Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation
<p dir="ltr">The process of using robotic technology to examine underwater systems is still a difficult undertaking because the majority of automated activities lack network connectivity. Therefore, the suggested approach finds the main hole in undersea systems and fills it using rob...
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2023
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| _version_ | 1864513534137729024 |
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| author | Hariprasath Manoharan (14157966) |
| author2 | Shitharth Selvarajan (14157976) Rajanikanth Aluvalu (22337599) Maha Abdelhaq (735574) Raed Alsaqour (735575) Mueen Uddin (4903510) |
| author2_role | author author author author author |
| author_facet | Hariprasath Manoharan (14157966) Shitharth Selvarajan (14157976) Rajanikanth Aluvalu (22337599) Maha Abdelhaq (735574) Raed Alsaqour (735575) Mueen Uddin (4903510) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hariprasath Manoharan (14157966) Shitharth Selvarajan (14157976) Rajanikanth Aluvalu (22337599) Maha Abdelhaq (735574) Raed Alsaqour (735575) Mueen Uddin (4903510) |
| dc.date.none.fl_str_mv | 2023-12-19T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.7717/peerj-cs.1709 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Diagnostic_structure_of_visual_robotic_inundated_systems_with_fuzzy_clustering_membership_correlation/30246037 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Communications engineering Control engineering, mechatronics and robotics Maritime engineering Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Robot Fuzzy clustering Underwater Error |
| dc.title.none.fl_str_mv | Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The process of using robotic technology to examine underwater systems is still a difficult undertaking because the majority of automated activities lack network connectivity. Therefore, the suggested approach finds the main hole in undersea systems and fills it using robotic automation. In the predicted model, an analytical framework is created to operate the robot within predetermined areas while maximizing communication ranges. Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. A cluster node is connected in each clustered region and provides the central control center with the necessary data. The weights are evenly distributed, and the designed robotic system is installed to prevent an uncontrolled operational state. Five different scenarios are used to test and validate the created model, and in each case, the proposed method is found to be superior to the current methodology in terms of range, energy, density, time periods, and total metrics of operation.</p><h2>Other Information</h2><p dir="ltr">Published in: PeerJ Computer Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.7717/peerj-cs.1709" target="_blank">https://dx.doi.org/10.7717/peerj-cs.1709</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_99a5a919b9ec68d43f8aec0fc5e2efc8 |
| identifier_str_mv | 10.7717/peerj-cs.1709 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30246037 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlationHariprasath Manoharan (14157966)Shitharth Selvarajan (14157976)Rajanikanth Aluvalu (22337599)Maha Abdelhaq (735574)Raed Alsaqour (735575)Mueen Uddin (4903510)EngineeringCommunications engineeringControl engineering, mechatronics and roboticsMaritime engineeringInformation and computing sciencesArtificial intelligenceData management and data scienceDistributed computing and systems softwareRobotFuzzy clusteringUnderwaterError<p dir="ltr">The process of using robotic technology to examine underwater systems is still a difficult undertaking because the majority of automated activities lack network connectivity. Therefore, the suggested approach finds the main hole in undersea systems and fills it using robotic automation. In the predicted model, an analytical framework is created to operate the robot within predetermined areas while maximizing communication ranges. Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. A cluster node is connected in each clustered region and provides the central control center with the necessary data. The weights are evenly distributed, and the designed robotic system is installed to prevent an uncontrolled operational state. Five different scenarios are used to test and validate the created model, and in each case, the proposed method is found to be superior to the current methodology in terms of range, energy, density, time periods, and total metrics of operation.</p><h2>Other Information</h2><p dir="ltr">Published in: PeerJ Computer Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.7717/peerj-cs.1709" target="_blank">https://dx.doi.org/10.7717/peerj-cs.1709</a></p>2023-12-19T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.7717/peerj-cs.1709https://figshare.com/articles/journal_contribution/Diagnostic_structure_of_visual_robotic_inundated_systems_with_fuzzy_clustering_membership_correlation/30246037CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302460372023-12-19T03:00:00Z |
| spellingShingle | Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation Hariprasath Manoharan (14157966) Engineering Communications engineering Control engineering, mechatronics and robotics Maritime engineering Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Robot Fuzzy clustering Underwater Error |
| status_str | publishedVersion |
| title | Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation |
| title_full | Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation |
| title_fullStr | Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation |
| title_full_unstemmed | Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation |
| title_short | Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation |
| title_sort | Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation |
| topic | Engineering Communications engineering Control engineering, mechatronics and robotics Maritime engineering Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Robot Fuzzy clustering Underwater Error |