Write Strategy Learning for Optical Dye Recording

Yao, Leehter; Huang, Chun-Kai; Chen, Yihong

doi:10.1109/tmech.2008.2008988

Cited by 4 publications

(1 citation statement)

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“…A less complicated GA-based learning approach based on on-line measurements was applied to the optical dye recording device, CD Recordable (CD-R) in [ 21 ]. The write strategy learning approach proposed in this paper for phase change recording media is essentially different from the write strategy proposed for the dye recording media in [ 21 ], since phase change recording media have entirely differently characteristics from dye recording media. This leads to entirely different write strategy parameters for both recording media.…”

Section: Introductionmentioning

confidence: 99%

On-Line Learning of Write Strategy for Ultra-Speed CD-RW Optical Recorder

Yao

Huang²

2018

Sensors

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An on-line machine learning approach integrating the genetic algorithm (GA) and jitter measurements is proposed to learn the write strategy for the infrared diode of ultra-speed CD-RW recorders. The recording performance differs significantly for the CD-RW discs recorded for the first, second, or third time above. It is difficult to learn one set of write strategy parameters for the infrared diode of ultra-speed CD-RW recorder that satisfies the recording specifications for three different types of discs. The GA is applied to the on-line learning of write strategy. However, the convergence of GA stagnates at the final stage of the learning process due to the fact that the write strategy parameters learned by the GA need to satisfy the recording specifications for discs recorded for the first time, second time and third time within one recording trial. To overcome this difficulty, a scheme called dynamic parameter encoding is proposed. This scheme improves the GA convergence and explores the search space much better than the conventional GA.

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Section: Introductionmentioning

confidence: 99%