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
Autor*in: |
Zheng, Xu [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019transfer abstract |
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Schlagwörter: |
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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.] |
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Übergeordnetes Werk: |
volume:187 ; year:2019 ; day:1 ; month:09 ; pages:0 |
Links: |
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DOI / URN: |
10.1016/j.oceaneng.2019.106141 |
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Katalog-ID: |
ELV047820489 |
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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. | ||
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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 |
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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 |
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Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
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Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks |
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Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks |
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Zheng, Xu |
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Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
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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. |
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title_short |
Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks |
url |
https://doi.org/10.1016/j.oceaneng.2019.106141 |
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Feng, Chen Li, Tengyue He, Bo |
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