Machine learning approach to analyze the status of forklift vehicles with irregular movement in a shipyard
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hyeonju | - |
dc.contributor.author | Lee, Jongho | - |
dc.contributor.author | An, Minji | - |
dc.contributor.author | Park, Gunil | - |
dc.contributor.author | Choi, Sungchul | - |
dc.date.accessioned | 2021-10-13T01:40:08Z | - |
dc.date.available | 2021-10-13T01:40:08Z | - |
dc.date.created | 2021-10-09 | - |
dc.date.issued | 2021-12 | - |
dc.identifier.issn | 0166-3615 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82362 | - |
dc.description.abstract | In large shipyards, the management of equipment used to build ships is critical. Because orders vary year to year, shipyard managers are required to determine methods to make the most of their limited resources. A particular difficulty arises in the management of moving vehicles because of the nature and size of shipyards. In recent years, shipbuilding companies have attempted to manage and track the locations and movements of vehicles using global positioning system (GPS) modules. However, because certain vehicles, such as forklifts, move irregularly around a yard, identifying their working status without being onsite is difficult. Simple location information alone is insufficient to determine whether a vehicle is working, moving, waiting, or resting. Status information acquisition requires intelligence algorithms to distinguish GPS data. This study proposes an approach based on machine learning to identify the work status of each forklift. We use the DBSCAN and k-means algorithms to identify the area in which a particular forklift is operating and the type of work it is performing. We developed a business intelligence system to collect data on forklifts equipped with GPS and Internet of Things devices. The system provides visual information on the status of individual forklifts and helps in the efficient management of their movements within large shipyards. © 2021 Elsevier B.V. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.relation.isPartOf | Computers in Industry | - |
dc.title | Machine learning approach to analyze the status of forklift vehicles with irregular movement in a shipyard | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000702770600008 | - |
dc.identifier.doi | 10.1016/j.compind.2021.103544 | - |
dc.identifier.bibliographicCitation | Computers in Industry, v.133 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85116060344 | - |
dc.citation.title | Computers in Industry | - |
dc.citation.volume | 133 | - |
dc.contributor.affiliatedAuthor | Lee, Jongho | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Business intelligence | - |
dc.subject.keywordAuthor | Clustering | - |
dc.subject.keywordAuthor | Forklifts | - |
dc.subject.keywordAuthor | Shipbuilding | - |
dc.subject.keywordAuthor | Smart shipyard | - |
dc.subject.keywordPlus | Global positioning system | - |
dc.subject.keywordPlus | Information management | - |
dc.subject.keywordPlus | K-means clustering | - |
dc.subject.keywordPlus | Machine learning | - |
dc.subject.keywordPlus | Materials handling equipment | - |
dc.subject.keywordPlus | Ships | - |
dc.subject.keywordPlus | Shipyards | - |
dc.subject.keywordPlus | Clusterings | - |
dc.subject.keywordPlus | Information acquisitions | - |
dc.subject.keywordPlus | Intelligence algorithms | - |
dc.subject.keywordPlus | Location information | - |
dc.subject.keywordPlus | Machine learning approaches | - |
dc.subject.keywordPlus | Moving vehicles | - |
dc.subject.keywordPlus | Simple++ | - |
dc.subject.keywordPlus | Smart shipyard | - |
dc.subject.keywordPlus | Status informations | - |
dc.subject.keywordPlus | System modules | - |
dc.subject.keywordPlus | Shipbuilding | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.