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인공신경망을 이용한 움직이는 물체의 변위 추정
- Alternative Title
- Jun-Young Ko
- Author(s)
- 고준영
- Alternative Author(s)
- Jun-Young Ko
- Advisor
- 김재현
- Department
- 일반대학원 전자공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2019-02
- Language
- eng
- Alternative Abstract
- Recently, a ball flight analyzers using FM-CW (Frequency Modulation Continuous Wave) Doppler radar are used to measure data such as speed, launch angle and carry distance in screen golf or baseball games. Trackman is typical ball flight analyzer. It is a product that was recognized for its ability to measure data in professional baseball games or golf tours. However, Trackman is expensive, and it has a reluctance to purchase for ordinary users. Also, the algorithms of the product are business proprietary. Study on displacement estimation of moving objects will help develop low-cost and high-efficiency ball flight analyzers, it can be used to track projectiles. In this thesis, we proposed a physical model and a statistical model to estimate the displacement of a moving object. The first proposed model is the physical model. The forces at work in a ball during flight include lift, drag, and gravity. The moving object with a high spinning speed has a different drag and lift according to its size, depth, speed, and spin rate. The physical model was designed reflecting these differences. The second proposed model is an MLR (Multiple Linear Regression) and an ANN (Artificial Neural Network) models using statistical analysis method. In real environments, FM-CW Doppler radars have limitations in detecting objects that travel more than tens of meters. Based on interpretable predictive models, the statistical analysis method can explore hidden patterns and relationships between variables. The ANN models were changed to design the ANN structures with the best performance. The data was collected using two types of golf clubs, 7 iron and Driver. The RMSE (Root Mean Square Error) was calculated to put forward the best model. As a result, the proposed ANN model has the best performance both 7 iron and the Driver club with the RMSE of 0.55 and 0.72 respectively. This research can be used not only for ball flight analysis but also for moving object analysis such as baseball in the future.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Electronic Engineering > 3. Theses(Master)
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