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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.4
- 수록면
- 95 - 101 (7page)
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초록· 키워드
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In this paper, we consider a group testing (GT) framework which is to find a set of defective samples out of a large number of samples. To handle this framework, we propose a maximum product detection algorithm (MPDA) which is based on maximum a posteriori probability (MAP). The key idea of this algorithm exploits iterative detection to propagate belief to neighbor samples by exchanging marginal probabilities between samples and output results. The belief propagation algorithm as a conventional approach has been used to detect defective samples, but it has computational complexity to obtain the marginal probability in the output nodes which combine other marginal probabilities from the sample nodes. We show that the our proposed MPDA provides a benefit to reduce computational complexity up to 12% in runtime, while its performance is only slightly degraded compared to the belief propagation algorithm. And we verify the simulations to compare the difference of performance.
목차
Abstract
1. Introduction
2. Related Work
3. Group Testing Framework
4. Detection of Defective Samples
5. Conclusion
REFERENCES
참고문헌 (9)
참고문헌 신청
R. Dorfman / 1943 / The Detection of Defective Members of Large Populations / The Annals of Mathematical Statistics 14(4):436~440
D.-Z. Du / 2006 / Pooling Designs and Nonadaptive Group Testing: Important Tools for DNA Sequencing / World Scientific
C. L. Chan / 2011 / Non-adaptive probabilistic group testing with noisy measurements : near-optimal bounds with efficient algorithms / 49th Annual Allerton Conference on Communication, Control, and Computing :1832~1839
M. Aldridge / 2014 / Group Testing Algorithms : Bounds and Simulations / IEEE Transactions on Information Theory 60(6):3671~3687
L. Baldassini / 2013 / The capacity of adaptive group testing / IEEE International Symposium on Information Theory :2676~2680
D.-Z. Du / 2006 / Pooling Designs and Nonadaptive Group Testing: Important Tools for DNA Sequencing / World Scientific
C. L. Chan / 2011 / Non-adaptive probabilistic group testing with noisy measurements : near-optimal bounds with efficient algorithms / 49th Annual Allerton Conference on Communication, Control, and Computing :1832~1839
M. Aldridge / 2014 / Group Testing Algorithms : Bounds and Simulations / IEEE Transactions on Information Theory 60(6):3671~3687
L. Baldassini / 2013 / The capacity of adaptive group testing / IEEE International Symposium on Information Theory :2676~2680
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