Lin Dongmei, Professor of Computer Science, Master, Director of Information and Education Technology Center of Foshan Institute of Technology. In March of 20001year, he obtained the master's degree in engineering from Daqing Petroleum Institute. Main research interests: virtual reality and intelligent computing. Since entering Foshan University of Science and Technology in 2002, he has been undertaking the front-line work of teaching, actively carrying out teaching reform and research on teaching methods. In September 2009, I visited RMIT in Melbourne, Australia for half a year. In the past two years, I have been mainly engaged in the work and research of educational informatization.

Chinese name: Lin Dongmei

Nationality: China.

Occupation: Professor

Graduation school: Daqing Petroleum Institute

Representative works: C language training class

Character experience

Since 2005, he has presided over the provincial natural science foundation project 1, participated in 5 provincial and municipal scientific research projects, and published 5 scientific research papers 1, including 5 EI retrieval papers, which won the Foshan Excellent Achievement Award for Philosophy and Social Sciences 1, 2 provincial appraisal awards and Foshan Excellent Academic Paper Award1. He presided over 5 provincial and municipal teaching and research projects, edited 4 teaching materials, and presided over the construction of the school's key course "C Language Programming".

Won the honorary title of "Excellent Teacher in Southern Guangdong" in Guangdong Province, and won the awards of "Excellent Young Teacher", "Excellent party member" and "Excellent Teacher in Foshan Education System" in our school. He has won the excellent teaching quality award of the school five times, the second prize of the school-level teaching achievement award 1, the third prize 1, and the excellent teaching quality award 2. School-level training object of "Thousand Hundred and Ten" project in Guangdong Province.

Achievement honor

I. The main topics and achievements presided over and participated in are as follows:

1. Hosted the Guangdong Natural Science Foundation project "Research on Solving Strategy of Multi-Traveling Salesman Problem Based on Two-level Degeneration Model" (1015280000100029) (2010);

2. Mainly participated in the Guangdong Natural Science Foundation project "Research on Key Technologies of Distributed Digital Resources Usage Control and Rights Protection" (8452800001001086) (2008);

3. Mainly participated in the Guangdong Natural Science Foundation project "Research on Emotion Recognition Technology Based on Multimodal" (1025280000100001) (2010);

4. Mainly participated in the National Spark Project "Animal Disease Early Warning and Food Safety Traceability System Based on RFID" (2008GA780030) (2008); );

5. Presided over the teaching reform and practice project of Guangdong Provincial Department of Education "Three-dimensional Teaching Material Development of C Language Programming" (B05)(2009).

6. "Research on Optimal Computing System of Multi-Traveling Salesman Problem Based on Double Degeneration Model" passed the appraisal of Guangdong Science and Technology Department (York Jian ZiNo. 148);

Second, publish relevant teaching materials.

1. fundamentals of computer application (7-302-12282-2/tp.7895) (2006)

2. College Computer Basic Experiment Guidance and Problem Set (978-7-121-09308-1) (2009)

3.c language training course (978-7-04-030286-8) (2011)

Third, the main papers published

There are more than 40 articles, such as double unique population co-evolutionary genetic algorithm for traveling salesman problem, double degenerate hybrid algorithm for solving multiple traveling salesman problems, Monte Carlo model for determining partial edges of global optimal solution of TSP, Traveling Salesman Problem with Nearest Neighbor Boundary Coding Based on Particle Swarm Optimization, Exact Heuristic Algorithmic Forraving Salesman Problem, etc. The hybrid ant colony algorithm for solving TSP, hybrid genetic algorithm based on feature evolution and heuristic particle swarm optimization with local information are studied.