求职应聘的英语自我介绍( 三 ) 「问人人知识网」

Research experience and academic activity
When a sophomore, I joined the Aociation of AI Enthusiast and began to narrow down my interest for my future research.In 1997, I participated in simulation tool development for the scheduling system in prof.Wang’s lab.With the tool of OpenGL and Matlab, I designed a simulation program for transportation scheduling system.It is now widely used by different research groups in NUST.In 1998, I aumed and fulfilled a sewage analysis & dispose project for Nanjing sewage treatment plant.This was my first practice to convert a laboratory idea to a commercial product.
【求职应聘的英语自我介绍】In 1999, I joined the distinguished profeor Yu-Geng Xis research group aiming at Network flow problem solving and Heuristic algorithm research.Soon I was engaged in the FuDan Gene Database Design.My duty was to pick up the useful information among different kinds of gene matching format.Through the comparison and analysis for many heuristic algorithms, I introduced an improved evolutionary algorithm -- Multi-population Genetic Algorithm.By dividing a whole population into several sub-populations, this improved algorithm can effectively prevent GA from local convergence and promote various evolutionary orientations.It proved more efficiently than SGA in experiments, too.In the second semester, I joined the workshop-scheduling research in Shanghai Heavy Duty Tyre plant.The scheduling was designed for the rubber-making proce that covered not only discrete but also continuous circumstances.To make a balance point between optimization quality and time cost, I proposed a Dynamic Layered Scheduling method based on hybrid petri Nets.The practical application showed that the average makespan was shortened by a large scale.I also publicized two papers in core journals with this idea.Recently, I am doing research in the Composite predict of the Electrical power system aisted with the technology of Data Mining for Bao Steel.I try to combine the Decision Tree with Receding Optimization to provide a new solution for the Composite predictive problem.This project is now under construction.