Digital twin modeling
The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tao, Fei [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
18 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota - Bayulgen, Oksan ELSEVIER, 2021, Dearborn, Mich |
|---|---|
| Übergeordnetes Werk: |
volume:64 ; year:2022 ; pages:372-389 ; extent:18 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jmsy.2022.06.015 |
|---|
| Katalog-ID: |
ELV058888519 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV058888519 | ||
| 003 | DE-627 | ||
| 005 | 20230626051820.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 221103s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.jmsy.2022.06.015 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001931.pica |
| 035 | |a (DE-627)ELV058888519 | ||
| 035 | |a (ELSEVIER)S0278-6125(22)00110-8 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 620 |q VZ |
| 084 | |a 83.65 |2 bkl | ||
| 100 | 1 | |a Tao, Fei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Digital twin modeling |
| 264 | 1 | |c 2022transfer abstract | |
| 300 | |a 18 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. | ||
| 520 | |a The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. | ||
| 650 | 7 | |a Enabling technologies |2 Elsevier | |
| 650 | 7 | |a Digital twin |2 Elsevier | |
| 650 | 7 | |a Enabling tools |2 Elsevier | |
| 650 | 7 | |a Digital twin modeling |2 Elsevier | |
| 650 | 7 | |a Digital twin model |2 Elsevier | |
| 700 | 1 | |a Xiao, Bin |4 oth | |
| 700 | 1 | |a Qi, Qinglin |4 oth | |
| 700 | 1 | |a Cheng, Jiangfeng |4 oth | |
| 700 | 1 | |a Ji, Ping |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Soc |a Bayulgen, Oksan ELSEVIER |t Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota |d 2021 |g Dearborn, Mich |w (DE-627)ELV00685088X |
| 773 | 1 | 8 | |g volume:64 |g year:2022 |g pages:372-389 |g extent:18 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.jmsy.2022.06.015 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 936 | b | k | |a 83.65 |j Versorgungswirtschaft |q VZ |
| 951 | |a AR | ||
| 952 | |d 64 |j 2022 |h 372-389 |g 18 | ||
| author_variant |
f t ft |
|---|---|
| matchkey_str |
taofeixiaobinqiqinglinchengjiangfengjipi:2022----:iiatim |
| hierarchy_sort_str |
2022transfer abstract |
| bklnumber |
83.65 |
| publishDate |
2022 |
| allfields |
10.1016/j.jmsy.2022.06.015 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001931.pica (DE-627)ELV058888519 (ELSEVIER)S0278-6125(22)00110-8 DE-627 ger DE-627 rakwb eng 620 VZ 83.65 bkl Tao, Fei verfasserin aut Digital twin modeling 2022transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. Enabling technologies Elsevier Digital twin Elsevier Enabling tools Elsevier Digital twin modeling Elsevier Digital twin model Elsevier Xiao, Bin oth Qi, Qinglin oth Cheng, Jiangfeng oth Ji, Ping oth Enthalten in Soc Bayulgen, Oksan ELSEVIER Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota 2021 Dearborn, Mich (DE-627)ELV00685088X volume:64 year:2022 pages:372-389 extent:18 https://doi.org/10.1016/j.jmsy.2022.06.015 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 64 2022 372-389 18 |
| spelling |
10.1016/j.jmsy.2022.06.015 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001931.pica (DE-627)ELV058888519 (ELSEVIER)S0278-6125(22)00110-8 DE-627 ger DE-627 rakwb eng 620 VZ 83.65 bkl Tao, Fei verfasserin aut Digital twin modeling 2022transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. Enabling technologies Elsevier Digital twin Elsevier Enabling tools Elsevier Digital twin modeling Elsevier Digital twin model Elsevier Xiao, Bin oth Qi, Qinglin oth Cheng, Jiangfeng oth Ji, Ping oth Enthalten in Soc Bayulgen, Oksan ELSEVIER Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota 2021 Dearborn, Mich (DE-627)ELV00685088X volume:64 year:2022 pages:372-389 extent:18 https://doi.org/10.1016/j.jmsy.2022.06.015 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 64 2022 372-389 18 |
| allfields_unstemmed |
10.1016/j.jmsy.2022.06.015 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001931.pica (DE-627)ELV058888519 (ELSEVIER)S0278-6125(22)00110-8 DE-627 ger DE-627 rakwb eng 620 VZ 83.65 bkl Tao, Fei verfasserin aut Digital twin modeling 2022transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. Enabling technologies Elsevier Digital twin Elsevier Enabling tools Elsevier Digital twin modeling Elsevier Digital twin model Elsevier Xiao, Bin oth Qi, Qinglin oth Cheng, Jiangfeng oth Ji, Ping oth Enthalten in Soc Bayulgen, Oksan ELSEVIER Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota 2021 Dearborn, Mich (DE-627)ELV00685088X volume:64 year:2022 pages:372-389 extent:18 https://doi.org/10.1016/j.jmsy.2022.06.015 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 64 2022 372-389 18 |
| allfieldsGer |
10.1016/j.jmsy.2022.06.015 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001931.pica (DE-627)ELV058888519 (ELSEVIER)S0278-6125(22)00110-8 DE-627 ger DE-627 rakwb eng 620 VZ 83.65 bkl Tao, Fei verfasserin aut Digital twin modeling 2022transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. Enabling technologies Elsevier Digital twin Elsevier Enabling tools Elsevier Digital twin modeling Elsevier Digital twin model Elsevier Xiao, Bin oth Qi, Qinglin oth Cheng, Jiangfeng oth Ji, Ping oth Enthalten in Soc Bayulgen, Oksan ELSEVIER Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota 2021 Dearborn, Mich (DE-627)ELV00685088X volume:64 year:2022 pages:372-389 extent:18 https://doi.org/10.1016/j.jmsy.2022.06.015 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 64 2022 372-389 18 |
| allfieldsSound |
10.1016/j.jmsy.2022.06.015 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001931.pica (DE-627)ELV058888519 (ELSEVIER)S0278-6125(22)00110-8 DE-627 ger DE-627 rakwb eng 620 VZ 83.65 bkl Tao, Fei verfasserin aut Digital twin modeling 2022transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. Enabling technologies Elsevier Digital twin Elsevier Enabling tools Elsevier Digital twin modeling Elsevier Digital twin model Elsevier Xiao, Bin oth Qi, Qinglin oth Cheng, Jiangfeng oth Ji, Ping oth Enthalten in Soc Bayulgen, Oksan ELSEVIER Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota 2021 Dearborn, Mich (DE-627)ELV00685088X volume:64 year:2022 pages:372-389 extent:18 https://doi.org/10.1016/j.jmsy.2022.06.015 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 64 2022 372-389 18 |
| language |
English |
| source |
Enthalten in Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota Dearborn, Mich volume:64 year:2022 pages:372-389 extent:18 |
| sourceStr |
Enthalten in Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota Dearborn, Mich volume:64 year:2022 pages:372-389 extent:18 |
| format_phy_str_mv |
Article |
| bklname |
Versorgungswirtschaft |
| institution |
findex.gbv.de |
| topic_facet |
Enabling technologies Digital twin Enabling tools Digital twin modeling Digital twin model |
| dewey-raw |
620 |
| isfreeaccess_bool |
false |
| container_title |
Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota |
| authorswithroles_txt_mv |
Tao, Fei @@aut@@ Xiao, Bin @@oth@@ Qi, Qinglin @@oth@@ Cheng, Jiangfeng @@oth@@ Ji, Ping @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV00685088X |
| dewey-sort |
3620 |
| id |
ELV058888519 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV058888519</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626051820.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221103s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jmsy.2022.06.015</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001931.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058888519</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0278-6125(22)00110-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">83.65</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tao, Fei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Digital twin modeling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">18</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Enabling technologies</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Digital twin</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Enabling tools</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Digital twin modeling</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Digital twin model</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiao, Bin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qi, Qinglin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cheng, Jiangfeng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ji, Ping</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Soc</subfield><subfield code="a">Bayulgen, Oksan ELSEVIER</subfield><subfield code="t">Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota</subfield><subfield code="d">2021</subfield><subfield code="g">Dearborn, Mich</subfield><subfield code="w">(DE-627)ELV00685088X</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:64</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:372-389</subfield><subfield code="g">extent:18</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jmsy.2022.06.015</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">83.65</subfield><subfield code="j">Versorgungswirtschaft</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">64</subfield><subfield code="j">2022</subfield><subfield code="h">372-389</subfield><subfield code="g">18</subfield></datafield></record></collection>
|
| author |
Tao, Fei |
| spellingShingle |
Tao, Fei ddc 620 bkl 83.65 Elsevier Enabling technologies Elsevier Digital twin Elsevier Enabling tools Elsevier Digital twin modeling Elsevier Digital twin model Digital twin modeling |
| authorStr |
Tao, Fei |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV00685088X |
| format |
electronic Article |
| dewey-ones |
620 - Engineering & allied operations |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
620 VZ 83.65 bkl Digital twin modeling Enabling technologies Elsevier Digital twin Elsevier Enabling tools Elsevier Digital twin modeling Elsevier Digital twin model Elsevier |
| topic |
ddc 620 bkl 83.65 Elsevier Enabling technologies Elsevier Digital twin Elsevier Enabling tools Elsevier Digital twin modeling Elsevier Digital twin model |
| topic_unstemmed |
ddc 620 bkl 83.65 Elsevier Enabling technologies Elsevier Digital twin Elsevier Enabling tools Elsevier Digital twin modeling Elsevier Digital twin model |
| topic_browse |
ddc 620 bkl 83.65 Elsevier Enabling technologies Elsevier Digital twin Elsevier Enabling tools Elsevier Digital twin modeling Elsevier Digital twin model |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
b x bx q q qq j c jc p j pj |
| hierarchy_parent_title |
Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota |
| hierarchy_parent_id |
ELV00685088X |
| dewey-tens |
620 - Engineering |
| hierarchy_top_title |
Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV00685088X |
| title |
Digital twin modeling |
| ctrlnum |
(DE-627)ELV058888519 (ELSEVIER)S0278-6125(22)00110-8 |
| title_full |
Digital twin modeling |
| author_sort |
Tao, Fei |
| journal |
Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota |
| journalStr |
Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
372 |
| author_browse |
Tao, Fei |
| container_volume |
64 |
| physical |
18 |
| class |
620 VZ 83.65 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Tao, Fei |
| doi_str_mv |
10.1016/j.jmsy.2022.06.015 |
| dewey-full |
620 |
| title_sort |
digital twin modeling |
| title_auth |
Digital twin modeling |
| abstract |
The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. |
| abstractGer |
The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. |
| abstract_unstemmed |
The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
Digital twin modeling |
| url |
https://doi.org/10.1016/j.jmsy.2022.06.015 |
| remote_bool |
true |
| author2 |
Xiao, Bin Qi, Qinglin Cheng, Jiangfeng Ji, Ping |
| author2Str |
Xiao, Bin Qi, Qinglin Cheng, Jiangfeng Ji, Ping |
| ppnlink |
ELV00685088X |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth |
| doi_str |
10.1016/j.jmsy.2022.06.015 |
| up_date |
2024-07-06T20:21:38.382Z |
| _version_ |
1803862459949252608 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV058888519</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626051820.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221103s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jmsy.2022.06.015</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001931.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058888519</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0278-6125(22)00110-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">83.65</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tao, Fei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Digital twin modeling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">18</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Enabling technologies</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Digital twin</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Enabling tools</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Digital twin modeling</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Digital twin model</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiao, Bin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qi, Qinglin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cheng, Jiangfeng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ji, Ping</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Soc</subfield><subfield code="a">Bayulgen, Oksan ELSEVIER</subfield><subfield code="t">Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota</subfield><subfield code="d">2021</subfield><subfield code="g">Dearborn, Mich</subfield><subfield code="w">(DE-627)ELV00685088X</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:64</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:372-389</subfield><subfield code="g">extent:18</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jmsy.2022.06.015</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">83.65</subfield><subfield code="j">Versorgungswirtschaft</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">64</subfield><subfield code="j">2022</subfield><subfield code="h">372-389</subfield><subfield code="g">18</subfield></datafield></record></collection>
|
| score |
7.400201 |