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Predicting Cellular Branching with Inverse Cellular Automata via Recurrent Neural Network : 재귀 신경망이 적용된 역 셀룰러 오토마타 학습법을 활용한 세포 분지(分枝) 예측
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 전누리 | - |
dc.contributor.author | 윤경원 | - |
dc.date.accessioned | 2018-05-28T16:08:52Z | - |
dc.date.available | 2018-05-28T16:08:52Z | - |
dc.date.issued | 2018-02 | - |
dc.identifier.other | 000000151413 | - |
dc.identifier.uri | https://hdl.handle.net/10371/140570 | - |
dc.description | 학위논문 (박사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2018. 2. 전누리. | - |
dc.description.abstract | Biological processes are very complex so the predicting of such processes is
considered to be very difficult. When it comes to the image-based prediction, one of the main reason of the predicting difficulty comes from the cost that gathering enough visual data which is extremely expensive. In this thesis, we introduce a novel method to reduce the cost of the prediction of cellular branching with Inverse Cellular Automata (ICA). This method assumes the biological image sequence as the set of cellular automata rules between t and t+1. With this assumption, single biological image sequence can provide width * height * frame number of cellular automata set of rule samples so they can be used as traditional Recurrent Neural Network(RNN) based machine learning. Proposed method overcomes the problem of lack of samples for machine learning in biological image predicting. We first briefly review the traditional cellular automata and introduce the concept of the ICA. And we will check the learnability with existing automata image rules. Finally, we show the prediction images with proposed method. | - |
dc.description.tableofcontents | Chapter 1. Introduction 1
Chapter 2. Inverse Cellular Automata Training 15 Chapter 3. Learnability of ICA 32 Chapter 4. Biological Image Sequence Application 42 Chapter 5. Conclusion 47 Bibliography 48 Abstract in Korean 50 | - |
dc.format | application/pdf | - |
dc.format.extent | 1730441 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Cellular Automata | - |
dc.subject | Biological Imaging | - |
dc.subject | Image Prediction | - |
dc.subject | Artificial Neural Network | - |
dc.subject | Recurrent Neural Network | - |
dc.subject.ddc | 621 | - |
dc.title | Predicting Cellular Branching with Inverse Cellular Automata via Recurrent Neural Network | - |
dc.title.alternative | 재귀 신경망이 적용된 역 셀룰러 오토마타 학습법을 활용한 세포 분지(分枝) 예측 | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | KYUNGWON YUN | - |
dc.description.degree | Doctor | - |
dc.contributor.affiliation | 공과대학 기계항공공학부 | - |
dc.date.awarded | 2018-02 | - |
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