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Guofeng Qin
Tongji University

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 1 
K Nearest Neighbor Path Queries Based on Road Networks Lulin Chen; Guofeng Qin
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 11: November 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

The Island is a k nearest neighbor query algorithm of moving objects based on road networks and can effectively balance the performance of query and update. But the algorithm doesn’t consider the direction of moving object which is required in many scenarios. It traverses vertexes from all directions, meaning wasting a lot of time in visiting useless vertexes. Besides, it doesn’t return query path. In this paper, we propose an improved Island algorithm which takes full account of moving direction. It takes best point estimate and heuristic search method, effectively reducing the number of traversal vertexes. At the same time, we optimize the data structure and let it return the query path. Results show that the improved algorithm outperforms the original one. Finally, we describe electric vehicle charging guidance system based on the improved Island algorithm. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3509
A Multicore Load Balancing Model Based on Java NIO Yang Wang; Guofeng Qin
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 6: October 2012
Publisher : Institute of Advanced Engineering and Science

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First this paper points out two common problems of utilizing processors under multicore architecture, namely processors waiting for IO operation to finish and load balancing among cores. Then it makes an analysis of the reasons for them. In order to fully exploit multicore processors, this paper proposes a multicore load balancing model based on the Java NIO framework which offers a solution to above problems. This model mainly illustrates a task scheduling algorithm which uses a parallel computing framework, Java Fork/Join. At last, experiments and performance analysis prove the effectiveness of this model in utilizing the multicore processors. Although the model is constructed under the architecture of Java language, it can be extended to other languages without much being changed. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1431
Pavement Image Segmentation Based on FCM Algorithm Using Neighborhood Information Xinsong Wang; Guofeng Qin
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 7: November 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

Standard FCM algorithm takes the pixel gray-scale information into account only, while ignoring the spatial location of pixels, so the standard FCM algorithm is sensitive to noise. This paper present a pavement image segmentation algorithm based on FCM algorithm using neighborhood information. The presented algorithm introduces neighborhood information into membership function to improve the standard FCM algorithm. It can eliminate noise effectively and retain the boundary information. The experiments by synthetic images and real pavement images show that the presented algorithm in this paper performs more robust to noise than the standard FCM algorithm and retain the boundary information effectively. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1551
An Intelligent Course Scheduling Model Based on Genetic Algorithm Guofeng Qin; Haibin Ma
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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With the university expansion, how to maintain teaching order using limited resources make the intelligent course scheduling become a multiple-constraint and multi-objective optimization problem. Traditional intelligent course scheduling algorithm is inefficient, cannot solve curriculum conflict question and meet the requirements of the modern university education management. Given this situation, this paper analyzes the university timetabling problem, and establishes a general course scheduling model; then proposes an improved genetic algorithm to sovle the intelligent course scheduling problem. It can meet all of the education resources’ constraints and the teachers’ personal demands as much as possible. Test the performance of between the improved genetic algorithm and simple genetic algorithm under different scenarios, the experimental results show that the improved genetic algorithm has better performance, can schedule courses reasonable. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4798 
Co-Authors Haibin Ma Lulin Chen Xinsong Wang Yang Wang