An Intelligent System for Reconstructing the Ripped-up Paper-Moneys

Nan-Hsing Chiu, Chang-En Pu, Ming-Chang Hsieh


The paper-moneys may face the problems of shreds in the unexpected accidents or human negligence. The reconstruction of ripped-up paper-moneys can demonstrate the evidences for decision makers or forensic examiners in order to exchange the complete paper-moneys. However, the reconstruction of ripped-up paper-moneys is very difficult on the basis of a lot of shreds with different distance factors that are measured from neighbouring pieces. How to identify the suitable feature weight for each distance factor is a critical issue for reconstructing the ripped-up paper-moneys. Particle swarm optimization is a search algorithm which is successfully adopted for solving many combination optimization problems in many fields. This study utilizes particle swarm optimization for exploring the proper feature weight for each distance factor to improve the reconstructed abilities. The proposed approach demonstrates the automatically reconstructing abilities which enhance the effects and efficiencies on the reconstruction of ripped-up paper-moneys.


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Paper Citation

in Harvard Style

Chiu N., Pu C. and Hsieh M. (2013). An Intelligent System for Reconstructing the Ripped-up Paper-Moneys . In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-59-4, pages 395-400. DOI: 10.5220/0004409903950400

in Bibtex Style

author={Nan-Hsing Chiu and Chang-En Pu and Ming-Chang Hsieh},
title={An Intelligent System for Reconstructing the Ripped-up Paper-Moneys},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Intelligent System for Reconstructing the Ripped-up Paper-Moneys
SN - 978-989-8565-59-4
AU - Chiu N.
AU - Pu C.
AU - Hsieh M.
PY - 2013
SP - 395
EP - 400
DO - 10.5220/0004409903950400