Mathematical Contest in Modeling (MCM) and Interdisciplinary Contest in Modeling (ICM) template template for Northeastern University at Qinhuangdao (NEUQ)The updated original mcmthesis template can be found here.
Modified for NEUQ based on the original mcmthesis class by Liam Huang, Zhaoli Wang
Esta presentación algunas definiciones y resultados del análisis complejo; todas ellas presentadas con el fin de dar una prueba completa del principio de identidad y del principio del argumento.
Referencias de la presentación: Basic Complex ANalysis, 3rd Ed. Jerrold E. Marsden, Michael J. Hoffman.
This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d occupancy grid and 3d octomap was created from a provided simulated environment. Next, a personal simulated environment was created for mapping as well. In this appearance based method, a process called Loop Closure is used to determine whether a robot has seen a location before or not. In this paper, it is seen that RTAB-Map is optimized for large scale and long term SLAM by using multiple strategies to allow for loop closure to be done in real time and the results depict that it can be an excellent solution for SLAM to develop robots that can map an environment in both 2d and 3d.
Supported Vectored Machine (SVM) is one of the most historical, but also most commonly used machine learning models in supervised learning. In this project, I built a SVM model with the Sequential Minimal Optimization (SMO) algorithm using SAS IML procedure. Also, I simulated some linearly separable data using data step and compared the result of the SVM model with the SAS build-in Logistic Procedure. Finally, I applied the model to a famous dataset called credit.