openalex.org/C127413603 |
| concepts[10].level | 0 |
| concepts[10].score | 0.3012681007385254 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[10].display_name | Engineering |
| concepts[11].id | https://openalex.org/C165801399 |
| concepts[11].level | 2 |
| concepts[11].score | 0.2982614040374756 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q25428 |
| concepts[11].display_name | Voltage |
| concepts[12].id | https://openalex.org/C119599485 |
| concepts[12].level | 1 |
| concepts[12].score | 0.14980322122573853 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[12].display_name | Electrical engineering |
| concepts[13].id | https://openalex.org/C146978453 |
| concepts[13].level | 1 |
| concepts[13].score | 0.10308894515037537 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[13].display_name | Aerospace engineering |
| concepts[14].id | https://openalex.org/C62520636 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[14].display_name | Quantum mechanics |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C134306372 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[16].display_name | Mathematical analysis |
| concepts[17].id | https://openalex.org/C121332964 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[17].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.597179651260376 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/electric-power-system |
| keywords[1].score | 0.5178983211517334 |
| keywords[1].display_name | Electric power system |
| keywords[2].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[2].score | 0.5130347013473511 |
| keywords[2].display_name | Convolutional neural network |
| keywords[3].id | https://openalex.org/keywords/control-engineering |
| keywords[3].score | 0.4658893644809723 |
| keywords[3].display_name | Control engineering |
| keywords[4].id | https://openalex.org/keywords/converters |
| keywords[4].score | 0.4640798568725586 |
| keywords[4].display_name | Converters |
| keywords[5].id | https://openalex.org/keywords/propulsion |
| keywords[5].score | 0.4600616991519928 |
| keywords[5].display_name | Propulsion |
| keywords[6].id | https://openalex.org/keywords/generalization |
| keywords[6].score | 0.45725032687187195 |
| keywords[6].display_name | Generalization |
| keywords[7].id | https://openalex.org/keywords/system-dynamics |
| keywords[7].score | 0.4164232611656189 |
| keywords[7].display_name | System dynamics |
| keywords[8].id | https://openalex.org/keywords/power |
| keywords[8].score | 0.3403158187866211 |
| keywords[8].display_name | Power (physics) |
| keywords[9].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[9].score | 0.31253695487976074 |
| keywords[9].display_name | Artificial intelligence |
| keywords[10].id | https://openalex.org/keywords/engineering |
| keywords[10].score | 0.3012681007385254 |
| keywords[10].display_name | Engineering |
| keywords[11].id | https://openalex.org/keywords/voltage |
| keywords[11].score | 0.2982614040374756 |
| keywords[11].display_name | Voltage |
| keywords[12].id | https://openalex.org/keywords/electrical-engineering |
| keywords[12].score | 0.14980322122573853 |
| keywords[12].display_name | Electrical engineering |
| keywords[13].id | https://openalex.org/keywords/aerospace-engineering |
| keywords[13].score | 0.10308894515037537 |
| keywords[13].display_name | Aerospace engineering |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2312.03850 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2312.03850 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2312.03850 |
| locations[1].id | doi:10.48550/arxiv.2312.03850 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2312.03850 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5101569204 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2360-4945 |
| authorships[0].author.display_name | Xiaoyu Ge |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ge, Xiaoyu |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5082103973 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8571-128X |
| authorships[1].author.display_name | Ali Hosseinipour |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Hosseinipour, Ali |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5066971136 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0199-0395 |
| authorships[2].author.display_name | Saskia A. Putri |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Putri, Saskia |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5007059001 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3178-2055 |
| authorships[3].author.display_name | Faegheh Moazeni |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Moazeni, Faegheh |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5041147963 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-5595-7641 |
| authorships[4].author.display_name | Javad Khazaei |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Khazaei, Javad |
| authorships[4].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2312.03850 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Learning the Dynamics of Future Marine Microgrids Using Temporal Convolutional Neural Network |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12126 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9793999791145325 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2305 |
| primary_topic.subfield.display_name | Environmental Engineering |
| primary_topic.display_name | Maritime Transport Emissions and Efficiency |
| related_works | https://openalex.org/W631083485, https://openalex.org/W4313452936, https://openalex.org/W2097026685, https://openalex.org/W2480068220, https://openalex.org/W2070883797, https://openalex.org/W2102542442, https://openalex.org/W1986220761, https://openalex.org/W2042495646, https://openalex.org/W4386859288, https://openalex.org/W2378383478 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2312.03850 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2312.03850 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2312.03850 |
| primary_location.id | pmh:oai:arXiv.org:2312.03850 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2312.03850 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2312.03850 |
| publication_date | 2023-12-06 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 55, 77, 83 |
| abstract_inverted_index.In | 74 |
| abstract_inverted_index.an | 51, 115 |
| abstract_inverted_index.as | 37 |
| abstract_inverted_index.be | 21 |
| abstract_inverted_index.in | 14, 68 |
| abstract_inverted_index.is | 54, 89 |
| abstract_inverted_index.of | 28, 50, 62, 101, 128, 136 |
| abstract_inverted_index.on | 82 |
| abstract_inverted_index.to | 20, 25, 59, 91 |
| abstract_inverted_index.SMG | 53 |
| abstract_inverted_index.and | 32, 41, 66, 106, 118, 133 |
| abstract_inverted_index.are | 6, 18, 104 |
| abstract_inverted_index.due | 24, 58 |
| abstract_inverted_index.for | 10, 126 |
| abstract_inverted_index.the | 7, 47, 60, 69, 93, 107, 137 |
| abstract_inverted_index.MVDC | 52 |
| abstract_inverted_index.from | 96 |
| abstract_inverted_index.high | 26, 119 |
| abstract_inverted_index.load | 34, 39 |
| abstract_inverted_index.show | 110 |
| abstract_inverted_index.such | 36 |
| abstract_inverted_index.task | 57 |
| abstract_inverted_index.that | 111 |
| abstract_inverted_index.this | 75, 112 |
| abstract_inverted_index.thus | 122 |
| abstract_inverted_index.(PPL) | 40 |
| abstract_inverted_index.(TCN) | 88 |
| abstract_inverted_index.These | 16 |
| abstract_inverted_index.based | 81 |
| abstract_inverted_index.data. | 98 |
| abstract_inverted_index.kinds | 100 |
| abstract_inverted_index.learn | 92 |
| abstract_inverted_index.model | 49 |
| abstract_inverted_index.navy. | 15 |
| abstract_inverted_index.power | 12, 29 |
| abstract_inverted_index.time. | 73 |
| abstract_inverted_index.(MVDC) | 2 |
| abstract_inverted_index.(SMGs) | 5 |
| abstract_inverted_index.demand | 44 |
| abstract_inverted_index.highly | 22 |
| abstract_inverted_index.models | 65, 71 |
| abstract_inverted_index.motors | 43 |
| abstract_inverted_index.neural | 86 |
| abstract_inverted_index.paper, | 76 |
| abstract_inverted_index.system | 63, 94 |
| abstract_inverted_index.control | 131 |
| abstract_inverted_index.dynamic | 23, 48, 70, 78 |
| abstract_inverted_index.holding | 123 |
| abstract_inverted_index.network | 87 |
| abstract_inverted_index.onboard | 11 |
| abstract_inverted_index.promise | 125 |
| abstract_inverted_index.results | 109 |
| abstract_inverted_index.system. | 138 |
| abstract_inverted_index.systems | 17 |
| abstract_inverted_index.testing | 102, 108 |
| abstract_inverted_index.through | 72 |
| abstract_inverted_index.ability, | 121 |
| abstract_inverted_index.achieves | 114 |
| abstract_inverted_index.advanced | 129 |
| abstract_inverted_index.approach | 113 |
| abstract_inverted_index.dynamics | 95 |
| abstract_inverted_index.patterns | 35 |
| abstract_inverted_index.temporal | 84 |
| abstract_inverted_index.volatile | 33 |
| abstract_inverted_index.Different | 99 |
| abstract_inverted_index.Obtaining | 46 |
| abstract_inverted_index.developed | 90 |
| abstract_inverted_index.framework | 80 |
| abstract_inverted_index.scenarios | 103 |
| abstract_inverted_index.stability | 134 |
| abstract_inverted_index.components | 64 |
| abstract_inverted_index.considered | 19 |
| abstract_inverted_index.converters | 31 |
| abstract_inverted_index.electronic | 30 |
| abstract_inverted_index.microgrids | 4 |
| abstract_inverted_index.prediction | 135 |
| abstract_inverted_index.propulsion | 42 |
| abstract_inverted_index.ship-board | 3 |
| abstract_inverted_index.strategies | 132 |
| abstract_inverted_index.variation. | 45 |
| abstract_inverted_index.challenging | 56 |
| abstract_inverted_index.data-driven | 130 |
| abstract_inverted_index.development | 127 |
| abstract_inverted_index.exceptional | 116 |
| abstract_inverted_index.measurement | 97 |
| abstract_inverted_index.penetration | 27 |
| abstract_inverted_index.performance | 117 |
| abstract_inverted_index.substantial | 124 |
| abstract_inverted_index.uncertainty | 67 |
| abstract_inverted_index.architecture | 9 |
| abstract_inverted_index.distribution | 13 |
| abstract_inverted_index.implemented, | 105 |
| abstract_inverted_index.pulsed-power | 38 |
| abstract_inverted_index.convolutional | 85 |
| abstract_inverted_index.Medium-voltage | 0 |
| abstract_inverted_index.direct-current | 1 |
| abstract_inverted_index.generalization | 120 |
| abstract_inverted_index.identification | 79 |
| abstract_inverted_index.confidentiality | 61 |
| abstract_inverted_index.state-of-the-art | 8 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile |