Ajou University repository

A Design and Implementaion of Carry Distance Prediction Model using Artificial Neural Network
Citations

SCOPUS

0

Citation Export

DC Field Value Language
dc.contributor.authorKo, Jun Young-
dc.contributor.authorKim, Kyeong Rok-
dc.contributor.authorKim, Jae Hyun-
dc.date.issued2018-11-16-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/36292-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85059463687&origin=inward-
dc.description.abstractA golf shot pattern analyzer, which can derive a golf ball speed, a launch angle, and a spin, measures parameters using a high frequency radar or a high speed camera. But it is difficult to measure a carry distance of golf ball moving several tens of meters. Therefore, the carry distance of golf ball is calculated by various variables such as an initial velocity of golf ball, a launch angle, a spin rate, etc. In this paper, we calculate the carry distance of golf ball based on an Artificial Neural Network (ANN). The ANN model uses five dependent variables (club speed, attack angle, golf ball speed, launch angle, and spin rate) as input variables. A structure of the ANN model consists of one input layer, four hidden layers, and one output layer. Hidden nodes of the hidden layer are composed of 10, 20, 20, and 20 nodes, respectively. A Root Mean Square Error (RMSE) is used for performance evaluation and the RMSE of the ANN model is 0.8.-
dc.description.sponsorshipACKNOWLEDGMENT This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2017R1A2A2A05001404 ).-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.subject.meshDependent variables-
dc.subject.meshGolf balls-
dc.subject.meshHigh frequency radar-
dc.subject.meshInitial velocities-
dc.subject.meshInput variables-
dc.subject.meshPerformance evaluations-
dc.subject.meshPrediction model-
dc.subject.meshRoot mean square errors-
dc.titleA Design and Implementaion of Carry Distance Prediction Model using Artificial Neural Network-
dc.typeConference-
dc.citation.conferenceDate2018.10.17. ~ 2018.10.19.-
dc.citation.conferenceName9th International Conference on Information and Communication Technology Convergence, ICTC 2018-
dc.citation.edition9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018-
dc.citation.endPage185-
dc.citation.startPage183-
dc.citation.title9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018-
dc.identifier.bibliographicCitation9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018, pp.183-185-
dc.identifier.doi10.1109/ictc.2018.8539694-
dc.identifier.scopusid2-s2.0-85059463687-
dc.identifier.urlhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8509497-
dc.subject.keywordArtificial Neural Network (ANN)-
dc.subject.keywordgolf ball carry distance-
dc.subject.keywordRoot Mean Square Error (RMSE)-
dc.type.otherConference Paper-
dc.description.isoafalse-
dc.subject.subareaComputer Networks and Communications-
dc.subject.subareaComputer Science Applications-
dc.subject.subareaInformation Systems-
dc.subject.subareaInformation Systems and Management-
dc.subject.subareaArtificial Intelligence-
Show simple item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Kim, Jae-Hyun Image
Kim, Jae-Hyun김재현
Department of Electrical and Computer Engineering
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.