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236 results on '"Cho, Dong-Ho"'

2. Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning

Author

Song, Yong Joon, Ji, Dong Jin, Seo, Hye In, Han, Gyu Bum, and Cho, Dong Ho

Subjects
Quantitative Biology - Quantitative Methods, Quantitative Biology - Genomics
Abstract

Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used for comparative analysis of biological genomes. However, the traditional sequence alignment method is considerably complicated in proportion to the sequences' length, and it is significantly challenging to align long sequences such as a human genome. Currently, several multiple sequence alignment algorithms are available that can reduce the complexity and improve the alignment performance of various genomes. However, there have been relatively fewer attempts to improve the alignment performance of the pairwise alignment algorithm. After grasping these problems, we intend to propose a new sequence alignment method using deep reinforcement learning. This research shows the application method of the deep reinforcement learning to the sequence alignment system and the way how the deep reinforcement learning can improve the conventional sequence alignment method., Comment: 20pages, 9figures

Published
2020

5. Deep Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks

Author

Lee, Woongsup, Kim, Minhoe, and Cho, Dong-Ho

Subjects
Computer Science - Networking and Internet Architecture, 68T99
Abstract

In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user (PU) which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS which is hard to compute and also hard to use in practice, the strategy for combining the individual sensing results of the SUs is learned with a CNN using training sensing samples. Accordingly, an environment-specific CSS which considers both spectral and spatial correlation of individual sensing outcomes, is found in an adaptive manner regardless of whether the individual sensing results are quantized or not. Through simulation, we show that the performance of CSS can be improved by the proposed DCS with low complexity even when the number of training samples is moderate.

Published
2017

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