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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(3): 219-224 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.510

Roundness Measurement in Spring Clamps Based on a Novel Particle Swarm Optimization

Xia Zhu and Renwen Chen
Abstract—One important inspection step in spring clamp is the determination of roundness value. Detect the circle from digital images is very important in computer vision. In view of clamp and the geometrical characteristics of the circular, a new optimization algorithm was proposed to extract image circle. Circle extraction is equivalent to the points search and extraction in the circle. Point search strategy is an optimization problem. Particle swarm optimization (PSO) is an effective global optimization method. Self-tuning parameters were achieved by using the improved particle swarm algorithm along with the geometric properties of the round, and detected effective point of band image so as to realize the recognition and detection of the circle. A large number of tests show that the proposed algorithm has good robustness and precision compared with the existing PSO algorithms. The main advantages of this method as compared with other methods include the considerable lower cost of facilities, easier maintenance and getting more accurate.

Index Terms—Spring clamp, diameter, circle detection, particle swarm optimization.

The authors are with the State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China (e-mail: snail2024@163.com, rwchen@nuaa.edu.cn).

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Cite: Xia Zhu and Renwen Chen, "Roundness Measurement in Spring Clamps Based on a Novel Particle Swarm Optimization," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 219-224, 2015.

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