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A Design and Implementaion of Carry Distance Prediction Model using Artificial Neural Network
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Publication Year
2018-11-16
Journal
9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018, pp.183-185
Keyword
Artificial Neural Network (ANN)golf ball carry distanceRoot Mean Square Error (RMSE)
Mesh Keyword
Dependent variablesGolf ballsHigh frequency radarInitial velocitiesInput variablesPerformance evaluationsPrediction modelRoot mean square errors
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsComputer Science ApplicationsInformation SystemsInformation Systems and ManagementArtificial Intelligence
Abstract
A 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.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36292
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85059463687&origin=inward
DOI
https://doi.org/10.1109/ictc.2018.8539694
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8509497
Type
Conference
Funding
ACKNOWLEDGMENT 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 ).
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Kim, Jae-Hyun김재현
Department of Electrical and Computer Engineering
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