scholarly journals Building a Classifier of Onset Stroke Prediction Using Random Tree Algorithm

2017 ◽  
Vol 7 (4) ◽  
pp. 61-66 ◽  
Author(s):  
Yu-Chen Chen ◽  
◽  
Takashi Suzuki ◽  
Masaaki Suzuki ◽  
Hiroyuki Takao ◽  
...  
Keyword(s):  
Random Tree ◽  
Tree Algorithm

scholarly journals Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yangyang Shi ◽  
Qiongqiong Li ◽  
Shengqiang Bu ◽  
Jiafu Yang ◽  
Linfeng Zhu
Keyword(s):  
Convergence Speed ◽  
Random Tree ◽  
Tree Search ◽  
Vehicle Model ◽  
Turning Angle ◽  
Tree Algorithm ◽  
Slow Convergence ◽  
Joint Simulation ◽  
Simulation Based ◽  
Vehicle Path

Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B-spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.

scholarly journals An Empirical Methodology to Examine the Effect of Meta Classifiers in J48 and Random Tree in Weather Data

2020 ◽  
Vol 9 (6) ◽  
pp. 153-157
Keyword(s):  
Human Activity ◽  
Data Interpretation ◽  
Random Tree ◽  
Weather Data ◽  
Peninsular India ◽  
Tree Algorithm ◽  
Implementation Phase ◽  
Empirical Methodology

Weather data interpretation has become vitally important in most domains of human activity and this is because in recent years, major changes have begun to impact climate globally – peninsular India is among the regions seriously affected with this and prediction has become a particularly urgent concern. In this work to bring out a better methodology to examine the weather data using Meta classifiers, a method is postulated by formulating it with Tree classifiers – J48 and Random Tree. Implementation phase has shown distinct results for both the classifiers. Regardless, we could conclude from this work that the effect of Meta Classifiers in J48 and Random Tree algorithm shows that efficiency can be improved by applying the same.

scholarly journals The Online Path Planning Method of UAV Autonomous Inspection in Distribution Network

2021 ◽  
Vol 256 ◽  
pp. 01047
Author(s):  
Li Li ◽  
Hong Zhan ◽  
Yongjing Hao
Keyword(s):  
Path Planning ◽  
Distribution Network ◽  
Dynamic Environment ◽  
Planning Method ◽  
Random Tree ◽  
Mountainous Areas ◽  
Tree Algorithm ◽  
Distribution Lines ◽  
Surrounding Environment ◽  
Autonomous Inspection

In this paper, the problem of online path planning for autonomous inspection of distribution network lines by UAV is studied. Because the distribution lines are mostly distributed around cities, counties and mountainous areas, the lines and their surrounding environment are uncertain and dynamic. These factors will affect the safety of UAV inspection, making the off-line pre-planned path for UAV unavailable. This paper designs an improved iteration random tree algorithm (IRRT) algorithm, which can quickly plan the path of UAV in dynamic environment.

Sign in / Sign up

Export Citation Format

Share Document