Subjects: Mathematics >> Theoretical Computer Science submitted time 2022-09-27 Cooperative journals: 《桂林电子科技大学学报》
Abstract: To solve the reasonable allocation of campus resources, optimize the operation mode of the campus public transportation system, and meet the daily convenient travel of teachers and students, an optimization scheme of campus bus system based on 0-1 integer programming model was proposed.The study took Guilin University of Electronic Technology as an example, firstly, the present situation of student travel was investigated, the result shows that most students are willing to use campus bus, which means campus bus has certain development prospects. Through field measurement and collection of relevant geographical data, 0-1 integer planning was used to select the location of bus stops and ant colony algorithm was applied to optimize bus routes, and made a series of simulation experiments to test the carrying capacity of the campus bus system. Finally, it was concluded that the distribution location of 19 bus stations and the loop length generated by the optimal bus route is 4 805 meters, and under the restriction of the vehicle driving speed within 20km/h, at least 15 buses should be arranged to meet the time needs of most students. The results show that the optimized campus bus system planning is more reasonable, which meets the ravel needs of most teachers and students and is suitable for small and medium-sized campus traffic route planning.
Subjects: Mathematics >> Theoretical Computer Science submitted time 2021-07-26
Abstract: "
Peer Review Status:Awaiting Review
Subjects: Mathematics >> Theoretical Computer Science submitted time 2020-10-10
Abstract: In this paper, we give a method to express the descending degree sum of squares of univariate positive semi-definite polynomials, and give an algorithm to get the descending degree sum of squares from known positive semi-definite polynomials. In the fourth section, we apply the idea and algorithm of the descending degree sum of squares to multivariate polynomials successfully.
Peer Review Status:Awaiting Review
Subjects: Mathematics >> Theoretical Computer Science submitted time 2017-11-17
Abstract: This paper proposes a new linear classification method named Focusing Classification, with the goal of taking the place of Logistic Regression. Focusing Classification has some advantages: length of its normal vector is limited, intuitional geometrical explanation, parameters' initial values are close to the best values. numerical experiments on the MNIST dataset demonstrate that Focusing Classification has better performance than Logistic Regression on length of its normal vector, accuracy and rate of convergence. With initial parameter values, Focusing Classification gains an accuracy of 97.31%.
Peer Review Status:Awaiting Review