Effect of Air Resonance by Wind Speed Difference on Falling fruit
This study completes an air vibration equation expressed wind speed slope and wind speed. First, preliminary experiments identified air vibrations when wind speed differences occurred over distance. Several air fans were connected in series and the rotational speed of the air fan was adjusted to vary the wind speed with distance. At this time, only certain pendulum oscillates during a particular wind speed slope. It was expected that the pendulum would shake because the frequency of the air due to the slope of the wind speed was equal to the natural frequency of the pendulum. In addition, relatively short pendulum swings in large wind speed slope, long pendulum swings in short wind speed slope. After calculating the natural frequency of the seasonal growth of fruit using the physical factors model, we experiment how resonant frequency was related with cone length, angular width, wind speed, velocity and secondary derivative. the actual experiment analyzed the natural frequency of the fruit and resonance from the air vibration as the linear function of the wind speed, velocity, and secondary derivative. The experiment determined that the pendulum of a specified number of frequencies resonated with a particular wind speed pattern. It is judged that the vibration of air is related to first derivative of wind speed depending on speed and distance. However, it is very difficult to express the flow of nonlinear fluids as a function of simple function, particularly the effects of air vibrations caused by wind speed second derivative, which appeared to be associated with forces. This is a task that needs to be solved through further research.
圓周上跳躍回歸問題之研究
圓周上相異n個點，將圓周分割成n段弧，每次每個點沿逆時針方向變換成與下一點所成弧之中點，若某點經m次變換後回到初始點，則m的最小值以及m的所有可能值為何？我們發現，m的最小值為n+2。更進一步發現，m的充要條件為m≧n+2且m≠kn-1, kn, kn+1，其中k為正奇數。接著，我們將問題一般化，圓周上相異n個點，沿逆時針方向變換成與下一點所成弧之p:q處，若某點經m次變換後回到初始點，則m的最小值以及m的所有可能值為何？我們發現，若p, q∈N，(p,q)=1，當變換次數r足夠大時，此n個點的位置會收斂至圓周上n等分點，同時，此n個點會在變換T=n(p+q)/(n,p)次後再次收斂至相同的位置。在這篇研究中，我們推導出任意點Pi變換r次後的點之位置坐標Ai(r)的一般式，不失一般性，我們針對P0求出A0(r)的最小極端值Lr與最大極端值Ur，在變換次數r足夠大時，透過觀察Lr與Ur對應到圓周上的收斂位置所形成的區間是否涵蓋原點，可預期P0變換r次後可否回歸。此外，我們也針對n個點具特殊初始位置座標來研究其回歸性質。
Multiple Time-step Predictive Models for Hurricanes in the North Atlantic Basin Based on Machine Learning Algorithms
The cost of damage caused by hurricanes in 2017 is estimated to be over 200 billion dollars. Quick and accurate prediction of the path of a hurricane and its strength would be very valuable in alleviating these losses. Machine learning based prediction models, in contrast to models based on physics, have been developed successfully in many problem domains. A machine learning system infers the modeling function from a training dataset. This project developed machine learning based prediction models to forecast the path and strength of hurricanes in the North Atlantic basin. Feature analysis was performed on the HURDAT2 dataset, which contains paths and strengths of past hurricanes. Artificial Neural Networks (ANNs) and Generalized Linear Model (GLM) approaches such as Tikhonov regularization were investigated to develop nine hurricane prediction models. Prediction accuracy of these models was compared using a testing dataset, disjoint from the training dataset. The coefficient of determination and the mean squared error were used as performance metrics. Post-processing metrics, such as geodesic error in path prediction and the mean wind speed error, were also used to compare different models. TLS linear regression model performed the best of out the nine models for one and two time steps, while the ANNs made more accurate predictions for longer periods. All models predicted location and strength with greater than .95 coefficient of determination for up to two days. My models predicted hurricane path in under a second with accuracy comparable to that of current models.