Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks
To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zheng, Xu [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy - Chang, Guanru ELSEVIER, 2015, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:187 ; year:2019 ; day:1 ; month:09 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.oceaneng.2019.106141 |
|---|
| Katalog-ID: |
ELV047820489 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV047820489 | ||
| 003 | DE-627 | ||
| 005 | 20230626020535.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191023s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.oceaneng.2019.106141 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000801.pica |
| 035 | |a (DE-627)ELV047820489 | ||
| 035 | |a (ELSEVIER)S0029-8018(18)31309-X | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 540 |q VZ |
| 082 | 0 | 4 | |a 660 |q VZ |
| 082 | 0 | 4 | |a 540 |q VZ |
| 084 | |a BIODIV |q DE-30 |2 fid | ||
| 084 | |a 42.13 |2 bkl | ||
| 100 | 1 | |a Zheng, Xu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks |
| 264 | 1 | |c 2019transfer abstract | |
| 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 To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. | ||
| 520 | |a To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. | ||
| 650 | 7 | |a AUV |2 Elsevier | |
| 650 | 7 | |a Pro-DPCA |2 Elsevier | |
| 650 | 7 | |a Complex networks |2 Elsevier | |
| 650 | 7 | |a Navigational states |2 Elsevier | |
| 700 | 1 | |a Feng, Chen |4 oth | |
| 700 | 1 | |a Li, Tengyue |4 oth | |
| 700 | 1 | |a He, Bo |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Chang, Guanru ELSEVIER |t Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |d 2015 |g Amsterdam [u.a.] |w (DE-627)ELV01276728X |
| 773 | 1 | 8 | |g volume:187 |g year:2019 |g day:1 |g month:09 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.oceaneng.2019.106141 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a FID-BIODIV | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 42.13 |j Molekularbiologie |q VZ |
| 951 | |a AR | ||
| 952 | |d 187 |j 2019 |b 1 |c 0901 |h 0 | ||
| author_variant |
x z xz |
|---|---|
| matchkey_str |
zhengxufengchenlitengyuehebo:2019----:nlssfuooosnewtreilavaiainlttsa |
| hierarchy_sort_str |
2019transfer abstract |
| bklnumber |
42.13 |
| publishDate |
2019 |
| allfields |
10.1016/j.oceaneng.2019.106141 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000801.pica (DE-627)ELV047820489 (ELSEVIER)S0029-8018(18)31309-X DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Zheng, Xu verfasserin aut Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. AUV Elsevier Pro-DPCA Elsevier Complex networks Elsevier Navigational states Elsevier Feng, Chen oth Li, Tengyue oth He, Bo oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:187 year:2019 day:1 month:09 pages:0 https://doi.org/10.1016/j.oceaneng.2019.106141 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 187 2019 1 0901 0 |
| spelling |
10.1016/j.oceaneng.2019.106141 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000801.pica (DE-627)ELV047820489 (ELSEVIER)S0029-8018(18)31309-X DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Zheng, Xu verfasserin aut Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. AUV Elsevier Pro-DPCA Elsevier Complex networks Elsevier Navigational states Elsevier Feng, Chen oth Li, Tengyue oth He, Bo oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:187 year:2019 day:1 month:09 pages:0 https://doi.org/10.1016/j.oceaneng.2019.106141 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 187 2019 1 0901 0 |
| allfields_unstemmed |
10.1016/j.oceaneng.2019.106141 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000801.pica (DE-627)ELV047820489 (ELSEVIER)S0029-8018(18)31309-X DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Zheng, Xu verfasserin aut Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. AUV Elsevier Pro-DPCA Elsevier Complex networks Elsevier Navigational states Elsevier Feng, Chen oth Li, Tengyue oth He, Bo oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:187 year:2019 day:1 month:09 pages:0 https://doi.org/10.1016/j.oceaneng.2019.106141 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 187 2019 1 0901 0 |
| allfieldsGer |
10.1016/j.oceaneng.2019.106141 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000801.pica (DE-627)ELV047820489 (ELSEVIER)S0029-8018(18)31309-X DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Zheng, Xu verfasserin aut Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. AUV Elsevier Pro-DPCA Elsevier Complex networks Elsevier Navigational states Elsevier Feng, Chen oth Li, Tengyue oth He, Bo oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:187 year:2019 day:1 month:09 pages:0 https://doi.org/10.1016/j.oceaneng.2019.106141 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 187 2019 1 0901 0 |
| allfieldsSound |
10.1016/j.oceaneng.2019.106141 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000801.pica (DE-627)ELV047820489 (ELSEVIER)S0029-8018(18)31309-X DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Zheng, Xu verfasserin aut Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. AUV Elsevier Pro-DPCA Elsevier Complex networks Elsevier Navigational states Elsevier Feng, Chen oth Li, Tengyue oth He, Bo oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:187 year:2019 day:1 month:09 pages:0 https://doi.org/10.1016/j.oceaneng.2019.106141 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 187 2019 1 0901 0 |
| language |
English |
| source |
Enthalten in Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy Amsterdam [u.a.] volume:187 year:2019 day:1 month:09 pages:0 |
| sourceStr |
Enthalten in Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy Amsterdam [u.a.] volume:187 year:2019 day:1 month:09 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Molekularbiologie |
| institution |
findex.gbv.de |
| topic_facet |
AUV Pro-DPCA Complex networks Navigational states |
| dewey-raw |
540 |
| isfreeaccess_bool |
false |
| container_title |
Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
| authorswithroles_txt_mv |
Zheng, Xu @@aut@@ Feng, Chen @@oth@@ Li, Tengyue @@oth@@ He, Bo @@oth@@ |
| publishDateDaySort_date |
2019-01-01T00:00:00Z |
| hierarchy_top_id |
ELV01276728X |
| dewey-sort |
3540 |
| id |
ELV047820489 |
| 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">ELV047820489</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626020535.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.oceaneng.2019.106141</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/GBV00000000000801.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV047820489</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0029-8018(18)31309-X</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">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.13</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zheng, Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</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">To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">AUV</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Pro-DPCA</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Complex networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Navigational states</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Feng, Chen</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Tengyue</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">He, Bo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Chang, Guanru ELSEVIER</subfield><subfield code="t">Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy</subfield><subfield code="d">2015</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV01276728X</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:187</subfield><subfield code="g">year:2019</subfield><subfield code="g">day:1</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.oceaneng.2019.106141</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="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.13</subfield><subfield code="j">Molekularbiologie</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">187</subfield><subfield code="j">2019</subfield><subfield code="b">1</subfield><subfield code="c">0901</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Zheng, Xu |
| spellingShingle |
Zheng, Xu ddc 540 ddc 660 fid BIODIV bkl 42.13 Elsevier AUV Elsevier Pro-DPCA Elsevier Complex networks Elsevier Navigational states Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks |
| authorStr |
Zheng, Xu |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV01276728X |
| format |
electronic Article |
| dewey-ones |
540 - Chemistry & allied sciences 660 - Chemical engineering |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
540 VZ 660 VZ BIODIV DE-30 fid 42.13 bkl Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks AUV Elsevier Pro-DPCA Elsevier Complex networks Elsevier Navigational states Elsevier |
| topic |
ddc 540 ddc 660 fid BIODIV bkl 42.13 Elsevier AUV Elsevier Pro-DPCA Elsevier Complex networks Elsevier Navigational states |
| topic_unstemmed |
ddc 540 ddc 660 fid BIODIV bkl 42.13 Elsevier AUV Elsevier Pro-DPCA Elsevier Complex networks Elsevier Navigational states |
| topic_browse |
ddc 540 ddc 660 fid BIODIV bkl 42.13 Elsevier AUV Elsevier Pro-DPCA Elsevier Complex networks Elsevier Navigational states |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
c f cf t l tl b h bh |
| hierarchy_parent_title |
Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
| hierarchy_parent_id |
ELV01276728X |
| dewey-tens |
540 - Chemistry 660 - Chemical engineering |
| hierarchy_top_title |
Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV01276728X |
| title |
Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks |
| ctrlnum |
(DE-627)ELV047820489 (ELSEVIER)S0029-8018(18)31309-X |
| title_full |
Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks |
| author_sort |
Zheng, Xu |
| journal |
Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
| journalStr |
Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Zheng, Xu |
| container_volume |
187 |
| class |
540 VZ 660 VZ BIODIV DE-30 fid 42.13 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Zheng, Xu |
| doi_str_mv |
10.1016/j.oceaneng.2019.106141 |
| dewey-full |
540 660 |
| title_sort |
analysis of autonomous underwater vehicle (auv) navigational states based on complex networks |
| title_auth |
Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks |
| abstract |
To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. |
| abstractGer |
To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. |
| abstract_unstemmed |
To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA |
| title_short |
Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks |
| url |
https://doi.org/10.1016/j.oceaneng.2019.106141 |
| remote_bool |
true |
| author2 |
Feng, Chen Li, Tengyue He, Bo |
| author2Str |
Feng, Chen Li, Tengyue He, Bo |
| ppnlink |
ELV01276728X |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1016/j.oceaneng.2019.106141 |
| up_date |
2024-07-06T17:12:16.505Z |
| _version_ |
1803850546158764032 |
| 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">ELV047820489</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626020535.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.oceaneng.2019.106141</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/GBV00000000000801.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV047820489</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0029-8018(18)31309-X</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">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.13</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zheng, Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</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">To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">AUV</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Pro-DPCA</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Complex networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Navigational states</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Feng, Chen</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Tengyue</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">He, Bo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Chang, Guanru ELSEVIER</subfield><subfield code="t">Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy</subfield><subfield code="d">2015</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV01276728X</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:187</subfield><subfield code="g">year:2019</subfield><subfield code="g">day:1</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.oceaneng.2019.106141</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="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.13</subfield><subfield code="j">Molekularbiologie</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">187</subfield><subfield code="j">2019</subfield><subfield code="b">1</subfield><subfield code="c">0901</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.3995867 |